600 Technik, Technologie
Refine
Year of publication
Document Type
- Article (85)
- Postprint (55)
- Doctoral Thesis (4)
- Conference Proceeding (2)
- Other (1)
Language
- English (147) (remove)
Is part of the Bibliography
- yes (147)
Keywords
- human behaviour (5)
- Environmental sciences (4)
- animal personality (4)
- deep reinforcement learning (4)
- exploratory-behavior (4)
- expression (4)
- production control (4)
- activation (3)
- disease (3)
- human-robot interaction (3)
Institute
- Mathematisch-Naturwissenschaftliche Fakultät (26)
- Institut für Biochemie und Biologie (25)
- Institut für Physik und Astronomie (21)
- Institut für Geowissenschaften (11)
- Institut für Chemie (8)
- Strukturbereich Kognitionswissenschaften (8)
- Fachgruppe Betriebswirtschaftslehre (7)
- Hasso-Plattner-Institut für Digital Engineering GmbH (7)
- Wirtschaftswissenschaften (7)
- Institut für Informatik und Computational Science (5)
Organizational commitments to equality change how people view women’s and men’s professional success
(2024)
To address women’s underrepresentation in high-status positions, many organizations have committed to gender equality. But is women’s professional success viewed less positively when organizations commit to women’s advancement? Do equality commitments have positive effects on evaluations of successful men? We fielded a survey experiment with a national probability sample in Germany (N = 3229) that varied employees’ gender and their organization’s commitment to equality. Respondents read about a recently promoted employee and rated how decisive of a role they thought intelligence and effort played in getting the employee promoted from 1 “Not at all decisive” to 7 “Very decisive” and the fairness of the promotion from 1 “Very unfair” to 7 “Very fair.” When organizations committed to women’s advancement rather than uniform performance standards, people believed intelligence and effort were less decisive in women’s promotions, but that intelligence was more decisive in men’s promotions. People viewed women’s promotions as least fair and men’s as most fair in organizations committed to women’s advancement. However, women’s promotions were still viewed more positively than men’s in all conditions and on all outcomes, suggesting people believed that organizations had double standards for success that required women to be smarter and work harder to be promoted, especially in organizations that did not make equality commitments.
“Ick bin een Berlina”
(2024)
Background: Robots are increasingly used as interaction partners with humans. Social robots are designed to follow expected behavioral norms when engaging with humans and are available with different voices and even accents. Some studies suggest that people prefer robots to speak in the user’s dialect, while others indicate a preference for different dialects.
Methods: Our study examined the impact of the Berlin dialect on perceived trustworthiness and competence of a robot. One hundred and twenty German native speakers (Mage = 32 years, SD = 12 years) watched an online video featuring a NAO robot speaking either in the Berlin dialect or standard German and assessed its trustworthiness and competence.
Results: We found a positive relationship between participants’ self-reported Berlin dialect proficiency and trustworthiness in the dialect-speaking robot. Only when controlled for demographic factors, there was a positive association between participants’ dialect proficiency, dialect performance and their assessment of robot’s competence for the standard German-speaking robot. Participants’ age, gender, length of residency in Berlin, and device used to respond also influenced assessments. Finally, the robot’s competence positively predicted its trustworthiness.
Discussion: Our results inform the design of social robots and emphasize the importance of device control in online experiments.
Fast Holocene slip and localized strain along the Liquiñe-Ofqui strike-slip fault system, Chile
(2021)
In active tectonic settings dominated by strike-slip kinematics, slip partitioning across subparallel faults is a common feature; therefore, assessing the degree of partitioning and strain localization is paramount for seismic hazard assessments. Here, we estimate a slip rate of 18.8 +/- 2.0 mm/year over the past 9.0 +/- 0.1 ka for a single strand of the Liquirie-Ofqui Fault System, which straddles the Main Cordillera in Southern Chile. This Holocene rate accounts for similar to 82% of the trench-parallel component of oblique plate convergence and is similar to million-year estimates integrated over the entire fault system. Our results imply that strain localizes on a single fault at millennial time scale but over longer time scales strain localization is not sustained. The fast millennial slip rate in the absence of historical Mw> 6.5 earthquakes along the Liquine-Ofqui Fault System implies either a component of aseismic slip or Mw similar to 7 earthquakes involving multi-trace ruptures and > 150-year repeat times. Our results have implications for the understanding of strike-slip fault system dynamics within volcanic arcs and seismic hazard assessments.
After initial detection of target archival DNA of a 116-year-old syntype specimen of the smooth lantern shark, Etmopterus pusillus, in a single-stranded DNA library, we shotgun-sequenced additional 9 million reads from this same DNA library. Sequencing reads were used for extracting mitochondrial sequence information for analyses of mitochondrial DNA characteristics and reconstruction of the mitochondrial genome. The archival DNA is highly fragmented. A total of 4599 mitochondrial reads were available for the genome reconstruction using an iterative mapping approach. The resulting genome sequence has 12 times coverage and a length of 16 741 bp. All 37 vertebrate mitochondrial loci plus the control region were identified and annotated. The mitochondrial NADH2 gene was subsequently used to place the syntype haplotype in a network comprising multiple E. pusillus samples from various distant localities as well as sequences from a morphological similar species, the shortfin smooth lantern shark Etmopterus joungi. Results confirm the almost global distribution of E. pusillus and suggest E. joungi to be a junior synonym of E. pusillus. As mitochondrial DNA often represents the only available reference information in non-model organisms, this study illustrates the importance of mitochondrial DNA from an aged, wet collection type specimen for taxonomy.
Fragmentation of peptides leaves characteristic patterns in mass spectrometry data, which can be used to identify protein sequences, but this method is challenging for mutated or modified sequences for which limited information exist. Altenburg et al. use an ad hoc learning approach to learn relevant patterns directly from unannotated fragmentation spectra.
Mass spectrometry-based proteomics provides a holistic snapshot of the entire protein set of living cells on a molecular level. Currently, only a few deep learning approaches exist that involve peptide fragmentation spectra, which represent partial sequence information of proteins.
Commonly, these approaches lack the ability to characterize less studied or even unknown patterns in spectra because of their use of explicit domain knowledge.
Here, to elevate unrestricted learning from spectra, we introduce 'ad hoc learning of fragmentation' (AHLF), a deep learning model that is end-to-end trained on 19.2 million spectra from several phosphoproteomic datasets. AHLF is interpretable, and we show that peak-level feature importance values and pairwise interactions between peaks are in line with corresponding peptide fragments.
