Multivariate Skew Normal Copula for Non-exchangeable Dependence

AbstractThe exchangeability assumption on the dependence structure of the multivariate data is restrictive in practical situations where the variables of interest are not likely to be associated to each other in an identical manner. In this paper, we propose a flexible class of multivariate skew normal copulas to model high-dimensional non-exchangeable dependence patterns. The proposed copulas have two sets of parameters capturing non-exchangeable dependence, one for association between the variables and the other for skewness of the variables. In order to efficiently estimate the two sets of parameters, we introduce the block coordinate ascent algorithm. The proposed class of multivariate skew normal copulas is illustrated using a real data set

A microfluidic concentrator array for quantitative predation assays of predatory microbes

We present a microfabricated concentrator array device that makes it possible to quantify the predation rate of Bdellovibrio bacteriovorus, a predatory microbe, toward its prey, Escherichia coli str. MG1655. The device can accumulate both prey and predator microbes sequentially within a series of concentrator arrays using the motility of the microbes and microfabricated arrowhead-shaped ratchet structures. Since the device can constrain both prey and predator cells within 200 pL chambers at a desired range of cell densities, it was demonstrated that the device cannot only enhance the possibility of studying predation processes/cycles directly at a single cell level but can also quantify the predation rates indirectly by measuring the time-dependent fluorescent intensity signals from the prey. Furthermore, the device can produce a wide range of initial prey to predator density ratios within various concentrator arrays through the use of microfluidic mixer structures on a single array chip, which allows us to study many different conditions with a single set of cultures, and quantitatively characterize the predation behaviour/rate. Lastly, we note that this novel concentrator array device can be a very powerful tool facilitating studies of microbial predations and microbe-microbe interaction and may be broadly used in other microbial biotechnological applications.close6

Microfabricated ratchet structure integrated concentrator arrays for synthetic bacterial cell-to-cell communication assays

We describe a microfluidic concentrator array device that is integrated with microfabricated ratchet structures to concentrate motile bacterial cells in desired destinations with required cell densities. The device consists of many pairs of concentrators with a wide range of spacing distances on a chip, and allows cells in one concentrator to be physically separated from but chemically connected to cells in the other concentrator. Therefore, the device facilitates quantification of the effect of spacing distance on the cell-to-cell communication of synthetically engineered bacterial cells. In addition, the device enables us to control the cell number density in each concentrator unit by adjusting the concentration time and the density of cell suspensions, and the basic concentrator unit of the device can be repeatedly duplicated on a chip. Hence, the device not only facilitates an investigation of the effect of cell densities on cell-to-cell communication, but it can also be further applied to an investigation of cellular communication among multiple types of cells. Lastly, the device can be easily fabricated using a single-layered soft-lithography technology so that we believe it would provide a simple but robust means for many synthetic and systems biologists to simplify and speed up their investigations of the synthetic genetic circuits in bacterial cells.close5

Microfluidic Technologies for Synthetic Biology

Microfluidic technologies have shown powerful abilities for reducing cost, time, and labor, and at the same time, for increasing accuracy, throughput, and performance in the analysis of biological and biochemical samples compared with the conventional, macroscale instruments. Synthetic biology is an emerging field of biology and has drawn much attraction due to its potential to create novel, functional biological parts and systems for special purposes. Since it is believed that the development of synthetic biology can be accelerated through the use of microfluidic technology, in this review work we focus our discussion on the latest microfluidic technologies that can provide unprecedented means in synthetic biology for dynamic profiling of gene expression/regulation with high resolution, highly sensitive on-chip and off-chip detection of metabolites, and whole-cell analysis

Automatic Extraction of Indoor Spatial Information from Floor Plan Image: A Patch-Based Deep Learning Methodology Application on Large-Scale Complex Buildings

Automatic floor plan analysis has gained increased attention in recent research. However, numerous studies related to this area are mainly experiments conducted with a simplified floor plan dataset with low resolution and a small housing scale due to the suitability for a data-driven model. For practical use, it is necessary to focus more on large-scale complex buildings to utilize indoor structures, such as reconstructing multi-use buildings for indoor navigation. This study aimed to build a framework using CNN (Convolution Neural Networks) for analyzing a floor plan with various scales of complex buildings. By dividing a floor plan into a set of normalized patches, the framework enables the proposed CNN model to process varied scale or high-resolution inputs, which is a barrier for existing methods. The model detected building objects per patch and assembled them into one result by multiplying the corresponding translation matrix. Finally, the detected building objects were vectorized, considering their compatibility in 3D modeling. As a result, our framework exhibited similar performance in detection rate (87.77%) and recognition accuracy (85.53%) to that of existing studies, despite the complexity of the data used. Through our study, the practical aspects of automatic floor plan analysis can be expanded.Y

Automatic Extraction of Indoor Spatial Information from Floor Plan Image: A Patch-Based Deep Learning Methodology Application on Large-Scale Complex Buildings

Automatic floor plan analysis has gained increased attention in recent research. However, numerous studies related to this area are mainly experiments conducted with a simplified floor plan dataset with low resolution and a small housing scale due to the suitability for a data-driven model. For practical use, it is necessary to focus more on large-scale complex buildings to utilize indoor structures, such as reconstructing multi-use buildings for indoor navigation. This study aimed to build a framework using CNN (Convolution Neural Networks) for analyzing a floor plan with various scales of complex buildings. By dividing a floor plan into a set of normalized patches, the framework enables the proposed CNN model to process varied scale or high-resolution inputs, which is a barrier for existing methods. The model detected building objects per patch and assembled them into one result by multiplying the corresponding translation matrix. Finally, the detected building objects were vectorized, considering their compatibility in 3D modeling. As a result, our framework exhibited similar performance in detection rate (87.77%) and recognition accuracy (85.53%) to that of existing studies, despite the complexity of the data used. Through our study, the practical aspects of automatic floor plan analysis can be expanded