2 research outputs found
Calicioid lichens and fungi in amber : Tracing extant lineages back to the Paleogene
Calicioid lichens and fungi are a polyphyletic grouping of tiny ascomycetes that accumulate a persistent spore mass (mazaedium) on top of their usually well-stalked ascomata ('mazaediate fungi'). In addition to extant forms, six fossils of the group were previously known from European Paleogene amber. Here we report nine new fossils and analyze the preserved features of all fossils to assess their applicability for dating molecular phylogenies. Many fossils are extremely well preserved, allowing detailed comparisons with modern taxa. SEM investigation reveals that even fine details of ascospore wall ultrastructure correspond to those seen in extant specimens. All fossils can confidently be assigned to modern genera: three to Calicium (Caliciaceae, Lecanoromycetes), five to Chaenotheca (Coniocybaceae, Coniocybomycetes), six to Chaenothecopsis (Mycocaliciaceae, Eurotiales), and one to Phaeocalicium (Mycocaliciaceae, Eurotiales). Several Calicium and Chaenotheca fossils are assignable to specific lineages within their genera, while the Chaenothecopsis fossils demonstrate the extent of intraspecific variation within one such lineage. Some features in the morphology of Chaenotheca succina nov. sp. seem to be ancestral as they have not been reported from modern species of the genus. (C) 2018 Elsevier Masson SAS. All rights reserved.Peer reviewe
Recognizing Affiliation: Using Behavioural Traces to Predict the Quality of Social Interactions in Online Games
Online social interactions in multiplayer games can be supportive and
positive or toxic and harmful; however, few methods can easily assess
interpersonal interaction quality in games. We use behavioural traces to
predict affiliation between dyadic strangers, facilitated through their social
interactions in an online gaming setting. We collected audio, video, in-game,
and self-report data from 23 dyads, extracted 75 features, trained Random
Forest and Support Vector Machine models, and evaluated their performance
predicting binary (high/low) as well as continuous affiliation toward a
partner. The models can predict both binary and continuous affiliation with up
to 79.1% accuracy (F1) and 20.1% explained variance (R2) on unseen data, with
features based on verbal communication demonstrating the highest potential. Our
findings can inform the design of multiplayer games and game communities, and
guide the development of systems for matchmaking and mitigating toxic behaviour
in online games.Comment: CHI '2