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Global science education reform calls for developing student knowledge-in-use that applies the integrated knowledge of core ideas and scientific practices to make sense of phenomena or solve problems. Knowledge-in-use development requires a long-term, standards-aligned, coherent learning system, including curriculum and instruction, assessment, and professional learning. This paper addresses the challenge of transforming standards into classrooms for knowledge-in-use and presents an iterative design process for developing a coherent and standards-aligned learning system. Using a project-based learning approach, we present a theory-driven, empirically validated learning system aligned with the U.S. science standards, consisting of four consecutive curriculum and instruction materials, assessments, and professional learning to support students’ knowledge-in-use in high school chemistry. We also present the iterative development and testing process with empirical evidence to support the effectiveness of our learning system in a five-year NSF-funded research project. This paper discusses the theoretical perspectives of developing an NGSS-aligned, coherent, and effective learning system and recaps the development and testing process by unpacking all essential components in our learning system. We conclude that our theory-driven and empirically validated learning system would inform high school teachers and researchers across countries in transforming their local science standards into curriculum materials to support students’ knowledge-in-use development.

 

Abstract SARS-CoV-2 receptor binding domains (RBDs) interact with both the ACE2 receptor and heparan sulfate on the surface of host cells to enhance SARS-CoV-2 infection. We show that suramin, a polysulfated synthetic drug, binds to the ACE2 receptor and heparan sulfate binding sites on the RBDs of wild-type, Delta, and Omicron variants. Specifically, heparan sulfate and suramin had enhanced preferential binding for Omicron RBD, and suramin is most potent against the live SARS-CoV-2 Omicron variant (B.1.1.529) when compared to wild type and Delta (B.1.617.2) variants in vitro. These results suggest that inhibition of live virus infection occurs through dual SARS-CoV-2 targets of S-protein binding and previously reported RNA-dependent RNA polymerase inhibition and offers the possibility for this and other polysulfated molecules to be used as potential therapeutic and prophylactic options against COVID-19. 

The now prevalent Omicron variant and its subvariants/sub-lineages have led to a significant increase in COVID-19 cases and raised serious concerns about increased risk of infectivity, immune evasion, and reinfection. Heparan sulfate (HS), located on the surface of host cells, plays an important role as a co-receptor for virus–host cell interaction. The ability of heparin and HS to compete for binding of the SARS-CoV-2 spike (S) protein to cell surface HS illustrates the therapeutic potential of agents targeting protein–glycan interactions. In the current study, phylogenetic tree of variants and mutations in S protein receptor-binding domain (RBD) of Omicron BA.2.12.1, BA.4 and BA.5 were described. The binding affinity of Omicron S protein RBD to heparin was further investigated by surface plasmon resonance (SPR). Solution competition studies on the inhibitory activity of heparin oligosaccharides and desulfated heparins at different sites on S protein RBD–heparin interactions revealed that different sub-lineages tend to bind heparin with different chain lengths and sulfation patterns. Furthermore, blind docking experiments showed the contribution of basic amino acid residues in RBD and sulfo groups and carboxyl groups on heparin to the interaction. Finally, pentosan polysulfate and mucopolysaccharide polysulfate were evaluated for inhibition on the interaction of heparin and S protein RBD of Omicron BA.2.12.1, BA.4/BA.5, and both showed much stronger inhibition than heparin. 

Abstract Complex carbohydrates (glycans) are major players in all organisms due to their structural, energy, and communication roles. This last essential role involves interacting and/or signaling through a plethora of glycan-binding proteins. The design and synthesis of glycans as potential drug candidates that selectively alter or perturb metabolic processes is challenging. Here we describe the first reported sulfur-linked polysaccharides with potentially altered conformational state(s) that are recalcitrant to digestion by heparanase, an enzyme important in human health and disease. An artificial sugar donor with a sulfhydryl functionality is synthesized and enzymatically incorporated into polysaccharide chains utilizing heparosan synthase. Used alone, this donor adds a single thio-sugar onto the termini of nascent chains. Surprisingly, in chain co-polymerization reactions with a second donor, this thiol-terminated heparosan also serves as an acceptor to form an unnatural thio-glycosidic bond (‘ S -link’) between sugar residues in place of a natural ‘ O -linked’ bond. S -linked heparan sulfate analogs are not cleaved by human heparanase. Furthermore, the analogs act as competitive inhibitors with > ~200-fold higher potency than expected; as a rationale, molecular dynamic simulations suggest that the S -link polymer conformations mimic aspects of the transition state. Our analogs form the basis for future cancer therapeutics and modulators of protein/sugar interactions. 