We demonstrate our approach by detecting post-translational modifications, specifically protein phosphorylation based on only the fragmentation spectrum without a database search. AHLF increases the area under the receiver operating characteristic curve (AUC) by an average of 9.4% on recent phosphoproteomic data compared with the current state of the art on this task.
Furthermore, use of AHLF in rescoring search results increases the number of phosphopeptide identifications by a margin of up to 15.1% at a constant false discovery rate. To show the broad applicability of AHLF, we use transfer learning to also detect cross-linked peptides, as used in protein structure analysis, with an AUC of up to 94%.
Inorganic perovskite solar cells show excellent thermal stability, but the reported power conversion efficiencies are still lower than for organic-inorganic perovskites. This is mainly caused by lower open-circuit voltages (V(OC)s). Herein, the reasons for the low V-OC in inorganic CsPbI2Br perovskite solar cells are investigated. Intensity-dependent photoluminescence measurements for different layer stacks reveal that n-i-p and p-i-n CsPbI2Br solar cells exhibit a strong mismatch between quasi-Fermi level splitting (QFLS) and V-OC. Specifically, the CsPbI2Br p-i-n perovskite solar cell has a QFLS-e center dot V-OC mismatch of 179 meV, compared with 11 meV for a reference cell with an organic-inorganic perovskite of similar bandgap. On the other hand, this study shows that the CsPbI2Br films with a bandgap of 1.9 eV have a very low defect density, resulting in an efficiency potential of 20.3% with a MeO-2PACz hole-transporting layer and 20.8% on compact TiO2. Using ultraviolet photoelectron spectroscopy measurements, energy level misalignment is identified as a possible reason for the QFLS-e center dot V-OC mismatch and strategies for overcoming this V-OC limitation are discussed. This work highlights the need to control the interfacial energetics in inorganic perovskite solar cells, but also gives promise for high efficiencies once this issue is resolved.
A detailed investigation of the energy levels of perylene-3,4,9,10-tetracarboxylic tetraethylester as a representative compound for the whole family of perylene esters was performed. It was revealed via electrochemical measurements that one oxidation and two reductions take place. The bandgaps determined via the electrochemical approach are in good agreement with the optical bandgap obtained from the absorption spectra via a Tauc plot. In addition, absorption spectra in dependence of the electrochemical potential were the basis for extensive quantum-chemical calculations of the neutral, monoanionic, and dianionic molecules. For this purpose, calculations based on density functional theory were compared with post-Hartree-Fock methods and the CAM-B3LYP functional proved to be the most reliable choice for the calculation of absorption spectra. Furthermore, spectral features found experimentally could be reproduced with vibronic calculations and allowed to understand their origins. In particular, the two lowest energy absorption bands of the anion are not caused by absorption of two distinct electronic states, which might have been expected from vertical excitation calculations, but both states exhibit a strong vibronic progression resulting in contributions to both bands.
Increasingly fast development cycles and individualized products pose major challenges for today's smart production systems in times of industry 4.0. The systems must be flexible and continuously adapt to changing conditions while still guaranteeing high throughputs and robustness against external disruptions. Deep reinforcement learning (RL) algorithms, which already reached impressive success with Google DeepMind's AlphaGo, are increasingly transferred to production systems to meet related requirements. Unlike supervised and unsupervised machine learning techniques, deep RL algorithms learn based on recently collected sensorand process-data in direct interaction with the environment and are able to perform decisions in real-time. As such, deep RL algorithms seem promising given their potential to provide decision support in complex environments, as production systems, and simultaneously adapt to changing circumstances. While different use-cases for deep RL emerged, a structured overview and integration of findings on their application are missing. To address this gap, this contribution provides a systematic literature review of existing deep RL applications in the field of production planning and control as well as production logistics. From a performance perspective, it became evident that deep RL can beat heuristics significantly in their overall performance and provides superior solutions to various industrial use-cases. Nevertheless, safety and reliability concerns must be overcome before the widespread use of deep RL is possible which presumes more intensive testing of deep RL in real world applications besides the already ongoing intensive simulations.
In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios.
In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios.
Copolyesterurethanes (PDLCLs) based on oligo(epsilon-caprolactone) (OCL) and oligo(omega-pentadecalactone) (OPDL) segments are biodegradable thermoplastic temperature-memory polymers. The temperature-memory capability in these polymers with crystallizable control units is implemented by a thermomechanical programming process causing alterations in the crystallite arrangement and chain organization. These morphological changes can potentially affect degradation. Initial observations on the macroscopic level inspire the hypothesis that switching of the controlling units causes an accelerated degradation of the material, resulting in programmable degradation by sequential coupling of functions. Hence, detailed degradation studies on Langmuir films of a PDLCL with 40 wt% OPDL content are carried out under enzymatic catalysis. The temperature-memory creation procedure is mimicked by compression at different temperatures. The evolution of the chain organization and mechanical properties during the degradation process is investigated by means of polarization-modulated infrared reflection absorption spectroscopy, interfacial rheology and to some extend by X-ray reflectivity. The experiments on PDLCL Langmuir films imply that degradability is not enhanced by thermal switching, as the former depends on the temperature during cold programming. Nevertheless, the thin film experiments show that the leaching of OCL segments does not induce further crystallization of the OPDL segments, which is beneficial for a controlled and predictable degradation.
We use the prolonged Greek crisis as a case study to understand how a lasting economic shock affects the innovation strategies of firms in economies with moderate innovation activities. Adopting the 3-stage CDM model, we explore the link between R&D, innovation, and productivity for different size groups of Greek manufacturing firms during the prolonged crisis. At the first stage, we find that the continuation of the crisis is harmful for the R&D engagement of smaller firms while it increased the willingness for R&D activities among the larger ones. At the second stage, among smaller firms the knowledge production remains unaffected by R&D investments, while among larger firms the R&D decision is positively correlated with the probability of producing innovation, albeit the relationship is weakened as the crisis continues. At the third stage, innovation output benefits only larger firms in terms of labor productivity, while the innovation-productivity nexus is insignificant for smaller firms during the lasting crisis.
