98 research outputs found
Predictors of Employment Status Among Adults With Autism Spectrum Disorder
No full textAbstract
Date Presented 3/31/2017
Multivariate logistic regression analyses of a national employment survey of adults with autism spectrum disorder (ASD) revealed disability disclosure and higher education increased the participants’ likelihood of employment. This information may prove useful to occupational therapists working with adults with ASD.
Primary Author and Speaker: Alisha Ohl</jats:p
North to the future
No full text13 p.Short story written by Alisha Bjorklund in the Fall semester 2009 at the University of Wisconsin-River Falls for Dr. Jennifer Brantley's English 382 Writing Workshop class. In the story, the author weaves together several episodes of various outdoor activities and life in Alaska by presenting each story individually and then ending each story by creating a fleeting glimpse of something that is explained by the next story
Crocodile's power
No full text14 p.Short story written by Alisha Bjorklund in the Fall semester 2009 at the University of Wisconsin-River Falls for Dr. Jennifer Brantley's English 382 Writing Workshop class. In the story, the author tells of a young boy, who being an outcast in his village is destined to be hated by his village because of the actions of his father. In order to overcome his destiny, he seeks to capture a true crocodile god in order to harness its power, but in the end, he finds something more powerful then he expected
Anecdotes and Antidotes
No full textBy Alisha Rankin How did early modern individuals test and try their recipes and cures? This question is at the heart of the special issue of the Bulletin of the History of Medicine, “Testing Drugs and Trying Cures in Medieval and Early Modern Europe,” in which I participated as both a co-editor and an author. My article, “On Anecdote and Antidotes: Poison Trials in Early Modern Europe,” examines the ways in which early modern practitioners tested a specific kind of cure: antidotes to poison...
Engineering measurement tools to advance quantitative single-cell biology and pathogen inactivation
No full textEngineering measurement tools to advance quantitative single-cell biology and pathogen inactivation
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Engineering measurement tools to advance quantitative single-cell biology and pathogen inactivation
Full text linkQuantitative measurement techniques are critical to new biological discoveries and reproducibility in science and medicine. Research advances are often driven by novel measurement capabilities or improvements in the sensitivity, specificity, or multiplexing of existing approaches. Improvements in throughput and precision can enable more accessible and accurate validation techniques to support reproducibility. Here, we introduce and optimize measurement approaches to advance two fields of quantitative biology: (1) single- and few-cell molecular profiling, and (2) germicidal ultraviolet-C (UV-C) pathogen inactivation. The measurement techniques developed here support research to understand the cellular heterogeneity driving development and disease, as well as safe and effective UV-C decontamination in clinical settings. First, we focus on advancing proteomic characterization of single cells with high specificity. Different proteoforms – different molecular forms of a protein arising from the same gene – often have unique roles in disease progression and other important biological processes. However, many assays cannot distinguish between proteoforms due to a lack of proteoform-specific antibodies. Electrophoretic cytometry increases proteoform specificity by using electrophoretic separations to spatially resolve proteoforms by mass or charge prior to antibody-based detection. To facilitate quantitative comparison of the ~100s of single-cell protein measurements which can be made on a single electrophoretic cytometry device, is it important to characterize and minimize measurement error. Here, we first investigate approaches to minimize and control for technical variation in both protein abundance and molecular mass measurements made by electrophoretic cytometry. We identify physicochemical mechanisms which contribute to intra-assay technical variation in protein immobilization and antibody binding within the electrophoretic sieving matrix, and use this fundamental understanding to develop strategies to improve the precision of single-cell protein abundance measurements. To improve the precision of molecular mass measurements, we develop protein-loaded microparticles which can be co-loaded with single-cells to act as a molecular mass ladder and control for technical variation in protein electromigration. Overall, these strategies allow finer biological differences in protein abundance and proteoform molecular mass to be distinguished.
Next, we extend electrophoretic cytometry approaches to a range of biological sample types and incorporate multimodal detection capabilities. First, to support study of the roles of different proteoforms in mammalian development, we develop and apply an electrophoretic cytometry approach to characterize proteins expressed in single mouse embryos and blastomeres. To understand the relationship between protein expression and upstream nucleic acids (DNA, mRNA), we also develop an approach to fractionate a single cell or embryo and measure both cytoplasmic proteins and nuclear DNA or mRNA from the same single cell or embryo. While these platforms advance molecular profiling of detached cells in suspension, measurements of adherent cells are also valuable to understand spatial variation in protein expression and to understand cell-microenvironment interactions. To characterize proteoforms from adherent cells while preserving information about the starting cell locations, we investigate the use of projection electrophoresis to separate proteoforms in the Z-dimension while maintaining spatial context information in the X-Y plane. Because adherent cell projection electrophoresis has a different assay geometry and boundary conditions than traditional electrophoretic cytometry platforms in which detached cells are isolated and lysed within microwells, we compare the sensitivity and lateral spatial resolution of adherent cell and microwell-based projection electrophoresis platforms using simulation and fluorescent protein imaging. Informed by this characterization, we demonstrate a proof-of-concept projection electrophoretic separation of subconfluent adherent breast cancer cells. Overall, this work extends electrophoretic cytometry to new sample types and offers a new approach to couple nucleic acid and proteoform measurements from the same single or few cells.
