Light regimen-induced variability of photosynthetic pigments and UV-B absorbing compounds in Luzula sylvatica from Arcto-Alpine tundra

The aim of this study was to evaluate the effects of different in situ light regimen on ecophysiological parameters of Luzula sylvatica leaves. Plants of L. sylvatica grown under natural sunny and shade conditions in arcto-alpine tundra were analyzed with respect to their leaf anatomy, content of photosynthetic pigments, UV absorbing compounds and phenanthrenoid compounds. Relationship between chlorophyll concentrations (Chla+b) and SPAD values was determined for sun and shade leaves measured repeatedly within summer and autumn seasons 2019 and 2020. Pooled data showed curvilinear Chla+b to SPAD relationship with the highest Chla+b and SPAD values found for shade leaves. Sun leaves had higher UV-B absorbing compounds contents than shade ones. The HPLC-DAD analysis revealed significant amount of soluble flavonoids in Luzula sylvatica leaves, amongst others the flavone-luteolin and its derivatives (e.g. tentatively identified luteolin-methyl-glucoside and luteolin-glucoside). The accumulation of luteolin based compounds in sun acclimated leaves is also plausible explanation for the higher antioxidant activity determined in sun leaf extraxts. Such response of flavonoid metabolism may help L.S. to cope with excessive-light stress through UV-attenuation mechanism and ROS scavanging. Additionally, phenanthrenoid compounds contents in L. sylvatica leaves were determined. Altogether, 9 phenanthrenoid compounds were identified by HPLC-HRMS. Their content was markedly different (up to the factor of 5) between sun and shade leaves of L.sylvatica

Interaction detection with depth sensing and body tracking cameras in physical rehabilitation

Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Methodologies, Models and Algorithms for Patients Rehabilitation”. Objectives: This paper presents a camera based method for identifying the patient and detecting interactions between the patient and the therapist during therapy. Detecting interactions helps to discriminate between active and passive motion of the patient as well as to estimate the accuracy of the skeletal data. Methods: Continuous face recognition is used to detect, recognize and track the patient with other people in the scene (e.g. the therapist, or a clinician). We use a method based on local binary patterns (LBP). After identifying users in the scene we identify interactions between the patient and other people. We use a depth map/point cloud for estimating the distance between two people. Our method uses the association of depth regions to user identities and computes the minimal distance between the regions. Results: Our results show state-of-the-art performance of real-time face recognition using low-resolution images that is sufficient to use in adaptive systems. Our proposed approach for detecting interactions shows 91.9% overall recognition accuracy what is sufficient for applications in the context of serious games. We also discuss limitations of the proposed method as well as general limitations of using depth cameras for serious games. Conclusions: We introduced a new method for frame-by-frame automated identification of the patient and labeling reliable sequences of the patient’s data recorded during rehabilitation (games). Our method improves automated rehabilitation systems by detecting the identity of the patient as well as of the therapist and by detecting the distance between both over time.SCOPUS: ar.jinfo:eu-repo/semantics/publishe