Tutor In-sight: Guiding and Visualizing Students Attention with Mixed Reality Avatar Presentation Tools

Remote conferencing systems are increasingly used to supplement or even replace in-person teaching. However, prevailing conferencing systems restrict the teacher’s representation to a webcam live-stream, hamper the teacher’s use of body-language, and result in students’ decreased sense of co-presence and participation. While Virtual Reality (VR) systems may increase student engagement, the teacher may not have the time or expertise to conduct the lecture in VR. To address this issue and bridge the requirements between students and teachers, we have developed Tutor In-sight, a Mixed Reality (MR) avatar augmented into the student’s workspace based on four design requirements derived from the existing literature, namely: integrated virtual with physical space, improved teacher’s co-presence through avatar, direct attention with auto-generated body language, and usable workfow for teachers. Two user studies were conducted from the perspectives of students and teachers to determine the advantages of Tutor In-sight in comparison to two existing conferencing systems, Zoom (video-based) and Mozilla Hubs (VR-based). The participants of both studies favoured Tutor In-sight. Among others, this main fnding indicates that Tutor Insight satisfed the needs of both teachers and students. In addition, the participants’ feedback was used to empirically determine the four main teacher requirements and the four main student requirements in order to improve the future design of MR educational tools

Comparison of Raman and Mid-Infrared Spectroscopy for Real-Time Monitoring of Yeast Fermentations: A Proof-of-Concept for Multi-Channel Photometric Sensors

Raman and mid-infrared (MIR) spectroscopy are useful tools for the specific detection of molecules, since both methods are based on the excitation of fundamental vibration modes. In this study, Raman and MIR spectroscopy were applied simultaneously during aerobic yeast fermentations of Saccharomyces cerevisiae. Based on the recorded Raman intensities and MIR absorption spectra, respectively, temporal concentration courses of glucose, ethanol, and biomass were determined. The chemometric methods used to evaluate the analyte concentrations were partial least squares (PLS) regression and multiple linear regression (MLR). In view of potential photometric sensors, MLR models based on two (2D) and four (4D) analyte-specific optical channels were developed. All chemometric models were tested to predict glucose concentrations between 0 and 30 g L−1, ethanol concentrations between 0 and 10 g L−1, and biomass concentrations up to 15 g L−1 in real time during diauxic growth. Root-mean-squared errors of prediction (RMSEP) of 0.68 g L−1, 0.48 g L−1, and 0.37 g L−1 for glucose, ethanol, and biomass were achieved using the MIR setup combined with a PLS model. In the case of Raman spectroscopy, the corresponding RMSEP values were 0.92 g L−1, 0.39 g L−1, and 0.29 g L−1. Nevertheless, the simple 4D MLR models could reach the performance of the more complex PLS evaluation. Consequently, the replacement of spectrometer setups by four-channel sensors were discussed. Moreover, the advantages and disadvantages of Raman and MIR setups are demonstrated with regard to process implementation

Targeting the Active Rhizosphere Microbiome of Trifolium pratense in Grassland Evidences a Stronger-Than-Expected Belowground Biodiversity-Ecosystem Functioning Link

The relationship between biodiversity and ecosystem functioning (BEF) is a central issue in soil and microbial ecology. To date, most belowground BEF studies focus on the diversity of microbes analyzed by barcoding on total DNA, which targets both active and inactive microbes. This approach creates a bias as it mixes the part of the microbiome currently steering processes that provide actual ecosystem functions with the part not directly involved. Using experimental extensive grasslands under current and future climate, we used the bromodeoxyuridine (BrdU) immunocapture technique combined with pair-end Illumina sequencing to characterize both total and active microbiomes (including both bacteria and fungi) in the rhizosphere of Trifolium pratense. Rhizosphere function was assessed by measuring the activity of three microbial extracellular enzymes (β-glucosidase, N-acetyl-glucosaminidase, and acid phosphatase), which play central roles in the C, N, and P acquisition. We showed that the richness of overall and specific functional groups of active microbes in rhizosphere soil significantly correlated with the measured enzyme activities, while total microbial richness did not. Active microbes of the rhizosphere represented 42.8 and 32.1% of the total bacterial and fungal taxa, respectively, and were taxonomically and functionally diverse. Nitrogen fixing bacteria were highly active in this system with 71% of the total operational taxonomic units (OTUs) assigned to this group detected as active. We found the total and active microbiomes to display different responses to variations in soil physicochemical factors in the grassland, but with some degree of resistance to a manipulation mimicking future climate. Our findings provide critical insights into the role of active microbes in defining soil ecosystem functions in a grassland ecosystem. We demonstrate that the relationship between biodiversity-ecosystem functioning in soil may be stronger than previously thought

