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1. Guanine and cytosine‐containing acrylic supramolecular networks

   https://doi.org/10.1002/pol.20230320  

   Liu, Boer; Spiering, Glenn A.; McDonough, Rose K.; Moore, Robert B.; Long, Timothy E. (August 2023, Journal of Polymer Science)

   Abstract This manuscript describes the synthesis and characterization of guanine and cytosine‐containing supramolecular copolymers, which are inspired from the guanine and cytosine nucleobase pair in deoxyribonucleic acid. Regioselective Michael‐addition allowed the efficient installation of the nucleobases on acrylate‐containing monomers, which enabled the preparation of a series of nucleobase‐functionalized acrylate andn‐butyl acrylate copolymers using conventional free radical copolymerization. Guanine‐containing copolymers exhibited superior thermal properties, thermomechanical performance, and more defined morphological structure than cytosine‐containing copolymer analogs due to the relatively strong guanine self‐association, thus expanding the potential applications for mechanically reinforced polymeric networks. Blending guanine‐ and cytosine‐containing copolymers formed a supramolecular structure through multiple hydrogen bonding between guanine and cytosine units. The supramolecular blend exhibited intermediate thermomechanical and morphological properties, which suggested that guanine and cytosine units were not fully associated in the random copolymer composition. This work provides valuable fundamental understanding of structure–property‐morphology relationships in acrylic copolymers with the presence of guanine‐cytosine self‐ and complementary interactions, suggesting new understanding in supramolecular design for enhanced mechanical and morphological properties. 

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2. SiPhyNetwork : An R package for simulating phylogenetic networks

   https://doi.org/10.1111/2041-210X.14116  

   Justison, Joshua A.; Solis‐Lemus, Claudia; Heath, Tracy A. (May 2023, Methods in Ecology and Evolution)

   Abstract Gene flow is increasingly recognized as an important macroevolutionary process. The many mechanisms that contribute to gene flow (e.g. introgression, hybridization, lateral gene transfer) uniquely affect the diversification of dynamics of species, making it important to be able to account for these idiosyncrasies when constructing phylogenetic models. Existing phylogenetic‐network simulators for macroevolution are limited in the ways they model gene flow.We presentSiPhyNetwork, an R package for simulating phylogenetic networks under a birth–death‐hybridization process.Our package unifies the existing birth–death‐hybridization models while also extending the toolkit for modelling gene flow. This tool can create patterns of reticulation such as hybridization, lateral gene transfer, and introgression.Specifically, we model different reticulate events by allowing events to either add, remove or keep constant the number of lineages. Additionally, we allow reticulation events to be trait dependent, creating the ability to model the expanse of isolating mechanisms that prevent gene flow. This tool makes it possible for researchers to model many of the complex biological factors associated with gene flow in a phylogenetic context. 

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3. Simultaneous cosegmentation of tumors in PET ‐ CT images using deep fully convolutional networks

   https://doi.org/10.1002/mp.13331  

   Zhong, Zisha; Kim, Yusung; Plichta, Kristin; Allen, Bryan G.; Zhou, Leixin; Buatti, John; Wu, Xiaodong (February 2019, Medical Physics)

   null (Ed.)
4. Interactive Investigation of Traffic Congestion on Fat-Tree Networks Using T ree S cope

   https://doi.org/10.1111/cgf.13442  

   Bhatia, H.; Jain, N.; Bhatele, A.; Livnat, Y.; Domke, J.; Pascucci, V.; Bremer, P.-T. (June 2018, Computer Graphics Forum)
5. Time‐Varying Spatial Propagation of Brain Networks in fMRI Data

   https://doi.org/10.1002/hbm.70131  

   Bostami, Biozid; Lewis, Noah; Agcaoglu, Oktay; Turner, Jessica_A; van_Erp, Theo; Ford, Judith_M; Fouladivanda, Mahshid; Calhoun, Vince; Iraji, Armin (January 2025, Human Brain Mapping)

   ABSTRACT Spontaneous neural activity coherently relays information across the brain. Several efforts have been made to understand how spontaneous neural activity evolves at the macro‐scale level as measured by resting‐state functional magnetic resonance imaging (rsfMRI). Previous studies observe the global patterns and flow of information in rsfMRI using methods such as sliding window or temporal lags. However, to our knowledge, no studies have examined spatial propagation patterns evolving with time across multiple overlapping 4D networks. Here, we propose a novel approach to study how dynamic states of the brain networks spatially propagate and evaluate whether these propagating states contain information relevant to mental illness. We implement a lagged windowed correlation approach to capture voxel‐wise network‐specific spatial propagation patterns in dynamic states. Results show systematic spatial state changes over time, which we confirmed are replicable across multiple scan sessions using human connectome project data. We observe networks varying in propagation speed; for example, the default mode network (DMN) propagates slowly and remains positively correlated with blood oxygenation level‐dependent (BOLD) signal for 6–8 s, whereas the visual network propagates much quicker. We also show that summaries of network‐specific propagative patterns are linked to schizophrenia. More specifically, we find significant group differences in multiple dynamic parameters between patients with schizophrenia and controls within four large‐scale networks: default mode, temporal lobe, subcortical, and visual network. Individuals with schizophrenia spend more time in certain propagating states. In summary, this study introduces a promising general approach to exploring the spatial propagation in dynamic states of brain networks and their associated complexity and reveals novel insights into the neurobiology of schizophrenia. 

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