Search for: All records

This Research-to-Practice full paper presents findings from the ASSETS program – a comprehensive support ecosystem developed to improve retention and reduce time to graduation for engineering transfer students. ASSETS builds on the momentum established by two statewide initiatives in Tennessee that place transfer students at the forefront: (1) Tennessee Promise – a nationally recognized scholarship program launched in 2015 that provides last-dollar scholarships for low-income students to attend any state community college, and (2) Tennessee Reconnect – a lastdollar grant established in 2018 that allows adults who do not have an associate degree to attend a community or technical college tuition-free. With over 100,000 students enrolled in these programs to date, the number of students transferring to four-year institutions is expected to increase exponentially in the coming years. Historically, transfer students have been at higher risk of attrition due to known academic and social barriers. This is especially true for the Engineering disciplines. In an effort to address these obstacles, we have developed the Academic Intervention, Social Supports, and Scholarships for Engineering Transfer Students (ASSETS) program. In its third year of operation, with 35 enrolled ASSETS scholars, the program is well underway. Among our findings, we have recognized the critical importance of nurturing a community of transfer students that emphasizes equity, diversity, and inclusion. Establishing such a community involves more than just adopting established best practices. It requires a shift in mindset on behalf of the student regarding what is required to succeed, as well as on the part of faculty on what is expected of incoming students. This paper presents the findings and outcomes of the ASSETS program towards providing support to and enhancing the success of engineering transfer students. 

Synopsis Identifying individual animals is crucial for many biological investigations. In response to some of the limitations of current identification methods, new automated computer vision approaches have emerged with strong performance. Here, we review current advances of computer vision identification techniques to provide both computer scientists and biologists with an overview of the available tools and discuss their applications. We conclude by offering recommendations for starting an animal identification project, illustrate current limitations, and propose how they might be addressed in the future. 

Autofluorescence has historically been considered a nuisance in medical imaging. Many endogenous fluorophores, specifically, collagen, elastin, NADH, and FAD, are found throughout the human body. Diagnostically, these signals can be prohibitive since they can outcompete signals introduced for diagnostic purposes. Recent advances in hyperspectral imaging have allowed the acquisition of significantly more data in a shorter time period by scanning the excitation spectra of fluorophores. The reduced acquisition time and increased signal-to-noise ratio allow for separation of significantly more fluorophores than previously possible. Here, we propose to utilize excitation-scanning of autofluorescence to examine tissues and diagnose pathologies. Spectra of autofluorescent molecules were obtained using a custom inverted microscope (TE-2000, Nikon Instruments) with a Xe arc lamp and thin film tunable filter array (VersaChrome, Semrock, Inc.) Scans utilized excitation wavelengths from 360 nm to 550 nm in 5 nm increments. The resultant spectra were used to examine hyperspectral image stacks from various collaborative studies, including an atherosclerotic rat model and a colon cancer study. Hyperspectral images were analyzed with ENVI and custom Matlab scripts including linear spectral unmixing (LSU) and principal component analysis (PCA). Initial results suggest the ability to separate the signals of endogenous fluorophores and measure the relative concentrations of fluorophores among healthy and diseased states of similar tissues. These results suggest pathology-specific changes to endogenous fluorophores can be detected using excitationscanning hyperspectral imaging. Future work will expand the library of pure molecules and will examine more defined disease states. 

Abstract Spectral imaging approaches provide new possibilities for measuring and discriminating fluorescent molecules in living cells and tissues. These approaches often employ tunable filters and robust image processing algorithms to identify many fluorescent labels in a single image set. Here, we present results from a novel spectral imaging technology that scans the fluorescence excitation spectrum, demonstrating that excitation‐scanning hyperspectral image data can discriminate among tissue types and estimate the molecular composition of tissues. This approach allows fast, accurate quantification of many fluorescent species from multivariate image data without the need of exogenous labels or dyes. We evaluated the ability of the excitation‐scanning approach to identify endogenous fluorescence signatures in multiple unlabeled tissue types. Signatures were screened using multi‐pass principal component analysis. Endmember extraction techniques revealed conserved autofluorescent signatures across multiple tissue types. We further examined the ability to detect known molecular signatures by constructing spectral libraries of common endogenous fluorophores and applying multiple spectral analysis techniques on test images from lung, liver and kidney. Spectral deconvolution revealed structure‐specific morphologic contrast generated from pure molecule signatures. These results demonstrate that excitation‐scanning spectral imaging, coupled with spectral imaging processing techniques, provides an approach for discriminating among tissue types and assessing the molecular composition of tissues. Additionally, excitation scanning offers the ability to rapidly screen molecular markers across a range of tissues without using fluorescent labels. This approach lays the groundwork for translation of excitation‐scanning technologies to clinical imaging platforms.