The ficolins recognize carbohydrates and acetylated compounds on microorganisms and dying host cells and are able to activate the lectin pathway of the complement system. In humans, three ficolin genes have been identified: FCN1, FCN2 and FCN3, which encode ficolin-1, ficolin-2 and ficolin-3, respectively. Rodents have only two ficolins designated ficolin-A and ficolin-B that are closely relate...

Quantitative assessment of carbon (C) storage by forests requires an understanding of climatic controls over respiratory C loss. Ecosystem respiration can be estimated biometrically as the sum (R Sigma) of soil (Rs), leaf (Rl) and wood (Rw) respiration, and meteorologically by measuring above-canopy nocturnal CO2 fluxes (Fcn). Here we estimated R Sigma over 5 yr in a forest in Michigan, USA, an...

The quenching of excited singlet methylene blue (IMB + * ) by N,N-dimethylaminomethylferrocene (FcN) in acetonitrile at room temperature has been studied using femtosecond pump-probe absorption spectroscopy. At high FcN concentration static quenching via an intermolecular electron transfer mechanism constitutes the predominating decay channel for l MB + * The time constants of the large amplitu...

In this work, we propose a technique that utilizes a fully convolutional network (FCN) to localize image splicing attacks. We first evaluated a single-task FCN (SFCN) trained only on the surface label. Although the SFCN is shown to provide superior performance over existing methods, it still provides a coarse localization output in certain cases. Therefore, we propose the use of a multi-task FC...

In this paper, we propose a novel learning based method for automated segmentation of brain tumor in multimodal MRI images. The machine learned features from fully convolutional neural network (FCN) and hand-designed texton features are used to classify the MRI image voxels. The score map with pixelwise predictions is used as a feature map which is learned from multimodal MRI training dataset u...

This paper proposes a novel image segmentation approach that integrates fully convolutional networks (FCNs) with a level set model. Compared with a FCN, the integrated method can incorporate smoothing and prior information to achieve an accurate segmentation. Furthermore, different than using the level set model as a post-processing tool, we integrate it into the training phase to fine-tune the...

We propose a weakly supervised method using two algorithms to predict object bounding boxes given only an image classification dataset. First algorithm is a simple Fully Convolutional Network (FCN) trained to classify object instances. We use the property of FCN to return a mask for images larger than training images to get a primary output segmentation mask during test time by passing an image...

Face detection has achieved great success using the region-based methods. In this report, we propose a region-based face detector applying deep networks in a fully convolutional fashion, named Face R-FCN. Based on Region-based Fully Convolutional Networks (R-FCN), our face detector is more accurate and computationally efficient compared with the previous R-CNN based face detectors. In our appro...

We detected phase jumps of the Free Core Nutation (FCN) of the Earth directly from the analysis of the Very Long Baseline Interferometer (VLBI) observation of the Earth rotation for the period 1984-2003 by applying the Weighted Wavelet Z-Transform (WWZ) method and the Short-time Periodogram with the Gabor function (SPG) method. During the period, the FCN had two significant phase jumps in 1992 ...