RWTH Aachen University
  • Aachen, NRW, Germany
Recent publications
Additive manufacturing has received attention for the fabrication of medical implants that have customized and complicated structures. Biodegradable Zn metals are revolutionary materials for orthopedic implants. In this study, pure Zn porous scaffolds with diamond structures were fabricated using customized laser powder bed fusion (L-PBF) technology. First, the mechanical properties, corrosion behavior, and biocompatibility of the pure Zn porous scaffolds were characterized in vitro. The scaffolds were then implanted into the rabbit femur critical-size bone defect model for 24 weeks. The results showed that the pure Zn porous scaffolds had compressive strength and rigidity comparable to those of cancellous bone, as well as relatively suitable degradation rates for bone regeneration. A benign host response was observed using hematoxylin and eosin (HE) staining of the heart, liver, spleen, lungs, and kidneys. Moreover, the pure Zn porous scaffold showed good biocompatibility and osteogenic promotion ability in vivo. This study showed that pure Zn porous scaffolds with customized structures fabricated using L-PBF represent a promising biodegradable solution for treating large bone defects.
This paper proposed a precast concrete pavement structure with the composite base layer comprised of the concrete beam and the filling low strength materials (B-PCP). The mechanical responses of the B-PCP induced by moving wheel loads coupled with temperature loads were analysed using the finite element method. Then the optimal geometry of the concrete beam was determined based on the sensitivity analysis, considering the tensile stress of both the slab and the beam of the base layer, the slab curling and the faulting. Furthermore, the performance evaluation of the B-PCP was conducted by the numerical analysis and the scale experiment testing, respectively. The results showed that the optical concrete beam was 0.8 ∼ 1.0 m wide and 0.2 ∼ 0.3 m thick. The numerical results showed that the optimal B-PCP structure had a good fatigue resistance performance, and the corresponding slab curling decreased by almost 40% compared with that of the traditional precast concrete pavement. Meanwhile, the scale experiment testing verified that the optimal B-PCP structure exhibited an excellent ability to resist the deformation. The results also showed that the bearing capacity of the optimal B-PCP structure was larger than 350kN, indicating a good bearing capacity.
Natural fibre–reinforced composites are more sustainable than other composites with respect to the raw materials. Their properties are attractive due to high specific properties, and especially so wherever high damping is valued. As the interphase between fibre and matrix is the region of highest stresses, a strong bond between fibre and matrix is essential for any composites’ properties. The present study compares two methods of determining the interfacial shear stress in natural fibre–reinforced composites: the single fibre fragmentation test and the single fibre pullout test. The studied composites are flax fibre reinforced epoxy. For a variety of fibre–matrix interaction, the fibres are treated with a laccase enzyme and dopamine, which is known to improve the fibre–matrix shear strength. In the observed samples, single fibre fragmentation test data, i.e. of fracture mode and fragment length, scatter when compared to pullout data. In single fibre pullout tests, the local interfacial shear strength showed a 30% increase in the laccase-treated samples, compared to the control samples. The method also permitted an evaluation of the frictional stress occurring after surface failure.
Magic-angle twisted trilayer graphene (MATTG) recently emerged as a highly tunable platform for studying correlated phases of matter, such as correlated insulators and superconductivity. Superconductivity occurs in a range of doping levels that is bounded by van Hove singularities, which stimulates the debate of the origin and nature of superconductivity in this material. In this work, we discuss the role of spin-fluctuations arising from atomic-scale correlations in MATTG for the superconducting state. We show that in a phase diagram as a function of doping ( ν ) and temperature, nematic superconducting regions are surrounded by ferromagnetic states and that a superconducting dome with T c ≈ 2 K appears between the integer fillings ν = −2 and ν = −3. Applying a perpendicular electric field enhances superconductivity on the electron-doped side which we relate to changes in the spin-fluctuation spectrum. We show that the nematic unconventional superconductivity leads to pronounced signatures in the local density of states detectable by scanning tunneling spectroscopy measurements.
