AN INVESTIGATION INTO THE RELATIONSHIP BETWEEN TEACHER GRIT, TECHNOLOGY SELF-EFFICACY, AND TECHNOLOGY INTEGRATION

The use of educational technology applications has grown tremendously in the last decade. Instructors are now equipped with hardware and software applications previously unavailable, such as mobile and interactive technologies. These tools can have tremendous impact on students’ learning and teacher practices. Teachers can improve their assessment capabilities through technology integration, provide better learning opportunities for students with learning disabilities, and promote deeper learning practices. Due to these benefits, budgets at the federal, state, and local levels of the United States now have specific allocations regarding technology-related purchases. Nevertheless, barriers remain regarding the effective integration of technologies in public schools. Student and teacher access to technology can be limited when at school versus at home. Internet access or slow speeds can drastically impact educational access in rural communities. Such differences in access can limit teachers\u27 and students\u27 experiences with technologies, restricting instructor technology background and student learning outcomes. School district policies regarding testing requirements can constrain teachers\u27 use of technology for instruction. Additionally, professional development opportunities for technology training can focus solely on introducing new technologies and not on effective integration strategies. While some of these variables can be addressed by increasing access to technology and shifting technology policies to increase teachers’ daily use, non-cognitive factors, such as teacher levels of technology self-efficacy and grit, may play a role in helping teachers use technology more effectively. This study addressed non-cognitive factors of self-efficacy and grit and their role in teacher levels of technology integration. A rural school district was chosen to evaluate high school teachers\u27 level of technology integration, technology self-efficacy, and grit. Exploratory Factor analysis, Correlation analysis, and hierarchical linear regression modeling were used to determine the correlations of grit and self-efficacy with technology integration. While self-efficacy correlates with technology integration for providing students with content, grit is correlated with how teachers use technology for tasks relating to higher-order thinking processes such as student publication. This study offers a foray into understanding the relationship between grit and technology integration across multiple high school locations in a rural district. The application of non-cognitive psychometrics on technology integration may support educators in advancing student use of technology to become deep-conceptual, metacognitive learners

Biconed graphs, edge-rooted forests, and h-vectors of matroid complexes

A well-known conjecture of Richard Stanley posits that the $h$-vector of the independence complex of a matroid is a pure ${\mathcal O}$-sequence. The conjecture has been established for various classes but is open for graphic matroids. A biconed graph is a graph with two specified `coning vertices', such that every vertex of the graph is connected to at least one coning vertex. The class of biconed graphs includes coned graphs, Ferrers graphs, and complete multipartite graphs. We study the $h$-vectors of graphic matroids arising from biconed graphs, providing a combinatorial interpretation of their entries in terms of `edge-rooted forests' of the underlying graph. This generalizes constructions of Kook and Lee who studied the M\"obius coinvariant (the last nonzero entry of the $h$-vector) of graphic matroids of complete bipartite graphs. We show that allowing for partially edge-rooted forests gives rise to a pure multicomplex whose face count recovers the $h$-vector, establishing Stanley's conjecture for this class of matroids.Comment: 15 pages, 3 figures; V2: added omitted author to metadat

Assessing and adjusting for differences between HIV prevalence estimates derived from national population-based surveys and antenatal care surveillance, with applications for Spectrum 2013

Objective(s): To assess differences between HIV prevalence estimates derived from national population surveys and antenatal care (ANC) surveillance sites and to improve the calibration of ANC-derived estimates in Spectrum 2013 to more appropriately account for differences between these data. Design: Retrospective analysis of national population survey and ANC surveillance data from 25 countries with generalized epidemics in sub-Saharan Africa and 8 countries with concentrated epidemics. Methods: Adult national population survey and ANC surveillance HIV prevalence estimates were compared for all available national population survey data points for the years 1999–2012. For sub-Saharan Africa, a mixed-effects linear regression model determined whether the relationship between national population and ANC estimates was constant across surveys. A new calibration method was developed to incorporate national population survey data directly into the likelihood for HIV prevalence in countries with generalized epidemics. Results were used to develop default rules for adjusting ANC data for countries with no national population surveys. Results: ANC surveillance data typically overestimate population prevalence, although a wide variation, particularly in rural areas, is observed across countries and survey years. The new calibration method yields similar point estimates to previous approaches, but leads to an average 44% increase in the width of 95% uncertainty intervals. Conclusion: Important biases remain in ANC surveillance data for HIV prevalence. The new approach to model-fitting in Spectrum 2013 more appropriately accounts for this bias when producing national estimates in countries with generalized epidemics. In countries with concentrated epidemics, local sex ratios should be used to calibrate ANC surveillance estimates

