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Given the disruptions COVID-19 caused to normal research processes, including inter- national collaboration, this study sought to understand scientists’ experiences collabo- rating internationally during the pandemic on COVID-19-related research. Specifically, it explored US scientists’ tie formation and reasons for international research collabora- tion with Chinese scientists. The study employed a sequential exploratory mixed methods design collecting interview and survey data from US scientists who co-published articles related to COVID-19 with Chinese scientists. The findings revealed the role of network mechanisms, such as transitivity, opportunity of contact, and homophily, in promoting rela- tionship formation and maintenance. Moreover, they showed the greater role that bonding social capital played in helping scientists access valuable knowledge, skills, and resources to enhance their research potential. Lastly, they demonstrated how particularized trust based on prior interactions and experiences encouraged relationship formation and col- laboration between US and Chinese scientists. Together, these results provide new insights in informing future policies and guidelines related to supporting international collaboration and, ultimately, shared pandemic challenges. 

As generalist parasitoid wasps, Leptopilina heterotoma are highly successful on many species of fruit flies of the genus Drosophila. The parasitoids produce specialized multi-strategy extracellular vesicle (EV)-like structures in their venom. Proteomic analysis identified several immunity-associated proteins, including the knottin peptide, LhKNOT, containing the structurally conserved inhibitor cysteine knot (ICK) fold, which is present in proteins from diverse taxa. Our structural and docking analysis of LhKNOT’s 36-residue core knottin fold revealed that in addition to the knottin motif itself, it also possesses a Cation–Polar–Cation (CPC) clip. The CPC clip motif is thought to facilitate antimicrobial activity in heparin-binding proteins. Surprisingly, a majority of ICKs tested also possess the CPC clip motif, including 75 bona fide plant and arthropod knottin proteins that share high sequence and/or structural similarity with LhKNOT. Like LhKNOT and these other 75 knottin proteins, even the Drosophila Drosomycin antifungal peptide, a canonical target gene of the fly’s Toll-NF-kappa B immune pathway, contains this CPC clip motif. Together, our results suggest a possible defensive function for the parasitoid LhKNOT. The prevalence of the CPC clip motif, intrinsic to the cysteine knot within the knottin proteins examined here, suggests that the resultant 3D topology is important for their biochemical functions. The CPC clip is likely a highly conserved structural motif found in many diverse proteins with reported heparin binding capacity, including amyloid proteins. Knottins are targets for therapeutic drug development, and insights into their structure–function relationships will advance novel drug design. 

Two-dimensional space embeddings such as Multi-Dimensional Scaling (MDS) are a popular means to gain insight into high-dimensional data relationships. However, in all but the simplest cases these embeddings suffer from significant distortions, which can lead to misinterpretations of the high-dimensional data. These distortions occur both at the global inter-cluster and the local intra-cluster levels. The former leads to misinterpretation of the distances between the various N-D cluster populations, while the latter hampers the appreciation of their individual shapes and composition, which we call cluster appearance. The distortion of cluster appearance incurred in the 2-D embedding is unavoidable since such low-dimensional embeddings always come at the loss of some of the intra-cluster variance. In this paper, we propose techniques to overcome these limitations by conveying the N-D cluster appearance via a framework inspired by illustrative design. Here we make use of Scagnostics which offers a set of intuitive feature descriptors to describe the appearance of 2-D scatterplots. We extend the Scagnostics analysis to N-D and then devise and test via crowd-sourced user studies a set of parameterizable texture patterns that map to the various Scagnostics descriptors. Finally, we embed these N-D Scagnostics-informed texture patterns into shapes derived from N-D statistics to yield what we call Cluster Appearance Glyphs. We demonstrate our framework with a dataset acquired to analyze program execution times in file systems.