Background Owing to the large amounts of energy, greenhouse gases, and waste that it generates, the construction industry is fundamental to the transition towards a circular economy. Indicators which show the circularity of products—and thus make them comparable with each other—can be used to support the implementation of such an economy. In this article, we have adapted the material circularity indicator of the Ellen MacArthur Foundation in order to analyze the circularity of construction products available in the German environmental database ÖKOBAUDAT. Results The adapted indicator is applied to 89 building products from the categories of insulation materials, plastics, metals, and mineral building materials. More than half of the products receive the lowest score of 0.10, indicating poor implementation of circular strategies in the German construction industry to date. Conclusion Circular material flows are most likely to be employed for metals. However, the overall low circularity scores indicate a big need for better implementing circularity strategies.
Background Although kidney transplantation improves patient survival and quality of life, long-term results are hampered by both immune- and non-immune-mediated complications. Current biomarkers of post-transplant complications, such as allograft rejection, chronic renal allograft dysfunction, and cutaneous squamous cell carcinoma, have a suboptimal predictive value. DNA methylation is an epigenetic modification that directly affects gene expression and plays an important role in processes such as ischemia/reperfusion injury, fibrosis, and alloreactive immune response. Novel techniques can quickly assess the DNA methylation status of multiple loci in different cell types, allowing a deep and interesting study of cells’ activity and function. Therefore, DNA methylation has the potential to become an important biomarker for prediction and monitoring in kidney transplantation. Purpose of the study The aim of this study was to evaluate the role of DNA methylation as a potential biomarker of graft survival and complications development in kidney transplantation. Material and Methods A systematic review of several databases has been conducted. The Newcastle–Ottawa scale and the Jadad scale have been used to assess the risk of bias for observational and randomized studies, respectively. Results Twenty articles reporting on DNA methylation as a biomarker for kidney transplantation were included, all using DNA methylation for prediction and monitoring. DNA methylation pattern alterations in cells isolated from different tissues, such as kidney biopsies, urine, and blood, have been associated with ischemia–reperfusion injury and chronic renal allograft dysfunction. These alterations occurred in different and specific loci. DNA methylation status has also proved to be important for immune response modulation, having a crucial role in regulatory T cell definition and activity. Research also focused on a better understanding of the role of this epigenetic modification assessment for regulatory T cells isolation and expansion for future tolerance induction-oriented therapies. Conclusions Studies included in this review are heterogeneous in study design, biological samples, and outcome. More coordinated investigations are needed to affirm DNA methylation as a clinically relevant biomarker important for prevention, monitoring, and intervention.
Background In individuals suffering from a rare disease the diagnostic process and the confirmation of a final diagnosis often extends over many years. Factors contributing to delayed diagnosis include health care professionals' limited knowledge of rare diseases and frequent (co-)occurrence of mental disorders that may complicate and delay the diagnostic process. The ZSE-DUO study aims to assess the benefits of a combination of a physician focusing on somatic aspects with a mental health expert working side by side as a tandem in the diagnostic process. Study design This multi-center, prospective controlled study has a two-phase cohort design. Methods Two cohorts of 682 patients each are sequentially recruited from 11 university-based German Centers for Rare Diseases (CRD): the standard care cohort (control, somatic expertise only) and the innovative care cohort (experimental, combined somatic and mental health expertise). Individuals aged 12 years and older presenting with symptoms and signs which are not explained by current diagnoses will be included. Data will be collected prior to the first visit to the CRD’s outpatient clinic (T0), at the first visit (T1) and 12 months thereafter (T2). Outcomes Primary outcome is the percentage of patients with one or more confirmed diagnoses covering the symptomatic spectrum presented. Sample size is calculated to detect a 10 percent increase from 30% in standard care to 40% in the innovative dual expert cohort. Secondary outcomes are (a) time to diagnosis/diagnoses explaining the symptomatology; (b) proportion of patients successfully referred from CRD to standard care; (c) costs of diagnosis including incremental cost effectiveness ratios; (d) predictive value of screening instruments administered at T0 to identify patients with mental disorders; (e) patients’ quality of life and evaluation of care; and f) physicians’ satisfaction with the innovative care approach. Conclusions This is the first multi-center study to investigate the effects of a mental health specialist working in tandem with a somatic expert physician in CRDs. If this innovative approach proves successful, it will be made available on a larger scale nationally and promoted internationally. In the best case, ZSE-DUO can significantly shorten the time to diagnosis for a suspected rare disease. Trial registration ClinicalTrials.gov; Identifier: NCT03563677; First posted: June 20, 2018, https://clinicaltrials.gov/ct2/show/NCT03563677 .
