212 research outputs found

    Geometry Processing of Conventionally Produced Mouse Brain Slice Images

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    Brain mapping research in most neuroanatomical laboratories relies on conventional processing techniques, which often introduce histological artifacts such as tissue tears and tissue loss. In this paper we present techniques and algorithms for automatic registration and 3D reconstruction of conventionally produced mouse brain slices in a standardized atlas space. This is achieved first by constructing a virtual 3D mouse brain model from annotated slices of Allen Reference Atlas (ARA). Virtual re-slicing of the reconstructed model generates ARA-based slice images corresponding to the microscopic images of histological brain sections. These image pairs are aligned using a geometric approach through contour images. Histological artifacts in the microscopic images are detected and removed using Constrained Delaunay Triangulation before performing global alignment. Finally, non-linear registration is performed by solving Laplace's equation with Dirichlet boundary conditions. Our methods provide significant improvements over previously reported registration techniques for the tested slices in 3D space, especially on slices with significant histological artifacts. Further, as an application we count the number of neurons in various anatomical regions using a dataset of 51 microscopic slices from a single mouse brain. This work represents a significant contribution to this subfield of neuroscience as it provides tools to neuroanatomist for analyzing and processing histological data.Comment: 14 pages, 11 figure

    Deliberative Democracy, Perspective from Indo-Pacific Blogosphere: A Survey

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    Deliberation and communication within the national space have had numerous implications on how citizens online and offline perceive government. It has also impacted the relationship between opposition and incumbent governments in the Indo-Pacific region. Authoritarian regimes have historically had control over the dissemination of information, thereby controlling power and limiting challenges from citizens who are not comfortable with the status quo. Social media and blogs have allowed citizens of these countries to find a way to communicate, and the exchange of information continues to rise. The quest by both authoritarian and democratic regimes to control or influence the discussion in the public sphere has given rise to concepts like cybertroopers, congressional bloggers, and commentator bloggers, among others. Cybertroopers have become the de facto online soldiers of authoritarian regimes who must embrace democracy. While commentator and congressional bloggers have acted with different strategies, commentator bloggers educate online citizens with knowledgeable information to influence the citizens. Congressional bloggers are political officeholders who use blogging to communicate their positions on ongoing national issues. Therefore, this work has explored various concepts synonymous with the Indo-Pacific public sphere and how it shapes elections and democracy

    Collective Learning Paradigm for Rapidly Evolving Curriculum: Facilitating Student and Content Engagement via Social Media

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    Curriculum in the information systems discipline has been rapidly evolving. This is not only challenging for the instructors to cope with the velocity of change in the curriculum, but also for the students. This paper illustrates a model that leverages the integrated use of social media technologies to facilitate collective learning in a university teaching/learning environment. However, the model could be adapted to other organizational environments. The model demonstrates how various challenges encountered in collective learning can be addressed with the help of social media technologies. A case study is presented to demonstrate the model’s applicability, feasibility, utility, and success in a senior-level social computing course at the University of Arkansas at Little Rock. An evolving, non-linear, and self-sustaining wiki portal is developed to encourage engagement between the content, students, and instructor. We further outline the student-centric, content-centric, and learning-centric advantages of the proposed model for the next generation learning environment

    A Computational Framework for Analyzing Social Behavior in Online Connective Action: A COVID-19 Lockdown Protest Case Study

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    Online social networks (OSN’s) have shaped collective action into a new form of organizing and engagement known as connective action. Protests, demonstrations, and social movements have largely relied on social media as their primary organizational process for resource mobilization. These platforms also provide a method to coordinate and influence behavior. Most social science research on connective action has taken a qualitative approach. There are some quantitative studies, but most focus on statistical validation of the qualitative approach (e.g., survey’s) or focus on only one aspect of connective action. Computational analysis as a complement to existing survey methods offer in-depth insights about the role of identity and provide insights into the underlying behaviors we see as catalysts for these online movements. This paper presents an interdisciplinary computational approach to analyze connective action by exploring the key features of collective identity, network organization, and mobilization in connective action movements

    Generation, detection and applications of in vitro oxygen gradients

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    Oxygen homeostasis is critical for the functioning of multicellular organisms. Deficiency of oxygen or hypoxia can lead to several pathological conditions such as ischemia, tumorigenesis and drug resistance. Most studies utilize specialized O2 incubators to generate singular oxygen concentrations that vary significantly from the physiological conditions where hypoxic gradients exist within the tissue e.g. in solid tumors. Current microfluidic technology using polydimethylsiloxane-based (PDMS) devices enables generation of such oxygen concentration gradients, but yield low-to-moderate spatial resolution, involve tedious device assembly and are not feasible for practical research or pharmaceutical screening. We have developed a novel and simplistic approach of reproducibly and rapidly generating stable biomimetic oxygen gradients with high spatial resolution and integrated detection capability. The microfluidic split and recombine strategy utilizing O2-rich and O2-depleted media allows generation of prolonged dissolved oxygen (DO) gradients while an underlying platinum based sensor layer (PtOEPK) allows real-time detection of DO gradients generated. Deposition of an approximately 5-7µm thick three-sided glass coating prevents multi-directional diffusion of ambient oxygen through PDMS maintaining the gradient stability for hours or days. Two variations of the gradient devices have been developed, one offering the ability to generate continuous gradients within a single channel while another containing multiple outlet chambers each maintaining a specific concentration of DO (Figure 1). Please click Additional Files below to see the full abstract

    A domain adaptive stochastic collocation approach for analysis of MEMS under uncertainties

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    This work proposes a domain adaptive stochastic collocation approach for uncertainty quantification, suitable for effective handling of discontinuities or sharp variations in the random domain. The basic idea of the proposed methodology is to adaptively decompose the random domain into subdomains. Within each subdomain, a sparse grid interpolant is constructed using the classical Smolyak construction [S. Smolyak, Quadrature and interpo- lation formulas for tensor products of certain classes of functions, Soviet Math. Dokl. 4 (1963) 240–243], to approximate the stochastic solution locally. The adaptive strategy is governed by the hierarchical surpluses, which are computed as part of the interpolation procedure. These hierarchical surpluses then serve as an error indicator for each subdo- main, and lead to subdivision whenever it becomes greater than a threshold value. The hierarchical surpluses also provide information about the more important dimensions, and accordingly the random elements can be split along those dimensions. The proposed adaptive approach is employed to quantify the effect of uncertainty in input parameters on the performance of micro-electromechanical systems (MEMS). Specifically, we study the effect of uncertain material properties and geometrical parameters on the pull-in behavior and actuation properties of a MEMS switch. Using the adaptive approach, we resolve the pull-in instability in MEMS switches. The results from the proposed approach are verified using Monte Carlo simulations and it is demonstrated that it computes the required statistics effectively
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