Search Results

You are looking at 1-10 of 38

INSPIRE: Development of an Interdisciplinary Science Program in Research and Entrepreneurship
T. Sedighi,
T. Radu,
Q. F. Ashraf,
B. Kumar,
E. J. Quilates,
R. Rahmatullah, and
J. N. Milstein
Article Category: Research Article
Volume/Issue: Volume 4: Issue 2
Online Publication Date: Dec 07, 2023
DOI: 10.35459/tbp.2023.000248
Page Range: 89 – 102

cite these career-readiness skills as the most desirable asset in an employee, with a special emphasis on the need for employees who can work on a diverse variety of tasks. This need for graduates with an interdisciplinary skill set should be considered in contrast to most university curricula, which tend to segregate the sciences into distinct silos with labels such as physics, chemistry, and biology, but fail to show the students how these disciplines interrelate. This segregation of the disciplines extends from undergraduate to graduate school. There are

Download PDF
G. Paci,
E. Haas,
L. Kornau,
D. Marchetti,
L. Wang,
R. Prevedel, and
A. Szmolenszky
Article Category: Research Article
Volume/Issue: Volume 2: Issue 3
Online Publication Date: Oct 07, 2021
Page Range: 55 – 73

learning materials, including reading resources, videos, and assignment options, engage visual, auditory, and verbal learners. The provided teaching and learning materials give teachers the freedom to adjust the MiA resource to their teaching methodology and to choose between an inquiry-based approach or a more guided approach when using the resource. MiA makes students aware of the connections between research, technology, and applied sciences. It facilitates the development of interdisciplinary and scientific thinking and, most importantly, stimulates

Timothy E. Saunders,
Robert A. Cross, and
Andrew J. Bowman
Article Category: Research Article
Volume/Issue: Volume -1: Issue aop
Online Publication Date: Jul 25, 2024
Page Range:

Yee-Hung Mark Chan,
Michelle Phillips,
Katherine Nielsen, and
Diana S. Chu
Article Category: Research Article
Volume/Issue: Volume -1: Issue aop
Online Publication Date: Jul 25, 2024
Page Range:

Alexandra Bermudez,
Samanta Negrete Muñoz,
Rita Blaik,
Amy C. Rowat,
Jimmy Hu, and
Neil Y.C. Lin
Article Category: Research Article
Volume/Issue: Volume 5: Issue 1
Online Publication Date: Dec 07, 2023
Page Range: 1 – 14

a teaching tip to facilitate motivation for the module, students can experiment with some bubbles, or foam, to visually observe how the system packs. Alternatively, counting vertex-forming boundaries of foam can be issued as a prelab for more independent students. Due to its complexity and interdisciplinary nature, we recommend reserving nucleus-to-cytoplasm ratio discussions for college-level students. Table 1. Tips for teaching: suggested teaching concepts for each education level. a Regarding hands-on skills covered in the image acquisition and

Fig 2; Data acquisition and analysis overview. (A) Workflow describing the 3 key experimental steps. Phase contrast images of frog skin epithelial cells are acquired (top) and used as an input into Cellpose, which is an AI-based segmentation tool for nuclear and cytoplasmic segmentation (middle). The segmentation outlines exported from Cellpose were then read into ImageJ to obtain morphologic measurements for downstream analyses, which can be performed by using various platforms, including MATLAB or Excel (bottom). (B) Cross-section image of frog skin illustrating the 3 main layers of the tissue. In this work, we focused on the topmost layer of the skin, the stratum corneum, because it can be approximated as a 2D system. Scale bar = 200 μm. (C) The 10× phase contrast image of flat-mount frog skin, illustrating the overall shape and dimension of the sample. Out-of-focus regions represent portions of the sample that are not in the same optical plane due to sample wrinkling. The red box denotes the region of interest (ROI) shown in (D–F). Scale bar = 100 μm. (D) The 20× phase contrast image of ROI shown in (C). (E) The 40× phase contrast image of the same ROI. (F) The 40× bright field image of the same ROI, but this produced a more out-of-focus background and less defined boundaries, which may reduce the segmentation robustness. Scale bars in (D–F) = 50 μm.
Alexandra Bermudez,
Samanta Negrete Muñoz,
Rita Blaik,
Amy C. Rowat,
Jimmy Hu, and
Neil Y.C. Lin
Fig 2
Fig 2

