Adaptive or non-adaptive? Cranial evolution in a radiation of miniaturized day geckos.

Lygodactylus geckos represent a well-documented radiation of miniaturized lizards with diverse life-history traits that are widely distributed in Africa, Madagascar, and South America. The group has diversified into numerous species with high levels of morphological similarity. The evolutionary processes underlying such diversification remain enigmatic, because species live in different ecological biomes, ecoregions and microhabitats, while suggesting strikingly high levels of homoplasy.

Where, why, and how is bias learned in medical image analysis models? A study of bias encoding within convolutional networks using synthetic data.

Background: Understanding the mechanisms of algorithmic bias is highly challenging due to the complexity and uncertainty of how various unknown sources of bias impact deep learning models trained with medical images. This study aims to bridge this knowledge gap by studying where, why, and how biases from medical images are encoded in these models.

Methods: We systematically studied layer-wise bias encoding in a convolutional neural network for disease classification using synthetic brain magnetic resonance imaging data with known disease and bias effects.

Molecular basis of neurodegeneration in a mouse model of -related disease.

Pathogenic variants in subunits of RNA polymerase (Pol) III cause a spectrum of -related neurodegenerative diseases including 4H leukodystrophy. Disease onset occurs from infancy to early adulthood and is associated with a variable range and severity of neurological and non-neurological features. The molecular basis of -related disease pathogenesis is unknown.

Analysis and visualization of the effect of multiple sclerosis on biological brain age.

Introduction: The rate of neurodegeneration in multiple sclerosis (MS) is an important biomarker for disease progression but can be challenging to quantify. The brain age gap, which quantifies the difference between a patient's chronological and their estimated biological brain age, might be a valuable biomarker of neurodegeneration in patients with MS. Thus, the aim of this study was to investigate the value of an image-based prediction of the brain age gap using a deep learning model and compare brain age gap values between healthy individuals and patients with MS.

Clinician Response to the 2021 USPSTF Recommendation for Colorectal Cancer Screening in Average Risk Adults Aged 45-49 Years.

Introduction: In 2021, the USPSTF lowered the recommended age of colorectal cancer (CRC) screening initiation from 50 to 45 years. This study assessed clinician response to the updated guideline in a major health system.

Methods: This was a retrospective cohort study of average-risk, CRC screening-naïve adults aged 45-50 years with a primary care appointment between July 2018 and February 2023.