Exogenous Leukemia Inhibitory Factor Stimulates Oligodendrocyte Progenitor Cell Proliferation and Enhances Hippocampal Remyelination

New CNS neurons and glia are generated throughout adulthood from endogenous neural stem and progenitor cells. These progenitors can respond to injury, but their ability to proliferate, migrate, differentiate, and survive is usually insufficient to replace lost cells and restore normal function. Potentiating the progenitor response with exogenous factors is an attractive strategy for the treatment of nervous system injuries and neurodegenerative and demyelinating disorders. Previously, we reported that delivery of leukemia inhibitory factor (LIF) to the CNS stimulates the self-renewal of neural stem cells and the proliferation of parenchymal glial progenitors. Here we identify these parenchymal glia as oligodendrocyte (OL) progenitor cells (OPCs) and show that LIF delivery stimulates their proliferation through the activation of gp130 receptor signaling within these cells. Importantly, this effect of LIF on OPC proliferation can be harnessed to enhance the generation of OLs that express myelin proteins and reform nodes of Ranvier in the context of chronic demyelination in the adult mouse hippocampus. Our findings, considered together with the known beneficial effects of LIF on OL and neuron survival, suggest that LIF has both reparative and protective activities that make it a promising potential therapy for CNS demyelinating disorders and injuries

Hybrid RANS/LES of flow in a rib-roughened channel with rotation

The aim of the present study is to verify the reliability of a k-ω based hybrid RANS/LES model in reproducing the flow in a rib-roughened rotating channel. The numerical results obtained with the hybrid RANS/LES model are compared to experimental data by Coletti and Arts (2011) and to the results obtained with the RANS k-ω model of Wilcox (2008). We demonstrate that the hybrid RANS/LES model gives realistic results for simulation of the rotating ribbed duct flow, without the necessity to add ad hoc corrections for system rotation to the underlying RANS mode

A large geometric distortion in the first photointermediate of rhodopsin, determined by double-quantum solid-state NMR

Double-quantum magic-angle-spinning NMR experiments were performed on 11,12-C-13(2)-retinylidene-rhodopsin under illumination at low temperature, in order to characterize torsional angle changes at the C11-C12 photoisomerization site. The sample was illuminated in the NMR rotor at low temperature (similar to 120 K) in order to trap the primary photointermediate, bathorhodopsin. The NMR data are consistent with a strong torsional twist of the HCCH moiety at the isomerization site. Although the HCCH torsional twist was determined to be at least 40A degrees, it was not possible to quantify it more closely. The presence of a strong twist is in agreement with previous Raman observations. The energetic implications of this geometric distortion are discussed

Algebraic shortcuts for leave-one-out cross-validation in supervised network inference

Supervised machine learning techniques have traditionally been very successful at reconstructing biological networks, such as protein-ligand interaction, protein-protein interaction and gene regulatory networks. Many supervised techniques for network prediction use linear models on a possibly nonlinear pairwise feature representation of edges. Recently, much emphasis has been placed on the correct evaluation of such supervised models. It is vital to distinguish between using a model to either predict new interactions in a given network or to predict interactions for a new vertex not present in the original network. This distinction matters because (i) the performance might dramatically differ between the prediction settings and (ii) tuning the model hyperparameters to obtain the best possible model depends on the setting of interest. Specific cross-validation schemes need to be used to assess the performance in such different prediction settings. In this work we discuss a state-of-the-art kernel-based network inference technique called two-step kernel ridge regression. We show that this regression model can be trained efficiently, with a time complexity scaling with the number of vertices rather than the number of edges. Furthermore, this framework leads to a series of cross-validation shortcuts that allow one to rapidly estimate the model performance for any relevant network prediction setting. This allows computational biologists to fully assess the capabilities of their models

Simple connectome inference from partial correlation statistics in calcium imaging

In this work, we propose a simple yet effective solution to the problem of connectome inference in calcium imaging data. The proposed algorithm consists of two steps. First, processing the raw signals to detect neural peak activities. Second, inferring the degree of association between neurons from partial correlation statistics. This paper summarises the methodology that led us to win the Connectomics Challenge, proposes a simplified version of our method, and finally compares our results with respect to other inference methods

Magnitude control of commutator errors

Non-uniform filtering of the Navier-Stokes equations expresses itself, next to the turbulent stresses, in additional closure terms known as commutator errors. These terms require explicit subgrid modeling if the non-uniformity of the filter is sufficiently pronounced. We derive expressions for the magnitude of the mean flux, the turbulent stress flux and the commutator error for individual Fourier modes. This gives rise to conditions for the spatial variations in the filter-width and the filter-skewness subject to which the magnitude of the commutator errors can be controlled. These conditions are translated into smoothness requirements of the computational grid, that involve ratios of first -, second - and third order derivatives of the grid mapping

Gray matter imaging in multiple sclerosis: what have we learned?

At the early onset of the 20th century, several studies already reported that the gray matter was implicated in the histopathology of multiple sclerosis (MS). However, as white matter pathology long received predominant attention in this disease, and histological staining techniques for detecting myelin in the gray matter were suboptimal, it was not until the beginning of the 21st century that the true extent and importance of gray matter pathology in MS was finally recognized. Gray matter damage was shown to be frequent and extensive, and more pronounced in the progressive disease phases. Several studies subsequently demonstrated that the histopathology of gray matter lesions differs from that of white matter lesions. Unfortunately, imaging of pathology in gray matter structures proved to be difficult, especially when using conventional magnetic resonance imaging (MRI) techniques. However, with the recent introduction of several more advanced MRI techniques, the detection of cortical and subcortical damage in MS has considerably improved. This has important consequences for studying the clinical correlates of gray matter damage. In this review, we provide an overview of what has been learned about imaging of gray matter damage in MS, and offer a brief perspective with regards to future developments in this field

T2 lesion location really matters: a 10 year follow-up study in primary progressive multiple sclerosis

Objectives: Prediction of long term clinical outcome in patients with primary progressive multiple sclerosis (PPMS) using imaging has important clinical implications, but remains challenging. We aimed to determine whether spatial location of T2 and T1 brain lesions predicts clinical progression during a 10-year follow-up in PPMS. Methods: Lesion probability maps of the T2 and T1 brain lesions were generated using the baseline scans of 80 patients with PPMS who were clinically assessed at baseline and then after 1, 2, 5 and 10 years. For each patient, the time (in years) taken before bilateral support was required to walk (time to event (TTE)) was used as a measure of progression rate. The probability of each voxel being ‘lesional’ was correlated with TTE, adjusting for age, gender, disease duration, centre and spinal cord cross sectional area, using a multiple linear regression model. To identify the best, independent predictor of progression, a Cox regression model was used. Results: A significant correlation between a shorter TTE and a higher probability of a voxel being lesional on T2 scans was found in the bilateral corticospinal tract and superior longitudinal fasciculus, and in the right inferior fronto-occipital fasciculus (p<0.05). The best predictor of progression rate was the T2 lesion load measured along the right inferior fronto-occipital fasciculus (p=0.016, hazard ratio 1.00652, 95% CI 1.00121 to 1.01186). Conclusion: Our results suggest that the location of T2 brain lesions in the motor and associative tracts is an important contributor to the progression of disability in PPMS, and is independent of spinal cord involvement