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AbstractSickle cell disease (SCD) is canonically characterized by reduced red blood cell (RBC) deformability, leading to microvascular obstruction and inflammation. Although the biophysical properties of sickle RBCs are known to influence SCD vasculopathy, the contribution of poor RBC deformability to endothelial dysfunction has yet to be fully explored. Leveraging interrelated in vitro and in silico approaches, we introduce a new paradigm of SCD vasculopathy in which poorly deformable sickle RBCs directly cause endothelial dysfunction via mechanotransduction, during which endothelial cells sense and pathophysiologically respond to aberrant physical forces independently of microvascular obstruction, adhesion, or hemolysis. We demonstrate that perfusion of sickle RBCs or pharmacologically-dehydrated healthy RBCs into small venule-sized “endothelialized” microfluidics leads to pathologic physical interactions with endothelial cells that directly induce inflammatory pathways. Using a combination of computational simulations and large venule-sized endothelialized microfluidics, we observed that perfusion of heterogeneous sickle RBC subpopulations with varying deformability, as well as suspensions of dehydrated normal RBCs admixed with normal RBCs, leads to aberrant margination of the less-deformable RBC subpopulations toward the vessel walls, causing localized, increased shear stress. Increased wall stress is dependent on the degree of subpopulation heterogeneity and oxygen tension and leads to inflammatory endothelial gene expression via mechanotransductive pathways. Our multifaceted approach demonstrates that the presence of sickle RBCs with reduced deformability leads directly to pathological physical (ie, direct collisions and/or compressive forces) and shear-mediated interactions with endothelial cells and induces an inflammatory response, thereby elucidating the ubiquity of vascular dysfunction in SCD. 

Abstract Despite the importance of developing elementary science teachers' content knowledge for teaching (CKT), there are limited assessments that have been designed to measure the full breadth of their CKT at scale. Our overall research project addressed this gap by developing an online assessment to measure elementary preservice teachers' CKT about matter and its interactions. This study, which was part of our larger project, reports on findings from one component of the item development process examining the construct validity of 118 different CKT about matter assessment items. In this study, 86 elementary teachers participated in cognitive interviews to examine: (a) the knowledge and reasoning they used when responding to these CKT about matter assessment items and (b) the nature of the content challenges and the content teaching challenges they encountered. Findings showed that over 80% of participant interview responses indicated that the CKT about matter items functioned as hypothesized, providing evidence to support future use of these items on a large‐scale assessment and in studies of science teachers' CKT. When responding to the items, participants showed evidence of four main challenges with the science content: (a) using scientific concepts to reason about science tasks, (b) using adequate evidence to reason about science phenomenon, (c) drawing upon examples of scientific phenomena, and (d) drawing upon science vocabulary. Findings also showed that participants experienced challenges regarding the following content teaching aspects when responding to these items: (a) connecting to key scientific concepts involved in the work of teaching science, (b) attending to instructional goal(s), and (c) recognizing features of grade‐level appropriateness. Implications for using CKT items as part of large‐scale science assessment systems and identifying areas to target in elementary science teachers' CKT development are addressed. 

Abstract We develop a decentralized colouring approach to diversify the nodes in a complex network. The key is the introduction of a local conflict index (LCI) that measures the colour conflicts arising at each node which can be efficiently computed using only local information. We demonstrate via both synthetic and real-world networks that the proposed approach significantly outperforms random colouring as measured by the size of the largest colour-induced connected component. Interestingly, for scale-free networks further improvement of diversity can be achieved by tuning a degree-biasing weighting parameter in the LCI.