Multivariate Image Analysis in Gaussian Multi-Scale Space for Defect Detection

Inspired by the coarse-to-fine visual perception process of human vision system,a new approach based on Gaussianmulti-scale space for defect detection of industrial products was proposed.By selecting different scale parameters of theGaussian kernel,the multi-scale representation of the original image data could be obtained and used to constitute the multi-variate image,in which each channel could represent a perceptual observation of the original image from different scales.TheMultivariate Image Analysis (MIA) techniques were used to extract defect features information.The MIA combined PrincipalComponent Analysis (PCA) to obtain the principal component scores of the multivariate test image.The Q-statistic image,derived from the residuals after the extraction of the first principal component score and noise,could be used to efficiently revealthe surface defects with an appropriate threshold value decided by training images.Experimental results show that the proposedmethod performs better than the gray histogram-based method.It has less sensitivity to the inhomogeneous of illumination,andhas more robustness and reliability of defect detection with lower pseudo reject rate.     

The Multi-Scale Network Landscape of Collaboration

Propelled by the increasing availability of large-scale high-quality data, advanced data modeling and analysis techniques are enabling many novel and significant scientific understanding of a wide range of complex social, natural, and technological systems. These developments also provide opportunities for studying cultural systems and phenomena—which can be said to refer to all products of human creativity and way of life. An important characteristic of a cultural product is that it does not exist in isolation from others, but forms an intricate web of connections on many levels. In the creation and dissemination of cultural products and artworks in particular, collaboration and communication of ideas play an essential role, which can be captured in the heterogeneous network of the creators and practitioners of art. In this paper we propose novel methods to analyze and uncover meaningful patterns from such a network using the network of western classical musicians constructed from a large-scale comprehensive Compact Disc recordings data. We characterize the complex patterns in the network landscape of collaboration between musicians across multiple scales ranging from the macroscopic to the mesoscopic and microscopic that represent the diversity of cultural styles and the individuality of the artists.     

Link Community Detection Using Generative Model and Nonnegative Matrix Factorization

Discovery of communities in complex networks is a fundamental data analysis problem with applications in various domains. While most of the existing approaches have focused on discovering communities of nodes, recent studies have shown the advantages and uses of link community discovery in networks. Generative models provide a promising class of techniques for the identification of modular structures in networks, but most generative models mainly focus on the detection of node communities rather than link communities. In this work, we propose a generative model, which is based on the importance of each node when forming links in each community, to describe the structure of link communities. We proceed to fit the model parameters by taking it as an optimization problem, and solve it using nonnegative matrix factorization. Thereafter, in order to automatically determine the number of communities, we extend the above method by introducing a strategy of iterative bipartition. This extended method not only finds the number of communities all by itself, but also obtains high efficiency, and thus it is more suitable to deal with large and unexplored real networks. We test this approach on both synthetic benchmarks and real-world networks including an application on a large biological network, and compare it with two highly related methods. Results demonstrate the superior performance of our approach over competing methods for the detection of link communities.     

Targeting the Biophysical Properties of the Myeloma Initiating Cell Niches: A Pharmaceutical Synergism Analysis Using Multi-Scale Agent-Based Modeling

Multiple myeloma, the second most common hematological cancer, is currently incurable due to refractory disease relapse and development of multiple drug resistance. We and others recently established the biophysical model that myeloma initiating (stem) cells (MICs) trigger the stiffening of their niches via SDF-1/CXCR4 paracrine; The stiffened niches then promote the colonogenesis of MICs and protect them from drug treatment. In this work we examined in silico the pharmaceutical potential of targeting MIC niche stiffness to facilitate cytotoxic chemotherapies. We first established a multi-scale agent-based model using the Markov Chain Monte Carlo approach to recapitulate the niche stiffness centric, pro-oncogenetic positive feedback loop between MICs and myeloma-associated bone marrow stromal cells (MBMSCs), and investigated the effects of such intercellular chemo-physical communications on myeloma development. Then we used AMD3100 (to interrupt the interactions between MICs and their stroma) and Bortezomib (a recently developed novel therapeutic agent) as representative drugs to examine if the biophysical properties of myeloma niches are drugable. Results showed that our model recaptured the key experimental observation that the MBMSCs were more sensitive to SDF-1 secreted by MICs, and provided stiffer niches for these initiating cells and promoted their proliferation and drug resistance. Drug synergism analysis suggested that AMD3100 treatment undermined the capability of MICs to modulate the bone marrow microenvironment, and thus re-sensitized myeloma to Bortezomib treatments. This work is also the first attempt to virtually visualize in 3D the dynamics of the bone marrow stiffness during myeloma development. In summary, we established a multi-scale model to facilitate the translation of the niche-stiffness centric myeloma model as well as experimental observations to possible clinical applications. We concluded that targeting the biophysical properties of stem cell niches is of high clinical potential since it may re-sensitize tumor initiating cells to chemotherapies and reduce risks of cancer relapse.     

