MONAI: An open-source framework for deep learning in healthcare

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.Comment: www.monai.i

Synaptic Proteins Linked to HIV-1 Infection and Immunoproteasome Induction: Proteomic Analysis of Human Synaptosomes

Infection of the central nervous system with human immunodeficiency virus type 1 (HIV-1) can produce morphological changes in the neocortical synaptodendritic arbor that are correlated with neurocognitive impairment. To determine whether HIV-1 infection influences the protein composition of human synapses, a proteomic study of isolated nerve endings was undertaken. Synaptosomes from frontal neocortex were isolated using isopyknic centrifugation from 19 human brain specimens. Purity and enrichment were assessed by measuring pre- and postsynaptic protein markers. Two-dimensional polyacrylamide gel electrophoresis and matrix-assisted laser desorption ionization time-of-flight mass spectrometry was used to screen for proteins differentially expressed in HIV/AIDS. The concentrations of 31 candidate protein spots were potentially abnormal in HIV-infected decedents with HIV encephalitis and/or increased expression of immunoproteasome subunits. Immunoblots showed that the concentration of some of them was related to HIV-1 infection of the brain and immunoproteasome (IPS) induction. Synapsin 1b and stathmin were inversely related to brain HIV-1 load; 14-3-3ζ and 14-4-4ε proteins were higher in subjects with HIV-1 loads. Perturbed synaptosome proteins were linked with IPS subunit composition, and 14-3-3ζ was histologically colocalized with IPS subunits in stained neocortical neurons. Proteomics illustrates that certain human proteins within the synaptic compartment are involved with changes in the synaptodendritic arbor and neurocognitive impairment in HIV-1-infected people

Neutron cross sections.

Brookhaven National Laboratory [operated by] Associated Universities Inc. under contract with the U.S. Atomic Energy Commission."Physics--TID-4500, 32nd Ed."Includes bibliographical references.Mode of access: Internet