SPH Simulations of Direct Impact Accretion in the Ultracompact AM CVn Binaries

The ultracompact binary systems V407 Vul (RX J1914.4+2456) and HM Cnc (RX J0806.3+1527) - a two-member subclass of the AM CVn stars - continue to pique interest because they defy unambiguous classification. Three proposed models remain viable at this time, but none of the three is significantly more compelling than the remaining two, and all three can satisfy the observational constraints if parameters in the models are tuned. One of the three proposed models is the direct impact model of Marsh & Steeghs (2002), in which the accretion stream impacts the surface of a rapidly-rotating primary white dwarf directly but at a near-glancing angle. One requirement of this model is that the accretion stream have a high enough density to advect its specific kinetic energy below the photosphere for progressively more-thermalized emission downstream, a constraint that requires an accretion spot size of roughly 1.2x10^5 km^2 or smaller. Having at hand a smoothed particle hydrodynamics code optimized for cataclysmic variable accretion disk simulations, it was relatively straightforward for us to adapt it to calculate the footprint of the accretion stream at the nominal radius of the primary white dwarf, and thus to test this constraint of the direct impact model. We find that the mass flux at the impact spot can be approximated by a bivariate Gaussian with standard deviation \sigma_{\phi} = 164 km in the orbital plane and \sigma_{\theta} = 23 km in the perpendicular direction. The area of the the 2\sigma ellipse into which 86% of the mass flux occurs is roughly 47,400 km^2, or roughly half the size estimated by Marsh & Steeghs (2002). We discuss the necessary parameters of a simple model of the luminosity distribution in the post-impact emission region.Comment: 24 pages, 5 figures, Accepted for publication in Ap

Development and validation of a deep learning model to quantify glomerulosclerosis in kidney biopsy specimens

Importance: A chronic shortage of donor kidneys is compounded by a high discard rate, and this rate is directly associated with biopsy specimen evaluation, which shows poor reproducibility among pathologists. A deep learning algorithm for measuring percent global glomerulosclerosis (an important predictor of outcome) on images of kidney biopsy specimens could enable pathologists to more reproducibly and accurately quantify percent global glomerulosclerosis, potentially saving organs that would have been discarded. Objective: To compare the performances of pathologists with a deep learning model on quantification of percent global glomerulosclerosis in whole-slide images of donor kidney biopsy specimens, and to determine the potential benefit of a deep learning model on organ discard rates. Design, Setting, and Participants: This prognostic study used whole-slide images acquired from 98 hematoxylin-eosin-stained frozen and 51 permanent donor biopsy specimen sections retrieved from 83 kidneys. Serial annotation by 3 board-certified pathologists served as ground truth for model training and for evaluation. Images of kidney biopsy specimens were obtained from the Washington University database (retrieved between June 2015 and June 2017). Cases were selected randomly from a database of more than 1000 cases to include biopsy specimens representing an equitable distribution within 0% to 5%, 6% to 10%, 11% to 15%, 16% to 20%, and more than 20% global glomerulosclerosis. Main Outcomes and Measures: Correlation coefficient (r) and root-mean-square error (RMSE) with respect to annotations were computed for cross-validated model predictions and on-call pathologists\u27 estimates of percent global glomerulosclerosis when using individual and pooled slide results. Data were analyzed from March 2018 to August 2020. Results: The cross-validated model results of section images retrieved from 83 donor kidneys showed higher correlation with annotations (r = 0.916; 95% CI, 0.886-0.939) than on-call pathologists (r = 0.884; 95% CI, 0.825-0.923) that was enhanced when pooling glomeruli counts from multiple levels (r = 0.933; 95% CI, 0.898-0.956). Model prediction error for single levels (RMSE, 5.631; 95% CI, 4.735-6.517) was 14% lower than on-call pathologists (RMSE, 6.523; 95% CI, 5.191-7.783), improving to 22% with multiple levels (RMSE, 5.094; 95% CI, 3.972-6.301). The model decreased the likelihood of unnecessary organ discard by 37% compared with pathologists. Conclusions and Relevance: The findings of this prognostic study suggest that this deep learning model provided a scalable and robust method to quantify percent global glomerulosclerosis in whole-slide images of donor kidneys. The model performance improved by analyzing multiple levels of a section, surpassing the capacity of pathologists in the time-sensitive setting of examining donor biopsy specimens. The results indicate the potential of a deep learning model to prevent erroneous donor organ discard

Deep learning quantification of percent steatosis in donor liver biopsy frozen sections

