A new Late Jurassic docodontan shows specializations for a subterranean lifestyle. It is similar to extant subterranean golden moles in having reduced digit segments as compared to the ancestral phalangeal pattern of mammaliaforms and extant mammals. The reduction of digit segments can occur in mammals by fusion of the proximal and intermediate phalangeal precursors, a developmental process for which a gene and signaling network have been characterized in mouse and human. Docodontans show a positional shift of thoracolumbar ribs, a developmental variation that is controlled by Hox9 and Myf5 genes in extant mammals. We argue that these morphogenetic mechanisms of modern mammals were operating before the rise of modern mammals, driving the morphological disparity in the earliest mammaliaform diversification.
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http://dx.doi.org/10.1126/science.1260880 | DOI Listing |
Digit Health
January 2025
Department of Urology, General Hospital of Northern Theater Command, Shenyang, China.
Purpose: Prostate cancer (PCa) is the second most common cancer in males worldwide, requiring improvements in diagnostic imaging to identify and treat it at an early stage. Bi-parametric magnetic resonance imaging (bpMRI) is recognized as an essential diagnostic technique for PCa, providing shorter acquisition times and cost-effectiveness. Nevertheless, accurate diagnosis using bpMRI images is difficult due to the inconspicuous and diverse characteristics of malignant tumors and the intricate structure of the prostate gland.
View Article and Find Full Text PDFAm J Neurodegener Dis
December 2024
Department of Radiology, Carver College of Medicine, University of Iowa Iowa, IA 52242, USA.
Objectives: This study aims to explore the capabilities of dendritic learning within feedforward tree networks (FFTN) in comparison to traditional synaptic plasticity models, particularly in the context of digit recognition tasks using the MNIST dataset.
Methods: We employed FFTNs with nonlinear dendritic segment amplification and Hebbian learning rules to enhance computational efficiency. The MNIST dataset, consisting of 70,000 images of handwritten digits, was used for training and testing.
Neurology
February 2025
Department of Advanced Biomedical Sciences, University "Federico II," Naples, Italy.
Background And Objectives: Although multiple sclerosis (MS) can be conceptualized as a network disorder, brain network analyses typically require advanced MRI sequences not commonly acquired in clinical practice. Using conventional MRI, we assessed cross-sectional and longitudinal structural disconnection and morphometric similarity networks in people with MS (pwMS), along with their relationship with clinical disability.
Methods: In this longitudinal monocentric study, 3T structural MRI of pwMS and healthy controls (HC) was retrospectively analyzed.
Eur Heart J Digit Health
January 2025
Department of Cardiovascular Surgery of Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Aims: Accurate heart function estimation is vital for detecting and monitoring cardiovascular diseases. While two-dimensional echocardiography (2DE) is widely accessible and used, it requires specialized training, is prone to inter-observer variability, and lacks comprehensive three-dimensional (3D) information. We introduce CardiacField, a computational echocardiography system using a 2DE probe for precise, automated left ventricular (LV) and right ventricular (RV) ejection fraction (EF) estimations, which is especially easy to use for non-cardiovascular healthcare practitioners.
View Article and Find Full Text PDFNPJ Digit Med
January 2025
School of Mechanical Engineering, Shandong University, Jinan, China.
Extensive research on retinal layer segmentation (RLS) using deep learning (DL) is mostly approaching a performance plateau, primarily due to reliance on structural information alone. To address the present situation, we conduct the first study on the impact of multi-spectral information (MSI) on RLS. Our experimental results show that incorporating MSI significantly improves segmentation accuracy for retinal layer optical coherence tomography (OCT) images.
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