Simulation of Left Atrial Function Using a Multi-Scale Model of the Cardiovascular System

During a full cardiac cycle, the left atrium successively behaves as a reservoir, a conduit and a pump. This complex behavior makes it unrealistic to apply the time-varying elastance theory to characterize the left atrium, first, because this theory has known limitations, and second, because it is still uncertain whether the load independence hypothesis holds. In this study, we aim to bypass this uncertainty by relying on another kind of mathematical model of the cardiac chambers. In the present work, we describe both the left atrium and the left ventricle with a multi-scale model. The multi-scale property of this model comes from the fact that pressure inside a cardiac chamber is derived from a model of the sarcomere behavior. Macroscopic model parameters are identified from reference dog hemodynamic data. The multi-scale model of the cardiovascular system including the left atrium is then simulated to show that the physiological roles of the left atrium are correctly reproduced. This include a biphasic pressure wave and an eight-shaped pressure-volume loop. We also test the validity of our model in non basal conditions by reproducing a preload reduction experiment by inferior vena cava occlusion with the model. We compute the variation of eight indices before and after this experiment and obtain the same variation as experimentally observed for seven out of the eight indices. In summary, the multi-scale mathematical model presented in this work is able to correctly account for the three roles of the left atrium and also exhibits a realistic left atrial pressure-volume loop. Furthermore, the model has been previously presented and validated for the left ventricle. This makes it a proper alternative to the time-varying elastance theory if the focus is set on precisely representing the left atrial and left ventricular behaviors.     

Multi-Scale Compositionality: Identifying the Compositional Structures of Social Dynamics Using Deep Learning

Objective

Social media exhibit rich yet distinct temporal dynamics which cover a wide range of different scales. In order to study this complex dynamics, two fundamental questions revolve around (1) the signatures of social dynamics at different time scales, and (2) the way in which these signatures interact and form higher-level meanings.

Method

In this paper, we propose the Recursive Convolutional Bayesian Model (RCBM) to address both of these fundamental questions. The key idea behind our approach consists of constructing a deep-learning framework using specialized convolution operators that are designed to exploit the inherent heterogeneity of social dynamics. RCBM’s runtime and convergence properties are guaranteed by formal analyses.

Results

Experimental results show that the proposed method outperforms the state-of-the-art approaches both in terms of solution quality and computational efficiency. Indeed, by applying the proposed method on two social network datasets, Twitter and Yelp, we are able to identify the compositional structures that can accurately characterize the complex social dynamics from these two social media. We further show that identifying these patterns can enable new applications such as anomaly detection and improved social dynamics forecasting. Finally, our analysis offers new insights on understanding and engineering social media dynamics, with direct applications to opinion spreading and online content promotion.     

Detection of Adult Green Sturgeon Using Environmental DNA Analysis

Environmental DNA (eDNA) is an emerging sampling method that has been used successfully for detection of rare aquatic species. The Identification of sampling tools that are less stressful for target organisms has become increasingly important for rare and endangered species. A decline in abundance of the Southern Distinct Population Segment (DPS) of North American Green Sturgeon located in California’s Central Valley has led to its listing as Threatened under the Federal Endangered Species Act in 2006. While visual surveys of spawning Green Sturgeon in the Central Valley are effective at monitoring fish densities in concentrated pool habitats, results do not scale well to the watershed level, providing limited spatial and temporal context. Unlike most traditional survey methods, environmental DNA analysis provides a relatively quick, inexpensive tool that could efficiently monitor the presence and distribution of aquatic species. We positively identified Green Sturgeon DNA at two locations of known presence in the Sacramento River, proving that eDNA can be effective for monitoring the presence of adult sturgeon. While further study is needed to understand uncertainties of the sampling method, our study represents the first documented detection of Green Sturgeon eDNA, indicating that eDNA analysis could provide a new tool for monitoring Green Sturgeon distribution in the Central Valley, complimenting traditional on-going survey methods.     

Using Sex Pheromone and a Multi-Scale Approach to Predict the Distribution of a Rare Saproxylic Beetle

The European red click beetle, Elater ferrugineus L., is associated with wood mould in old hollow deciduous trees. As a result of severe habitat fragmentation caused by human disturbance, it is threatened throughout its distribution range. A new pheromone-based survey method, which is very efficient in detecting the species, was used in the present study to relate the occurrence of E. ferrugineus to the density of deciduous trees. The latter data were from a recently completed regional survey in SE Sweden recording >120,000 deciduous trees. The occurrence of E. ferrugineus increased with increasing amount of large hollow and large non-hollow trees in the surrounding landscape. Quercus robur (oak) was found to be the most important substrate for E. ferrugineus, whereas two groups of tree species (Carpinus betulus, Fagus sylvatica, Ulmus glabra, vs. Acer platanoides, Aesculus hippocastanum, Fraxinus excelsior, Tilia cordata) were less important but may be a complement to oak in sustaining populations of the beetle. The occurrence of E. ferrugineus was explained by the density of oaks at two different spatial scales, within the circle radii 327 m and 4658 m. In conclusion, priority should be given to oaks in conservation management of E. ferrugineus, and then to the deciduous trees in the genera listed above. Conservation planning at large spatial and temporal scales appears to be essential for long-term persistence of E. ferrugineus. We also show that occurrence models based on strategic sampling might result in pessimistic predictions. This study demonstrates how pheromone-based monitoring make insects excellent tools for sustained feedback to models for landscape conservation management.     

