The next generation fungal diversity researcher

Fungi are more important to our lives than is assumed by the general public. They can comprise both devastating pathogens and plant-associated mutualists in nature, and several species have also become important workhorses of biotechnology. Fungal diversity research has in a short time transcended from a low-tech research area to a method-intensive high-tech discipline. With the advent of the new genomic and post-genomic methodologies, large quantities of new fungal data are currently becoming available each year. Whilst these new data and methodologies may help modern fungal diversity researchers to explore and discover the yet hidden diversity within a context of biological processes and organismal diversity, they need to be reconciled with the traditional approaches. Such a synthesis is actually difficult to accomplish given the current discouraging situation of fungal biology education, especially in the areas of biodiversity and taxonomic research. The number of fungal diversity researchers and taxonomists in academic institutions is decreasing, as are opportunities for mycological education in international curricula. How can we educate and stimulate students to pursue a career in fungal diversity research and taxonomy and avoid the situation whereby only those few institutions with strong financial support are able to conduct excellent research? Our short answer is that we need a combination of increased specialization and increased collaboration, i.e. that scientists with specialized expertise (e.g., in data generation, compilation, interpretation, and communication) consistently work together to generate and deliver new fungal knowledge in a more integrative manner – closing the gap between both traditional and modern approaches and academic and non-academic environments. Here we discuss how this perspective could be implemented in the training of the ‘next generation fungal diversity researcher’

Entropy Ordered Shapes as Bivariate Glyphs

The natural ordering of shapes is not historically used in visualization applications. It could be helpful to show if an order exists among shapes, as this would provide an additional visual channel for presenting ordered bivariate data. Objective—we rigorously evaluate the use of visual entropy allowing us to construct an ordered scale of shape glyphs. Method—we evaluate the visual entropy glyphs in replicated trials online and at two different global locations. Results—an exact binomial analysis of a pair-wise comparison of the glyphs showed a majority of participants (n = 87) ordered the glyphs as predicted by the visual entropy score with large effect size. In a further signal detection experiment participants (n = 15) were able to find glyphs representing uncertainty with high sensitivity and low error rates. Conclusion—Visual entropy predicts shape order and provides a visual channel with the potential to support ordered bivariate data

Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium.

Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection, foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates.Twenty healthy older adults, 20 people with Parkinson's disease, 20 with multiple sclerosis, 19 with proximal femoral fracture, 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.5 h in the real-world, using a single wearable device worn on the lower back. A reference system combining inertial modules with distance sensors and pressure insoles was used for comparison of DMOs from the single wearable device. We assessed and validated three algorithms for gait sequence detection, four for ICD, three for CAD and four for SL by concurrently comparing their performances (e.g., accuracy, specificity, sensitivity, absolute and relative errors). Additionally, the effects of walking bout (WB) speed and duration on algorithm performance were investigated.We identified two cohort-specific top performing algorithms for gait sequence detection and CAD, and a single best for ICD and SL. Best gait sequence detection algorithms showed good performances (sensitivity > 0.73, positive predictive values > 0.75, specificity > 0.95, accuracy > 0.94). ICD and CAD algorithms presented excellent results, with sensitivity > 0.79, positive predictive values > 0.89 and relative errors < 11% for ICD and < 8.5% for CAD. The best identified SL algorithm showed lower performances than other DMOs (absolute error < 0.21 m). Lower performances across all DMOs were found for the cohort with most severe gait impairments (proximal femoral fracture). Algorithms' performances were lower for short walking bouts; slower gait speeds (< 0.5 m/s) resulted in reduced performance of the CAD and SL algorithms.Overall, the identified algorithms enabled a robust estimation of key DMOs. Our findings showed that the choice of algorithm for estimation of gait sequence detection and CAD should be cohort-specific (e.g., slow walkers and with gait impairments). Short walking bout length and slow walking speed worsened algorithms' performances. Trial registration ISRCTN - 12246987