Beyond Questionnaires: Innovative Approaches to Evaluating Mixed Reality

Mixed Reality (MR) technologies, including augmented and virtual reality, are increasingly used in a number of sectors, thanks to their capabilities to immerse users in multisensory interaction environments. However, as indicated by some systematic reviews, questionnaire remains the main method for evaluating the interaction quality of MR. There is a lack of innovative approaches addressing unique features of MR. It can dampen the advances of MR, as evaluation feedback can inform its future development. In this workshop we aim to explore this issue by inviting participants to share their practical experiences or conceptual ideas of evaluating MR in various contexts, using different methods and tools. The ultimate aim is to produce a research agenda on this topic for the community to examine it further in the future

Biotic interactions as mediators of context-dependent biodiversity-ecosystem functioning relationships

Biodiversity drives the maintenance and stability of ecosystem functioning as well as many of nature’s benefits to people, yet people cause substantial biodiversity change. Despite broad consensus about a positive relationship between biodiversity and ecosystem functioning (BEF), the underlying mechanisms and their context-dependencies are not well understood. This proposal, submitted to the European Research Council (ERC), aims at filling this knowledge gap by providing a novel conceptual framework for integrating biotic interactions across guilds of organisms, i.e. plants and mycorrhizal fungi, to explain the ecosystem consequences of biodiversity change. The overarching hypothesis is that EF increases when more tree species associate with functionally dissimilar mycorrhizal fungi. Taking a whole-ecosystem perspective, we propose to explore the role of tree-mycorrhiza interactions in driving BEF across environmental contexts and how this relates to nutrient dynamics. Given the significant role that mycorrhizae play in soil nutrient and water uptake, BEF relationships will be investigated under normal and drought conditions. Resulting ecosystem consequences will be explored by studying main energy channels and ecosystem multifunctionality using food web energy fluxes and by assessing carbon storage. Synthesising drivers of biotic interactions will allow us to understand context-dependent BEF relationships. This interdisciplinary and integrative project spans the whole gradient from local-scale process assessments to global relationships by building on unique experimental infrastructures like the MyDiv Experiment, iDiv Ecotron and the global network TreeDivNet, to link ecological mechanisms to reforestation initiatives. This innovative combination of basic scientific research with real-world interventions links trait-based community ecology, global change research and ecosystem ecology, pioneering a new generation of BEF research and represents a significant step towards implementing BEF theory for human needs

The community ecology perspective of omics data

The measurement of uncharacterized pools of biological molecules through techniques such as metabarcoding, metagenomics, metatranscriptomics, metabolomics, and metaproteomics produces large, multivariate datasets. Analyses of these datasets have successfully been borrowed from community ecology to characterize the molecular diversity of samples (ɑ-diversity) and to assess how these profiles change in response to experimental treatments or across gradients (β-diversity). However, sample preparation and data collection methods generate biases and noise which confound molecular diversity estimates and require special attention. Here, we examine how technical biases and noise that are introduced into multivariate molecular data affect the estimation of the components of diversity (i.e., total number of different molecular species, or entities; total number of molecules; and the abundance distribution of molecular entities). We then explore under which conditions these biases affect the measurement of ɑ- and β-diversity and highlight how novel methods commonly used in community ecology can be adopted to improve the interpretation and integration of multivariate molecular data. Video Abstract

TNF-related apoptosis-inducing ligand, interferon gamma-induced protein 10, and C-reactive protein in predicting the progression of SARS-CoV-2 infection : a prospective cohort study

Background: Early prognostication of COVID-19 severity will potentially improve patient care. Biomarkers, such as TNF-related apoptosis-inducing ligand (TRAIL), interferon gamma-induced protein 10 (IP-10), and C-reactive protein (CRP), might represent possible tools for point-of-care testing and severity prediction. Methods: In this prospective cohort study, we analyzed serum levels of TRAIL, IP-10, and CRP in patients with COVID-19, compared them with control subjects, and investigated the association with disease sever ity. Results: A total of 899 measurements were performed in 132 patients (mean age 64 years, 40.2% females). Among patients with COVID-19, TRAIL levels were lower (49.5 vs 87 pg/ml, P = 0.0142), whereas IP-10 and CRP showed higher levels (667.5 vs 127 pg/ml, P <0.001; 75.3 vs 1.6 mg/l, P <0.001) than healthy controls. TRAIL yielded an inverse correlation with length of hospital and intensive care unit (ICU) stay, Simplified Acute Physiology Score II, and National Early Warning Score, and IP-10 showed a positive cor relation with disease severity. Multivariable regression revealed that obesity (adjusted odds ratio [aOR] 5.434, 95% confidence interval [CI] 1.005-29.38), CRP (aOR 1.014, 95% CI 1.002-1.027), and peak IP-10 (aOR 1.001, 95% CI 1.00-1.002) were independent predictors of in-ICU mortality

