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Aim:Although clinicians primarily diagnose dementia based on a combination of metrics such as medical history and formal neuropsychological tests, recent work using linguistic analysis of narrative speech to identify dementia has shown promising results. We aim to build upon research by Thomas JA & Burkardt HA et al. (J Alzheimers Dis. 2020;76:905–2) and Alhanai et al. (arXiv:1710.07551v1. 2020) on the Framingham Heart Study (FHS) Cognitive Aging Cohort by 1) demonstrating the predictive capability of linguistic analysis in differentiating cognitively normal from cognitively impaired participants and 2) comparing the performance of the original linguistic features with the performance of expanded features.Methods:Data were derived from a subset of the FHS Cognitive Aging Cohort. We analyzed a sub-selection of 98 participants, which provided 127 unique audio files and clinical observations (n = 127, female = 47%, cognitively impaired = 43%). We built on previous work which extracted original linguistic features from transcribed audio files by extracting expanded features. We used both feature sets to train logistic regression classifiers to distinguish cognitively normal from cognitively impaired participants and compared the predictive power of the original and expanded linguistic feature sets, and participants’ Mini-Mental State Examination (MMSE) scores.Results:Based on the area under the receiver-operator characteristic curve (AUC) of the models, both the original (AUC = 0.882) and expanded (AUC = 0.883) feature sets outperformed MMSE (AUC = 0.870) in classifying cognitively impaired and cognitively normal participants. Although the original and expanded feature sets had similar AUC, the expanded feature set showed better positive and negative predictive value [expanded: positive predictive value (PPV) = 0.738, negative predictive value (NPV) = 0.889; original: PPV = 0.701, NPV = 0.869].Conclusions:Linguistic analysis has been shown to be a potentially powerful tool for clinical use in classifying cognitive impairment. This study expands the work of several others, but further studies into the plausibility of speech analysis in clinical use are vital to ensure the validity of speech analysis for clinical classification of cognitive impairment. 

Intermediate-depth intraseasonal variability (ISV) at a 20–90-day period, as detected in velocity measurements from seven subsurface moorings in the tropical western Pacific, is interpreted in terms of equatorial Rossby waves. The moorings were deployed between 08 and 7.58N along 1428E from September 2014 to October 2015. The strongest ISV energy at 1200m occurs at 4.58N. Peak energy at 4.58N is also seen in an eddy-resolving global circulation model. An analysis of the model output identifies the source of the ISV as short equatorial Rossby waves with westward phase speed but southeastward and downward group velocity. Additionally, it is shown that a superposition of first three baroclinic modes is required to represent the ISV energy propagation. Further analysis using a 1.5-layer shallow water model suggests that the first meridional mode Rossby wave accounts for the specific meridional distribution of ISV in the western Pacific. The same model suggests that the tilted coastlines of Irian Jaya and Papua New Guinea, which lie to the south of the moorings, shift the location of the northern peak of meridional velocity oscillation from 38N to near 4.58N. The tilt of this boundary with respect to a purely zonal alignment therefore needs to be taken into account to explain this meridional shift of the peak. Calculation of the barotropic conversion rate indicates that the intraseasonal kinetic energy below 1000m can be transferred into the mean flows, suggesting a possible forcing mechanism for intermediate-depth zonal jets.