Drought and the availability of mineable phosphorus minerals used for fertilization are two of the important issues agriculture is facing in the future. High phosphorus availability in soils is necessary to maintain high agricultural yields. Drought is one of the major threats for terrestrial ecosystem performance and crop production in future. Among the measures proposed to cope with the upcoming challenges of intensifying drought stress and to decrease the need for phosphorus fertilizer application is the fertilization with silica (Si). Here we tested the importance of soil Si fertilization on wheat phosphorus concentration as well as wheat performance during drought at the field scale. Our data clearly showed a higher soil moisture for the Si fertilized plots. This higher soil moisture contributes to a better plant performance in terms of higher photosynthetic activity and later senescence as well as faster stomata responses ensuring higher productivity during drought periods. The plant phosphorus concentration was also higher in Si fertilized compared to control plots. Overall, Si fertilization or management of the soil Si pools seem to be a promising tool to maintain crop production under predicted longer and more serve droughts in the future and reduces phosphorus fertilizer requirements.
Enhanced charge selectivity via anodic-C60 layer reduces nonradiative losses in organic solar cells
(2021)
Interfacial layers in conjunction with suitable charge-transport layers can significantly improve the performance of optoelectronic devices by facilitating efficient charge carrier injection and extraction.
This work uses a neat C-60 interlayer on the anode to experimentally reveal that surface recombination is a significant contributor to nonradiative recombination losses in organic solar cells.
These losses are shown to proportionally increase with the extent of contact between donor molecules in the photoactive layer and a molybdenum oxide (MoO3) hole extraction layer, proven by calculating voltage losses in low- and high-donor-content bulk heterojunction device architectures.
Using a novel in-device determination of the built-in voltage, the suppression of surface recombination, due to the insertion of a thin anodic-C-60 interlayer on MoO3, is attributed to an enhanced built-in potential.
The increased built-in voltage reduces the presence of minority charge carriers at the electrodes-a new perspective on the principle of selective charge extraction layers.
The benefit to device efficiency is limited by a critical interlayer thickness, which depends on the donor material in bilayer devices.
Given the high popularity of MoO3 as an efficient hole extraction and injection layer and the increasingly popular discussion on interfacial phenomena in organic optoelectronic devices, these findings are relevant to and address different branches of organic electronics, providing insights for future device design.
Developing a new product generation requires the transfer of knowledge among various knowledge carriers. Several factors influence knowledge transfer, e.g., the complexity of engineering tasks or the competence of employees, which can decrease the efficiency and effectiveness of knowledge transfers in product engineering. Hence, improving those knowledge transfers obtains great potential, especially against the backdrop of experienced employees leaving the company due to retirement, so far, research results show, that the knowledge transfer velocity can be raised by following the Knowledge Transfer Velocity Model and implementing so-called interventions in a product engineering context. In most cases, the implemented interventions have a positive effect on knowledge transfer speed improvement. In addition to that, initial theoretical findings describe factors influencing the quality of knowledge transfers and outline a setting to empirically investigate how the quality can be improved by introducing a general description of knowledge transfer reference situations and principles to measure the quality of knowledge artifacts. To assess the quality of knowledge transfers in a product engineering context, the Knowledge Transfer Quality Model (KTQM) is created, which serves as a basis to develop and implement quality-dependent interventions for different knowledge transfer situations. As a result, this paper introduces the specifications of eight situation-adequate interventions to improve the quality of knowledge transfers in product engineering following an intervention template. Those interventions are intended to be implemented in an industrial setting to measure the quality of knowledge transfers and validate their effect.
Protected cultivation in greenhouses or polytunnels offers the potential for sustainable production of high-yield, high-quality vegetables. This is related to the ability to produce more on less land and to use resources responsibly and efficiently. Crop yield has long been considered the most important factor. However, as plant-based diets have been proposed for a sustainable food system, the targeted enrichment of health-promoting plant secondary metabolites should be addressed. These metabolites include carotenoids and flavonoids, which are associated with several health benefits, such as cardiovascular health and cancer protection.
Cover materials generally have an influence on the climatic conditions, which in turn can affect the levels of secondary metabolites in vegetables grown underneath. Plastic materials are cost-effective and their properties can be modified by incorporating additives, making them the first choice. However, these additives can migrate and leach from the material, resulting in reduced service life, increased waste and possible environmental release. Antifogging additives are used in agricultural films to prevent the formation of droplets on the film surface, thereby increasing light transmission and preventing microbiological contamination.
This thesis focuses on LDPE/EVA covers and incorporated antifogging additives for sustainable protected cultivation, following two different approaches. The first addressed the direct effects of leached antifogging additives using simulation studies on lettuce leaves (Lactuca sativa var capitata L). The second determined the effect of antifog polytunnel covers on lettuce quality. Lettuce is usually grown under protective cover and can provide high nutritional value due to its carotenoid and flavonoid content, depending on the cultivar.
To study the influence of simulated leached antifogging additives on lettuce leaves, a GC-MS method was first developed to analyze these additives based on their fatty acid moieties. Three structurally different antifogging additives (reference material) were characterized outside of a polymer matrix for the first time. All of them contained more than the main fatty acid specified by the manufacturer. Furthermore, they were found to adhere to the leaf surface and could not be removed by water or partially by hexane.
The incorporation of these additives into polytunnel covers affects carotenoid levels in lettuce, but not flavonoids, caffeic acid derivatives and chlorophylls. Specifically, carotenoids were higher in lettuce grown under polytunnels without antifog than with antifog. This has been linked to their effect on the light regime and was suggested to be related to carotenoid function in photosynthesis.
In terms of protected cultivation, the use of LDPE/EVA polytunnels affected light and temperature, and both are closely related. The carotenoid and flavonoid contents of lettuce grown under polytunnels was reversed, with higher carotenoid and lower flavonoid levels. At the individual level, the flavonoids detected in lettuce did not differ however, lettuce carotenoids adapted specifically depending on the time of cultivation. Flavonoid reduction was shown to be transcriptionally regulated (CHS) in response to UV light (UVR8). In contrast, carotenoids are thought to be regulated post-transcriptionally, as indicated by the lack of correlation between carotenoid levels and transcripts of the first enzyme in carotenoid biosynthesis (PSY) and a carotenoid degrading enzyme (CCD4), as well as the increased carotenoid metabolic flux. Understanding the regulatory mechanisms and metabolite adaptation strategies could further advance the strategic development and selection of cover materials.
Droughts in São Paulo
(2023)
Literature has suggested that droughts and societies are mutually shaped and, therefore, both require a better understanding of their coevolution on risk reduction and water adaptation. Although the Sao Paulo Metropolitan Region drew attention because of the 2013-2015 drought, this was not the first event. This paper revisits this event and the 1985-1986 drought to compare the evolution of drought risk management aspects. Documents and hydrological records are analyzed to evaluate the hazard intensity, preparedness, exposure, vulnerability, responses, and mitigation aspects of both events. Although the hazard intensity and exposure of the latter event were larger than the former one, the policy implementation delay and the dependency of service areas in a single reservoir exposed the region to higher vulnerability. In addition to the structural and non-structural tools implemented just after the events, this work raises the possibility of rainwater reuse for reducing the stress in reservoirs.
Adverse environmental conditions are detrimental to plant growth and development. Acclimation to abiotic stress conditions involves activation of signaling pathways which often results in changes in gene expression via networks of transcription factors (TFs). Mediator is a highly conserved co-regulator complex and an essential component of the transcriptional machinery in eukaryotes. Some Mediator subunits have been implicated in stress-responsive signaling pathways; however, much remains unknown regarding the role of plant Mediator in abiotic stress responses. Here, we use RNA-seq to analyze the transcriptional response of Arabidopsis thaliana to heat, cold and salt stress conditions. We identify a set of common abiotic stress regulons and describe the sequential and combinatorial nature of TFs involved in their transcriptional regulation. Furthermore, we identify stress-specific roles for the Mediator subunits MED9, MED16, MED18 and CDK8, and putative TFs connecting them to different stress signaling pathways. Our data also indicate different modes of action for subunits or modules of Mediator at the same gene loci, including a co-repressor function for MED16 prior to stress. These results illuminate a poorly understood but important player in the transcriptional response of plants to abiotic stress and identify target genes and mechanisms as a prelude to further biochemical characterization.
Organic solar cells (OSC) nowadays match their inorganic competitors in terms of current production but lag behind with regards to their open-circuit voltage loss and fill-factor, with state-of-the-art OSCs rarely displaying fill-factor of 80% and above. The fill-factor of transport-limited solar cells, including organic photovoltaic devices, is affected by material and device-specific parameters, whose combination is represented in terms of the established figures of merit, such as theta and alpha. Herein, it is demonstrated that these figures of merit are closely related to the long-range carrier drift and diffusion lengths. Further, a simple approach is presented to devise these characteristic lengths using steady-state photoconductance measurements. This yields a straightforward way of determining theta and alpha in complete cells and under operating conditions. This approach is applied to a variety of photovoltaic devices-including the high efficiency nonfullerene acceptor blends-and show that the diffusion length of the free carriers provides a good correlation with the fill-factor. It is, finally, concluded that most state-of-the-art organic solar cells exhibit a sufficiently large drift length to guarantee efficient charge extraction at short circuit, but that they still suffer from too small diffusion lengths of photogenerated carriers limiting their fill factor.
Energy system models are advancing rapidly. However, it is not clear whether models are becoming better, in the sense that they address the questions that decision-makers need to be answered to make well-informed decisions. Therefore, we investigate the gap between model improvements relevant from the perspective of modellers compared to what users of model results think models should address. Thus, we ask: What are the differences between energy model improvements as perceived by modellers, and the actual needs of users of model results? To answer this question, we conducted a literature review, 32 interviews, and an online survey. Our results show that user needs and ongoing improvements of energy system models align to a large degree so that future models are indeed likely to be better than current models. We also find mismatches between the needs of modellers and users, especially in the modelling of social, behavioural and political aspects, the trade-off between model complexity and understandability, and the ways that model results should be communicated. Our findings suggest that a better understanding of user needs and closer cooperation between modellers and users is imperative to truly improve models and unlock their full potential to support the transition towards climate neutrality in Europe.
Due to climate change the frequency and character of precipitation are changing as the hydrological cycle intensifies. With regards to snowfall, global warming has two opposing influences; increasing humidity enables intense snowfall, whereas higher temperatures decrease the likelihood of snowfall. Here we show an intensification of extreme snowfall across large areas of the Northern Hemisphere under future warming. This is robust across an ensemble of global climate models when they are bias-corrected with observational data. While mean daily snowfall decreases, both the 99th and the 99.9th percentiles of daily snowfall increase in many regions in the next decades, especially for Northern America and Asia. Additionally, the average intensity of snowfall events exceeding these percentiles as experienced historically increases in many regions. This is likely to pose a challenge to municipalities in mid to high latitudes. Overall, extreme snowfall events are likely to become an increasingly important impact of climate change in the next decades, even if they will become rarer, but not necessarily less intense, in the second half of the century.
In our fast-changing world, human-machine interfaces (HMIs) are of ever-increasing importance. Among the most ubiquitous examples are touchscreens that most people are familiar with from their smartphones. The quality of such an HMI can be improved by adding haptic feedback-an imitation of using mechanical buttons-to the touchscreen. Thin-film actuators on the basis of electro-mechanically active polymers (EAPs), with the electroactive material sandwiched between two compliant electrodes, offer a promising technology for haptic surfaces. In thin-film technology, the thickness and the number of stacked layers of the electroactive dielectric are key parameters for tuning a system. Therefore, we have experimentally investigated the influence of the thickness of a single EAP layer on the electrical and the electro-mechanical performance of the transducer. In order to achieve high electro-mechanical actuator outputs, we have employed relaxor-ferroelectric ter-fluoropolymers that can be screen-printed. By means of a model-based approach, we have also directly compared single- and multi-layer actuators, thus providing guidelines for optimized transducer configurations with respect to the system requirements of haptic applications for which the operation frequency is of particular importance.
Among various types of perovskite-based tandem solar cells (TSCs), all-perovskite TSCs are of particular attractiveness for building- and vehicle-integrated photovoltaics, or space energy areas as they can be fabricated on flexible and lightweight substrates with a very high power-to-weight ratio. However, the efficiency of flexible all-perovskite tandems is lagging far behind their rigid counterparts primarily due to the challenges in developing efficient wide-bandgap (WBG) perovskite solar cells on the flexible substrates as well as their low open-circuit voltage (V-OC). Here, it is reported that the use of self-assembled monolayers as hole-selective contact effectively suppresses the interfacial recombination and allows the subsequent uniform growth of a 1.77 eV WBG perovskite with superior optoelectronic quality. In addition, a postdeposition treatment with 2-thiopheneethylammonium chloride is employed to further suppress the bulk and interfacial recombination, boosting the V-OC of the WBG top cell to 1.29 V. Based on this, the first proof-of-concept four-terminal all-perovskite flexible TSC with a power conversion efficiency of 22.6% is presented. When integrating into two-terminal flexible tandems, 23.8% flexible all-perovskite TSCs with a superior V-OC of 2.1 V is achieved, which is on par with the V-OC reported on the 28% all-perovskite tandems grown on the rigid substrate.
