Writing about accessibility

In this forum we celebrate research that helps to successfully bring the benefits of computing technologies to children, older adults, people with disabilities, and other populations that are often ignored in the design of mass-marketed products. --- Juan Pablo Hourcade, Editor </jats:p

Canopy spectral reflectance detects oak wilt at the landscape scale using phylogenetic discrimination

The oak wilt disease caused by the invasive fungal pathogen Bretziella fagacearum is one of the greatest threats to oak-dominated forests across the Eastern United States. Accurate detection and monitoring over large areas are necessary for management activities to effectively mitigate and prevent the spread of oak wilt. Canopy spectral reflectance contains both phylogenetic and physiological information across the visible near-infrared (VNIR) and short-wave infrared (SWIR) ranges that can be used to identify diseased red oaks. We develop partial least square discriminant analysis (PLS-DA) models using airborne hyperspectral reflectance to detect diseased canopies and assess the importance of VNIR, SWIR, phylogeny, and physiology for oak wilt detection. We achieve high accuracy through a three-step phylogenetic process in which we first distinguish oaks from other species (90% accuracy), then red oaks from white oaks (Quercus macrocarpa) (93% accuracy), and, lastly, infected from non-infected trees (80% accuracy). Including SWIR wavelengths increased model accuracy by ca. 20% relative to models based on VIS-NIR wavelengths alone; using a phylogenetic approach also increased model accuracy by ca. 20% over a single-step classification. SWIR wavelengths include spectral information important in differentiating red oaks from other species and in distinguishing diseased red oaks from healthy red oaks. We determined the most important wavelengths to identify oak species, red oaks, and diseased red oaks. We also demonstrated that several multispectral indices associated with physiological decline can detect differences between healthy and diseased trees. The wavelengths in these indices also tended to be among the most important wavelengths for disease detection within PLS-DA models, indicating a convergence of the methods. Indices were most significant for detecting oak wilt during late August, especially those associated with canopy photosynthetic activity and water status. Our study suggests that coupling phylogenetics, physiology, and canopy spectral reflectance provides an interdisciplinary and comprehensive approach that enables detection of forest diseases at large scales. These results have potential for direct application by forest managers for detection to initiate actions to mitigate the disease and prevent pathogen spread

Coupling spectral and resource-use complementarity in experimental grassland and forest communites

Reflectance spectra provide integrative measures of plant phenotypes by capturing chemical, morphological, anatomical and architectural trait information. Here, we investigate the linkages between plant spectral variation, and spectral and resource-use complementarity that contribute to ecosystem productivity. In both a forest and prairie grassland diversity experiment, we delineated n-dimensional hypervolumes using wavelength bands of reflectance spectra to test the association between the spectral space occupied by individual plants and their growth, as well as between the spectral space occupied by plant communities and ecosystem productivity. We show that the spectral space occupied by individuals increased with their growth, and the spectral space occupied by plant communities increased with ecosystem productivity. Furthermore, ecosystem productivity was better explained by inter-individual spectral complementarity than by the large spectral space occupied by productive individuals. Our results indicate that spectral hypervolumes of plants can reflect ecological strategies that shape community composition and ecosystem function, and that spectral complementarity can reveal resource-use complementarity

Remotely detected aboveground plant function predicts belowground processes in two prairie diversity experiments

Imaging spectroscopy provides the opportunity to incorporate leaf and canopy optical data into ecological studies, but the extent to which remote sensing of vegetation can enhance the study of belowground processes is not well understood. In terrestrial systems, aboveground and belowground vegetation quantity and quality are coupled, and both influence belowground microbial processes and nutrient cycling. We hypothesized that ecosystem productivity, and the chemical, structural and phylogenetic-functional composition of plant communities would be detectable with remote sensing and could be used to predict belowground plant and soil processes in two grassland biodiversity experiments: the BioDIV experiment at Cedar Creek Ecosystem Science Reserve in Minnesota and the Wood River Nature Conservancy experiment in Nebraska. We tested whether aboveground vegetation chemistry and productivity, as detected from airborne sensors, predict soil properties, microbial processes and community composition. Imaging spectroscopy datawere used to map aboveground biomass, green vegetation cover, functional traits and phylogenetic-functional community composition of vegetation. We examined the relationships between the image-derived variables and soil carbon and nitrogen concentration, microbial community composition, biomass and extracellular enzyme activity, and soil processes, including net nitrogen mineralization. In the BioDIV experiment—which has low overall diversity and productivity despite high variation in each—belowground processes were driven mainly by variation in the amount of organic matter inputs to soils. As a consequence, soil respiration, microbial biomass and enzyme activity, and fungal and bacterial composition and diversity were significantly predicted by remotely sensed vegetation cover and biomass. In contrast, at Wood River—where plant diversity and productivity were consistently higher—belowground processes were driven mainly by variation in the quality of aboveground inputs to soils. Consequently, remotely sensed functional, chemical and phylogenetic composition of vegetation predicted belowground extracellular enzyme activity, microbial biomass, and net nitrogen mineralization rates but aboveground biomass (or cover) did not. The contrasting associations between the quantity (productivity) and quality (composition) of aboveground inputs with belowground soil attributes provide a basis for using imaging spectroscopy to understand belowground processes across productivity gradients in grassland systems. However, a mechanistic understanding of how above and belowground components interact among different ecosystems remains critical to extending these results broadly

Speeching: Mobile Crowdsourced Speech Assessment to Support Self-Monitoring and Management for People with Parkinson's

