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1. Coordination of NMCP1- and NMCP2-class proteins within the plant nucleoskeleton

   https://doi.org/10.1091/mbc.E19-08-0464  

   Blunt, Endia L. ; Shandler, Jason A. ; Hughes, Erika J. ; Sussman, Hayley ; Christopherson, Rachel C. ; Richards, Eric J. ( December 2020 , Molecular Biology of the Cell)

   Misteli, Tom (Ed.)

   Plants lack lamin proteins but contain a class of coiled-coil proteins that serve as analogues to form a laminal structure at the nuclear periphery. These nuclear matrix constituent proteins (NMCPs) play important roles in regulating nuclear morphology and are partitioned into two distinct groups. We investigated Arabidopsis NMCPs (called CRWNs) to study the interrelationship between the three NMCP1-type paralogues (CRWN1, 2, and 3) and the lone NMCP2-type paralogue, CRWN4. An examination of crwn mutants using protein immunoblots demonstrated that CRWN4 abundance depends on the presence of the NMCP1-type proteins, particularly CRWN1. The possibility that CRWN4 is coimported into the nucleus with nuclear localization signal (NLS)-bearing paralogues in the NMCP1-clade was discounted based on recovery of a crwn4-2 missense allele that disrupts a predicted NLS and lowers the abundance of CRWN4 in the nucleus. Further, a screen for mutations that suppress the effects of the crwn4-2 mutation led to the discovery of a missense allele, impa-1 G146E , in one of the nine importin-α genes in the Arabidopsis genome. Our results indicate that the CRWN4 carries a functional NLS that interacts with canonic nuclear import machinery. Once imported, the level of CRWN4 within the nucleus is modulated by the abundance of NMCP1 proteins. 

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2. A class of event-triggered coordination algorithms for multi-agent systems on weight-balanced digraphs

   https://doi.org/10.23919/ACC.2018.8431314  

   Xu, Ping ; Nowzari, Cameron ; Tian, Zhi ( June 2018 , 2018 Annual American Control Conference (ACC))

   null (Ed.)
3. Aligning Multiple PPI Networks with Representation Learning on Networks

   Bo Song, Jianliang Gao ( December 2018 , 2018 IEEE BIBM Conference Proceeding)

   Abstract—Protein-protein interaction (PPI) network alignment has been motivating researches for the comprehension of the underlying crucial biological knowledge, such as conserved evolutionary pathways and functionally conserved proteins throughout different species. Existing PPI network alignment methods have tried to improve the coverage ratio by aligning all proteins from different species. However, there is a fundamental biological justification needed to be acknowledged, that not every protein in a species can, nor should, find homologous proteins in other species. In this paper, we propose a novel approach for multiple PPI network alignment that tries to align only those proteins with the most similarities. To provide more comprehensive supports in computing the similarity, we integrate structural features of the networks together with biological characteristics during the alignment. For the structural features, we apply on PPI networks a representation learning method, which creates a low-dimensional vector embedding with the surrounding topologies of each protein in the network. This approach quantifies the structural features, and provides a new way to determine the topological similarity of the networks by transferring which as calculations in vector similarities. We also propose a new metric for the topological evaluation which can better assess the topological quality of the alignment results across different networks. Both biological and topological evaluations demonstrate our approach is promising and preferable against previous multiple alignment methods. 

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4. Aligning Multiple PPI Networks with Representation Learning on Networks

   Bo Song, Jianliang Gao ( December 2018 , 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM))

   Protein-protein interaction (PPI) network alignment has been motivating researches for the comprehension of the underlying crucial biological knowledge, such as conserved evolutionary pathways and functionally conserved proteins throughout different species. Existing PPI network alignment methods have tried to improve the coverage ratio by aligning all proteins from different species. However, there is a fundamental biological justification needed to be acknowledged, that not every protein in a species can, nor should, find homologous proteins in other species. In this paper, we propose a novel approach for multiple PPI network alignment that tries to align only those proteins with the most similarities. To provide more comprehensive supports in computing the similarity, we integrate structural features of the networks together with biological characteristics during the alignment. For the structural features, we apply on PPI networks a representation learning method, which creates a low-dimensional vector embedding with the surrounding topologies of each protein in the network. This approach quantifies the structural features, and provides a new way to determine the topological similarity of the networks by transferring which as calculations in vector similarities. We also propose a new metric for the topological evaluation which can better assess the topological quality of the alignment results across different networks. Both biological and topological evaluations demonstrate our approach is promising and preferable against previous multiple alignment methods. 

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5. Aligning learning objectives and approaches in global engineering graduate programs: Review and recommendations by an interdisciplinary working group

   https://doi.org/10.1016/j.deveng.2022.100095  

   MacDonald, Laura ; Thomas, Evan ; Javernick-Will, Amy ; Austin-Breneman, Jesse ; Aranda, Iana ; Salvinelli, Carlo ; Klees, Rita ; Walters, Jeffrey ; Parmentier, Mary Jane ; Schaad, David ; et al ( January 2022 , Development Engineering)