Enzymes can support the synthesis or degradation of biomacromolecules in natural processes. Here, we demonstrate that enzymes can induce a macroscopic-directed movement of microstructured hydrogels following a mechanism that we call a "Jack-in-the-box" effect. The material's design is based on the formation of internal stresses induced by a deformation load on an architectured microscale, which are kinetically frozen by the generation of polyester locking domains, similar to a Jack-in-thebox toy (i.e., a compressed spring stabilized by a closed box lid). To induce the controlled macroscopic movement, the locking domains are equipped with enzyme-specific cleavable bonds (i.e., a box with a lock and key system). As a result of enzymatic reaction, a transformed shape is achieved by the release of internal stresses. There is an increase in entropy in combination with a swelling-supported stretching of polymer chains within the microarchitectured hydrogel (i.e., the encased clown pops-up with a pre-stressed movement when the box is unlocked). This utilization of an enzyme as a physiological stimulus may offer new approaches to create interactive and enzyme-specific materials for different applications such as an optical indicator of the enzyme's presence or actuators and sensors in biotechnology and in fermentation processes.
Objective We propose a data-driven method to detect temporal patterns of disease progression in high-dimensional claims data based on gradient boosting with stability selection. Materials and methods We identified patients with chronic obstructive pulmonary disease in a German health insurance claims database with 6.5 million individuals and divided them into a group of patients with the highest disease severity and a group of control patients with lower severity. We then used gradient boosting with stability selection to determine variables correlating with a chronic obstructive pulmonary disease diagnosis of highest severity and subsequently model the temporal progression of the disease using the selected variables. Results We identified a network of 20 diagnoses (e.g. respiratory failure), medications (e.g. anticholinergic drugs) and procedures associated with a subsequent chronic obstructive pulmonary disease diagnosis of highest severity. Furthermore, the network successfully captured temporal patterns, such as disease progressions from lower to higher severity grades. Discussion The temporal trajectories identified by our data-driven approach are compatible with existing knowledge about chronic obstructive pulmonary disease showing that the method can reliably select relevant variables in a high-dimensional context. Conclusion We provide a generalizable approach for the automatic detection of disease trajectories in claims data. This could help to diagnose diseases early, identify unknown risk factors and optimize treatment plans.
From an active labor market policy perspective, start-up subsidies for unemployed individuals are very effective in improving long-term labor market outcomes for participants. From a business perspective, however, the assessment of these public programs is less clear since they might attract individuals with low entrepreneurial abilities and produce businesses with low survival rates and little contribution to job creation, economic growth, and innovation. In this paper, we use a rich data set to compare participants of a German start-up subsidy program for unemployed individuals to a group of regular founders who started from non-unemployment and did not receive the subsidy. The data allows us to analyze their business performance up until 40 months after business formation. We find that formerly subsidized founders lag behind not only in survival and job creation, but especially also in innovation activities. The gaps in these business outcomes are relatively constant or even widening over time. Hence, we do not see any indication of catching up in the longer run. While the gap in survival can be entirely explained by initial differences in observable start-up characteristics, the gap in business development remains and seems to be the result of restricted access to capital as well as differential business strategies and dynamics. Considering these conflicting results for the assessment of the subsidy program from an ALMP and business perspective, policy makers need to carefully weigh the costs and benefits of such a strategy to find the right policy mix.
How insoluble inclusions and intersecting layers affect the leaching process within potash seams
(2021)
Potash seams are a valuable resource containing several economically interesting, but also highly soluble minerals. In the presence of water, uncontrolled leaching can occur, endangering subsurface mining operations. In the present study, the influence of insoluble inclusions and intersecting layers on leaching zone evolution was examined by means of a reactive transport model. For that purpose, a scenario analysis was carried out, considering different rock distributions within a carnallite-bearing potash seam. The results show that reaction-dominated systems are not affected by heterogeneities at all, whereas transport-dominated systems exhibit a faster advance in homogeneous rock compositions. In return, the ratio of permeated rock in vertical direction is higher in heterogeneous systems. Literature data indicate that most natural potash systems are transport-dominated. Accordingly, insoluble inclusions and intersecting layers can usually be seen as beneficial with regard to reducing hazard potential as long as the mechanical stability of leaching zones is maintained. Thereby, the distribution of insoluble areas is of minor impact unless an inclined, intersecting layer occurs that accelerates leaching zone growth in one direction. Moreover, it is found that the saturation dependency of dissolution rates increases the growth rate in the long term, and therefore must be considered in risk assessments.
The PNPLA3 reference single-nucleotide polymorphism rs738409 has been identified as a predisposing factor for nonalcoholic fatty liver disease. A simple method based on PCR and restriction fragment length polymorphism (RFLP) analysis had been published to detect the nonpathogenic allele PNPLA3 rs738409 variant. The presence of the pathogenic variant was deduced by the indigestibility of the corresponding PCR product with BtsCI recognizing the nonpathogenic allele. However, one cannot exclude that an enzymatic reaction does not occur for other, more trivial, reasons. For safe and secure detection of the pathogenic PNPLA3 rs738409, we have further developed the PCR-restriction fragment length polymorphism method by adding a second restriction enzyme digest, clearly identifying the correct PNPLA3 alleles and in particular the pathogenic variant. <br /> METHOD SUMMARY <br /> The method presented here represents an improved genetic diagnosis of the PNPLA3 rs738409 alleles based on conventional and inexpensive molecular biological methods. We used methodology based on PCR and restriction fragment length polymorphisms and clearly identified both described alleles by the use of two restriction enzymes. Digestion of individuals' specific PNPLA3 PCR fragments with both enzymes in independent reactions clearly showed the PNPLA3 rs738409 genotype.
Monitoring the response of volcanic CO2 emissions to changes in the Los Humeros hydrothermal system
(2021)
Carbon dioxide is the most abundant, non-condensable gas in volcanic systems, released into the atmosphere through either diffuse or advective fluid flow. The emission of substantial amounts of CO2 at Earth's surface is not only controlled by volcanic plumes during periods of eruptive activity or fumaroles, but also by soil degassing along permeable structures in the subsurface. Monitoring of these processes is of utmost importance for volcanic hazard analyses, and is also relevant for managing geothermal resources. Fluid-bearing faults are key elements of economic value for geothermal power generation. Here, we describe for the first time how sensitively and quickly natural gas emissions react to changes within a deep hydrothermal system due to geothermal fluid reinjection. For this purpose, we deployed an automated, multi-chamber CO2 flux monitoring system within the damage zone of a deep-rooted major normal fault in the Los Humeros Volcanic Complex (LHVC) in Mexico and recorded data over a period of five months. After removing the atmospheric effects on variations in CO2 flux, we calculated correlation coefficients between residual CO2 emissions and reinjection rates, identifying an inverse correlation of rho = - 0.51 to - 0.66. Our results indicate that gas emissions respond to changes in reinjection rates within 24 h, proving an active hydraulic communication between the hydrothermal system and Earth's surface. This finding is a promising indication not only for geothermal reservoir monitoring but also for advanced long-term volcanic risk analysis. Response times allow for estimation of fluid migration velocities, which is a key constraint for conceptual and numerical modelling of fluid flow in fracture-dominated systems.