Data acquisition and analysis overview. (A) Workflow describing the 3 key experimental steps. Phase contrast images of frog skin epithelial cells are acquired (top) and used as an input into Cellpose, which is an AI-based segmentation tool for nuclear and cytoplasmic segmentation (middle). The segmentation outlines exported from Cellpose were then read into ImageJ to obtain morphologic measurements for downstream analyses, which can be performed by using various platforms, including MATLAB or Excel (bottom). (B) Cross-section image of frog skin illustrating the 3 main layers of the tissue. In this work, we focused on the topmost layer of the skin, the stratum corneum, because it can be approximated as a 2D system. Scale bar = 200 μm. (C) The 10× phase contrast image of flat-mount frog skin, illustrating the overall shape and dimension of the sample. Out-of-focus regions represent portions of the sample that are not in the same optical plane due to sample wrinkling. The red box denotes the region of interest (ROI) shown in (D–F). Scale bar = 100 μm. (D) The 20× phase contrast image of ROI shown in (C). (E) The 40× phase contrast image of the same ROI. (F) The 40× bright field image of the same ROI, but this produced a more out-of-focus background and less defined boundaries, which may reduce the segmentation robustness. Scale bars in (D–F) = 50 μm.


Alexandra Bermudez,
Samanta Negrete Muñoz,
Rita Blaik,
Amy C. Rowat,
Jimmy Hu, and
Neil Y.C. Lin
Fig 3
Fig 3

Image segmentation and morphology analysis. (A) Screenshot of Cellpose 2.0 displaying nuclear masks identified by using a user-trained model. (B) Cytoplasmic and nuclear segmentation overlay (yellow) superimposed on the original image using ImageJ. The region of interest (ROI) manager (left window) allows users to look at each outline individually. Users can then press measure to obtain the results shown in the right window. (C) Zoom in of the red boxed region of the Cellpose user interface shown in (A). Here, users can chose from a pretrained model or optimize segmentation parameters. (D) Enlargement of the orange boxed region shown in (B) illustrating the ImageJ ROI manager. (E) Enlargement of the green boxed region in (B) demonstrating the ImageJ measurement results.


Alexandra Bermudez,
Samanta Negrete Muñoz,
Rita Blaik,
Amy C. Rowat,
Jimmy Hu, and
Neil Y.C. Lin
Fig 4
Fig 4

Epithelial cell vertex and AR analyses. (A) The 3-cell vertex. An example phase contrast image of 3 cell boundaries forming a vertex. (B) Associated vertices. An example phase contrast image of two 3-cell vertices that are closely associated. (C) Vertex distribution in frog skin samples. The 108 vertices were analyzed and roughly 85% of all vertices were well separated. (D) Representative phase contrast images of high AR cells. (E) Representative phase contrast images of low AR cells. (F) Probability density function (PDF) demonstrating the spread in AR within the frog skin samples. (G) PDFs normalized by using the rescaling parameter AR − 1AR − 1 where AR represents the average AR. Distribution was fit to a gamma curve with κ=2.40. Scale bar for panels (A), (B), and (E) = 20 μm. Scale bar for panel (D) = 10 μm.


Alexandra Bermudez,
Samanta Negrete Muñoz,
Rita Blaik,
Amy C. Rowat,
Jimmy Hu, and
Neil Y.C. Lin
Fig 1
Fig 1

Epithelium and jammed foam exhibit similar morphology. (A) Image of Madin–Darby canine kidney epithelial cells reproduced from He et al. (55). Scale bar is 10 μm. (B) Screenshot of a 2D wet foam, which represents a jammed physical system (56). Despite the different nature between these 2 physical and biologic examples, the analogous morphology suggests that interfacial tension largely regulates the structure in both systems.


Alexandra Bermudez,
Samanta Negrete Muñoz,
Rita Blaik,
Amy C. Rowat,
Jimmy Hu, and
Neil Y.C. Lin
Fig 5
Fig 5

Nuclear-to-cytoplasmic correlation analysis. (A) Scatter plot of nucleus area versus cell area used to obtain the Pearson correlation coefficient for varying sample sizes. The red dashed line denotes the best fit line. A higher correlation between nuclear and cell area was observed with increasing sample size N. (B) The Pearson correlation coefficient (linear) versus sample size (logarithmic). The middle blue curve denotes the mean, while the shaded blue band denotes the standard deviation. (C) The P value (linear) versus sample size (logarithmic). The tan-shaded region denotes a P value significance threshold of 0.05. The black dashed line denotes the corresponding sample size for a P value of 0.05.