24 research outputs found

    Experience-Based UDL Applications: Overcoming Barriers to Learning

    Get PDF
    The overall purpose of this study was to examine the autobiographical memory narrative as a way for graduate teacher candidates (TCs) to learn to identify (1) barriers to learning, (2) Universal Design for Learning (UDL) checkpoints to remove these barriers, and (3) strategies for addressing the UDL checkpoints and removing these barriers. This phenomenological study explored lived experiences of (a) UDL training in the graduate teacher preparation programs, (b) barriers to learning in the past experience, and (c) application of UDL principles to removing the self-identified barriers to learning among graduate TCs. Having a purposeful criterion sample at a site level to explore central phenomena in the study (Creswell & Poth, 2018), participants in the study included 63 graduate TCs in a teacher certification program at a university in the north eastern region of the United States. The participants dually took roles as a student, who identified barriers to their learning from the past experience, and as a teacher, who applied UDL principles to removing those self-identified barriers. Data were collected through each participant’s autobiographical narrative about (i) their past learning experience at any point in K-16 education, (ii) barrier to their own learning experience in the past, and (iii) UDL application to removing the identified learning barriers. Data were analyzed to identify frequency of barriers and types of strategies to remove these barriers across participants. Discussion includes identified (1) barriers to learning, (2) UDL checkpoints, and (3) strategies to apply the identified UDL checkpoints to removing these barriers. Emerging themes were aligned with the UDL guidelines (2018)

    Image_1_Detecting glaucoma from multi-modal data using probabilistic deep learning.jpeg

    No full text
    ObjectiveTo assess the accuracy of probabilistic deep learning models to discriminate normal eyes and eyes with glaucoma from fundus photographs and visual fields.DesignAlgorithm development for discriminating normal and glaucoma eyes using data from multicenter, cross-sectional, case-control study.Subjects and participantsFundus photograph and visual field data from 1,655 eyes of 929 normal and glaucoma subjects to develop and test deep learning models and an independent group of 196 eyes of 98 normal and glaucoma patients to validate deep learning models.Main outcome measuresAccuracy and area under the receiver-operating characteristic curve (AUC).MethodsFundus photographs and OCT images were carefully examined by clinicians to identify glaucomatous optic neuropathy (GON). When GON was detected by the reader, the finding was further evaluated by another clinician. Three probabilistic deep convolutional neural network (CNN) models were developed using 1,655 fundus photographs, 1,655 visual fields, and 1,655 pairs of fundus photographs and visual fields collected from Compass instruments. Deep learning models were trained and tested using 80% of fundus photographs and visual fields for training set and 20% of the data for testing set. Models were further validated using an independent validation dataset. The performance of the probabilistic deep learning model was compared with that of the corresponding deterministic CNN model.ResultsThe AUC of the deep learning model in detecting glaucoma from fundus photographs, visual fields, and combined modalities using development dataset were 0.90 (95% confidence interval: 0.89–0.92), 0.89 (0.88–0.91), and 0.94 (0.92–0.96), respectively. The AUC of the deep learning model in detecting glaucoma from fundus photographs, visual fields, and both modalities using the independent validation dataset were 0.94 (0.92–0.95), 0.98 (0.98–0.99), and 0.98 (0.98–0.99), respectively. The AUC of the deep learning model in detecting glaucoma from fundus photographs, visual fields, and both modalities using an early glaucoma subset were 0.90 (0.88,0.91), 0.74 (0.73,0.75), 0.91 (0.89,0.93), respectively. Eyes that were misclassified had significantly higher uncertainty in likelihood of diagnosis compared to eyes that were classified correctly. The uncertainty level of the correctly classified eyes is much lower in the combined model compared to the model based on visual fields only. The AUCs of the deterministic CNN model using fundus images, visual field, and combined modalities based on the development dataset were 0.87 (0.85,0.90), 0.88 (0.84,0.91), and 0.91 (0.89,0.94), and the AUCs based on the independent validation dataset were 0.91 (0.89,0.93), 0.97 (0.95,0.99), and 0.97 (0.96,0.99), respectively, while the AUCs based on an early glaucoma subset were 0.88 (0.86,0.91), 0.75 (0.73,0.77), and 0.92 (0.89,0.95), respectively.Conclusion and relevanceProbabilistic deep learning models can detect glaucoma from multi-modal data with high accuracy. Our findings suggest that models based on combined visual field and fundus photograph modalities detects glaucoma with higher accuracy. While probabilistic and deterministic CNN models provided similar performance, probabilistic models generate certainty level of the outcome thus providing another level of confidence in decision making.</p

    Image_2_Detecting glaucoma from multi-modal data using probabilistic deep learning.tif

    No full text
    ObjectiveTo assess the accuracy of probabilistic deep learning models to discriminate normal eyes and eyes with glaucoma from fundus photographs and visual fields.DesignAlgorithm development for discriminating normal and glaucoma eyes using data from multicenter, cross-sectional, case-control study.Subjects and participantsFundus photograph and visual field data from 1,655 eyes of 929 normal and glaucoma subjects to develop and test deep learning models and an independent group of 196 eyes of 98 normal and glaucoma patients to validate deep learning models.Main outcome measuresAccuracy and area under the receiver-operating characteristic curve (AUC).MethodsFundus photographs and OCT images were carefully examined by clinicians to identify glaucomatous optic neuropathy (GON). When GON was detected by the reader, the finding was further evaluated by another clinician. Three probabilistic deep convolutional neural network (CNN) models were developed using 1,655 fundus photographs, 1,655 visual fields, and 1,655 pairs of fundus photographs and visual fields collected from Compass instruments. Deep learning models were trained and tested using 80% of fundus photographs and visual fields for training set and 20% of the data for testing set. Models were further validated using an independent validation dataset. The performance of the probabilistic deep learning model was compared with that of the corresponding deterministic CNN model.ResultsThe AUC of the deep learning model in detecting glaucoma from fundus photographs, visual fields, and combined modalities using development dataset were 0.90 (95% confidence interval: 0.89–0.92), 0.89 (0.88–0.91), and 0.94 (0.92–0.96), respectively. The AUC of the deep learning model in detecting glaucoma from fundus photographs, visual fields, and both modalities using the independent validation dataset were 0.94 (0.92–0.95), 0.98 (0.98–0.99), and 0.98 (0.98–0.99), respectively. The AUC of the deep learning model in detecting glaucoma from fundus photographs, visual fields, and both modalities using an early glaucoma subset were 0.90 (0.88,0.91), 0.74 (0.73,0.75), 0.91 (0.89,0.93), respectively. Eyes that were misclassified had significantly higher uncertainty in likelihood of diagnosis compared to eyes that were classified correctly. The uncertainty level of the correctly classified eyes is much lower in the combined model compared to the model based on visual fields only. The AUCs of the deterministic CNN model using fundus images, visual field, and combined modalities based on the development dataset were 0.87 (0.85,0.90), 0.88 (0.84,0.91), and 0.91 (0.89,0.94), and the AUCs based on the independent validation dataset were 0.91 (0.89,0.93), 0.97 (0.95,0.99), and 0.97 (0.96,0.99), respectively, while the AUCs based on an early glaucoma subset were 0.88 (0.86,0.91), 0.75 (0.73,0.77), and 0.92 (0.89,0.95), respectively.Conclusion and relevanceProbabilistic deep learning models can detect glaucoma from multi-modal data with high accuracy. Our findings suggest that models based on combined visual field and fundus photograph modalities detects glaucoma with higher accuracy. While probabilistic and deterministic CNN models provided similar performance, probabilistic models generate certainty level of the outcome thus providing another level of confidence in decision making.</p

    Table_1_Detecting glaucoma from multi-modal data using probabilistic deep learning.docx

    No full text
    ObjectiveTo assess the accuracy of probabilistic deep learning models to discriminate normal eyes and eyes with glaucoma from fundus photographs and visual fields.DesignAlgorithm development for discriminating normal and glaucoma eyes using data from multicenter, cross-sectional, case-control study.Subjects and participantsFundus photograph and visual field data from 1,655 eyes of 929 normal and glaucoma subjects to develop and test deep learning models and an independent group of 196 eyes of 98 normal and glaucoma patients to validate deep learning models.Main outcome measuresAccuracy and area under the receiver-operating characteristic curve (AUC).MethodsFundus photographs and OCT images were carefully examined by clinicians to identify glaucomatous optic neuropathy (GON). When GON was detected by the reader, the finding was further evaluated by another clinician. Three probabilistic deep convolutional neural network (CNN) models were developed using 1,655 fundus photographs, 1,655 visual fields, and 1,655 pairs of fundus photographs and visual fields collected from Compass instruments. Deep learning models were trained and tested using 80% of fundus photographs and visual fields for training set and 20% of the data for testing set. Models were further validated using an independent validation dataset. The performance of the probabilistic deep learning model was compared with that of the corresponding deterministic CNN model.ResultsThe AUC of the deep learning model in detecting glaucoma from fundus photographs, visual fields, and combined modalities using development dataset were 0.90 (95% confidence interval: 0.89–0.92), 0.89 (0.88–0.91), and 0.94 (0.92–0.96), respectively. The AUC of the deep learning model in detecting glaucoma from fundus photographs, visual fields, and both modalities using the independent validation dataset were 0.94 (0.92–0.95), 0.98 (0.98–0.99), and 0.98 (0.98–0.99), respectively. The AUC of the deep learning model in detecting glaucoma from fundus photographs, visual fields, and both modalities using an early glaucoma subset were 0.90 (0.88,0.91), 0.74 (0.73,0.75), 0.91 (0.89,0.93), respectively. Eyes that were misclassified had significantly higher uncertainty in likelihood of diagnosis compared to eyes that were classified correctly. The uncertainty level of the correctly classified eyes is much lower in the combined model compared to the model based on visual fields only. The AUCs of the deterministic CNN model using fundus images, visual field, and combined modalities based on the development dataset were 0.87 (0.85,0.90), 0.88 (0.84,0.91), and 0.91 (0.89,0.94), and the AUCs based on the independent validation dataset were 0.91 (0.89,0.93), 0.97 (0.95,0.99), and 0.97 (0.96,0.99), respectively, while the AUCs based on an early glaucoma subset were 0.88 (0.86,0.91), 0.75 (0.73,0.77), and 0.92 (0.89,0.95), respectively.Conclusion and relevanceProbabilistic deep learning models can detect glaucoma from multi-modal data with high accuracy. Our findings suggest that models based on combined visual field and fundus photograph modalities detects glaucoma with higher accuracy. While probabilistic and deterministic CNN models provided similar performance, probabilistic models generate certainty level of the outcome thus providing another level of confidence in decision making.</p

    Image_4_Detecting glaucoma from multi-modal data using probabilistic deep learning.tif

    No full text
    ObjectiveTo assess the accuracy of probabilistic deep learning models to discriminate normal eyes and eyes with glaucoma from fundus photographs and visual fields.DesignAlgorithm development for discriminating normal and glaucoma eyes using data from multicenter, cross-sectional, case-control study.Subjects and participantsFundus photograph and visual field data from 1,655 eyes of 929 normal and glaucoma subjects to develop and test deep learning models and an independent group of 196 eyes of 98 normal and glaucoma patients to validate deep learning models.Main outcome measuresAccuracy and area under the receiver-operating characteristic curve (AUC).MethodsFundus photographs and OCT images were carefully examined by clinicians to identify glaucomatous optic neuropathy (GON). When GON was detected by the reader, the finding was further evaluated by another clinician. Three probabilistic deep convolutional neural network (CNN) models were developed using 1,655 fundus photographs, 1,655 visual fields, and 1,655 pairs of fundus photographs and visual fields collected from Compass instruments. Deep learning models were trained and tested using 80% of fundus photographs and visual fields for training set and 20% of the data for testing set. Models were further validated using an independent validation dataset. The performance of the probabilistic deep learning model was compared with that of the corresponding deterministic CNN model.ResultsThe AUC of the deep learning model in detecting glaucoma from fundus photographs, visual fields, and combined modalities using development dataset were 0.90 (95% confidence interval: 0.89–0.92), 0.89 (0.88–0.91), and 0.94 (0.92–0.96), respectively. The AUC of the deep learning model in detecting glaucoma from fundus photographs, visual fields, and both modalities using the independent validation dataset were 0.94 (0.92–0.95), 0.98 (0.98–0.99), and 0.98 (0.98–0.99), respectively. The AUC of the deep learning model in detecting glaucoma from fundus photographs, visual fields, and both modalities using an early glaucoma subset were 0.90 (0.88,0.91), 0.74 (0.73,0.75), 0.91 (0.89,0.93), respectively. Eyes that were misclassified had significantly higher uncertainty in likelihood of diagnosis compared to eyes that were classified correctly. The uncertainty level of the correctly classified eyes is much lower in the combined model compared to the model based on visual fields only. The AUCs of the deterministic CNN model using fundus images, visual field, and combined modalities based on the development dataset were 0.87 (0.85,0.90), 0.88 (0.84,0.91), and 0.91 (0.89,0.94), and the AUCs based on the independent validation dataset were 0.91 (0.89,0.93), 0.97 (0.95,0.99), and 0.97 (0.96,0.99), respectively, while the AUCs based on an early glaucoma subset were 0.88 (0.86,0.91), 0.75 (0.73,0.77), and 0.92 (0.89,0.95), respectively.Conclusion and relevanceProbabilistic deep learning models can detect glaucoma from multi-modal data with high accuracy. Our findings suggest that models based on combined visual field and fundus photograph modalities detects glaucoma with higher accuracy. While probabilistic and deterministic CNN models provided similar performance, probabilistic models generate certainty level of the outcome thus providing another level of confidence in decision making.</p

    Image_3_Detecting glaucoma from multi-modal data using probabilistic deep learning.tif

    No full text
    ObjectiveTo assess the accuracy of probabilistic deep learning models to discriminate normal eyes and eyes with glaucoma from fundus photographs and visual fields.DesignAlgorithm development for discriminating normal and glaucoma eyes using data from multicenter, cross-sectional, case-control study.Subjects and participantsFundus photograph and visual field data from 1,655 eyes of 929 normal and glaucoma subjects to develop and test deep learning models and an independent group of 196 eyes of 98 normal and glaucoma patients to validate deep learning models.Main outcome measuresAccuracy and area under the receiver-operating characteristic curve (AUC).MethodsFundus photographs and OCT images were carefully examined by clinicians to identify glaucomatous optic neuropathy (GON). When GON was detected by the reader, the finding was further evaluated by another clinician. Three probabilistic deep convolutional neural network (CNN) models were developed using 1,655 fundus photographs, 1,655 visual fields, and 1,655 pairs of fundus photographs and visual fields collected from Compass instruments. Deep learning models were trained and tested using 80% of fundus photographs and visual fields for training set and 20% of the data for testing set. Models were further validated using an independent validation dataset. The performance of the probabilistic deep learning model was compared with that of the corresponding deterministic CNN model.ResultsThe AUC of the deep learning model in detecting glaucoma from fundus photographs, visual fields, and combined modalities using development dataset were 0.90 (95% confidence interval: 0.89–0.92), 0.89 (0.88–0.91), and 0.94 (0.92–0.96), respectively. The AUC of the deep learning model in detecting glaucoma from fundus photographs, visual fields, and both modalities using the independent validation dataset were 0.94 (0.92–0.95), 0.98 (0.98–0.99), and 0.98 (0.98–0.99), respectively. The AUC of the deep learning model in detecting glaucoma from fundus photographs, visual fields, and both modalities using an early glaucoma subset were 0.90 (0.88,0.91), 0.74 (0.73,0.75), 0.91 (0.89,0.93), respectively. Eyes that were misclassified had significantly higher uncertainty in likelihood of diagnosis compared to eyes that were classified correctly. The uncertainty level of the correctly classified eyes is much lower in the combined model compared to the model based on visual fields only. The AUCs of the deterministic CNN model using fundus images, visual field, and combined modalities based on the development dataset were 0.87 (0.85,0.90), 0.88 (0.84,0.91), and 0.91 (0.89,0.94), and the AUCs based on the independent validation dataset were 0.91 (0.89,0.93), 0.97 (0.95,0.99), and 0.97 (0.96,0.99), respectively, while the AUCs based on an early glaucoma subset were 0.88 (0.86,0.91), 0.75 (0.73,0.77), and 0.92 (0.89,0.95), respectively.Conclusion and relevanceProbabilistic deep learning models can detect glaucoma from multi-modal data with high accuracy. Our findings suggest that models based on combined visual field and fundus photograph modalities detects glaucoma with higher accuracy. While probabilistic and deterministic CNN models provided similar performance, probabilistic models generate certainty level of the outcome thus providing another level of confidence in decision making.</p