We present Speeching, a mobile application that uses crowdsourcing to support the self-monitoring and management of speech and voice issues for people with Parkinson's (PwP). The application allows participants to audio record short voice tasks, which are then rated and assessed by crowd workers. Speeching then feeds these results back to provide users with examples of how they were perceived by listeners unconnected to them (thus not used to their speech patterns). We conducted our study in two phases. First we assessed the feasibility of utilising the crowd to provide ratings of speech and voice that are comparable to those of experts. We then conducted a trial to evaluate how the provision of feedback, using Speeching, was valued by PwP. Our study highlights how applications like Speeching open up new opportunities for self-monitoring in digital health and wellbeing, and provide a means for those without regular access to clinical assessment services to practice-and get meaningful feedback on-their speech

Leaf reflectance spectra capture the evolutionary history of seed plants

Leaf reflection spectra have been increasingly used to assess plant diversity. However, we do not yet understand how spectra vary across the tree of life or how the evolution of leaf traits affects the differentiation of spectra among species and lineages. Here we describe a framework that integrates spectra with phylogenies and apply it to aglobal dataset of over 16 000 leaf-level spectra (400–2400 nm) for 544 seed plant species. We test for phylogenetic signal in spectra, evaluate their ability to classify lineages, and characterize their evolutionary dynamics. We show that phylogenetic signal is present in leaf spectra but that the spectral regions most strongly associated with the phylogeny vary among lineages. Despite among-lineage heterogeneity, broad plant groups, orders, and families can be identified from reflectance spectra. Evolutionary models also reveal that different spectral regions evolve at different rates and under different constraint levels, mirroring the evolution of their underlying traits. Leaf spectra capture the phylogenetic history of seed plants and the evolutionary dynamics of leaf chemistry and structure. Consequently, spectra have the potential to provide breakthrough assessments of leaf evolution and plant phylogenetic diversity at global scales

Risk of cardiovascular events and death associated with initiation of SGLT2 inhibitors compared with DPP-4 inhibitors:an analysis from the CVD-REAL 2 multinational cohort study

Background Cardiovascular outcome trials have shown cardiovascular benefit with sodium-glucose co-transporter-2 (SGLT2) inhibitors in patients with type 2 diabetes, whereas dipeptidyl peptidase-4 (DPP-4) inhibitors have not shown an effect. We aimed to address knowledge gaps regarding the comparative effectiveness of SGLT2 inhibitor use in clinical practice (with DPP-4 inhibitor use as an active comparator) across a range of cardiovascular risks and in diverse geographical settings. Methods In this comparative cohort study, we used data from clinical practice from 13 countries in the Asia-Pacific, Middle East, European, and North American regions to assess the risk of cardiovascular events and death in adult patients with type 2 diabetes newly initiated on SGLT2 inhibitors compared with those newly initiated on DPP-4 inhibitors. De-identified health records were used to select patients who were initiated on these drug classes between Dec 1, 2012, and May 1, 2016, with follow-up until Dec 31, 2014, to Nov 30, 2017 (full range; dates varied by country). Non-parsimonious propensity scores for SGLT2 inhibitor initiation were developed for each country and patients who were initiated on an SGLT2 inhibitor were matched with those who were initiated on a DPP-4 inhibitor in a 1:1 ratio. Outcomes assessed were hospitalisation for heart failure, all-cause death, myocardial infarction, and stroke. Hazard ratios (HRs) were estimated by country and then pooled in a weighted meta-analysis. Findings Following propensity score matching, 193 124 new users of SGLT2 inhibitors and 193 124 new users of DPP-4 inhibitors were included in the study population. Participants had a mean age of 58 years (SD 12.2), 170 335 (44.1%) of 386 248 were women, and 111933 (30.1%) of 372 262 had established cardiovascular disease. Initiation of an SGLT2 inhibitor versus a DPP-4 inhibitor was associated with substantially lower risks of hospitalisation for heart failure (HR 0.69, 95% CI 0. 61-0. 77; p Interpretation In this large, international, observational study, initiation of SGLT2 inhibitors versus DPP-4 inhibitors was associated with lower risks of heart failure, death, myocardial infarction, and stroke, providing further support for the cardiovascular benefits associated with use of SGLT2 inhibitors in patients with type 2 diabetes. Copyright (C) 2020 Elsevier Ltd. All rights reserved

Review of quantitative empirical evaluations of technology for people with visual impairments

Addressing the needs of visually impaired people is of continued interest in Human Computer Interaction (HCI) research. Yet, one of the major challenges facing researchers in this field continues to be how to design adequate quantitative empirical evaluation for these users in HCI. In this paper, we analyse a corpus of 178 papers on technologies designed for people with visual impairments, published since 1988, and including at least one quantitative empirical evaluation (243 evaluations in total). To inform future research in this area, we provide an overview, historic trends and a unified terminology to design and report quantitative empirical evaluations. We identify open issues and propose a set of guidelines to address them. Our analysis aims to facilitate and stimulate future research on this topic

Lower Risk of Heart Failure and Death in Patients Initiated on Sodium-Glucose Cotransporter-2 Inhibitors Versus Other Glucose-Lowering DrugsClinical Perspective: The CVD-REAL Study (Comparative Effectiveness of Cardiovascular Outcomes in New Users of Sodium-Glucose Cotransporter-2 Inhibitors)

Reduction in cardiovascular death and hospitalization for heart failure (HHF) was recently reported with the sodium-glucose cotransporter-2 inhibitor (SGLT-2i) empagliflozin in patients with type 2 diabetes mellitus who have atherosclerotic cardiovascular disease. We compared HHF and death in patients newly initiated on any SGLT-2i versus other glucose-lowering drugs in 6 countries to determine if these benefits are seen in real-world practice and across SGLT-2i class