Developing a Clinical Literacy Experience That Improves Outcomes for Students and Preservice Teachers

Improving literacy outcomes so more students graduate from high school career- and college- ready is critical in today\u27s society. There is a wealth of evidence-based practices for teachers to utilize and yet student literacy outcomes fail to improve. This article provides an example of how a clinical model literacy clinic, housed in a partner elementary school, improved learning outcomes for preservice teachers and the at-risk students they instructed. During this twice weekly, semester-long literacy clinic, the preservice teachers explicitly taught all five critical literacy components to support struggling readers with the focus on using high-leverage practices for instruction. This taught the preservice teachers both the what and how to teach struggling readers. This program supported the needs of a partner school while developing new teachers confident and prepared to meet the literacy needs of struggling readers

Developing Growth Mindset and GRIT in Preservice Teachers

Educator preparation programs are charged with developing preservice teachers ready to meet the many challenges of today\u27s classrooms. Developing a growth mindset and GRIT provides future educators with important dispositions to increase their teaching effectiveness and improve the success of their students. A growth mindset helps learners realize that intellect is not fixed but through time and effort, skills will increase. Developing GRIT (i.e., growth, resilience, integrity, and tenacity) builds the perseverance to continue until goals are reached. Developing GRIT and a growth mindset helps teachers understand that all students, even diverse learners, can be successful if provided the appropriate instruction. This article provides an example of how an educator preparation program incorporated growth mindset and GRIT in the clinical model to support learning for the preservice teachers while promoting growth mindset and GRIT in the classroom

Power Law Stretching of Associating Polymers in Steady-State Extensional Flow

We present a tube model for the Brownian dynamics of associating polymers in extensional flow. In linear response, the model confirms the analytical predictions for the sticky diffusivity by Leibler-Rubinstein-Colby theory. Although a single-mode Doi-Edwards-Marrucci-Grizzuti approximation accurately describes the transient stretching of the polymers above a “sticky” Weissenberg number (product of the strain rate with the sticky-Rouse time), the preaveraged model fails to capture a remarkable development of a power law distribution of stretch in steady-state extensional flow: while the mean stretch is finite, the fluctuations in stretch may diverge. We present an analytical model that shows how strong stochastic forcing drives the long tail of the distribution, gives rise to rare events of reaching a threshold stretch, and constitutes a framework within which nucleation rates of flow-induced crystallization may be understood in systems of associating polymers under flow. The model also exemplifies a wide class of driven systems possessing strong, and scaling, fluctuations.We present a tube model for the Brownian dynamics of associating polymers in extensional flow. In linear response, the model confirms the analytical predictions for the sticky diffusivity by Leibler-Rubinstein-Colby theory. Although a single-mode Doi-Edwards-Marrucci-Grizzuti approximation accurately describes the transient stretching of the polymers above a “sticky” Weissenberg number (product of the strain rate with the sticky-Rouse time), the preaveraged model fails to capture a remarkable development of a power law distribution of stretch in steady-state extensional flow: while the mean stretch is finite, the fluctuations in stretch may diverge. We present an analytical model that shows how strong stochastic forcing drives the long tail of the distribution, gives rise to rare events of reaching a threshold stretch, and constitutes a framework within which nucleation rates of flow-induced crystallization may be understood in systems of associating polymers under flow. The model also exemplifies a wide class of driven systems possessing strong, and scaling, fluctuations

Silk Protein Solution : A Natural Example of Sticky Reptation

Silk is one of the most intriguing examples of biomolecular self-assembly, yet little is understood of molecular mechanisms behind the flow behavior generating these complex high-performance fibers. This work applies the polymer physics of entangled solution rheology to present a first microphysical understanding of silk in the linear viscoelastic regime. We show that silk solutions can be approximated as reptating polymers with "sticky" calcium bridges whose strength can be controlled through the potassium concentration. This approach provides a new window into critical microstructural parameters, in particular identifying the mechanism by which potassium and calcium ions are recruited as a powerful viscosity control in silk. Our model constitutes a viable starting point to understand not only the "flow-induced self-assembly" of silk fibers but also a broader range of phenomena in the emergent field of material-focused synthetic biology

