Endophyte Growth on Two Species of Conifers on the Shawangunk Ridge, Mid-Hudson Valley, New York

The Middle Silurian Shawangunk Formation is underlain by the Upper Ordovician Martinsburg Formation in the mid-Hudson Valley. The Shawangunk Ridge is composed of a very resistant quartz pebble conglomerate whereas the Martinsburg consists of less resistant shales and graywackes. Endophytes are fungi growing asymptomatically in plant tissues that are thought to act as a support system for the plant, protecting against desiccation and pathogens. We compared the diversity of endophytes colonizing two species of conifers, Pinus strobus and Tsuga canadensis, on two different substrates, conglomerate and shale. We hypothesized that there would be a marked difference in diversity between the two substrates, resulting in different microenvironments for the endophytes, since differences in diversity between species would occur if endophytes are host specific. We collected 7 individuals of each of the two conifer species along with pH readings of the soil at the base of the trees. Readings were taken at several sites on the Ridge last spring (2015). The needles of the specimens collected were surfaced sterilized and plated within 48 hours, then grown for 8 weeks on an agar plate. There was no difference in pH between the two substrates. To date, we have grown over 90 morphotypes of endophytes, including fungi from the genera Cladosporium, Chaetomium, Alternaria, Lophodermium, and Phoma. We expect to continue this project as we investigate endophyte growth on other genera such as Quercus, Hamamelis and Acer

Macular Telangiectasia Type 2: A Classification System Using MultiModal Imaging MacTel Project Report Number 10

Purpose: To develop a severity classification for macular telangiectasia type 2 (MacTel) disease using multimodal imaging. Design: An algorithm was used on data from a prospective natural history study of MacTel for classification development. Subjects: A total of 1733 participants enrolled in an international natural history study of MacTel. Methods: The Classification and Regression Trees (CART), a predictive nonparametric algorithm used in machine learning, analyzed the features of the multimodal imaging important for the development of a classification, including reading center gradings of the following digital images: stereoscopic color and red-free fundus photographs, fluorescein angiographic images, fundus autofluorescence images, and spectral-domain (SD)-OCT images. Regression models that used least square method created a decision tree using features of the ocular images into different categories of disease severity. Main Outcome Measures: The primary target of interest for the algorithm development by CART was the change in best-corrected visual acuity (BCVA) at baseline for the right and left eyes. These analyses using the algorithm were repeated for the BCVA obtained at the last study visit of the natural history study for the right and left eyes. Results: The CART analyses demonstrated 3 important features from the multimodal imaging for the classification: OCT hyper-reflectivity, pigment, and ellipsoid zone loss. By combining these 3 features (as absent, present, noncentral involvement, and central involvement of the macula), a 7-step scale was created, ranging from excellent to poor visual acuity. At grade 0, 3 features are not present. At the most severe grade, pigment and exudative neovascularization are present. To further validate the classification, using the Generalized Estimating Equation regression models, analyses for the annual relative risk of progression over a period of 5 years for vision loss and for progression along the scale were performed. Conclusions: This analysis using the data from current imaging modalities in participants followed in the MacTel natural history study informed a classification for MacTel disease severity featuring variables from SD-OCT. This classification is designed to provide better communications to other clinicians, researchers, and patients. Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references

Quality specifications in postgraduate medical e-learning: an integrative literature review leading to a postgraduate medical e-learning model

BACKGROUND: E-learning is driving major shifts in medical education. Prioritizing learning theories and quality models improves the success of e-learning programs. Although many e-learning quality standards are available, few are focused on postgraduate medical education. METHODS: We conducted an integrative review of the current postgraduate medical e-learning literature to identify quality specifications. The literature was thematically organized into a working model. RESULTS: Unique quality specifications (n = 72) were consolidated and re-organized into a six-domain model that we called the Postgraduate Medical E-learning Model (Postgraduate ME Model). This model was partially based on the ISO-19796 standard, and drew on cognitive load multimedia principles. The domains of the model are preparation, software design and system specifications, communication, content, assessment, and maintenance. CONCLUSION: This review clarified the current state of postgraduate medical e-learning standards and specifications. It also synthesized these specifications into a single working model. To validate our findings, the next-steps include testing the Postgraduate ME Model in controlled e-learning settings

Macular Telangiectasia Type 2: A Classification System Using MultiModal Imaging MacTel Project Report Number 10

To develop a severity classification for macular telangiectasia type 2 (MacTel) disease using multimodal imaging. An algorithm was used on data from a prospective natural history study of MacTel for classification development. A total of 1733 participants enrolled in an international natural history study of MacTel. The Classification and Regression Trees (CART), a predictive nonparametric algorithm used in machine learning, analyzed the features of the multimodal imaging important for the development of a classification, including reading center gradings of the following digital images: stereoscopic color and red-free fundus photographs, fluorescein angiographic images, fundus autofluorescence images, and spectral-domain (SD)-OCT images. Regression models that used least square method created a decision tree using features of the ocular images into different categories of disease severity. The primary target of interest for the algorithm development by CART was the change in best-corrected visual acuity (BCVA) at baseline for the right and left eyes. These analyses using the algorithm were repeated for the BCVA obtained at the last study visit of the natural history study for the right and left eyes. The CART analyses demonstrated 3 important features from the multimodal imaging for the classification: OCT hyper-reflectivity, pigment, and ellipsoid zone loss. By combining these 3 features (as absent, present, noncentral involvement, and central involvement of the macula), a 7-step scale was created, ranging from excellent to poor visual acuity. At grade 0, 3 features are not present. At the most severe grade, pigment and exudative neovascularization are present. To further validate the classification, using the Generalized Estimating Equation regression models, analyses for the annual relative risk of progression over a period of 5 years for vision loss and for progression along the scale were performed. This analysis using the data from current imaging modalities in participants followed in the MacTel natural history study informed a classification for MacTel disease severity featuring variables from SD-OCT. This classification is designed to provide better communications to other clinicians, researchers, and patients. Proprietary or commercial disclosure may be found after the references