Long-Term N Addition, Not Warming, Increases Net Ecosystem CO\u3csub\u3e2\u3c/sub\u3e Exchange in a Desert Steppe in Northern China

Grasslands cover a major part of the global terrestrial area and provide important ecosystem functions such as sequestration of carbon (C). Desert steppes are unique ecosystems with properties in between desert and grasslands. They are considered to be vulnerable ecosystems that are at risk of desertification due to global change. To provide a robust prediction of the effect of climate warming and increased nitrogen (N) deposition on desert steppe, long-term studies that capture the annual variation in precipitation are needed. We conducted a 12-year field experiment in a desert steppe which showed that warming did not change ecosystem C exchange whereas N addition increased ecosystem C storage. Moreover, warming did not change total aboveground biomass, mainly due to the contrasting responses of C4 and C3 plants, especially in the presence of additional N. Therefore, our study predicts that warming do not necessarily lead to degradation of the desert steppe and N addition may have a positive effect on CO2 sequestration, providing a negative feedback on climate change. However, these global change drivers do alter vegetation composition in the desert steppe, which can have consequences on a diversity of ecosystem functions

Reachability Analysis of Asynchronous Dynamic Pushdown Networks Based on Tree Semantics Approach

ADPN (Asynchronous Dynamic Pushdown Networks) are an abstract model for concurrent programs with recursive procedures and dynamic thread creation. Usually, asynchronous dynamic pushdown networks are described with interleaving semantics, in which the backward analysis is not effective. In order to improve interleaving semantics, tree semantics approach was introduced. This paper extends the tree semantics to ADPN. Because the reachability problem of ADPN is also undecidable, we address the context-bounded reachability problem and provide an algorithm for backward reachability analysis with tree-based semantics Approach

Relation Extraction Using Convolution Tree Kernel Expanded with Entity Features

PACLIC 21 / Seoul National University, Seoul, Korea / November 1-3, 200

Unsupervised Texture Segmentation Using Active Contour Model and Oscillating Information

Textures often occur in real-world images and may cause considerable difficulties in image segmentation. In order to segment texture images, we propose a new segmentation model that combines image decomposition model and active contour model. The former model is capable of decomposing structural and oscillating components separately from texture image, and the latter model can be used to provide smooth segmentation contour. In detail, we just replace the data term of piecewise constant/smooth approximation in CCV (convex Chan-Vese) model with that of image decomposition model-VO (Vese-Osher). Therefore, our proposed model can estimate both structural and oscillating components of texture images as well as segment textures simultaneously. In addition, we design fast Split-Bregman algorithm for our proposed model. Finally, the performance of our method is demonstrated by segmenting some synthetic and real texture images

Joint Learning-based Causal Relation Extraction from Biomedical Literature

Causal relation extraction of biomedical entities is one of the most complex tasks in biomedical text mining, which involves two kinds of information: entity relations and entity functions. One feasible approach is to take relation extraction and function detection as two independent sub-tasks. However, this separate learning method ignores the intrinsic correlation between them and leads to unsatisfactory performance. In this paper, we propose a joint learning model, which combines entity relation extraction and entity function detection to exploit their commonality and capture their inter-relationship, so as to improve the performance of biomedical causal relation extraction. Meanwhile, during the model training stage, different function types in the loss function are assigned different weights. Specifically, the penalty coefficient for negative function instances increases to effectively improve the precision of function detection. Experimental results on the BioCreative-V Track 4 corpus show that our joint learning model outperforms the separate models in BEL statement extraction, achieving the F1 scores of 58.4% and 37.3% on the test set in Stage 2 and Stage 1 evaluations, respectively. This demonstrates that our joint learning system reaches the state-of-the-art performance in Stage 2 compared with other systems.Comment: 15 pages, 3 figure

Proteomic analysis of the biomass hydrolytic potentials of Penicillium oxalicum lignocellulolytic enzyme system

Additional file 2: Table S1. The functional annotations of proteins identified in the proteome of SP. Mass spectrometry-based proteomics study was performed to comprehensively dissect the lignocellulolytic enzyme profile of SP. Accession, Protein name, PSM, Calc. MW, CBM, Calc. pI and CAZy family of identified proteins were shown

Active Contour Model Coupling with Higher Order Diffusion for Medical Image Segmentation

Active contour models are very popular in image segmentation. Different features such as mean gray and variance are selected for different purpose. But for image with intensity inhomogeneities, there are no features for segmentation using the active contour model. The images with intensity inhomogeneities often occurred in real world especially in medical images. To deal with the difficulties raised in image segmentation with intensity inhomogeneities, a new active contour model with higher-order diffusion method is proposed. With the addition of gradient and Laplace information, the active contour model can converge to the edge of the image even with the intensity inhomogeneities. Because of the introduction of Laplace information, the difference scheme becomes more difficult. To enhance the efficiency of the segmentation, the fast Split Bregman algorithm is designed for the segmentation implementation. The performance of our method is demonstrated through numerical experiments of some medical image segmentations with intensity inhomogeneities