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

Gut microbiome is not associated with mild cognitive impairment in Parkinson's disease

Gut microbiome differences between people with Parkinson's disease (PD) and control subjects without Parkinsonism are widely reported, but potential alterations related to PD with mild cognitive impairment (MCI) have yet to be comprehensively explored. We compared gut microbial features of PD with MCI (n = 58) to cognitively unimpaired PD (n = 60) and control subjects (n = 90) with normal cognition. Our results did not support a specific microbiome signature related to MCI in PD

Exploring real-time functional magnetic resonance imaging neurofeedback in adolescents with disruptive behavior disorder and callous unemotional traits

Introduction: Adolescents with increased callous unemotional traits (CU traits) in the context of disruptive behavior disorder (DBD) show a persistent pattern of antisocial behavior with shallow affect and a lack of empathy or remorse. The amygdala and insula as regions commonly associated with emotion processing, empathy and arousal are implicated in DBD with high CU traits. While behavioral therapies for DBD provide significant but small effects, individualized treatments targeting the implicated brain regions are missing. Methods: In this explorative randomized controlled trial we randomly assigned twenty-seven adolescents with DBD to individualized real-time functional magnetic resonance neurofeedback (rtfMRI-NF) or behavioral treatment as usual (TAU). Visual feedback of either amygdala or insula activity was provided during rtfMRI-NF by gauges and included a simple and concurrent video run plus a transfer run. A linear mixed model (LMM) was applied to determine improvement of self-regulation. Specificity was assessed by correlating individual self-regulation improvement with clinical outcomes. Results: The rtfMRI-NF (n = 11) and TAU (n = 10) completers showed comparable and significant clinical improvement indicating neither superiority nor inferiority of rtfMRI-NF. The exploratory LMM revealed successful learning of self-regulation along the course of training for participants who received feedback from the amygdala. A significant exploratory correlation between individual target region activity in the simple run and clinical improvement was found for one dimension of DBD. Conclusions: This exploratory study demonstrated feasibility and suggests clinical efficacy of individualized rtfMRI-NF comparable to active TAU for adolescents with DBD and increased CU traits. Further studies are needed to confirm efficacy, specificity and to clarify underlying learning mechanisms

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

Mild cognitive impairment is not associated with gut microbiota alterations in Parkinson’s disease

Abstract Gut microbiome differences between people with Parkinson’s disease (PD) and control subjects without parkinsonism are widely reported, but potential alterations related to PD with mild cognitive impairment (MCI) have yet to be comprehensively explored. We compared gut microbial features of PD with MCI (n=58) to cognitively unimpaired PD (n=60) and control subjects (n=90) without MCI. Our results did not support a specific microbiome signature related to MCI in PD.R-AGR-3870 - IAS - MCI-BIOME (01/01/2020 - 31/12/2023) - WILMES Paul3. Good health and well-beingv

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