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An unresolved question in our understanding of the medial temporal lobes is how functional differences between structures pertaining to stimulus category relate to the distinction between item-based and contextually based recognition-memory processes. Specifically, it remains unclear whether perirhinal cortex (PrC) supports item-based familiarity signals for all stimulus categories or whether parahippocampal cortex (PhC) may also play a role for stimulus categories that are known to engage this structure in other task contexts. Here, we used multivoxel pattern analyses of fMRI data to compare patterns of activity in humans that are associated with the perceived familiarity of faces, buildings, and chairs. During scanning, participants judged the familiarity of previously studied and novel items from all three categories. Instances in which recognition was based on recollection were removed from all analyses. In right PrC, we found patterns of activity that distinguished familiar from novel faces. By contrast, in right PhC, we observed such patterns for buildings. Familiarity signals for chairs were present in both structures but shared little overlap with the patterns observed for faces and buildings on a more fine-grained scale. In the hippocampus, we found no evidence for familiarity signals for any object category. Our findings show that both PrC and PhC contribute to the assessment of item familiarity. They suggest that PhC does not only represent episodic context but can also represent item information for some object categories in recognition-memory decisions. In turn, our findings also indicate that the involvement of PrC in representing item familiarity is not ubiquitous.
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http://dx.doi.org/10.1523/JNEUROSCI.0126-13.2013 | DOI Listing |
J Coll Sci Teach
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RCSB Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers University, Piscataway, New Jersey, United States.
Structure-function relationships are a core concept in many STEM disciplines. Most biology curricula introduce students to macromolecules, their building blocks, and other small molecules that play key roles in biological processes. However, the shapes, interactions, and functions of these molecules are often discussed using schematic diagrams, ignoring the vast amounts of three-dimensional structural and bioinformatics data freely available from public data resources.
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Molecular AI, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca Gothenburg Sweden
Incorporating non-natural amino acids (NNAAs) into peptides enhances therapeutic properties, including binding affinity, metabolic stability, and half-life time. The pursuit of novel NNAAs for improved peptide designs faces the challenge of effective synthesis of these building blocks as well as the entire peptide itself. Solid-Phase Peptide Synthesis (SPPS) is an essential technology for the automated assembly of peptides with NNAAs, necessitating careful protection for effective coupling of amino acids in the peptide chain.
View Article and Find Full Text PDFBMC Glob Public Health
September 2025
Connell School of Nursing, Boston College, Chestnut Hill, MA, USA.
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August 2025
Department of Social Sciences and Health Policy, Medical Center Boulevard, Wake Forest University School of Medicine, Winston-Salem, NC 27157, United States.
Minoritized racial, ethnic, sexual, and gender communities and populations face profound health disparities and their engagement in research remains low. In a randomized controlled trial, our community-based participatory research partnership tested the efficacy of ChiCAS, an HIV prevention intervention designed to increase pre-exposure prophylaxis use among Spanish-speaking transgender Latinas. Of 161 eligible Spanish-speaking transgender Latinas screened, we enrolled 144, achieving an 89% participation rate, and retained 94% at 6-month follow-up.
View Article and Find Full Text PDFRadiology
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Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck St, Boston, MA 02115.
Despite the rapid growth of Food and Drug Administration-cleared artificial intelligence (AI)- and machine learning-enabled medical devices for use in radiology, current tools remain limited in scope, often focusing on narrow tasks and lacking the ability to comprehensively assist radiologists. These narrow AI solutions face limitations in financial sustainability, operational efficiency, and clinical utility, hindering widespread adoption and constraining their long-term value in radiology practice. Recent advances in generative and multimodal AI have expanded the scope of image interpretation, prompting discussions on the development of generalist medical AI.
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