The microbial groups of nitrogen fixers, ammonia oxidizers, and denitrifiers largely drive the inorganic nitrogen cycle in temperate terrestrial ecosystems. Their spatial and temporal dynamics, however, vary depending on the studied scale. The present study aimed to fill a knowledge gap by providing an explicit picture of spatial and temporal dynamics of a subset of these soil microorganisms at the plot scale. We selected an unfertilized perennial grassland, where nitrogen cycling is considered to be efficient and tightly coupled to plant growth. At six times over one growing season 60 soil samples were taken from a 10 m × 10 m area and abundances of marker genes for total archaea and bacteria (16S rRNA), nitrogen fixing bacteria (nifH), ammonia oxidizing archaea (amoA AOA) and bacteria (amoA AOB), and denitrifying bacteria (nirS, nirK and nosZ) were determined by qPCR. Potential nitrification activity (PNA) and denitrifying enzyme activity (DEA) were determined. Seasonal changes in abundance patterns of marker genes were detected, and were associated with changes in substrate availability associated with plant growth stages. Potential nitrification and denitrification enzyme activities were strongly spatially structured at the studied scale, corresponding to periods of rapid plant growth, June and October, and their spatial distributions were similar, providing visual evidence of highly localized spatial and temporal conditions at this scale. Temporal variability in the N-cycling communities versus the stability of their respective potential activities provided evidence of both short-lived temporal niche partitioning and a degree of microbial functional redundancy. Our results indicate that in an unfertilized grassland, at the meter scale, abundances of microbial N-cycling organisms can exhibit transient changes, while nitrogen cycling processes remain stable.

Spatial and temporal dynamics of nitrogen fixing, nitrifying and denitrifying microbes in an unfertilized grassland soil

Cluster ensemble has been shown to be very effective in unsupervised classification learning by generating a large pool of different clustering solutions and then combining them into a final decision. However, the task of it becomes more difficult due to the inherent complexities among base cluster results, such as uncertainty, vagueness and overlapping. Granular computing is one of the fastest growing information-processing paradigms in the domain of computational intelligence and human-centric systems. As the core part of granular computing, the rough set theory dealing with inexact, uncertain, or vague information, has been widely applied in machine learning and knowledge discovery related areas in recent years. From these perspectives, in this paper, a hierarchical cluster ensemble model based on knowledge granulation is proposed with the attempt to provide a new way to deal with the cluster ensemble problem together with ensemble learning application of the knowledge granulation. A novel rough distance is introduced to measure the dissimilarity between base partitions and the notion of knowledge granulation is improved to measure the agglomeration degree of a given granule. Furthermore, a novel objective function for cluster ensembles is defined and the corresponding inferences are made. A hierarchical cluster ensemble algorithm based on knowledge granulation is designed. Experimental results on real-world data sets demonstrate the effectiveness for better cluster ensemble of the proposed method.

Hierarchical cluster ensemble model based on knowledge granulation

Accurate ROI localization and hierarchical hyper-sphere model for finger-vein recognition

Facial semantic descriptors based on information granules

Graphical abstractDisplay Omitted HighlightsProposed hybrid methodology is a coarse to fine approach to abdominal organs segmentation.The evolving organ contour is initialized by means of information granule.The image is fuzzified using Gaussian membership function.The granule-adjusted hybrid level set approach yields final segmentation.The approach effectiveness was confirmed based on 20 CT studies and 4 types of organs. Hybrid methodology that combines granular computing with a level set approach for image segmentation is introduced in this paper. The goal of the study is to provide an adjustable semi-automated method for 3D segmentation of abdominal organs in computed tomography studies. Based on initial guidance in terms of models or seed points the information granule is formulated for each anatomical structure under consideration. The granulation leads via spatial volume resampling and granule-driven image intensity fuzzification to the final segmentation stage employing a hybrid level set approach. Depending on the organ intensity and shape the segmentation runs with selected parameters. The algorithm has been evaluated by 20 computed tomography studies and four various structures delineated by an expert. The experimental part presents a comprehensive analysis of subsequent stages of the algorithm. The voxel-wise segmentation sensitivities reached 94.4% for the right kidney, 93.8% for the left kidney, 94.0% for the spleen, and 94.0% for the liver, with the Dice index at 93.7%, 94.2%, 91.0%, and 92.9%, respectively.

Calibrating level set approach by granular computing in computed tomography abdominal organs segmentation

The problem of generating a superresolution (SR) image from a single low-resolution (LR) input image is addressed via granular computing clustering in the paper. Firstly, and the training images are regarded as SR image and partitioned into some SR patches, which are resized into LS patches, the training set is composed of the SR patches and the corresponding LR patches. Secondly, the granular computing (GrC) clustering is proposed by the hypersphere representation of granule and the fuzzy inclusion measure compounded by the operation between two granules. Thirdly, the granule set (GS) including hypersphere granules with different granularities is induced by GrC and used to formthe relation between the LR image and the SR image by lasso. Experimental results showed that GrC achieved the least root mean square errors between the reconstructed SR image and the original image compared with bicubic interpolation, sparse representation, and NNLasso.

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Image superresolution reconstruction via granular computing clustering

Decision Tree Classifiers Based on Granular Computing

Bottom-up and top-down are two main computing models in granular computing by which the granule set including granules with different granularities. The top-down hyperbox granular computing classification algorithm based on isolation, or IHBGrC for short, is proposed in the framework of top-down computing model. Algorithm IHBGrC defines a novel function to measure the distance between two hyperbox hgranules, which is used to judge the inclusion relation between two hyperbox granules, the meet operation is used to isolate the ith class data from the other class data, and the hyperbox granule is partitioned into some hyperbox granules which include the ith class data. We compare the performance of IHBGrC with support vector machines and HBGrC, for a number of two-class problems and multiclass problems. Our computational experiments showed that IHBGrC can both speed up training and achieve comparable generalization performance.

https://journals.sagepub.com/doi/pdf/10.1177/1748301816676818

Fan Zhang

Papers

Image superresolution reconstruction via granular computing clustering

Decision Tree Classifiers Based on Granular Computing

Isolation-based hyperbox granular classification computing: