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129th ASEE Annual Conference and Exposition: Excellence Through Diversity, ASEE 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2046179

ABSTRACT

The COVID-19 pandemic forced education institutions everywhere to rapidly pivot to an online format in which students must often work remotely. The rapid transition has been especially challenging for STEM related courses in which students require access to physical devices to complete their work. We describe the initial steps of an NSF funded project focused on creating learning environments and materials designed to support engaged remote student learning. The approach utilizes IoT learning kits that are lent to students to provide hands-on learning experiences and promote remote engaged learning at students' own chosen environment. The IoT involves infrastructure in which a wide variety of physical devices interact with one another and share information. When designing, working with or combining these devices, engineering students must consider, among other things, sensors and signals, sensor and system integration, input and output interfaces, system functions, control, network management, system architecture and storage, power consumption and management issues, as well as testing and measurement for validation of proper functionality. Computer science students, on the other hand focus more on cloud infrastructure services for the support and management of IoT devices as well as the security and communications aspects of these systems;computer science students are also involved in, among other things, system architecture and storage, device control, real-time operation, system integration, user interface, and app development to facilitate the proper use of the IoT devices. This paper describes the initial efforts underway at two Hispanic Serving Institutions in South Texas to develop IoT-based hands-on engaged student learning environments and tools targeting students studying remotely in computer science, electrical engineering and mechanical engineering programs. Three aspects of remote learning are being investigated: 1) Hands-on active problem- and project-based learning (PBL) through the use of IoT kits, 2) Off-campus engaged student learning through hands-on projects using IoT kits, and 3) Scaffolding and Transfer Learning from mathematical concepts to explain the underlying physics theory of the sensors. © American Society for Engineering Education, 2022.

2.
129th ASEE Annual Conference and Exposition: Excellence Through Diversity, ASEE 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2046168

ABSTRACT

With advances in sensor, computing and communication technologies, Internet of Things (IoT) enabled devices are becoming more prevalent. COVID-19 forced many educational institutions to move their courses online, at least temporarily. While this rapid shift in teaching formats impacted all courses, it was especially challenging for lab-based courses in which students require physical access to equipment in order to complete their exercises and assignments. As universities transition back to “normal“instruction formats, many have continued to offer an increased amount of online course content, including lab based courses. IoT technologies can be utilized to enable hands-on learning opportunities for students, especially those who are learning remotely. To support remote student learning, IoT-based labs have been planned as part of the senior capstone design courses in computer science and electrical engineering at Texas A&M University-Kingsville, a Minority Serving Institution. These planned assignments will utilize a basic IoT learning kit comprised of a Raspberry Pi board (or similar basic processor board) along with a collection of sensors. The kits are available to be checked out to students, especially those who are participating in remote learning. The IoT-based lab topics include an introduction to IoT technology, connecting and reading data from sensors and logging it to a website, and remote access/control to an IoT enabled device via the internet. Utilizing the IoT learning kits, these exercises keep students engaged and involved with hands-on learning. Through this introduction to and experience with applications utilizing IoT devices and technology, students will gain a better understanding of and have the opportunity to integrate IoT technology in their senior capstone design projects. © American Society for Engineering Education, 2022.

