Reducing sensory experiences during the period that immediately follows learning improves long-term memory retention in healthy humans, and even preserves memory in patients with amnesia. To date, it is entirely unclear why this is the case, and identifying the neurobiological mechanisms underpinning this effect requires suitable animal models, which are currently lacking. Here, we describe a straightforward experimental procedure in rats that future studies can use to directly address this issue. Using this method, we replicated the central findings on quiet wakefulness obtained in humans: We show that rats that spent 1 h alone in a familiar dark and quiet chamber (the Black Box) after exploring two objects in an open field expressed long-term memory for the object locations 6 h later, while rats that instead directly went back into their home cage with their cage mates did not. We discovered that both visual stimulation and being together with conspecifics contributed to the memory loss in the home cage, as exposing rats either to light or to a cage mate in the Black Box was sufficient to disrupt memory for object locations. Our results suggest that in both rats and humans, everyday sensory experiences that normally follow learning in natural settings can interfere with processes that promote long-term memory retention, thereby causing forgetting in form of retroactive interference. The processes involved in this effect are not sleep-dependent because we prevented sleep in periods of reduced sensory experience. Our findings, which also have implications for research practices, describe a potentially useful method to study the neurobiological mechanisms that might explain why normal sensory processing after learning impairs memory both in healthy humans and in patients suffering from amnesia.
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http://dx.doi.org/10.1101/lm.053256.120 | DOI Listing |
Geroscience
January 2025
Department of Bioengineering and QB3, University of California, Berkeley, Berkeley, CA, 94720, USA.
Biological age estimation from DNA methylation and determination of relevant biomarkers is an active research problem which has predominantly been tackled with black-box penalized regression. Machine learning is used to select a small subset of features from hundreds of thousands of CpG probes and to increase generalizability typically lacking with ordinary least-squares regression. Here, we show that such feature selection lacks biological interpretability and relevance in the clocks of the first and next generations and clarify the logic by which these clocks systematically exclude biomarkers of aging and age-related disease.
View Article and Find Full Text PDFSci Rep
January 2025
Fischell Department of Bioengineering, University of Maryland, College Park, USA.
The development of optical sensors for label-free quantification of cell parameters has numerous uses in the biomedical arena. However, using current optical probes requires the laborious collection of sufficiently large datasets that can be used to calibrate optical probe signals to true metabolite concentrations. Further, most practitioners find it difficult to confidently adapt black box chemometric models that are difficult to troubleshoot in high-stakes applications such as biopharmaceutical manufacturing.
View Article and Find Full Text PDFPsychiatry Res
January 2025
University of California San Francisco, Department of Psychiatry and Behavioral Sciences, 675 18th Ave. San Francisco, CA 94121, USA; San Francisco Veteran's Affairs Medical Center, 4150 Clement St. San Francisco, CA 94121, USA.
A lack of diverse and representative participant samples in mental health intervention research perpetuates mental health disparities. This issue has become a salient concern in studies of psychedelic-assisted psychotherapy (PAT), which is emerging as a promising mental health intervention. This systematic review evaluates the reporting, representation, and analysis of participant sociodemographic characteristics in randomized controlled trials (RCTs) of PAT.
View Article and Find Full Text PDFPLoS One
January 2025
Business School, Huaqiao University, Quanzhou, Fujian, China.
Global climate change has become one of the most large-scale, widespread, and far-reaching challenges facing mankind. Against this background, China has proposed a "dual-carbon" target in 2020, which greatly demonstrates China's determination and commitment to carbon emission reduction, and the burden of realizing the "dual-carbon" target is mainly borne by heavy polluters. The burden of achieving the "dual-carbon" goal is mainly borne by the heavily polluting firms.
View Article and Find Full Text PDFBrief Bioinform
November 2024
School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, No. 800 Dong Chuan Road, Shanghai 200240, China.
Machine learning has emerged as a transformative tool for elucidating cellular heterogeneity in single-cell RNA sequencing. However, a significant challenge lies in the "black box" nature of deep learning models, which obscures the decision-making process and limits interpretability in cell status annotation. In this study, we introduced scGO, a Gene Ontology (GO)-inspired deep learning framework designed to provide interpretable cell status annotation for scRNA-seq data.
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