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1. Cell Modeling in Jupyter Notebook using CompuCell3D

   https://doi.org/10.5281/zenodo.7600786  

   Chung, Trinity; Sego, T. J.; Glazier, James A. (January 2023, Journal of advanced technological education)

   CompuCell3D (CC3D) is an open-source software framework for building and executing multi-cell biological virtual-tissue models. It represents cells using the Glazier–Graner–Hogeweg model, also known as Cellular Potts model. The primary CC3D application consists of two separate tools, a smart model editor (Twedit++) and a tool for model execution, visualization and steering (Player). The CompuCell3D version 4.x release introduces support for Jupyter Notebooks, an interactive computational environment, which brings the benefits of reproducibility, portability, and self-documentation. Since model specifications in CC3D are written in Python and CC3DML and Jupyter supports Python and other languages, Jupyter can naturally act as an integrated development environment (IDE) for CC3D users as well as a live document with embedded text and simulations. This update follows the trend in software to move away from monolithic freestanding applications to the distribution of methodologies in the form of libraries that can be used in conjunction with other libraries and packages. With these benefits, CC3D deployed inJupyter Notebook is a more natural and efficient platform for scientific publishing and education using CC3D. 

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2. Novel Sensing Methodology for Initial Alignment Using MmWave Phased Arrays

   https://doi.org/10.1109/IEEECONF59524.2023.10476851  

   Pote, Rohan R; Rao, Bhaskar D (October 2023, IEEE)

   A novel sensing approach for the single radio frequency (RF) chain millimeter wave systems is proposed. The sensing strategy is inspired from synthetic aperture radar systems, and synthesizes a virtual array manifold vector using a single phased array over temporal measurements. The geometry of the virtual array can be controlled enabling synthesis of both a virtual uniform linear array (ULA) and a virtual sparse linear array starting from a large physical array. Moreover, the proposed sensing can be realized using conventional phased array/analog combiner. Several design parameters of the sensing scheme provide flexibility and are discussed including their impact on initial alignment. The proposed sensing approach allows for a rich set of options for inference. Candidate detection and estimation algorithms are presented and their performance is evaluated. 

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3. Situational awareness of COVID pandemic data using virtual reality

   https://doi.org/10.2352/ISSN.2470-1173.2021.13.ERVR-177  

   Sharma, Sharad; Bodempudi, Sri Teja (March 2021, Electronic Imaging)

   null (Ed.)

   Situational awareness provides the decision making capability to identify, process, and comprehend big data. In our approach, situational awareness is achieved by integrating and analyzing multiple aspects of data using stacked bar graphs and geographic representations of the data. We provide a data visualization tool to represent COVID pandemic data on top of the geographical information. The combination of geospatial and temporal data provides the information needed to conduct situational analysis for the COVID-19 pandemic. By providing interactivity, geographical maps can be viewed from different perspectives and offer insight into the dynamical aspects of the COVID-19 pandemic for the fifty states in the USA. We have overlaid dynamic information on top of a geographical representation in an intuitive way for decision making. We describe how modeling and simulation of data increase situational awareness, especially when coupled with immersive virtual reality interaction. This paper presents an immersive virtual reality (VR) environment and mobile environment for data visualization using Oculus Rift head-mounted display and smartphones. This work combines neural network predictions with human-centric situational awareness and data analytics to provide accurate, timely, and scientific strategies in combatting and mitigating the spread of the coronavirus pandemic. Testing and evaluation of the data visualization tool have been done with real-time feed of COVID pandemic data set for immersive environment, non-immersive environment, and mobile environment. 

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4. Real-Time Data Analytics of COVID Pandemic Using Virtual Reality

   https://doi.org/10.1007/978-3-030-77599-5_9  

   Sharma, S; Bodempudi, S.T; Reehl, A (July 2021, Lecture notes in computer science)

   Chen, J.Y.C.; Fragomeni, G (Ed.)

   Visualizing data effectively is critical for the discovery process in the age of big data. We are exploring the use of immersive virtual reality platforms for scientific data visualization for COVID-19 pandemic. We are interested in finding ways to better understand, perceive and interact with multidimensional data in the field of cognition technology and human-computer interaction. Immersive visualization leads to a better understanding and perception of relationships in the data. This paper presents a data visualization tool for immersive data visualizations based on the Unity development platform. The data visualization tool is capable of visualizing the real-time COVID pandemic data for the fifty states in the USA. Immersion provides a better understanding of the data than traditional desktop visualization tools and leads to more human-centric situational awareness insights. This research effort aims to identify how graphical objects like charts and bar graphs depicted in Virtual Reality tools, developed in accordance with an analyst’s mental model can enhance an analyst’s situation awareness. Our results also suggest that users feel more satisfied when using immersive virtual reality data visualization tools and thus demonstrate the potential of immersive data analytics. 

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5. Modeling the impact of single-cell stochasticity and size control on the population growth rate in asymmetrically dividing cells

   https://doi.org/10.1371/journal.pcbi.1009080  

   Barber, Felix; Min, Jiseon; Murray, Andrew W.; Amir, Ariel (June 2021, PLOS Computational Biology)

   Csikász-Nagy, Attila (Ed.)

   Microbial populations show striking diversity in cell growth morphology and lifecycle; however, our understanding of how these factors influence the growth rate of cell populations remains limited. We use theory and simulations to predict the impact of asymmetric cell division, cell size regulation and single-cell stochasticity on the population growth rate. Our model predicts that coarse-grained noise in the single-cell growth rate λ decreases the population growth rate, as previously seen for symmetrically dividing cells. However, for a given noise in λ we find that dividing asymmetrically can enhance the population growth rate for cells with strong size control (between a “sizer” and an “adder”). To reconcile this finding with the abundance of symmetrically dividing organisms in nature, we propose that additional constraints on cell growth and division must be present which are not included in our model, and we explore the effects of selected extensions thereof. Further, we find that within our model, epigenetically inherited generation times may arise due to size control in asymmetrically dividing cells, providing a possible explanation for recent experimental observations in budding yeast. Taken together, our findings provide insight into the complex effects generated by non-canonical growth morphologies. 

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