Apolipoprotein E ɛ4 allele is a common susceptibility gene for late-onset Alzheimer's disease. Brain vascular and metabolic deficits can occur in cognitively normal apolipoprotein E ɛ4 carriers decades before the onset of Alzheimer's disease. The goal of this study was to determine whether early intervention using rapamycin could restore neurovascular and neurometabolic functions, and thus impede pathological progression of Alzheimer's disease-like symptoms in pre-symptomatic Apolipoprotein E ɛ4 transgenic mice. Using in vivo, multimodal neuroimaging, we found that apolipoprotein E ɛ4 mice treated with rapamycin had restored cerebral blood flow, blood–brain barrier integrity and glucose metabolism, compared to age- and gender-matched wild-type controls. The preserved vasculature and metabolism were associated with amelioration of incipient learning deficits. We also found that rapamycin restored the levels of the proinflammatory cyclophilin A in vasculature, which may contribute to the preservation of cerebrovascular function in the apolipoprotein E ɛ4 transgenics. Our results show that rapamycin improves functional outcomes in this mouse model and may have potential as an effective intervention to block progression of vascular, metabolic and early cognitive deficits in human Apolipoprotein E ɛ4 carriers. As rapamycin is FDA-approved and neuroimaging is readily used in humans, the results of the present study may provide the basis for future Alzheimer's disease intervention studies in human subjects.

Text is one of the most prevalent types of digital data that people create as they go about their lives. Digital footprints of people's language usage in social media posts were found to allow for inferences of their age and gender. However, the even more prevalent and potentially more sensitive text from instant messaging services has remained largely uninvestigated. We analyze language variations in instant messages with regard to individual differences in age and gender by replicating and extending the methods used in prior research on social media posts. Using a dataset of 309,229 WhatsApp messages from 226 volunteers, we identify unique age-and gender-linked language variations. We use cross-validated machine learning algorithms to predict volunteers' age (MAEMd = 3.95, rMd = 0.81, R 2 Md = 0.49) and gender (AccuracyMd = 85.7%, F1Md = 0.67, AUCMd = .82) significantly above baseline-levels and identify the most predictive language features. We discuss implications for psycholinguistic theory, present opportunities for application in author profiling, and suggest methodological approaches for making predictions from small text data sets. Given the recent trend towards the dominant use of private messaging and increasingly weaker user data protection, we highlight rising threats to individual privacy rights in instant messaging.

A Bayesian Study On Social Media Language During The First Wave of the COVID-19 Pandemic.Personality traits change over time, however research on it was sparse, since previous approaches were too time-consuming and expensive. Also, the necessary methodological complexity was beyond the capabilities of classical personality researchers, which resulted in contradictory results and lack of methodological standards. In this paper, we presented a simple and cost-effective method that overcame these restrictions.We introduced a machine learning approach for daily measurements to personality research, and developed a bespoke Bayesian algorithm to analyse the observed change. This resulted in uncovering concrete points of regime-shift that overlapped with relevant exogenous events for a Japanese sample of social media users.With it, we showed that personality measures displayed significant elasticity under extreme exogenous conditions during the first wave of COVID-19 and the subsequent societal countermeasures, which can be interpreted as a temporary shift from normal expression of latent psychological traits z to their respective emergency expression ze.Concretely, we found that the group of top 25% Conscientiousness users displayed a significant change in the FFM factors Agreeableness and Extraversion. We finally compared our findings with those from similar studies in other cultures, and discussed generalisability as well as future qualitative and quantitative directions for research.

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