In addition to advancing techniques to measure biological samples directly, we also advance research and implementation of germicidal UV-C pathogen inactivation through the development of quantitative, high-throughput, and accessible UV-C dosimetry techniques. To address shortages induced by the COVID-19 pandemic, UV-C decontamination has been identified as a promising approach to decontaminate N95 respirators for emergency reuse. Both pathogen inactivation and N95 degradation depend on UV-C dose. However, it is challenging to measure UV-C dose on N95 surfaces, as radiometers and other standard UV-C dose measurement techniques have insufficiently small form factor, and often have nonideal angular response. Here, we develop a high-throughput quantitative UV-C dosimetry approach using colorimetric indicators, characterize the impact of optical attenuators on dosimeter dynamic range and angular response, and apply the dosimetry approach to make first-in-kind paired measurements of on-N95 UV-C dose and SARS-CoV-2 viral inactivation. Improved UV-C dose measurement techniques facilitate research of UV-C pathogen inactivation and validation of UV-C decontamination protocols.
Taken together, the work covered in this dissertation advances the range and precision of measurements important to studying single-cell biology and pathogen inactivation, supporting a variety of research and clinical applications
Respectable from their intelligence: the education of Louisiana's gens de couleur libres, 1800 to 1860
Full text linkThis study provides a historical analysis of the socioeconomic and cultural conditions that influenced the unprecedented educational attainment of Louisiana’s gens de couleur libres (free people of color) from colonization to the dawn of the American Civil War. Many in this community came to possess notable wealth – to the extent that they have been esteemed as the wealthiest group of free blacks in the nation in the nineteenth century. Moreover, libres were able to attain the highest levels of education: private schools were created, pupils were sent north for schooling, tutors were hired, and many finished their schooling in France. Given that this community, on the whole, achieved substantially higher levels of wealth and education than any of their North American counterparts, this work relies on archival research methods to answer the central question: What enabled an entire community of color to find scholarly success in an overtly racially oppressive society?Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2019-05-01The student, Alisha Johnson, accepted the attached license on 2017-04-17 at 15:42.The student, Alisha Johnson, submitted this Dissertation for approval on 2017-04-17 at 16:32.This Dissertation was approved for publication on 2017-04-19 at 08:59.DSpace SAF Submission Ingestion Package generated from Vireo submission #10818 on 2017-08-10 at 14:30:59Made available in DSpace on 2017-08-10T19:52:03Z (GMT). No. of bitstreams: 3
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"High Noon": Developing a Video Game Production Pipeline for Chico State Game Studios
No full textABSTRACT\ud
HIGH NOON: DEVELOPING A VIDEO GAME PRODUCTION\ud
PIPELINE FOR CHICO STATE GAME STUDIOS\ud
by\ud
?? Alisha Thayer 2009\ud
Master of Science in Interdisciplinary Studies\ud
Applied Computer Graphics\ud
California State University, Chico\ud
Summer 2009\ud
Founded in 2004, Chico State Game Studios has produced four large-scale\ud
video games with team sizes ranging from eight to fifty-four students. The success of\ud
projects of this nature relies heavily on effective, documented production pipelines,\ud
something that past Chico State Game Studios projects have lacked due to the fact that\ud
no established knowledge base has ever been shared between incoming and exiting projects.\ud
High Noon, Chico State Game Studios??? fourth project, sought to provide a\ud
venue for testing and documenting experimental production pipelines for use by large,\ud
student-run projects. This was done through extensive research, planning, customizing,\ud
executing, and documenting processes used through the author???s tenure as Director of\ud
x\ud
High Noon and as Art Director on the following Chico State Game Studios project,\ud
D.A.V.I.S.\ud
The intention of this project is to present guidelines for incoming Chico State\ud
Game Studios teams by providing comprehensive documentation of the successes and\ud
failures of High Noon???s production. Moreover, this project stresses the importance of\ud
project documentation and aims to set a precedent for future Chico State Game Studios\ud
projects.CSU, Chic
A data-driven approach to object classification through fog
No full textThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (page 51).Identifying objects through fog is an important problem that is difficult even for the human eye. Solving this problem would make autonomous vehicles, drones, and other similar systems more resilient to changing natural weather conditions. While there are existing solutions for dehazing images occluded by light fog, these solutions are not effective in cases of very dense fog. Hence, we present a system that uses a combination of time resolved sensing, specifically using Single Photon Avalanche Photodiode (SPAD) cameras, and deep learning with convolutional neural networks to detect and classify objects when imaged through extreme scattering media like fog. This thesis describes our three-pronged approach to solving this problem: (1) building simulation software to gather sufficient training data, (2) verifying and benchmarking output of simulation with real-life fog data, (3) training deep learning models to classify objects occluded by fog.by Alisha Saxena.M. Eng
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