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The Next Generation Science Standards (NGSS) reflect an ambitious vision for science education where students investigate phenomena or solve problems through using and applying disciplinary core ideas in concert with science and engineering practices and crosscutting concepts. Because the NGSS are so different from prior standards, the need for high-quality curriculum materials is especially great. As new curricula go to scale, it will be important to conduct evidence-based research on their efficacy. We conducted a randomized experiment to examine the efficacy of a widely available NGSS-designed middle school curriculum for improving seventh grade students’ learning in physical science. A hierarchical linear modeling approach was applied to analyze student learning outcomes as measured by an NGSS-aligned assessment. Initial findings demonstrate evidence of promise of the curriculum materials for supporting three-dimensional teaching and learning. The findings provide support for further research on NGSS-designed materials at other grade levels and within other science domains. 

In this paper, we report on our study of the implementation of an NGSS-designed middle school curriculum and how it fits into the larger context of a district system, including how it may be impacted by other system components (e.g., district support or teacher PL), and how these, in turn, may impact NGSS instruction in the classroom. This study was part of a larger randomized controlled trial that was conducted across four school districts with seventh grade science teachers and their students. We examined the nature of curriculum use and NGSS instruction in treatment and control conditions via instructional logs and surveys. 

Sea turtles represent an ancient lineage of marine vertebrates that evolved from terrestrial ancestors over 100 Mya. The genomic basis of the unique physiological and ecological traits enabling these species to thrive in diverse marine habitats remains largely unknown. Additionally, many populations have drastically declined due to anthropogenic activities over the past two centuries, and their recovery is a high global conservation priority. We generated and analyzed high-quality reference genomes for the leatherback ( Dermochelys coriacea ) and green ( Chelonia mydas ) turtles, representing the two extant sea turtle families. These genomes are highly syntenic and homologous, but localized regions of noncollinearity were associated with higher copy numbers of immune, zinc-finger, and olfactory receptor (OR) genes in green turtles, with ORs related to waterborne odorants greatly expanded in green turtles. Our findings suggest that divergent evolution of these key gene families may underlie immunological and sensory adaptations assisting navigation, occupancy of neritic versus pelagic environments, and diet specialization. Reduced collinearity was especially prevalent in microchromosomes, with greater gene content, heterozygosity, and genetic distances between species, supporting their critical role in vertebrate evolutionary adaptation. Finally, diversity and demographic histories starkly contrasted between species, indicating that leatherback turtles have had a low yet stable effective population size, exhibit extremely low diversity compared with other reptiles, and harbor a higher genetic load compared with green turtles, reinforcing concern over their persistence under future climate scenarios. These genomes provide invaluable resources for advancing our understanding of evolution and conservation best practices in an imperiled vertebrate lineage. 

Cell shape provides both geometry for, and a reflection of, cell function. Numerous methods for describing and modeling cell shape have been described, but previous evaluation of these methods in terms of the accuracy of generative models has been limited.

Here we compare traditional methods and deep autoencoders to build generative models for cell shapes in terms of the accuracy with which shapes can be reconstructed from models. We evaluated the methods on different collections of 2D and 3D cell images, and found that none of the methods gave accurate reconstructions using low dimensional encodings. As expected, much higher accuracies were observed using high dimensional encodings, with outline-based methods significantly outperforming image-based autoencoders. The latter tended to encode all cells as having smooth shapes, even for high dimensions. For complex 3D cell shapes, we developed a significant improvement of a method based on the spherical harmonic transform that performs significantly better than other methods. We obtained similar results for the joint modeling of cell and nuclear shape. Finally, we evaluated the modeling of shape dynamics by interpolation in the shape space. We found that our modified method provided lower deformation energies along linear interpolation paths than other methods. This allows practical shape evolution in high dimensional shape spaces. We conclude that our improved spherical harmonic based methods are preferable for cell and nuclear shape modeling, providing better representations, higher computational efficiency and requiring fewer training images than deep learning methods.

All software and data is available at http://murphylab.cbd.cmu.edu/software.

Supplementary data are available at Bioinformatics online.

 

Hydrogels hold much promise for 3D printing of functional living materials; however, challenges remain in tailoring mechanical robustness as well as biological performance. In addressing this challenge, the modular synthesis of functional hydrogels from 3‐arm diblock copolypeptide stars composed of an inner poly(l‐glutamate) domain and outer poly(l‐tyrosine) or poly(l‐valine) blocks is described. Physical crosslinking due to ß‐sheet assembly of these star block copolymers gives mechanical stability during extrusion printing and the selective incorporation of methacrylate units allows for subsequent photocrosslinking to occur under biocompatible conditions. This permits direct ink writing (DIW) printing of bacteria‐based mixtures leading to 3D objects with high fidelity and excellent bacterial viability. The tunable stiffness of different copolypeptide networks enables control over proliferation and colony formation for embeddedEscherichia colibacteria as demonstrated via isopropyl ß‐d‐1‐thiogalactopyranoside (IPTG) induction of green fluorescent protein (GFP) expression. This translation of molecular structure to network properties highlights the versatility of these polypeptide hydrogel systems with the combination of writable structures and biological activity illustrating the future potential of these 3D‐printed biocomposites.

 

Shear‐recoverable hydrogels based on block copolypeptides with rapid self‐recovery hold potential in extrudable and injectable 3D‐printing applications. In this work, a series of 3‐arm star‐shaped block copolypeptides composed of an inner hydrophilic poly(l‐glutamate) domain and an outer β‐sheet forming domain is synthesized with varying side chains and block lengths. By changing the β‐sheet forming domains, hydrogels with diverse microstructures and mechanical properties are prepared and structure–function relationships are determined using scattering and rheological techniques. Differences in the properties of these materials are amplified during direct‐ink writing with a strong correlation observed between printability and material chemistry. Significantly, it is observed that non‐canonical β‐sheet blocks based on phenyl glycine form more stable networks with superior mechanical properties and writability compared to widely used natural amino acid counterparts. The versatile design available through block copolypeptide materials provides a robust platform to access tunable material properties based solely on molecular design. These systems can be exploited in extrusion‐based applications such as 3D‐printing without the need for additives.