Trade-off for survival
(2022)
The environmental micmbiota is increasingly exposed to chemical pollution. While the emergence of multi-resistant pathogens is recognized as a global challenge, our understanding of antimicrobial resistance (AMR) development from native microbiomes and the risks associated with chemical exposure is limited. By implementing a lichen as a bioindicator organism and model for a native microbiome, we systematically examined responses towards antimicrobials (colistin, tetracycline, glyphosate, and alkylpyrazine). Despite an unexpectedly high resilience, we identified potential evolutionary consequences of chemical exposure in terms of composition and functioning of native bacterial communities. Major shifts in bacterial composition were observed due to replacement of naturally abundant taxa; e.g. Chthoniobacterales by Pseudomonadales. A general response, which comprised activation of intrinsic resistance and parallel reduction of metabolic activity at RNA and protein levels was deciphered by a multi-omics approach. Targeted analyses of key taxa based on metagenome-assembled genomes reflected these responses but also revealed diversified strategies of their players. Chemical-specific responses were also observed, e.g., glyphosate enriched bacterial r-strategists and activated distinct ARGs. Our work demonstrates that the high resilience of the native micmbiota toward antimicrobial exposure is not only explained by the presence of antibiotic resistance genes but also adapted metabolic activity as a trade-off for survival. Moreover, our results highlight the importance of native microbiomes as important but so far neglected AMR reservoirs. We expect that this phenomenon is representative for a wide range of environmental microbiota exposed to chemicals that potentially contribute to the emergence of antibiotic-resistant bacteria from natural environments.
In this report we describe Cy5-dUTP labelling of recombinase-polymerase-amplification (RPA) products directly during the amplification process for the first time. Nucleic acid amplification techniques, especially polymerase-chain-reaction as well as various isothermal amplification methods such as RPA, becomes a promising tool in the detection of pathogens and target specific genes. Actually, RPA even provides more advantages. This isothermal method got popular in point of care diagnostics because of its speed and sensitivity but requires pre-labelled primer or probes for a following detection of the amplicons. To overcome this disadvantages, we performed an labelling of RPA-amplicons with Cy5-dUTP without the need of pre-labelled primers. The amplification results of various multiple antibiotic resistance genes indicating great potential as a flexible and promising tool with high specific and sensitive detection capabilities of the target genes. After the determination of an appropriate rate of 1% Cy5-dUTP and 99% unlabelled dTTP we were able to detect the bla(CTX-M15) gene in less than 1.6E-03 ng genomic DNA corresponding to approximately 200 cfu of Escherichia coli cells in only 40 min amplification time.
Fibrous shape-memory polymer (SMP) scaffolds were investigated considering the fiber as basic microstructural feature. By reduction of the fiber diameter in randomly oriented electrospun polyetherurethane (PEU) meshes from the micro-to the nano-scale, we observed changes in the molecular orientation within the fibers and its impact on the structural and shape-memory performance. It was assumed that a spatial restriction by reduction of the fiber diameter increases molecular orientation along the orientation of the fiber. The stress-strain relation of random PEU scaffolds is initially determined by the 3D arrangement of the fibers and thus is independent of the molecular orientation. Increasing the molecular orientation with decreasing single fiber diameter in scaffolds composed of randomly arranged fibers did not alter the initial stiffness and peak stress but strongly influenced the elongation at break and the stress increase above the Yield point. Reduction of the single fiber diameter also distinctly improved the shape-memory performance of the scaffolds. Fibers with nanoscale diameters (< 100 nm) possessed an almost complete shape recovery, high recovery stresses and fast relaxation kinetics, while the shape fixity was found to decrease with decreasing fiber diameter. Hence, the fiber diameter is a relevant design parameter for SMP.
Many knowledge representation tasks involve trees or similar structures as abstract datatypes. However, devising compact and efficient declarative representations of such structural properties is non-obvious and can be challenging indeed. In this article, we take a number of acyclicity properties into consideration and investigate various logic-based approaches to encode them. We use answer set programming as the primary representation language but also consider mappings to related formalisms, such as propositional logic, difference logic and linear programming. We study the compactness of encodings and the resulting computational performance on benchmarks involving acyclic or tree structures.
Ribosome biogenesis is tightly associated to plant metabolism due to the usage of ribosomes in the synthesis of proteins necessary to drive metabolic pathways. Given the central role of ribosome biogenesis in cell physiology, it is important to characterize the impact of different components involved in this process on plant metabolism. Double mutants of the Arabidopsis thaliana cytosolic 60S maturation factors REIL1 and REIL2 do not resume growth after shift to moderate 10 degrees C chilling conditions. To gain mechanistic insights into the metabolic effects of this ribosome biogenesis defect on metabolism, we developed TC-iReMet2, a constraint-based modelling approach that integrates relative metabolomics and transcriptomics time-course data to predict differential fluxes on a genome-scale level. We employed TC-iReMet2 with metabolomics and transcriptomics data from the Arabidopsis Columbia 0 wild type and the reil1-1 reil2-1 double mutant before and after cold shift. We identified reactions and pathways that are highly altered in a mutant relative to the wild type. These pathways include the Calvin-Benson cycle, photorespiration, gluconeogenesis, and glycolysis. Our findings also indicated differential NAD(P)/NAD(P)H ratios after cold shift. TC-iReMet2 allows for mechanistic hypothesis generation and interpretation of system biology experiments related to metabolic fluxes on a genome-scale level.
Today, firms pursuing a pioneering strategy are often engaged in supply chain relationships to benefit from external resources and to improve their innovation. However, this effort can be impeded by power asymmetries in such relationships and especially by the execution of coercive power by their partner firm. Contracts could potentially reduce this risk of opportunistic behavior. Our survey study on 778 small to medium-sized enterprises in the European packaging and medical equipment industries examines how coercive power of the partner and the contractual arrangement between firms moderate the pioneering strategy's innovation outcomes in the short and long run. Our results confirm the negative effect of coercive power on innovation performance in both the short and long term. However, the compensating effect of rather complete contracts differs temporally. Whereas, contract completeness protects against higher dependence at the beginning of the collaboration, their effect diminishes over time. In contrast, rather incomplete contracts enhance the innovation performance in the long term, possibly complemented with trust.
