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The Framework for K-12 Science Education recognizes modeling as an essential practice for building deep understanding of science. Modeling assessments should measure the ability to integrate Disciplinary Core Ideas and Crosscutting Concepts. Machine learning (ML) has been utilized to score and provide feedback on open-ended Learning Progression (LP)-aligned assessments. Analytic rubrics have been shown to be easier to evaluate the validity of ML-based scores. A possible drawback of using analytic rubrics is the potential for oversimplification of integrated ideas. We demonstrate the deconstruction of a 3D holistic rubric for modeling assessments aligned LP for Physical Science. We describe deconstructing this rubric into analytic categories for ML training and to preserve its 3D nature. 

Topologically ordered phases of matter elude Landau's symmetry-breaking theory, featuring a variety of intriguing properties such as long-range entanglement and intrinsic robustness against local perturbations. Their extension to periodically driven systems gives rise to exotic new phenomena that are forbidden in thermal equilibrium. Here, we report the observation of signatures of such a phenomenon -- a prethermal topologically ordered time crystal -- with programmable superconducting qubits arranged on a square lattice. By periodically driving the superconducting qubits with a surface-code Hamiltonian, we observe discrete time-translation symmetry breaking dynamics that is only manifested in the subharmonic temporal response of nonlocal logical operators. We further connect the observed dynamics to the underlying topological order by measuring a nonzero topological entanglement entropy and studying its subsequent dynamics. Our results demonstrate the potential to explore exotic topologically ordered nonequilibrium phases of matter with noisy intermediate-scale quantum processors. 

A widely applicable approach was developed to synthesize ketones, esters, amides via the oxidative C−C bond cleavage of readily available alkyl aldehydes. Green and abundant molecular oxygen (O₂) was used as the oxidant, and base metals (cobalt and copper) were used as the catalysts. This strategy can be extended to the one‐pot synthesis of ketones from primary alcohols and α‐ketoamides from aldehydes.

 

Microbial communities comprised of phototrophs and heterotrophs hold great promise for sustainable biotechnology. Successful application of these communities relies on the selection of appropriate partners. Here we construct four community metabolic models to guide strain selection, pairing phototrophic, sucrose-secretingSynechococcus elongatuswith heterotrophicEscherichia coliK-12,Escherichia coliW,Yarrowia lipolytica, orBacillus subtilis. Model simulations reveae metabolic exchanges that sustain the heterotrophs in minimal media devoid of any organic carbon source, pointing toS. elongatus-E. coliK-12 as the most active community. Experimental validation of flux predictions for this pair confirms metabolic interactions and potential production capabilities. Synthetic communities bypass member-specific metabolic bottlenecks (e.g. histidine- and transport-related reactions) and compensate for lethal genetic traits, achieving up to 27% recovery from lethal knockouts. The study provides a robust modelling framework for the rational design of synthetic communities with optimized growth sustainability using phototrophic partners.