Monkeypox virus (MPXV), a member of the Orthopoxvirus genus, has begun to spread into many countries worldwide. While the prevalence of monkeypox in Central and Western Africa is well-known, the recent rise in the number of cases spread through intimate personal contact, particularly in the United States, poses a grave international threat. Previous studies have shown that cell-surface heparan sulfate (HS) is important for vaccinia virus (VACV) infection, particularly the binding of VACV A27, which appears to mediate the binding of virus to cellular HS. Some other glycosaminoglycans (GAGs) also bind to proteins on Orthopoxviruses. In this study, by using surface plasmon resonance, we demonstrated that MPXV A29 protein (a homolog of VACV A27) binds to GAGs including heparin and chondroitin sulfate/dermatan sulfate. The negative charges on GAGs are important for GAG–MPXV A29 interaction. GAG analogs, pentosan polysulfate and mucopolysaccharide polysulfate, show strong inhibition of MPXV A29–heparin interaction. A detailed understanding on the molecular interactions involved in this disease should accelerate the development of therapeutics and drugs for the treatment of MPXV. 

N-glycolylated carbohydrates are amino sugars with an N-glycolyl amide group. These glycans have not been well studied due to their surprising rarity in nature in comparison with N-acetylated carbohydrates. Recently, however, there has been increasing interest in N-glycolylated sugars because the non-human sialic acid N-glycolylneuraminic acid (Neu5Gc), apparently the only source of all N-glycolylated sugars in deuterostomes, appears to be involved in xenosialitis (inflammation associated with consumption of Neu5Gc-rich red meats). Xenosialitis has been implicated in cancers as well as other diseases including atherosclerosis. Furthermore, metabolites of Neu5Gc have been shown to be incorporated into glycosaminoglycans (GAGs), resulting in N-glycolylated GAGs. These N-glycolylated GAGs have important potential applications, such as dating the loss of the Neu5Gc-generating CMAH gene in humans and being explored as a xenosialitis biomarker and/or estimate of the body burden of diet-derived Neu5Gc, to understand the risks associated with the consumption of red meats. This review explores N-glycolylated carbohydrates, how they are metabolized to N-glycolylglucosamine and N-glycolylgalactosamine, and how these metabolites can be incorporated into N-glycolylated GAGs in human tissues. We also discuss other sources of N-glycolylated sugars, such as recombinant production from microorganisms using metabolic engineering as well as chemical synthesis.

 

Involving students in scientific modeling practice is one of the most effective approaches to achieving the next generation science education learning goals. Given the complexity and multirepresentational features of scientific models, scoring student-developed models is time- and cost-intensive, remaining one of the most challenging assessment practices for science education. More importantly, teachers who rely on timely feedback to plan and adjust instruction are reluctant to use modeling tasks because they could not provide timely feedback to learners. This study utilized machine learn- ing (ML), the most advanced artificial intelligence (AI), to develop an approach to automatically score student- drawn models and their written descriptions of those models. We developed six modeling assessment tasks for middle school students that integrate disciplinary core ideas and crosscutting concepts with the modeling practice. For each task, we asked students to draw a model and write a description of that model, which gave students with diverse backgrounds an opportunity to represent their understanding in multiple ways. We then collected student responses to the six tasks and had human experts score a subset of those responses. We used the human-scored student responses to develop ML algorithmic models (AMs) and to train the computer. Validation using new data suggests that the machine-assigned scores achieved robust agreements with human consent scores. Qualitative analysis of student-drawn models further revealed five characteristics that might impact machine scoring accuracy: Alternative expression, confusing label, inconsistent size, inconsistent position, and redundant information. We argue that these five characteristics should be considered when developing machine-scorable modeling tasks.