Large-scale biochemical models are of increasing sizes due to the consideration of interacting organisms and tissues. Model reduction approaches that preserve the flux phenotypes can simplify the analysis and predictions of steady-state metabolic phenotypes. However, existing approaches either restrict functionality of reduced models or do not lead to significant decreases in the number of modelled metabolites. Here, we introduce an approach for model reduction based on the structural property of balancing of complexes that preserves the steady-state fluxes supported by the network and can be efficiently determined at genome scale. Using two large-scale mass-action kinetic models of Escherichia coli, we show that our approach results in a substantial reduction of 99% of metabolites. Applications to genome-scale metabolic models across kingdoms of life result in up to 55% and 85% reduction in the number of metabolites when arbitrary and mass-action kinetics is assumed, respectively. We also show that predictions of the specific growth rate from the reduced models match those based on the original models. Since steady-state flux phenotypes from the original model are preserved in the reduced, the approach paves the way for analysing other metabolic phenotypes in large-scale biochemical networks.
Reaction lumping in metabolic networks for application with thermodynamic metabolic flux analysis
(2021)
Thermodynamic metabolic flux analysis (TMFA) can narrow down the space of steady-state flux distributions, but requires knowledge of the standard Gibbs free energy for the modelled reactions. The latter are often not available due to unknown Gibbs free energy change of formation ,Delta fG0, of metabolites. To optimize the usage of data on thermodynamics in constraining a model, reaction lumping has been proposed to eliminate metabolites with unknown Delta fG0. However, the lumping procedure has not been formalized nor implemented for systematic identification of lumped reactions. Here, we propose, implement, and test a combined procedure for reaction lumping, applicable to genome-scale metabolic models. It is based on identification of groups of metabolites with unknown Delta fG0 whose elimination can be conducted independently of the others via: (1) group implementation, aiming to eliminate an entire such group, and, if this is infeasible, (2) a sequential implementation to ensure that a maximal number of metabolites with unknown Delta fG0 are eliminated. Our comparative analysis with genome-scale metabolic models of Escherichia coli, Bacillus subtilis, and Homo sapiens shows that the combined procedure provides an efficient means for systematic identification of lumped reactions. We also demonstrate that TMFA applied to models with reactions lumped according to the proposed procedure lead to more precise predictions in comparison to the original models. The provided implementation thus ensures the reproducibility of the findings and their application with standard TMFA.
When inferring on the magnitude of future heat-related mortality due to climate change, human adaptation to heat should be accounted for. We model long-term changes in minimum mortality temperatures (MMT), a well-established metric denoting the lowest risk of heat-related mortality, as a function of climate change and socio-economic progress across 3820 cities. Depending on the combination of climate trajectories and socio-economic pathways evaluated, by 2100 the risk to human health is expected to decline in 60% to 80% of the cities against contemporary conditions. This is caused by an average global increase in MMTs driven by long-term human acclimatisation to future climatic conditions and economic development of countries. While our adaptation model suggests that negative effects on health from global warming can broadly be kept in check, the trade-offs are highly contingent to the scenario path and location-specific. For high-forcing climate scenarios (e.g. RCP8.5) the maintenance of uninterrupted high economic growth by 2100 is a hard requirement to increase MMTs and level-off the negative health effects from additional scenario-driven heat exposure. Choosing a 2 degrees C-compatible climate trajectory alleviates the dependence on fast growth, leaving room for a sustainable economy, and leads to higher reductions of mortality risk.
Trade-offs are inherent to biochemical networks governing diverse cellular functions, from gene expression to metabolism. Yet, trade-offs between fluxes of biochemical reactions in a metabolic network have not been formally studied. Here, we introduce the concept of absolute flux trade-offs and devise a constraint-based approach, termed FluTO, to identify and enumerate flux trade-offs in a given genome-scale metabolic network. By employing the metabolic networks of Escherichia coli and Saccharomyces cerevisiae, we demonstrate that the flux trade-offs are specific to carbon sources provided but that reactions involved in the cofactor and prosthetic group biosynthesis are present in trade-offs across all carbon sources supporting growth. We also show that absolute flux trade-offs depend on the biomass reaction used to model the growth of Arabidopsis thaliana under different carbon and nitrogen conditions. The identified flux trade-offs reflect the tight coupling between nitrogen, carbon, and sulphur metabolisms in leaves of C-3 plants. Altogether, FluTO provides the means to explore the space of alternative metabolic routes reflecting the constraints imposed by inherent flux trade-offs in large-scale metabolic networks.
The use of acidic ionic liquids and solids as electrolytes in fuel cells is an emerging field due to their efficient proton conductivity and good thermal stability. Despite multiple reports describing conducting properties of acidic ILs, little is known on the charge-transport mechanism in the vicinity of liquid-glass transition and the structural factors governing the proton hopping. To address these issues, we studied two acidic imidazolium-based ILs with the same cation, however, different anions-bulk tosylate vs small methanesulfonate. High-pressure dielectric studies of anhydrous and water-saturated materials performed in the close vicinity of T-g have revealed significant differences in the charge-transport mechanism in these two systems being undetectable at ambient conditions. Thereby, we demonstrated the effect of molecular architecture on proton hopping, being crucial in the potential electrochemical applications of acidic ILs.
In this work, which is part of a larger research program, a framework called "virtual data fusion" was developed to provide an automated and consistent crack detection method that allows for the cross-comparison of results from large quantities of X-ray computed tomography (CT) data. A partial implementation of this method in a custom program was developed for use in research focused on crack quantification in alkali-silica reaction (ASR)-sensitive concrete aggregates. During the CT image processing, a series of image analyses tailored for detecting specific, individual crack-like characteristics were completed. The results of these analyses were then "fused" in order to identify crack-like objects within the images with much higher accuracy than that yielded by any individual image analysis procedure. The results of this strategy demonstrated the success of the program in effectively identifying crack-like structures and quantifying characteristics, such as surface area and volume. The results demonstrated that the source of aggregate has a very significant impact on the amount of internal cracking, even when the mineralogical characteristics remain very similar. River gravels, for instance, were found to contain significantly higher levels of internal cracking than quarried stone aggregates of the same mineralogical type.
