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Generation of Customizable Micro-wavy Pattern through Grayscale Direct Image Lithography.

ABSTRACT: With the increasing amount of research work in surface studies, a more effective method of producing patterned microstructures is highly desired due to the geometric limitations and complex fabricating process of current techniques. This paper presents an efficient and cost-effective method to generate customizable micro-wavy pattern using direct image lithography. This method utilizes a grayscale Gaussian distribution effect to model inaccuracies inherent in the polymerization process, which are normally regarded as trivial matters or errors. The measured surface profiles and the mathematical prediction show a good agreement, demonstrating the ability of this method to generate wavy patterns with precisely controlled features. An accurate pattern can be generated with customizable parameters (wavelength, amplitude, wave shape, pattern profile, and overall dimension). This mask-free photolithography approach provides a rapid fabrication method that is capable of generating complex and non-uniform 3D wavy patterns with the wavelength ranging from 12 μm to 2100 μm and an amplitude-to-wavelength ratio as large as 300%. Microfluidic devices with pure wavy and wavy-herringbone patterns suitable for capture of circulating tumor cells are made as a demonstrative application. A completely customized microfluidic device with wavy patterns can be created within a few hours without access to clean room or commercial photolithography equipment.

SUBMITTER: He R

PROVIDER: S-EPMC4763239 | biostudies-other | 2016 Feb

REPOSITORIES: biostudies-other

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Project description:BackgroundPattern recognition algorithms are useful in bioimage informatics applications such as quantifying cellular and subcellular objects, annotating gene expressions, and classifying phenotypes. To provide effective and efficient image classification and annotation for the ever-increasing microscopic images, it is desirable to have tools that can combine and compare various algorithms, and build customizable solution for different biological problems. However, current tools often offer a limited solution in generating user-friendly and extensible tools for annotating higher dimensional images that correspond to multiple complicated categories.ResultsWe develop the BIOimage Classification and Annotation Tool (BIOCAT). It is able to apply pattern recognition algorithms to two- and three-dimensional biological image sets as well as regions of interest (ROIs) in individual images for automatic classification and annotation. We also propose a 3D anisotropic wavelet feature extractor for extracting textural features from 3D images with xy-z resolution disparity. The extractor is one of the about 20 built-in algorithms of feature extractors, selectors and classifiers in BIOCAT. The algorithms are modularized so that they can be "chained" in a customizable way to form adaptive solution for various problems, and the plugin-based extensibility gives the tool an open architecture to incorporate future algorithms. We have applied BIOCAT to classification and annotation of images and ROIs of different properties with applications in cell biology and neuroscience.ConclusionsBIOCAT provides a user-friendly, portable platform for pattern recognition based biological image classification of two- and three- dimensional images and ROIs. We show, via diverse case studies, that different algorithms and their combinations have different suitability for various problems. The customizability of BIOCAT is thus expected to be useful for providing effective and efficient solutions for a variety of biological problems involving image classification and annotation. We also demonstrate the effectiveness of 3D anisotropic wavelet in classifying both 3D image sets and ROIs.

Project description:BACKGROUND: With the rapid growth of genome sequencing projects, genome browser is becoming indispensable, not only as a visualization system but also as an interactive platform to support open data access and collaborative work. Thus a customizable genome browser framework with rich functions and flexible configuration is needed to facilitate various genome research projects. RESULTS: Based on next-generation web technologies, we have developed a general-purpose genome browser framework ABrowse which provides interactive browsing experience, open data access and collaborative work support. By supporting Google-map-like smooth navigation, ABrowse offers end users highly interactive browsing experience. To facilitate further data analysis, multiple data access approaches are supported for external platforms to retrieve data from ABrowse. To promote collaborative work, an online user-space is provided for end users to create, store and share comments, annotations and landmarks. For data providers, ABrowse is highly customizable and configurable. The framework provides a set of utilities to import annotation data conveniently. To build ABrowse on existing annotation databases, data providers could specify SQL statements according to database schema. And customized pages for detailed information display of annotation entries could be easily plugged in. For developers, new drawing strategies could be integrated into ABrowse for new types of annotation data. In addition, standard web service is provided for data retrieval remotely, providing underlying machine-oriented programming interface for open data access. CONCLUSIONS: ABrowse framework is valuable for end users, data providers and developers by providing rich user functions and flexible customization approaches. The source code is published under GNU Lesser General Public License v3.0 and is accessible at http://www.abrowse.org/. To demonstrate all the features of ABrowse, a live demo for Arabidopsis thaliana genome has been built at http://arabidopsis.cbi.edu.cn/.

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Generation of Customizable Micro-wavy Pattern through Grayscale Direct Image Lithography.

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