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Diabetes can lead to serious microvascular complications like proliferative diabetic retinopathy (PDR), which is the leading cause of blindness in adults. The proteomic changes that occur during PDR cannot be measured in the human retina for ethical reasons, but could be reflected by proteomic changes in vitreous humor. Thus, we considered that comparisons between the proteome profiles of the vitreous humors of PDR and nondiabetic controls could lead to the discovery of novel pathogenic proteins and clinical biomarkers. In this study, the authors used several proteomic methods to comprehensively examine vitreous humor proteomes of PDR patients and nondiabetic controls. These methods included immunoaffinity subtraction (IS)/2-DE/ MALDI-MS, nano-LC-MALDI-MS/MS, and nano-LC-ESI-MS/MS. The identified proteins were subjected to the Trans-Proteomic Pipeline validation process. Resultantly, 531 proteins were identified, i.e., 415 and 346 proteins were identified in PDR and nondiabetic control vitreous humor samples, respectively, and of these 531 proteins, 240 were identified for the first time in this study. The PDR vitreous proteome was also found to contain many proteins possibly involved in the pathogenesis of PDR. The proteins described provide the most comprehensive proteome listing in the vitreous humor samples of PDR and nondiabetic control patients.

Data augmentation has recently emerged as an essential component of modern training recipes for visual recognition tasks. However, data augmentation for video recognition has been rarely explored despite its effectiveness. Few existing augmentation recipes for video recognition naively extend the image augmentation methods by applying the same operations to the whole video frames. Our main idea is that the magnitude of augmentation operations for each frame needs to be changed over time to capture the real-world video's temporal variations. These variations should be generated as diverse as possible using fewer additional hyperparameters during training. Through this motivation, we propose a simple yet effective video data augmentation framework, DynaAugment. The magnitude of augmentation operations on each frame is changed by an effective mechanism, Fourier Sampling that parameterizes diverse, smooth, and realistic temporal variations. DynaAugment also includes an extended search space suitable for video for automatic data augmentation methods. DynaAugment experimentally demonstrates that there are additional performance rooms to be improved from static augmentations on diverse video models. Specifically, we show the effectiveness of DynaAugment on various video datasets and tasks: large-scale video recognition (Kinetics-400 and Something-Something-v2), small-scale video recognition (UCF-101 and HMDB-51), fine-grained video recognition (Diving-48 and FineGym), video action segmentation on Breakfast, video action localization on THUMOS'14, and video object detection on MOT17Det. DynaAugment also enables video models to learn more generalized representation to improve the model robustness on the corrupted videos.Preprint. Under review.

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We report unique thermo-optical characteristics of DNA-Cetyl tri-methyl ammonium (DNA-CTMA) thin solid film with a large negative thermo-optical coefficient of -3.4×10^-4/°C in the temperature range from 20°C to 70°C without any observable thermal hysteresis. By combining this thermo-optic DNA film and fiber optic multimode interference (MMI) device, we experimentally demonstrated a highly sensitive compact temperature sensor with a large spectral shift of 0.15 nm/°C. The fiber optic MMI device was a concatenated structure with single-mode fiber (SMF)-coreless silica fiber (CSF)-single mode fiber (SMF) and the DNA-CTMA film was deposited on the CSF. The spectral shifts of the device in experiments were compared with the beam propagation method, which showed a good agreement.

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