Background Persistence is a key criterion for the risk assessment of chemicals. In degradation tests, microbial biodegradation of labeled test chemicals leads to the incorporation of the label in microbial biomass, resulting in biogenic non-extractable residues (bioNER), which are not considered as harmful in persistence assessment. The amount of bioNER can be estimated using the microbial turnover to biomass (MTB) model. MTB estimates the biomass growth during productive degradation of a compound from theoretical growth yield and CO2-formation and gives an upper and a lower value for bioNER formation. The aim of this study is use available experimental data for bioNER to assess the validity, accuracy and precision of the MTB method as new tool in persistence assessment. Results We collected experimental data in order to test accuracy and precision of this estimation method. In total, 16 experimental studies were found in literature where bioNER was experimentally quantified. Hereof, 13 studies used the amount of label recovered from total amino acid (tAA) content as proxy for bioNER. Unfortunately, the comparison with experimental data was difficult due to the variety of employed methods. A conversion factor is required to extrapolate from tAA on bioNER, and this factor may vary during the experiment and between experiments. The bioNER formation for all compounds tested was calculated with the MTB method, and the outcome was compared to measured tAA as proxy for bioNER. The relation between predicted and measured bioNER was significant, but no better correlation was obtained than with CO2 to tAA. The mean absolute error of the prediction (low MTB versus tAA) was 5% applied label (range 0.3 to 16%). Some deviation between measured results and calculated bioNER could be contributed to uncertainties in the experimental determination, as shown by variance in replicates (bromoxynil) or high background of label in sterile samples (sulfadiazine). Conclusions MTB thus provides a robust model for determining of the potential amounts of biomass and bioNER formed from the degradation of organic chemicals.
Physical activity impacts immune homeostasis and leads to rapid and marked increase in cell-free DNA (cfDNA). However, the origin of cfDNA during exercise remains elusive and it is unknown if physical activity could improve or interfere with methylation based liquid biopsy. We analyzed the methylation levels of four validated CpGs representing cfDNA from granulocytes, lymphocytes, monocytes, and non-hematopoietic cells, in healthy individuals in response to exercise, and in patients with hematological malignancies under resting conditions. The analysis revealed that physical activity almost exclusively triggered DNA release from granulocytes, highlighting the relevance as a pre-analytical variable which could compromise diagnostic accuracy. Graphical Abstract
The chemical pollution crisis severely threatens human and environmental health globally. To tackle this challenge the establishment of an overarching international science–policy body has recently been suggested. We strongly support this initiative based on the awareness that humanity has already likely left the safe operating space within planetary boundaries for novel entities including chemical pollution. Immediate action is essential and needs to be informed by sound scientific knowledge and data compiled and critically evaluated by an overarching science–policy interface body. Major challenges for such a body are (i) to foster global knowledge production on exposure, impacts and governance going beyond data-rich regions (e.g., Europe and North America), (ii) to cover the entirety of hazardous chemicals, mixtures and wastes, (iii) to follow a one-health perspective considering the risks posed by chemicals and waste on ecosystem and human health, and (iv) to strive for solution-oriented assessments based on systems thinking. Based on multiple evidence on urgent action on a global scale, we call scientists and practitioners to mobilize their scientific networks and to intensify science–policy interaction with national governments to support the negotiations on the establishment of an intergovernmental body based on scientific knowledge explaining the anticipated benefit for human and environmental health.
