Describing Videos by Exploiting Temporal Structure

Recent progress in using recurrent neural networks (RNNs) for image description has motivated the exploration of their application for video description. However, while images are static, working with videos requires modeling their dynamic temporal structure and then properly integrating that information into a natural language description. In this context, we propose an approach that successfully takes into account both the local and global temporal structure of videos to produce descriptions. First, our approach incorporates a spatial temporal 3-D convolutional neural network (3-D CNN) representation of the short temporal dynamics. The 3-D CNN representation is trained on video action recognition tasks, so as to produce a representation that is tuned to human motion and behavior. Second we propose a temporal attention mechanism that allows to go beyond local temporal modeling and learns to automatically select the most relevant temporal segments given the text-generating RNN. Our approach exceeds the current state-of-art for both BLEU and METEOR metrics on the Youtube2Text dataset. We also present results on a new, larger and more challenging dataset of paired video and natural language descriptions.Comment: Accepted to ICCV15. This version comes with code release and supplementary materia

ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation

We propose a structured prediction architecture, which exploits the local generic features extracted by Convolutional Neural Networks and the capacity of Recurrent Neural Networks (RNN) to retrieve distant dependencies. The proposed architecture, called ReSeg, is based on the recently introduced ReNet model for image classification. We modify and extend it to perform the more challenging task of semantic segmentation. Each ReNet layer is composed of four RNN that sweep the image horizontally and vertically in both directions, encoding patches or activations, and providing relevant global information. Moreover, ReNet layers are stacked on top of pre-trained convolutional layers, benefiting from generic local features. Upsampling layers follow ReNet layers to recover the original image resolution in the final predictions. The proposed ReSeg architecture is efficient, flexible and suitable for a variety of semantic segmentation tasks. We evaluate ReSeg on several widely-used semantic segmentation datasets: Weizmann Horse, Oxford Flower, and CamVid; achieving state-of-the-art performance. Results show that ReSeg can act as a suitable architecture for semantic segmentation tasks, and may have further applications in other structured prediction problems. The source code and model hyperparameters are available on https://github.com/fvisin/reseg.Comment: In CVPR Deep Vision Workshop, 201

FK506-binding protein-like and FK506-binding protein 8 regulate dual leucine zipper kinase degradation and neuronal responses to axon injury

The dual leucine zipper kinase (DLK) is a key regulator of axon regeneration and degeneration in response to neuronal injury; however, regulatory mechanisms of the DLK function via its interacting proteins are largely unknown. To better understand the molecular mechanism of DLK function, we performed yeast two-hybrid screening analysis and identified FK506-binding protein-like (FKBPL, also known as WAF-1/CIP1 stabilizing protein 39) as a DLK-binding protein. FKBPL binds to the kinase domain of DLK and inhibits its kinase activity. In addition, FKBPL induces DLK protein degradation through ubiquitin-dependent pathways. We further assessed other members in the FKBP protein family and found that FK506-binding protein 8 (FKBP8) also induced DLK degradation. We identified the lysine 271 residue in the kinase domain as a major site of DLK ubiquitination and SUMO3 conjugation and was thus responsible for regulating FKBP8-mediated proteasomal degradation that was inhibited by the substitution of the lysine 271 to arginine. FKBP8-mediated degradation of DLK is mediated by autophagy pathway because knockdown of Atg5 inhibited DLK destabilization. We show that in vivo overexpression of FKBP8 delayed the progression of axon degeneration and suppressed neuronal death after axotomy in sciatic and optic nerves. Taken together, this study identified FKBPL and FKBP8 as novel DLK-interacting proteins that regulate DLK stability via the ubiquitin-proteasome and lysosomal protein degradation pathways

Plasma Biomarkers for Brain Injury in Extracorporeal Membrane Oxygenation

Extracorporeal membrane oxygenation (ECMO) is a life-saving intervention for patients with refractory cardiorespiratory failure. Despite its benefits, ECMO carries a significant risk of neurological complications, including acute brain injury (ABI). Although standardized neuromonitoring and neurological care have been shown to improve early detection of ABI, the inability to perform neuroimaging in a timely manner is a major limitation in the accurate diagnosis of neurological complications. Therefore, blood-based biomarkers capable of detecting ongoing brain injury at the bedside are of great clinical significance. This review aims to provide a concise review of the current literature on plasma biomarkers for ABI in patients on ECMO support