Fungal Planet description sheets: 1614-1696.

Novel species of fungi described in this study include those from various countries as follows: , in leaves of , among deep leaf litter, from uredinium of on , on well-rotted twigs and litter in mixed wet sclerophyll and subtropical rainforest. , on twigs of , on bark, in savannas with shrubs and trees. , on leaves of , (incl.

Vulnerability of Thalamic Nuclei at CSF Interface During the Entire Course of Multiple Sclerosis.

Background And Objectives: Thalamic atrophy can be used as a proxy for neurodegeneration in multiple sclerosis (MS). Some data point toward thalamic nuclei that could be affected more than others. However, the dynamic of their changes during MS evolution and the mechanisms driving their differential alterations are still uncertain.

Staging of progressive supranuclear palsy-Richardson syndrome using MRI brain charts for the human lifespan.

Brain charts for the human lifespan have been recently proposed to build dynamic models of brain anatomy in normal aging and various neurological conditions. They offer new possibilities to quantify neuroanatomical changes from preclinical stages to death, where longitudinal MRI data are not available. In this study, we used brain charts to model the progression of brain atrophy in progressive supranuclear palsy-Richardson syndrome.

Lifespan neurodegeneration of the human brain in multiple sclerosis.

Atrophy related to multiple sclerosis (MS) has been found at the early stages of the disease. However, the archetype dynamic trajectories of the neurodegenerative process, even prior to clinical diagnosis, remain unknown. We modeled the volumetric trajectories of brain structures across the entire lifespan using 40,944 subjects (38,295 healthy controls and 2649 MS patients).

Longitudinal detection of new MS lesions using deep learning.

The detection of new multiple sclerosis (MS) lesions is an important marker of the evolution of the disease. The applicability of learning-based methods could automate this task efficiently. However, the lack of annotated longitudinal data with new-appearing lesions is a limiting factor for the training of robust and generalizing models.