Background Imprinting disorders are a group of congenital diseases which are characterized by molecular alterations affecting differentially methylated regions (DMRs). To date, at least twelve imprinting disorders have been defined with overlapping but variable clinical features including growth and metabolic disturbances, cognitive dysfunction, abdominal wall defects and asymmetry. In general, a single specific DMR is affected in an individual with a given imprinting disorder, but there are a growing number of reports on individuals with so-called multilocus imprinting disturbances (MLID), where aberrant imprinting marks (most commonly loss of methylation) occur at multiple DMRs. However, as the literature is fragmented, we reviewed the molecular and clinical data of 55 previously reported or newly identified MLID families with putative pathogenic variants in maternal effect genes (NLRP2, NLRP5, NLRP7, KHDC3L, OOEP, PADI6) and in other candidate genes (ZFP57, ARID4A, ZAR1, UHRF1, ZNF445). Results In 55 families, a total of 68 different candidate pathogenic variants were identified (7 in NLRP2, 16 in NLRP5, 7 in NLRP7, 17 in PADI6, 15 in ZFP57, and a single variant in each of the genes ARID4A, ZAR1, OOEP, UHRF1, KHDC3L and ZNF445). Clinical diagnoses of affected offspring included Beckwith–Wiedemann syndrome spectrum, Silver–Russell syndrome spectrum, transient neonatal diabetes mellitus, or they were suspected for an imprinting disorder (undiagnosed). Some families had recurrent pregnancy loss. Conclusions Genomic maternal effect and foetal variants causing MLID allow insights into the mechanisms behind the imprinting cycle of life, and the spatial and temporal function of the different factors involved in oocyte maturation and early development. Further basic research together with identification of new MLID families will enable a better understanding of the link between the different reproductive issues such as recurrent miscarriages and preeclampsia in maternal effect variant carriers/families and aneuploidy and the MLID observed in the offsprings. The current knowledge can already be employed in reproductive and genetic counselling in specific situations.
We introduce a Python package that provides simple and unified access to a collection of datasets from fundamental physics research—including particle physics, astroparticle physics, and hadron- and nuclear physics—for supervised machine learning studies. The datasets contain hadronic top quarks, cosmic-ray-induced air showers, phase transitions in hadronic matter, and generator-level histories. While public datasets from multiple fundamental physics disciplines already exist, the common interface and provided reference models simplify future work on cross-disciplinary machine learning and transfer learning in fundamental physics. We discuss the design and structure and line out how additional datasets can be submitted for inclusion. As showcase application, we present a simple yet flexible graph-based neural network architecture that can easily be applied to a wide range of supervised learning tasks. We show that our approach reaches performance close to dedicated methods on all datasets. To simplify adaptation for various problems, we provide easy-to-follow instructions on how graph-based representations of data structures, relevant for fundamental physics, can be constructed and provide code implementations for several of them. Implementations are also provided for our proposed method and all reference algorithms.
Atherosclerosis is the foundation of potentially fatal cardiovascular diseases and it is characterized by plaque formation in large arteries. Current treatments aimed at reducing atherosclerotic risk factors still allow room for a large residual risk; therefore, novel therapeutic candidates targeting inflammation are needed. The endothelium is the starting point of vascular inflammation underlying atherosclerosis and we could previously demonstrate that the chemokine axis CXCL12–CXCR4 plays an important role in disease development. However, the role of ACKR3, the alternative and higher affinity receptor for CXCL12 remained to be elucidated. We studied the role of arterial ACKR3 in atherosclerosis using western diet-fed Apoe−/− mice lacking Ackr3 in arterial endothelial as well as smooth muscle cells. We show for the first time that arterial endothelial deficiency of ACKR3 attenuates atherosclerosis as a result of diminished arterial adhesion as well as invasion of immune cells. ACKR3 silencing in inflamed human coronary artery endothelial cells decreased adhesion molecule expression, establishing an initial human validation of ACKR3’s role in endothelial adhesion. Concomitantly, ACKR3 silencing downregulated key mediators in the MAPK pathway, such as ERK1/2, as well as the phosphorylation of the NF-kB p65 subunit. Endothelial cells in atherosclerotic lesions also revealed decreased phospho-NF-kB p65 expression in ACKR3-deficient mice. Lack of smooth muscle cell-specific as well as hematopoietic ACKR3 did not impact atherosclerosis in mice. Collectively, our findings indicate that arterial endothelial ACKR3 fuels atherosclerosis by mediating endothelium-immune cell adhesion, most likely through inflammatory MAPK and NF-kB pathways.