The Community Structure of the Global Corporate Network

We investigate the community structure of the global ownership network of transnational corporations. We find a pronounced organization in communities that cannot be explained by randomness. Despite the global character of this network, communities reflect first of all the geographical location of firms, while the industrial sector plays only a marginal role. We also analyze the meta-network in which the nodes are the communities and the links are obtained by aggregating the links among firms belonging to pairs of communities. We analyze the network centrality of the top 50 communities and we provide a quantitative assessment of the financial sector role in connecting the global economy.     

Novel Approach to Quantitative Detection of Specific rRNA in a Microbial Community, Using Catalytic DNA

We developed a novel method for the quantitative detection of the 16S rRNA of a specific bacterial species in the microbial community by using deoxyribozyme (DNAzyme), which possesses the catalytic function to cleave RNA in a sequence-specific manner. A mixture of heterogeneous 16S rRNA containing the target 16S rRNA was incubated with a species-specific DNAzyme. The cleaved target 16S rRNA was separated from the intact 16S rRNA by electrophoresis, and then their amounts were compared for the quantitative detection of target 16S rRNA. This method was used to determine the abundance of the 16S rRNA of a filamentous bacterium, Sphaerotilus natans, in activated sludge, which is a microbial mixture used in wastewater treatment systems. The result indicated that this DNAzyme-based approach would be applicable to actual microbial communities.     

Multi-Scale Modeling in Morphogenesis: A Critical Analysis of the Cellular Potts Model

Cellular Potts models (CPMs) are used as a modeling framework to elucidate mechanisms of biological development. They allow a spatial resolution below the cellular scale and are applied particularly when problems are studied where multiple spatial and temporal scales are involved. Despite the increasing usage of CPMs in theoretical biology, this model class has received little attention from mathematical theory. To narrow this gap, the CPMs are subjected to a theoretical study here. It is asked to which extent the updating rules establish an appropriate dynamical model of intercellular interactions and what the principal behavior at different time scales characterizes. It is shown that the longtime behavior of a CPM is degenerate in the sense that the cells consecutively die out, independent of the specific interdependence structure that characterizes the model. While CPMs are naturally defined on finite, spatially bounded lattices, possible extensions to spatially unbounded systems are explored to assess to which extent spatio-temporal limit procedures can be applied to describe the emergent behavior at the tissue scale. To elucidate the mechanistic structure of CPMs, the model class is integrated into a general multiscale framework. It is shown that the central role of the surface fluctuations, which subsume several cellular and intercellular factors, entails substantial limitations for a CPM''s exploitation both as a mechanistic and as a phenomenological model.     

Analysis of Bacterial Community in the Ginseng Soil Using Denaturing Gradient Gel Electrophoresis (DGGE)

The objective of this work was to determine the shifts in the PCR-DGGE profiles of bacterial communities associated with the rhizosphere soil of ginseng at varying age levels. Differences in the dominance of intense DNA bands in the DGGE profile was observed over the age of the plants indicating the fluctuation in the microbial community structure. The bacterial orders of actinomycetales of Actinobacteria and Spingomonadales and Rhizobiales of α-Proteobacteria were predominant in the ginseng soil.     

Microarray-Based Analysis of Subnanogram Quantities of Microbial Community DNAs by Using Whole-Community Genome Amplification

Microarray technology provides the opportunity to identify thousands of microbial genes or populations simultaneously, but low microbial biomass often prevents application of this technology to many natural microbial communities. We developed a whole-community genome amplification-assisted microarray detection approach based on multiple displacement amplification. The representativeness of amplification was evaluated using several types of microarrays and quantitative indexes. Representative detection of individual genes or genomes was obtained with 1 to 100 ng DNA from individual or mixed genomes, in equal or unequal abundance, and with 1 to 500 ng community DNAs from groundwater. Lower concentrations of DNA (as low as 10 fg) could be detected, but the lower template concentrations affected the representativeness of amplification. Robust quantitative detection was also observed by significant linear relationships between signal intensities and initial DNA concentrations ranging from (i) 0.04 to 125 ng (r² = 0.65 to 0.99) for DNA from pure cultures as detected by whole-genome open reading frame arrays, (ii) 0.1 to 1,000 ng (r² = 0.91) for genomic DNA using community genome arrays, and (iii) 0.01 to 250 ng (r² = 0.96 to 0.98) for community DNAs from ethanol-amended groundwater using 50-mer functional gene arrays. This method allowed us to investigate the oligotrophic microbial communities in groundwater contaminated with uranium and other metals. The results indicated that microorganisms containing genes involved in contaminant degradation and immobilization are present in these communities, that their spatial distribution is heterogeneous, and that microbial diversity is greatly reduced in the highly contaminated environment.