Objective:This study investigated whether visual function is associated with cognitive activity engagement and mild cognitive impairment in middle-aged and elderly individuals. Method:This cross-sectional study was conducted on 120 individuals aged 50–89. The Florida Cognitive Activity Scale (FCAS) was used to assess cognitive activity engagement. Visual function was assessed by near visual acuity(nVA) and contrast sensitivity (CS), and both combined to obtain a visual function (VF) compound score. Multi-variable linear regression models, adjusted for confounders, were used to assess the association between the determinants and FCAS. Results:After confounder adjustment, nVA was not associated with overall cognitive activity engagement. CS was significantly associated with the FCAS“Higher Cognitive Abilities”subscale score (BHC= 5.5 [95% CI 1.3; 9.7]).Adjustment for nVA attenuated the association between CS and engagement in tasks of Higher Cognitive Abilities(BHC= 4.7 [95% CI 0.1; 9.3]).In retired individuals(N= 87), theVF compound score was associated with a lower Cognitive Activity Scale score(BCA=−1.2 [95% CI−2.3;−0.1]), lower Higher Cognitive Abilities score(BHC=−0.7 [95% CI−1.3;−0.1])and lower Frequent Cognitive Abilities score (BFA=−0.5 [95% CI−0.9;−0.1]). Conclusion:CS, but not nVA, plays a role in engagement in tasks associated with Higher Cognitive Abilities in middle-aged and elderly individuals. In retired individuals, the VF compound score is associated with lower Cognitive Activity score, lower Higher Cognitive Abilities score and lower Frequent Cognitive Abilities score.
LINK
The sensitivity of tropical forest carbon to climate is a key uncertainty in predicting global climate change. Although short-term drying and warming are known to affect forests, it is unknown if such effects translate into long-term responses. Here, we analyze 590 permanent plots measured across the tropics to derive the equilibrium climate controls on forest carbon. Maximum temperature is the most important predictor of aboveground biomass (−9.1 megagrams of carbon per hectare per degree Celsius), primarily by reducing woody productivity, and has a greater impact per °C in the hottest forests (>32.2°C). Our results nevertheless reveal greater thermal resilience than observations of short-term variation imply. To realize the long-term climate adaptation potential of tropical forests requires both protecting them and stabilizing Earth’s climate.
MULTIFILE
Ambient activity monitoring systems produce large amounts of data, which can be used for health monitoring. The problem is that patterns in this data reflecting health status are not identified yet. In this paper the possibility is explored of predicting the functional health status (the motor score of AMPS = Assessment of Motor and Process Skills) of a person from data of binary ambient sensors. Data is collected of five independently living elderly people. Based on expert knowledge, features are extracted from the sensor data and several subsets are selected. We use standard linear regression and Gaussian processes for mapping the features to the functional status and predict the status of a test person using a leave-oneperson-out cross validation. The results show that Gaussian processes perform better than the linear regression model, and that both models perform better with the basic feature set than with location or transition based features. Some suggestions are provided for better feature extraction and selection for the purpose of health monitoring. These results indicate that automated functional health assessment is possible, but some challenges lie ahead. The most important challenge is eliciting expert knowledge and translating that into quantifiable features.