Additive manufacturing (AM) technologies have been recognized for their capability to build complex components and hence have offered more freedom to designers for a long time. The ability to directly use a computer-aided design (CAD) model has allowed for fabricating and realizing complicated components, monolithic design, reducing the number of components in an assembly, decreasing time to market, and adding performance or comfort-enhancing functionalities. One of the features that can be introduced for boosting a component functionality using AM is the inclusion of surface texture on a given component. This inclusion is usually a difficult task as creating a CAD model resolving fine details of a given texture is difficult even using commercial software packages. This paper develops a methodology to include texture directly on the CAD model of a target surface using a patch-based sampling texture synthesis algorithm, which can be manufactured using AM. Input for the texture generation algorithm can be either a physical sample or an image with heightmap information. The heightmap information from a physical sample can be obtained by 3D scanning the sample and using the information from the acquired point cloud. After obtaining the required inputs, the patches are sampled for texture generation according to non-parametric estimation of the local conditional Markov random field (MRF) density function, which helps avoid mismatched features across the patch boundaries. While generating the texture, a design constraint to ensure AM producibility is considered, which is essential when manufacturing a component using, e.g., Fused Deposition Melting (FDM) or Laser Powder Bed Fusion (LPBF). The generated texture is then mapped onto the surface using the developed distance and angle preserving mapping algorithms. The implemented algorithms can be used to map the generated texture onto a mathematically defined surface. This paper maps the textures onto flat, curved, and sinusoidal surfaces for illustration. After the texture mapping, a stereolithography (STL) model is generated with the desired texture on the target surface. The generated STL model is printed using FDM technology as a final step.
Some COVID-19 patients experience dyspnea without objective impairment of pulmonary or cardiac function. This study determined diaphragm function and its central voluntary activation as a potential correlate with exertional dyspnea after COVID-19 acute respiratory distress syndrome (ARDS) in ten patients and matched controls. One year post discharge, both pulmonary function tests and echocardiography were normal. However, six patients with persisting dyspnea on exertion showed impaired volitional diaphragm function and control based on ultrasound, magnetic stimulation and balloon catheter-based recordings. Diaphragm dysfunction with impaired voluntary activation can be present 1 year after severe COVID-19 ARDS and may relate to exertional dyspnea. This prospective case–control study was registered under the trial registration number NCT04854863 April, 22 2021
Numerous research methods have been developed to detect anomalies in the areas of security and risk analysis. In healthcare, there are numerous use cases where anomaly detection is relevant. For example, early detection of sepsis is one such use case. Early treatment of sepsis is cost effective and reduces the number of hospital days of patients in the ICU. There is no single procedure that is sufficient for sepsis diagnosis, and combinations of approaches are needed. Detecting anomalies in patient time series data could help speed the development of some decisions. However, our algorithm must be viewed as complementary to other approaches based on laboratory values and physician judgments. The focus of this work is to develop a hybrid method for detecting anomalies that occur, for example, in multidimensional medical signals, sensor signals, or other time series in business and nature. The novelty of our approach lies in the extension and combination of existing approaches: Statistics, Self Organizing Maps and Linear Discriminant Analysis in a unique and unprecedented way with the goal of identifying different types of anomalies in real-time measurement data and defining the point where the anomaly occurs. The proposed algorithm not only has the full potential to detect anomalies, but also to find real points where an anomaly starts.
