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1.
Memory ; 31(3): 428-456, 2023 03.
Artículo en Inglés | MEDLINE | ID: mdl-36651851

RESUMEN

Familiar music facilitates memory retrieval in adults with dementia. However, mechanisms behind this effect, and its generality, are unclear because of a lack of parallel work in healthy aging. Exposure to familiar music enhances spontaneous recall of memories directly cued by the music, but it is unknown whether such effects extend to deliberate recall more generally - e.g., to memories not directly linked to the music being played. It is also unclear whether familiar music boosts recall of specific episodes versus more generalised semantic memories, or whether effects are driven by domain-general mechanisms (e.g., improved mood). In a registered report study, we examined effects of familiar music on deliberate recall in healthy adults ages 65-80 years (N = 75) by presenting familiar music from earlier in life, unfamiliar music, and non-musical audio clips across three sessions. After each clip, we assessed free recall of remote memories for pre-selected events. Contrary to our hypotheses, we found no effects of music exposure on recall of prompted events, though familiar music evoked spontaneous memories most often. These results suggest that effects of familiar music on recall may be limited to memories specifically evoked in response to the music (Preprint and registered report protocol at https://osf.io/kjnwd/).


Asunto(s)
Envejecimiento Saludable , Memoria Episódica , Humanos , Adulto , Anciano , Anciano de 80 o más Años , Semántica , Recuerdo Mental/fisiología , Señales (Psicología)
2.
J Chem Inf Model ; 59(4): 1338-1346, 2019 04 22.
Artículo en Inglés | MEDLINE | ID: mdl-30821455

RESUMEN

A featurization algorithm based on functional class fingerprints has been implemented within the DeepChem machine learning framework. It is based on descriptors more appropriate for solvation, taking into account intermolecular properties, and has been used in the prediction of free energies of solvation. Tests carried out on solvents with a range of polarity from the FreeSolv and MNSol data sets have shown slightly better accuracy than the commonly used topology-based extended connectivity fingerprint algorithm for hydration free energies. However, improvement was not as significant as hoped and less clear for less polar solvents suggesting that further solvent-specific descriptors may need to be taken into consideration.


Asunto(s)
Quimioinformática/métodos , Aprendizaje Automático , Solventes/química , Termodinámica
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