BACKGROUND: Pathologist evaluation of donor liver biopsies provides information for accepting or discarding potential donor livers. Due to the urgent nature of the decision process, this is regularly performed using frozen sectioning at the time of biopsy. The percent steatosis in a donor liver biopsy correlates with transplant outcome, however there is significant inter- and intra-observer variability in quantifying steatosis, compounded by frozen section artifact. We hypothesized that a deep learning model could identify and quantify steatosis in donor liver biopsies. METHODS: We developed a deep learning convolutional neural network that generates a steatosis probability map from an input whole slide image (WSI) of a hematoxylin and eosin-stained frozen section, and subsequently calculates the percent steatosis. Ninety-six WSI of frozen donor liver sections from our transplant pathology service were annotated for steatosis and used to train (n = 30 WSI) and test (n = 66 WSI) the deep learning model. FINDINGS: The model had good correlation and agreement with the annotation in both the training set (r of 0.88, intraclass correlation coefficient [ICC] of 0.88) and novel input test sets (r = 0.85 and ICC=0.85). These measurements were superior to the estimates of the on-service pathologist at the time of initial evaluation (r = 0.52 and ICC=0.52 for the training set, and r = 0.74 and ICC=0.72 for the test set). INTERPRETATION: Use of this deep learning algorithm could be incorporated into routine pathology workflows for fast, accurate, and reproducible donor liver evaluation. FUNDING: Mid-America Transplant Society

Inhibition of αvβ5 Integrin Attenuates Vascular Permeability and Protects against Renal Ischemia-Reperfusion Injury

Ischemia-reperfusion injury (IRI) is a leading cause of AKI. This common clinical complication lacks effective therapies and can lead to the development of CKD. The αvβ5 integrin may have an important role in acute injury, including septic shock and acute lung injury. To examine its function in AKI, we utilized a specific function-blocking antibody to inhibit αvβ5 in a rat model of renal IRI. Pretreatment with this anti-αvβ5 antibody significantly reduced serum creatinine levels, diminished renal damage detected by histopathologic evaluation, and decreased levels of injury biomarkers. Notably, therapeutic treatment with the αvβ5 antibody 8 hours after IRI also provided protection from injury. Global gene expression profiling of post-ischemic kidneys showed that αvβ5 inhibition affected established injury markers and induced pathway alterations previously shown to be protective. Intravital imaging of post-ischemic kidneys revealed reduced vascular leak with αvβ5 antibody treatment. Immunostaining for αvβ5 in the kidney detected evident expression in perivascular cells, with negligible expression in the endothelium. Studies in a three-dimensional microfluidics system identified a pericyte-dependent role for αvβ5 in modulating vascular leak. Additional studies showed αvβ5 functions in the adhesion and migration of kidney pericytes in vitro Initial studies monitoring renal blood flow after IRI did not find significant effects with αvβ5 inhibition; however, future studies should explore the contribution of vasomotor effects. These studies identify a role for αvβ5 in modulating injury-induced renal vascular leak, possibly through effects on pericyte adhesion and migration, and reveal αvβ5 inhibition as a promising therapeutic strategy for AKI

Effect of snow microstructure variability on Ku-band radar snow water equivalent retrievals

Spatial variability in snowpack properties negatively impacts our capacity to make direct measurements of snow water equivalent (SWE) using satellites. A comprehensive data set of snow microstructure (94 profiles at 36 sites) and snow layer thickness (9000 vertical profiles across 9 trenches) collected over two winters at Trail Valley Creek, NWT, Canada, were applied in synthetic radiative transfer experiments. This allowed robust assessment of the impact of estimation accuracy of unknown snow microstructural characteristics on the viability of SWE retrievals. Depth hoar layer thickness varied over the shortest horizontal distances, controlled by subnivean vegetation and topography, while variability of total snowpack thickness approximated that of wind slab layers. Mean horizontal correlation lengths of layer thickness were sub-metre for all layers. Depth hoar was consistently ~30% of total depth, and with increasing total depth the proportion of wind slab increased at the expense of the decreasing surface snow layer. Distinct differences were evident between distributions of layer properties; a single median value represented density and specific surface area (SSA) of each layer well. Spatial variability in microstructure of depth hoar layers dominated SWE retrieval errors. A depth hoar SSA estimate of around 7% under the median value was needed to accurately retrieve SWE. In shallow snowpacks <0.6m, depth hoar SSA estimates of ±5-10% around the optimal retrieval SSA allowed SWE retrievals within a tolerance of ±30 mm. Where snowpacks were deeper than ~30cm, accurate values of representative SSA for depth hoar became critical as retrieval errors were exceeded if the median depth hoar SSA was applied