The hippocampus is involved in many cognitive domains which are difficult for autistic individuals. Our previous study using a Structural Learning task that has been shown to depend on hippocampal functioning found that structural learning is diminished in autistic adults (Ring et al., 2017). The aim of the present study was to examine whether those results can be replicated in and extended to a sample of autistic and non-autistic children. We tested 43 autistic children and 38 non-autistic children with a subsample of 25 autistic and 28 non-autistic children who were well-matched on IQ. The children took part in a Simple Discrimination task which a simpler form of compound learning, and a Structural Learning task. We expected both groups to perform similarly in Simple Discrimination but reduced performance by the autism group on the Structural Learning task, which is what we found in both the well-matched and the non-matched sample. However, contrary to our prediction and the findings from autistic adults in our previous study, autistic children demonstrated a capacity for Structural Learning and showed an overall better performance in the tasks than was seen in earlier studies. We discuss developmental differences in autism as well as the role of executive functions that may have contributed to better than predicted task performance in this study.
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http://dx.doi.org/10.1007/s10803-024-06486-0 | DOI Listing |
Comput Med Imaging Graph
January 2025
CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; National Key Laboratory of Kidney Diseases, Beijing 100853, China. Electronic address:
In clinical optical molecular imaging, the need for real-time high frame rates and low excitation doses to ensure patient safety inherently increases susceptibility to detection noise. Faced with the challenge of image degradation caused by severe noise, image denoising is essential for mitigating the trade-off between acquisition cost and image quality. However, prevailing deep learning methods exhibit uncontrollable and suboptimal performance with limited interpretability, primarily due to neglecting underlying physical model and frequency information.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China.
In recent decades, covalent inhibitors have emerged as a promising strategy for therapeutic development, leveraging their unique mechanism of forming covalent bonds with target proteins. This approach offers advantages such as prolonged drug efficacy, precise targeting, and the potential to overcome resistance. However, the inherent reactivity of covalent compounds presents significant challenges, leading to off-target effects and toxicities.
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
Department of Biomedical Informatics, University of Colorado Anschutz School of Medicine, Aurora, Colorado, United States of America.
While single-cell experiments provide deep cellular resolution within a single sample, some single-cell experiments are inherently more challenging than bulk experiments due to dissociation difficulties, cost, or limited tissue availability. This creates a situation where we have deep cellular profiles of one sample or condition, and bulk profiles across multiple samples and conditions. To bridge this gap, we propose BuDDI (BUlk Deconvolution with Domain Invariance).
View Article and Find Full Text PDFPLoS One
January 2025
Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran.
CNN is considered an efficient tool in brain image segmentation. However, neonatal brain images require specific methods due to their nature and structural differences from adult brain images. Hence, it is necessary to determine the optimal structure and parameters for these models to achieve the desired results.
View Article and Find Full Text PDFPLOS Glob Public Health
January 2025
Institute of Anatomy, Faculty of Medicine, University of Zurich, Zurich, Switzerland.
Peru is among Latin American countries with the largest Indigenous population, yet ethnical health disparities persist, particularly in the Amazon region which comprises 60% of the national territory. Healthcare models that include Indigenous medicine and traditional healers present an important avenue for addressing such inequalities, as they increase cultural adequacy of services, healthcare access, and acknowledge Indigenous Rights for their perspectives to be represented in public healthcare. Understanding the underlying epistemologies of Indigenous medicine is a prerequisite for this purpose.
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