    High-accuracy determination of the neutron flux in the new experimental area n_TOF-EAR2 at CERN

    Get PDF
    A new high flux experimental area has recently become operational at the n_TOF facility at CERN. This new measuring station, n_TOF-EAR2, is placed at the end of a vertical beam line at a distance of approximately 20m from the spallation target. The characterization of the neutron beam, in terms of flux, spatial profile and resolution function, is of crucial importance for the feasibility study and data analysis of all measurements to be performed in the new area. In this paper, the measurement of the neutron flux, performed with different solid-state and gaseous detection systems, and using three neutron-converting reactions considered standard in different energy regions is reported. The results of the various measurements have been combined, yielding an evaluated neutron energy distribution in a wide energy range, from 2meV to 100MeV, with an accuracy ranging from 2%, at low energy, to 6% in the high-energy region. In addition, an absolute normalization of the n_TOF-EAR2 neutron flux has been obtained by means of an activation measurement performed with 197Au foils in the beam.Peer reviewe

    Soluble lymphotoxin is an important effector molecule in GVHD and GVL

    No full text
    Tumor necrosis factor (TNF) is a key cytokine in the effector phase of graft-versus-host disease (GVHD) after bone marrow transplantation, and TNF inhibitors have shown efficacy in clinical and experimental GVHD. TNF signals through the TNF receptors (TNFR), which also bind soluble lymphotoxin (LT alpha 3), a TNF family member with a previously unexamined role in GVHD pathogenesis. We have used preclinical models to investigate the role of LT in GVHD. We confirm that grafts deficient in LT alpha have an attenuated capacity to induce GVHD equal to that seen when grafts lack TNF. This is not associated with other defects in cytokine production or T-cell function, suggesting that LT alpha 3 exerts its pathogenic activity directly via TNFR signaling. We confirm that donor-derived LT alpha is required for graft-versus-leukemia (GVL) effects, with equal impairment in leukemic clearance seen in recipients of LT alpha- and TNF-deficient grafts. Further impairment in tumor clearance was seen using Tnf/Lta(-/-) donors, suggesting that these molecules play nonredundant roles in GVL. Importantly, donor TNF/LT alpha were only required for GVL where the recipient leukemia was susceptible to apoptosis via p55 TNFR signaling. These data suggest that antagonists neutralizing both TNF and LT alpha 3 may be effective for treatment of GVHD, particularly if residual leukemia lacks the p55 TNFR. (Blood. 2010;115:122-132

    References

    No full text

    Natural Biology of Polyomavirus Middle T Antigen

    No full text
    “It has been commented by someone that ‘polyoma’ is an adjective composed of a prefix and suffix, with no root between—a meatless linguistic sandwich” (C. J. Dawe). The very name “polyomavirus” is a vague mantel: a name given before our understanding of these viral agents was clear but implying a clear tumor life-style, as noted by the late C. J. Dawe. However, polyomavirus are not by nature tumor-inducing agents. Since it is the purpose of this review to consider the natural function of middle T antigen (MT), encoded by one of the seemingly crucial transforming genes of polyomavirus, we will reconsider and redefine the virus and its MT gene in the context of its natural biology and function. This review was motivated by our recent in vivo analysis of MT function. Using intranasal inoculation of adult SCID mice, we have shown that polyomavirus can replicate with an MT lacking all functions associated with transformation to similar levels to wild-type virus. These observations, along with an almost indistinguishable replication of all MT mutants with respect to wild-type viruses in adult competent mice, illustrate that MT can have a play subtle role in acute replication and persistence. The most notable effect of MT mutants was in infections of newborns, indicating that polyomavirus may be highly adapted to replication in newborn lungs. It is from this context that our current understanding of this well-studied virus and gene is presented
    corecore