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.
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Abstract Motivation 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.
Results 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.
Availability and implementation All software and data is available at http://murphylab.cbd.cmu.edu/software.
Supplementary information Supplementary data are available at Bioinformatics online.
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The killing of tumor cells by CD8+T cells is suppressed by the tumor microenvironment, and increased expression of inhibitory receptors, including programmed cell death protein-1 (PD-1), is associated with tumor-mediated suppression of T cells. To find cellular defects triggered by tumor exposure and associated PD-1 signaling, we established an ex vivo imaging approach to investigate the response of antigen-specific, activated effector CD8+tumor-infiltrating lymphocytes (TILs) after interaction with target tumor cells. Although TIL–tumor cell couples readily formed, couple stability deteriorated within minutes. This was associated with impaired F-actin clearing from the center of the cellular interface, reduced Ca2+signaling, increased TIL locomotion, and impaired tumor cell killing. The interaction of CD8+T lymphocytes with tumor cell spheroids in vitro induced a similar phenotype, supporting a critical role of direct T cell–tumor cell contact. Diminished engagement of PD-1 within the tumor, but not acute ex vivo blockade, partially restored cell couple maintenance and killing. PD-1 thus contributes to the suppression of TIL function by inducing a state of impaired subcellular organization.
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