Background The European environmental risk assessment of plant protection products considers aquatic model ecosystem studies (microcosms/mesocosms, M/M) as suitable higher tier approach to assess treatment-related effects and to derive regulatory acceptable concentrations (RAC). However, it is under debate to what extent these artificial test systems reflect the risks of pesticidal substances with potential harmful effects on natural macroinvertebrate communities, and whether the field communities are adequately protected by the results of the M/M studies. We therefore compared the composition, sensitivity and vulnerability of benthic macroinvertebrates established in control (untreated) groups of 47 selected M/M studies with natural stream communities at 26 reference field sites. Results Since 2013 the number of benthic macroinvertebrate taxa present in M/M studies has increased by 39% to a mean of 38 families per study. However, there is only an average of 4 families per study that comply with the recommendations provided by EFSA (EFSA J 11:3290, 2013), i.e.: (i) allowing statistical identification of treatment-related effects of at least 70% according to the minimum detectable difference (here criteria are slightly modified) and (ii) belonging to insects or crustaceans (potentially sensitive taxa for pesticidal substances). Applying the criterion of physiological sensitivity according to the SPEAR pesticides concept, the number of families decreases from 4 to 2.3 per study. Conclusions Most taxa established in recent M/M studies do not suitably represent natural freshwater communities. First, because their abundances are often not sufficient for statistical detection of treatment-related effects in order to determine an appropriate endpoint and subsequent RAC. Recommendations are given to improve the detectability of such effects and their reliability. Second, the taxa often do not represent especially sensitive or vulnerable taxa in natural communities in terms of their traits. The uncertainties linked to vulnerable taxa in M/M studies are especially high considering their representativity for field assemblages and the comparability of factors determining their recovery time. Thus considering recovery for deriving a RAC (i.e., ERO-RAC) is not recommended. In addition, this paper discusses further concerns regarding M/M studies in a broader regulatory context and recommends the development of alternative assessment tools and a shift towards a new paradigm.
The effects of bilingualism on executive functions (EFs) and intelligence are still controversially discussed. Most studies have focused on performance differences without considering the underlying structure of cognitive abilities. Thus, we examined whether the structure of EFs and the relations of EFs with intelligence differ between mono- and bilingual children. A total of 240 elementary school children (mean age = 8 years 6 months; 133 monolinguals and 95 bilinguals) performed two tasks measuring working memory, inhibition, cognitive flexibility, and fluid intelligence, respectively. Confirmatory factor analyses showed that one common EF factor provided the best fit to the data in both language groups, indicating that bilingualism is not associated with differences in the EF structure at this age. Moreover, there were no latent performance differences in either EFs or intelligence between mono- and bilingual children. However, we found a stronger relation between a common EF factor and fluid intelligence in bilingual children as compared with monolingual children, implying a closer coupling of EFs and intelligence abilities in bilingual children. This contributes to explaining the previous heterogeneous findings on the task level because more closely coupled cognitive functioning can be slightly beneficial for some tasks and irrelevant or even slightly obstructive for others.
Institution pages aggregate content on ResearchGate related to an institution. The members listed on this page have self-identified as being affiliated with this institution. Publications listed on this page were identified by our algorithms as relating to this institution. This page was not created or approved by the institution. If you represent an institution and have questions about these pages or wish to report inaccurate content, you can contact us here.
22,416 members
Christina Regenbogen
  • Department of Psychiatry, Psychotherapy and Psychosomatics
Oliver Budde
  • Forschungsinstitut für Rationalisierung e. V.
Ralf Klamma
  • Adavanced Community Information Systems Group
Information
Address
Templergraben 55, D-52056, Aachen, NRW, Germany
Head of institution
Dr. rer. nat. Dr. h. c. mult., Universitätsprofessor Ulrich Rüdiger
Website
http://www.rwth-aachen.de