The polarization of adipose tissue-resident macrophages toward the alternatively activated, anti-inflammatory M2 phenotype is believed to improve insulin sensitivity. However, the mechanisms controlling tissue macrophage activation remain unclear. Here we show that adipocytes are a source of Th2 cytokines, including IL-13 and to a lesser extent IL-4, which induce macrophage PPARdelta/beta (Ppard/b) expression through a STAT6 binding site on its promoter to activate alternative activation. Coculture studies indicate that Ppard ablation renders macrophages incapable of transition to the M2 phenotype, which in turns causes inflammation and metabolic derangement in adipocytes. Remarkably, a similar regulatory mechanism by hepatocyte-derived Th2 cytokines and macrophage PPARdelta is found to control hepatic lipid metabolism. The physiological relevance of this paracrine pathway is demonstrated in myeloid-specific PPARdelta(-/-) mice, which develop insulin resistance and show increased adipocyte lipolysis and severe hepatosteatosis. These findings provide a molecular basis to modulate tissue-resident macrophage activation and insulin sensitivity.

The state-of-the-art performance for object detection has been significantly improved over the past two years. Besides the introduction of powerful deep neural networks such as GoogleNet [1] and VGG [2], novel object detection frameworks such as R-CNN [3] and its successors, Fast R-CNN [4] and Faster R-CNN [5], play an essential role in improving the state-of-the-art. Despite their effectiveness on still images, those frameworks are not specifically designed for object detection from videos. Temporal and contextual information of videos are not fully investigated and utilized. In this work, we propose a deep learning framework that incorporates temporal and contextual information from tubelets obtained in videos, which dramatically improves the baseline performance of existing stillimage detection frameworks when they are applied to videos. It is called T-CNN, i.e. tubelets with convolutional neueral networks. The proposed framework won newly introduced object-detectionfrom-video (VID) task with provided data in the ImageNet Large-Scale Visual Recognition Challenge 2015 (ILSVRC 2015). Code is publicly available at https://github.com/myfavouritekk/T-CNN.

Deep Convolution Neural Networks (CNNs) have shown impressive performance in various vision tasks such as image classification, object detection and semantic segmentation. For object detection, particularly in still images, the performance has been significantly increased last year thanks to powerful deep networks (e.g. GoogleNet) and detection frameworks (e.g. Regions with CNN features (R-CNN)). The lately introduced ImageNet [6] task on object detection from video (VID) brings the object detection task into the video domain, in which objects' locations at each frame are required to be annotated with bounding boxes. In this work, we introduce a complete framework for the VID task based on still-image object detection and general object tracking. Their relations and contributions in the VID task are thoroughly studied and evaluated. In addition, a temporal convolution network is proposed to incorporate temporal information to regularize the detection results and shows its effectiveness for the task. Code is available at https://github.com/ myfavouritekk/vdetlib.

In an effort to elucidate the physiologic role of ABCA7, we generated mice lacking this transporter (Abca7 ؊/؊ mice). Homozygous null mice were produced from intercrosses of heterozygous null mice at the expected Mendelian frequency and developed normally without any obvious phenotypic abnormalities. Cholesterol and phospholipid efflux stimulated by apolipoprotein A-I from macrophages isolated from wild type and Abca7 ؊/؊ mice did not differ, suggesting that these activities may not be central to the physiological role of the transporter in vivo. Abca7 ؊/؊ females, but not males, had significantly less visceral fat and lower total serum and high density lipoprotein cholesterol levels than wild type, gender-matched littermates. ABCA7 expression was detected in hippocampal and cortical neurons by in situ hybridization and in brain and white adipose tissue by Western blotting. Induction of adipocyte differentiation from 3T3 fibroblasts in culture led to a marked increase in ABCA7 expression. These studies suggest that ABCA7 plays a novel role in lipid and fat metabolism that Abca7 ؊/؊ mice can be used to elucidate.

BackgroundIn Huntington's disease (HD), an expanded CAG repeat produces characteristic striatal neurodegeneration. Interestingly, the HD CAG repeat, whose length determines age at onset, undergoes tissue-specific somatic instability, predominant in the striatum, suggesting that tissue-specific CAG length changes could modify the disease process. Therefore, understanding the mechanisms underlying the tissue specificity of somatic instability may provide novel routes to therapies. However progress in this area has been hampered by the lack of sensitive high-throughput instability quantification methods and global approaches to identify the underlying factors.ResultsHere we describe a novel approach to gain insight into the factors responsible for the tissue specificity of somatic instability. Using accurate genetic knock-in mouse models of HD, we developed a reliable, high-throughput method to quantify tissue HD CAG repeat instability and integrated this with genome-wide bioinformatic approaches. Using tissue instability quantified in 16 tissues as a phenotype and tissue microarray gene expression as a predictor, we built a mathematical model and identified a gene expression signature that accurately predicted tissue instability. Using the predictive ability of this signature we found that somatic instability was not a consequence of pathogenesis. In support of this, genetic crosses with models of accelerated neuropathology failed to induce somatic instability. In addition, we searched for genes and pathways that correlated with tissue instability. We found that expression levels of DNA repair genes did not explain the tissue specificity of somatic instability. Instead, our data implicate other pathways, particularly cell cycle, metabolism and neurotransmitter pathways, acting in combination to generate tissue-specific patterns of instability.ConclusionOur study clearly demonstrates that multiple tissue factors reflect the level of somatic instability in different tissues. In addition, our quantitative, genome-wide approach is readily applicable to high-throughput assays and opens the door to widespread applications with the potential to accelerate the discovery of drugs that alter tissue instability.