Blind spots in global soil biodiversity and ecosystem function research

Soils harbor a substantial fraction of the world’s biodiversity, contributing to many crucial ecosystem functions. It is thus essential to identify general macroecological patterns related to the distribution and functioning of soil organisms to support their conservation and consideration by governance. These macroecological analyses need to represent the diversity of environmental conditions that can be found worldwide. Here we identify and characterize existing environmental gaps in soil taxa and ecosystem functioning data across soil macroecological studies and 17,186 sampling sites across the globe. These data gaps include important spatial, environmental, taxonomic, and functional gaps, and an almost complete absence of temporally explicit data. We also identify the limitations of soil macroecological studies to explore general patterns in soil biodiversity-ecosystem functioning relationships, with only 0.3% of all sampling sites having both information about biodiversity and function, although with different taxonomic groups and functions at each site. Based on this information, we provide clear priorities to support and expand soil macroecological research.This manuscript developed from discussions within the German Centre of Integrative Biodiversity Research funded by the German Research Foundation (DFG FZT118). CAG and NE acknowledge funding by iDiv (DFG FZT118) Flexpool proposal 34600850. C.A.G., A.H.B., J.S., A.C., N.G.R., S.C., L.B., M.C.R., F.B., J.O., G.P., H.R.P.P., M.W., T.W., K.K., and N.E. acknowledge funding by iDiv (DFG FZT118) Flexpool proposal 34600844. N.E. acknowledges funding by the DFG (FOR 1451) and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 677232). Finally we would like to acknowledge the contribution of all the authors that provided their datasets for analysis within this paper. Open access funding provided by Projekt DEAL

Large-scale drivers of relationships between soil microbial properties and organic carbon across Europe

[Aim] Quantify direct and indirect relationships between soil microbial community properties (potential basal respiration, microbial biomass) and abiotic factors (soil, climate) in three major land-cover types.[Location] Europe.[Time period] 2018.[Major taxa studied] Microbial community (fungi and bacteria).[Methods] We collected 881 soil samples from across Europe in the framework of the Land Use/Land Cover Area Frame Survey (LUCAS). We measured potential soil basal respiration at 20 ºC and microbial biomass (substrate-induced respiration) using an O2-microcompensation apparatus. Soil and climate data were obtained from the same LUCAS survey and online databases. Structural equation models (SEMs) were used to quantify relationships between variables, and equations extracted from SEMs were used to create predictive maps. Fatty acid methyl esters were measured in a subset of samples to distinguish fungal from bacterial biomass.[Results] Soil microbial properties in croplands were more heavily affected by climate variables than those in forests. Potential soil basal respiration and microbial biomass were correlated in forests but decoupled in grasslands and croplands, where microbial biomass depended on soil carbon. Forests had a higher ratio of fungi to bacteria than grasslands or croplands.[Main conclusions] Soil microbial communities in grasslands and croplands are likely carbon-limited in comparison with those in forests, and forests have a higher dominance of fungi indicating differences in microbial community composition. Notably, the often already-degraded soils of croplands could be more vulnerable to climate change than more natural soils. The provided maps show potentially vulnerable areas that should be explicitly accounted for in future management plans to protect soil carbon and slow the increasing vulnerability of European soils to climate change.The LUCAS Soil sample collection is supported by the Directorate-General Environment (DG-ENV), Directorate-General Agriculture and Rural Development (DG-AGRI), Directorate-General Climate Action (DG-CLIMA) and Directorate-General Eurostat (DG-ESTAT) of the European Commission. F. Bastida thanks the Spanish Ministry and European Regional Development Fund (FEDER) funds for the project AGL2017–85755-R (AEI/FEDER, UE), the i-LINK+ 2018 (LINKA20069) from CSIC, and funds from ‘Fundación Séneca’ from Murcia Province (19896/GERM/15). M.C.R. acknowledges support from an European Research Commission (ERC) Advanced Grant (694368). This project was funded by the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig of the German Research Foundation (FZT 118-202548816).Peer reviewe