Phase transition energetics in mesoscale photosynthetic condensates

The pyrenoid is a model two-component biomolecular condensate, vital for efficient photosynthesis in algae. Despite simulations predicting qualitative features of liquid-liquid phase separation driving their formation, the underlying energetics remain unclear. By modelling interactions between Rubisco protein carbon-capturing machinery inside pyrenoids as linker chemical and stretch potentials we explain spectroscopic and single-molecule data over physiological concentrations. This new parametrisation can be used for quantitative predictions in generalized emergent self-assembly of two-component condensates.Comment: v2: correction in the calculations v3: added experimental wor

Ligand-regulated oligomerisation of allosterically interacting proteins

The binding of ligands to distinct sites at proteins or at protein clusters is often cooperative or anti-cooperative due to allosteric signalling between those sites. The allostery is usually attributed to a configurational change of the proteins from a relaxed to a configurationally different tense state. Alternatively, as originally proposed by Cooper and Dryden, a tense state may be achieved by merely restricting the thermal vibrations of the protein around its mean configuration. In this work, we provide theoretical tools to investigate fluctuation allostery using cooling and titration experiments in which ligands regulate dimerisation, or ring or chain formation. We discuss in detail how ligands may regulate the supramolecular (co)polymerisation of liganded and unliganded proteins

Stretching of Bombyx mori Silk Protein in Flow

The flow-induced self-assembly of entangled Bombyx mori silk proteins is hypothesised to be aided by the ‘registration’ of aligned protein chains using intermolecularly interacting ‘sticky’ patches. This suggests that upon chain alignment, a hierarchical network forms that collectively stretches and induces nucleation in a precisely controlled way. Through the lens of polymer physics, we argue that if all chains would stretch to a similar extent, a clear correlation length of the stickers in the direction of the flow emerges, which may indeed favour such a registration effect. Through simulations in both extensional flow and shear, we show that there is, on the other hand, a very broad distribution of protein–chain stretch, which suggests the registration of proteins is not directly coupled to the applied strain, but may be a slow statistical process. This qualitative prediction seems to be consistent with the large strains (i.e., at long time scales) required to induce gelation in our rheological measurements under constant shear. We discuss our perspective of how the flow-induced self-assembly of silk may be addressed by new experiments and model development

Membraneless organelles formed by liquid-liquid phase separation increase bacterial fitness

Liquid-liquid phase separation is emerging as a crucial phenomenon in several fundamental cell processes. A range of eukaryotic systems exhibit liquid condensates. However, their function in bacteria, which in general lack membrane-bound compartments, remains less clear. Here, we used high-resolution optical microscopy to observe single bacterial aggresomes, nanostructured intracellular assemblies of proteins, to undercover their role in cell stress. We find that proteins inside aggresomes are mobile and undergo dynamic turnover, consistent with a liquid state. Our observations are in quantitative agreement with phase-separated liquid droplet formation driven by interacting proteins under thermal equilibrium that nucleate following diffusive collisions in the cytoplasm. We have discovered aggresomes in multiple species of bacteria, and show that these emergent, metastable liquid-structured protein assemblies increase bacterial fitness by enabling cells to tolerate environmental stresses

Morphology formation in binary mixtures upon gradual destabilisation

Spontaneous liquid-liquid phase separation is commonly understood in terms of phenomenological mean-field theories. These theories correctly predict the structural features of the fluid at sufficiently long time scales and wavelengths. However, these conditions are not met in various examples in biology and materials science where the mixture is slowly destabilised, and phase separation is strongly affected by critical thermal fluctuations. We propose a mechanism of pretransitional structuring of a mixture that approaches the miscibility gap and predict scaling relations that describe how the characteristic feature size of the emerging morphology decreases with an increasing quench rate. These predictions quantitatively agree with our kinetic Monte Carlo and molecular dynamics simulations of a phase-separating binary mixture, as well as with previously reported experimental observations. We discuss how these predictions are affected by non-conserved order parameters (e.g., due to chemical reactions or alignment of liquid-crystalline molecules), hydrodynamics and active transport