To exploit the whole potential of Additive Manufacturing (AM), a sound knowledge about the mechanical and especially cyclic properties of AM materials as well as their dependency on the process parameters is indispensable. In the presented work, the influence of chemical composition of the used powder on the fatigue behavior of Selectively Laser Melted (SLM) and Laser Deposition Welded (LDW) specimens made of austenitic stainless steel AISI 316L was investigated. Therefore, in each manufacturing process two variations of chemical composition of the used powder were utilized. For qualitative characterization of the materials cyclic deformation behavior, load increase tests (LITs) were performed and further used for the physically based lifetime calculation method (PhyBaLLIT), enabling an efficient determination of stress (S)–number of cycles to failure (Nf) curves (S–Nf), which show excellent correlation to additionally performed constant amplitude tests (CATs). Moreover, instrumented cyclic indentation tests (PhyBaLCHT) were utilized to characterize the materials’ defect tolerance in a comparably short time. All material variants exhibit a high influence of microstructural defects on the fatigue properties. Consequently, for the SLM process a higher fatigue lifetime at lower stress amplitudes could be observed for the batch with a higher defect tolerance, resulting from a more pronounced deformation induced austenite–α’-martensite transformation. In correspondence to that, the batch of LDW material with an increased defect tolerance exhibit a higher fatigue strength. However, the differences in defect tolerance between the LDW batches is only slightly influenced by phase transformation and seems to be mainly governed by differences in hardening potential of the austenitic microstructure. Furthermore, a significantly higher fatigue strength could be observed for SLM material in relation to LDW specimens, because of a refined microstructure and smaller microstructural defects of SLM specimens.

Cancer cells often harbor chromosomes in abnormal numbers and with aberrant structure. The consequences of these chromosomal aberrations are difficult to study in cancer, and therefore several model systems have been developed in recent years. We show that human cells with extra chromosome engineered via microcell-mediated chromosome transfer often gain massive chromosomal rearrangements. The rearrangements arose by chromosome shattering and rejoining as well as by replication-dependent mechanisms. We show that the isolated micronuclei lack functional lamin B1 and become prone to envelope rupture, which leads to DNA damage and aberrant replication. The presence of functional lamin B1 partly correlates with micronuclei size, suggesting that the proper assembly of nuclear envelope might be sensitive to membrane curvature. The chromosomal rearrangements in trisomic cells provide growth advantage compared to cells without rearrangements. Our model system enables to study mechanisms of massive chromosomal rearrangements of any chromosome and their consequences in human cells.

The Atacama Desert is one of the driest and probably oldest deserts on Earth where only a few extremophile organisms are able to survive. This study investigated two terricolous and two epiphytic lichens from the fog oasis “Las Lomitas” within the National Park Pan de Azúcar which represents a refugium for a few vascular desert plants and many lichens that can thrive on fog and dew alone. Ecophysiological measurements and climate records were combined with molecular data of the mycobiont, their green algal photobionts and lichenicolous fungi to gain information about the ecology of lichens within the fog oasis. Phylogenetic and morphological investigations led to the identification and description of the new lichen species Acarospora conafii sp. nov. as well as the lichenicolous fungi that accompanied them and revealed the trebouxioid character of all lichen photobionts. Their photosynthetic responses were compared during natural scenarios such as reactivation by high air humidity and in situ fog events to elucidate the activation strategies of this lichen community. Epiphytic lichens showed photosynthetic activity that was rapidly induced by fog and high relative air humidity whereas terricolous lichens were only activated by fog.

Green Innovation Areas have been developed in the US context of urban development in order to jump-start innovative solutions in abandoned areas. Prospective types of uses in these areas are not predetermined, but should be experimental and innovative. So far they can comprise vast greenhouse uses to less extensive clover fields, but their potential is not yet fully discovered. Implementing new and innovative economic uses in urban areas is relatively new in research for urban areas, in particular, when development types like bioeconomy are implemented. The joint German–Mexican research presented in this article aims at exploring the use of vacant inner urban spaces as Green Innovation Areas—discussing their potentials for sustainable development of shrinking cities.

Patients after total hip arthroplasty (THA) suffer from lingering musculoskeletal restrictions. Three-dimensional (3D) gait analysis in combination with machine-learning approaches is used to detect these impairments. In this work, features from the 3D gait kinematics, spatio temporal parameters (Set 1) and joint angles (Set 2), of an inertial sensor (IMU) system are proposed as an input for a support vector machine (SVM) model, to differentiate impaired and non-impaired gait. The features were divided into two subsets. The IMU-based features were validated against an optical motion capture (OMC) system by means of 20 patients after THA and a healthy control group of 24 subjects. Then the SVM model was trained on both subsets. The validation of the IMU system-based kinematic features revealed root mean squared errors in the joint kinematics from 0.24° to 1.25°. The validity of the spatio-temporal gait parameters (STP) revealed a similarly high accuracy. The SVM models based on IMU data showed an accuracy of 87.2% (Set 1) and 97.0% (Set 2). The current work presents valid IMU-based features, employed in an SVM model for the classification of the gait of patients after THA and a healthy control. The study reveals that the features of Set 2 are more significant concerning the classification problem. The present IMU system proves its potential to provide accurate features for the incorporation in a mobile gait-feedback system for patients after THA.