3.
Virologie ; 26(2):186, 2022.
Article in English | EMBASE | ID: covidwho-1912865

ABSTRACT

Bats are natural reservoirs for numerous coronaviruses, including the potential ancestor of SARS-CoV-2. Knowledge concerning the interaction of coronaviruses and bat cells is, however, sparse. There is thus a need to develop bat cellular models to understand cell tropism, viral replication and virus-induced cell responses. Here, we report the first molecular study of SARS-CoV-2 infection in chiropteran cells. We investigated the ability of primary cells from Rhinolophus and Myotis species, as well as of established and novel cell lines from Myotis myotis, Eptesicus serotinus, Tadarida brasiliensis and Nyctalus noctula, to support SARS-CoV-2 replication. None of these cells were permissive to infection, not even the ones expressing detectable levels of angiotensin-converting enzyme 2 (ACE2), which serves as the viral receptor in many mammalian species including humans. The resistance to infection was overcome by expression of human ACE2 (hACE2) in three cell lines, suggesting that the restriction to viral replication was due to a low expression of bat ACE2 (bACE2) or absence of bACE2 binding in these cells. By contrast, multiple restriction factors to viral replication exist in the three N. noctula cells since hACE2 expression was not sufficient to permit infection. Infectious virions were produced but not released from hACE2-transduced M. myotis brain cells. E. serotinus brain cells and M. myotis nasal epithelial cells expressing hACE2 efficiently controlled viral replication, which correlated with a potent interferon response. Together, our data highlight the existence of species-specific molecular barriers to viral replication in bat cells. Our newly developed chiropteran cellular models are useful tools to investigate the interplay between viruses belonging to the SARS-CoV- 2 lineage and their natural reservoir, including the identification of factors responsible for viral restriction.

4.
Clin Microbiol Infect ; 26(10): 1386-1394, 2020 Oct.
Article in English | MEDLINE | ID: covidwho-628848

ABSTRACT

OBJECTIVES: To validate the diagnostic accuracy of a Euroimmun SARS-CoV-2 IgG and IgA immunoassay for COVID-19. METHODS: In this unmatched (1:2) case-control validation study, we used sera of 181 laboratory-confirmed SARS-CoV-2 cases and 326 controls collected before SARS-CoV-2 emergence. Diagnostic accuracy of the immunoassay was assessed against a whole spike protein-based recombinant immunofluorescence assay (rIFA) by receiver operating characteristic (ROC) analyses. Discrepant cases between ELISA and rIFA were further tested by pseudo-neutralization assay. RESULTS: COVID-19 patients were more likely to be male and older than controls, and 50.3% were hospitalized. ROC curve analyses indicated that IgG and IgA had high diagnostic accuracies with AUCs of 0.990 (95% Confidence Interval [95%CI]: 0.983-0.996) and 0.978 (95%CI: 0.967-0.989), respectively. IgG assays outperformed IgA assays (p=0.01). Taking an assessed 15% inter-assay imprecision into account, an optimized IgG ratio cut-off > 2.5 displayed a 100% specificity (95%CI: 99-100) and a 100% positive predictive value (95%CI: 96-100). A 0.8 cut-off displayed a 94% sensitivity (95%CI: 88-97) and a 97% negative predictive value (95%CI: 95-99). Substituting the upper threshold for the manufacturer's, improved assay performance, leaving 8.9% of IgG ratios indeterminate between 0.8-2.5. CONCLUSIONS: The Euroimmun assay displays a nearly optimal diagnostic accuracy using IgG against SARS-CoV-2 in patient samples, with no obvious gains from IgA serology. The optimized cut-offs are fit for rule-in and rule-out purposes, allowing determination of whether individuals in our study population have been exposed to SARS-CoV-2 or not. IgG serology should however not be considered as a surrogate of protection at this stage.


Subject(s)
Antibodies, Viral/blood , Betacoronavirus/immunology , Clinical Laboratory Techniques/methods , Coronavirus Infections/diagnosis , Immunoassay/standards , Immunoglobulin A/blood , Immunoglobulin G/blood , Pneumonia, Viral/diagnosis , Adult , Area Under Curve , COVID-19 , COVID-19 Testing , Case-Control Studies , Child , Coronavirus Infections/immunology , Coronavirus Infections/physiopathology , Coronavirus Infections/virology , Female , Humans , Immune Sera/chemistry , Male , Pandemics , Pneumonia, Viral/immunology , Pneumonia, Viral/physiopathology , Pneumonia, Viral/virology , ROC Curve , SARS-CoV-2 , Sensitivity and Specificity , Severity of Illness Index
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