Since its inception, the concept of neurodiversity has been defined in a number of different ways, which can cause confusion among those hoping to educate themselves about the topic. Learning about neurodiversity can also be challenging because there is a lack of well-curated, appropriately contextualized information on the topic. To address such barriers, we present an annotated reading list that was developed collaboratively by a neurodiverse group of researchers. The nine themes covered in the reading list are: the history of neurodiversity; ways of thinking about neurodiversity; the importance of lived experience; a neurodiversity paradigm for autism science; beyond deficit views of ADHD; expanding the scope of neurodiversity; anti-ableism; the need for robust theory and methods; and integration with open and participatory work. We hope this resource can support readers in understanding some of the key ideas and topics within neurodiversity, and that it can further orient researchers towards more rigorous, destigmatizing, accessible, and inclusive scientific practices.
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http://dx.doi.org/10.7554/eLife.102467 | DOI Listing |
Arch Virol
December 2024
Department of Virology, Faculty of Biology, Lomonosov Moscow State University, Moscow, 119234, Russia.
A new badnavirus was discovered in nettle plants (Urtica dioica L., family Urticaceae) with vein banding symptoms using high-throughput sequencing. This virus was provisionally named "nettle badnavirus 1" (NBV 1).
View Article and Find Full Text PDFElife
December 2024
Ask Me, I'm an AAC user, United States, United States.
J Biomed Life Sci
November 2024
Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.
JMIR Form Res
November 2024
Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, 880 Kitakobayashi, Mibu-cho, Shimotsuga-gun, Tochigi, 321-0293, Japan, 81 282-86-1111, 81 282-86-4775.
Background: Diagnostic errors are significant problems in medical care. Despite the usefulness of artificial intelligence (AI)-based diagnostic decision support systems, the overreliance of physicians on AI-generated diagnoses may lead to diagnostic errors.
Objective: We investigated the safe use of AI-based diagnostic decision support systems with trust calibration by adjusting trust levels to match the actual reliability of AI.
BMC Biol
November 2024
State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, 361102, China.
Background: Accurate and comprehensive genomic annotation, including the full list of protein-coding genes, is vital for understanding the molecular mechanisms of human biology. We have previously shown that the genome contains a multitude of yet hidden functional exons and transcripts, some of which might represent novel mRNAs. These results resonate with those from other groups and strongly argue that two decades after the completion of the first draft of the human genome sequence, the current annotation of human genes and transcripts remains far from being complete.
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