Downregulated expression of lactate dehydrogenase in adult oligodendrocytes and its implication for the transfer of glycolysis products to axons.

Oligodendrocytes and astrocytes are metabolically coupled to neuronal compartments. Pyruvate and lactate can shuttle between glial cells and axons via monocarboxylate transporters. However, lactate can only be synthesized or used in metabolic reactions with the help of lactate dehydrogenase (LDH), a tetramer of LDHA and LDHB subunits in varying compositions. Here we show that mice with a cell type-specific disruption of both Ldha and Ldhb genes in oligodendrocytes lack a pathological phenotype that would be indicative of oligodendroglial dysfunctions or lack of axonal metabolic support. Indeed, when combining immunohistochemical, electron microscopical, and in situ hybridization analyses in adult mice, we found that the vast majority of mature oligodendrocytes lack detectable expression of LDH. Even in neurodegenerative disease models and in mice under metabolic stress LDH was not increased. In contrast, at early development and in the remyelinating brain, LDHA was readily detectable in immature oligodendrocytes. Interestingly, by immunoelectron microscopy LDHA was particularly enriched at gap junctions formed between adjacent astrocytes and at junctions between astrocytes and oligodendrocytes. Our data suggest that oligodendrocytes metabolize lactate during development and remyelination. In contrast, for metabolic support of axons mature oligodendrocytes may export their own glycolysis products as pyruvate rather than lactate. Lacking LDH, these oligodendrocytes can also "funnel" lactate through their "myelinic" channels between gap junction-coupled astrocytes and axons without metabolizing it. We suggest a working model, in which the unequal cellular distribution of LDH in white matter tracts facilitates a rapid and efficient transport of glycolysis products among glial and axonal compartments.

Automatic salient object detection via maximum entropy estimation.

This Letter proposes a rapid method for automatic salient object detection inspired by the idea that an image consists of redundant information and novelty fluctuations. We believe object detection can be achieved by removing the nonsalient parts and focusing on the salient object. Considering the relation between the composition of the image and the aim of object detection, we constructed what we believe is a more reliable saliency map to evaluate the image composition. The local energy feature is combined with a simple biologically inspired model (color, intensity, orientation) to strengthen the integrity of the object in the saliency map. We estimated the entropy of the object via the maximum entropy method. Then, we removed pixels of minimal intensity from the original image and compute the entropy of the resulting images, correlating this entropy with the object entropy. Our experimental results show that the algorithm outperforms the state-of-the-art methods and is more suitable in real-time applications.