In recent years, the search for more efficient and environmentally friendly materials to be employed in the next generation of thin film solar cell devices has seen a shift towards hybrid halide perovskites and chalcogenide materials crystallising in the kesterite crystal structure. Prime examples for the latter are Cu2ZnSnS4, Cu2ZnSnSe4, and their solid solution Cu2ZnSn(SxSe1-x)(4), where actual devices already demonstrated power conversion efficiencies of about 13 %. However, in their naturally occurring kesterite crystal structure, the so-called Cu-Zn disorder plays an important role and impacts the structural, electronic, and optical properties. To understand the influence of Cu-Zn disorder, we perform first-principles calculations based on density functional theory combined with special quasirandom structures to accurately model the cation disorder. Since the electronic band gaps and derived optical properties are severely underestimated by (semi)local exchange and correlation functionals, supplementary hybrid functional calculations have been performed. Concerning the latter, we additionally employ a recently devised technique to speed up structural relaxations for hybrid functional calculations. Our calculations show that the Cu-Zn disorder leads to a slight increase in the unit cell volume compared to the conventional kesterite structure showing full cation order, and that the band gap gets reduced by about 0.2 eV, which is in very good agreement with earlier experimental and theoretical findings. Our detailed results on structural, electronic, and optical properties will be discussed with respect to available experimental data, and will provide further insights into the atomistic origin of the disorder-induced band gap lowering in these promising kesterite type materials.
Procrastination is a self-regulatory problem of voluntarily and destructively delaying intended and necessary or personally important tasks. Previous studies showed that procrastination is associated with executive dysfunctions that seem to be particularly strong in punishing contexts. In the present event-related potential (ERP) study a monetary version of the parametric Go/No-Go task was performed by high and low academic procrastinators to verify the influence of motivational context (reward vs. punishment expectation) and task difficulty (easy vs. hard) on procrastination-related executive dysfunctions. The results revealed increased post-error slowing along with reduced P300 and error-related negativity (ERN) amplitudes in high (vs. low) procrastination participants-effects that indicate impaired attention and error-related processing in this group. This pattern of results did not differ as a function of task difficulty and motivation condition. However, when the task got more difficult executive attention deficits became even more apparent at the behavioral level in high procrastinators, as indexed by increased reaction time variability. The findings substantiate prior preliminary evidence that procrastinators show difficulties in certain aspects of executive functioning (in attention and error processing) during execution of task-relevant behavior, which may be more apparent in highly demanding situations.
Gene expression data is analyzed to identify biomarkers, e.g. relevant genes, which serve for diagnostic, predictive, or prognostic use. Traditional approaches for biomarker detection select distinctive features from the data based exclusively on the signals therein, facing multiple shortcomings in regards to overfitting, biomarker robustness, and actual biological relevance. Prior knowledge approaches are expected to address these issues by incorporating prior biological knowledge, e.g. on gene-disease associations, into the actual analysis. However, prior knowledge approaches are currently not widely applied in practice because they are often use-case specific and seldom applicable in a different scope. This leads to a lack of comparability of prior knowledge approaches, which in turn makes it currently impossible to assess their effectiveness in a broader context.
Our work addresses the aforementioned issues with three contributions. Our first contribution provides formal definitions for both prior knowledge and the flexible integration thereof into the feature selection process. Central to these concepts is the automatic retrieval of prior knowledge from online knowledge bases, which allows for streamlining the retrieval process and agreeing on a uniform definition for prior knowledge. We subsequently describe novel and generalized prior knowledge approaches that are flexible regarding the used prior knowledge and applicable to varying use case domains. Our second contribution is the benchmarking platform Comprior. Comprior applies the aforementioned concepts in practice and allows for flexibly setting up comprehensive benchmarking studies for examining the performance of existing and novel prior knowledge approaches. It streamlines the retrieval of prior knowledge and allows for combining it with prior knowledge approaches. Comprior demonstrates the practical applicability of our concepts and further fosters the overall development and comparability of prior knowledge approaches. Our third contribution is a comprehensive case study on the effectiveness of prior knowledge approaches. For that, we used Comprior and tested a broad range of both traditional and prior knowledge approaches in combination with multiple knowledge bases on data sets from multiple disease domains. Ultimately, our case study constitutes a thorough assessment of a) the suitability of selected knowledge bases for integration, b) the impact of prior knowledge being applied at different integration levels, and c) the improvements in terms of classification performance, biological relevance, and overall robustness.
In summary, our contributions demonstrate that generalized concepts for prior knowledge and a streamlined retrieval process improve the applicability of prior knowledge approaches. Results from our case study show that the integration of prior knowledge positively affects biomarker results, particularly regarding their robustness. Our findings provide the first in-depth insights on the effectiveness of prior knowledge approaches and build a valuable foundation for future research.
The mixture of ammonium nitrate (AN) prills and fuel oil (FO), usually referred to as ANFO, is extensively used in the mining industry as a bulk explosive. One of the major performance predictors of ANFO mixtures is the fuel oil retention, which is itself governed by the complex pore structure of the AN prills. In this study, we present how X-ray computed tomography (XCT), and the associated advanced data processing workflow, can be used to fully characterise the structure and morphology of AN prills. We show that structural parameters such as volume fraction of the different phases and morphological parameters such as specific surface area and shape factor can be reliably extracted from the XCT data, and that there is a good agreement with the measured oil retention values. Importantly, oil retention measurements (qualifying the efficiency of ANFO as explosives) correlate well with the specific surface area determined by XCT. XCT can therefore be employed non-destructively; it can accurately evaluate and characterise porosity in ammonium nitrate prills, and even predict their efficiency.
The effect of two types of scanning strategies on the grain structure and build-up of Residual Stress (RS) has been investigated in an as-built IN718 alloy produced by Laser Powder Bed Fusion (LPBF). The RS state has been investigated by X-ray diffraction techniques. The microstructural characterization was performed principally by Electron Backscatter Diffraction (EBSD), where the application of a post-measurement refinement technique enables small misorientations (< 2 degrees) to be resolved. Kernel average misorientation (KAM) distributions indicate that preferably oriented columnar grains contain higher levels of misorientation, when compared to elongated grains with lower texture. The KAM distributions combined with X-ray diffraction stress maps infer that the increased misorientation is induced via plastic deformation driven by the thermal stresses, acting to self-relieve stress. The possibility of obtaining lower RS states in the build direction as a consequence of the influence of the microstructure should be considered when envisaging scanning strategies aimed at the mitigation of RS.
Adverse environmental conditions are detrimental to plant growth and development. Acclimation to abiotic stress conditions involves activation of signaling pathways which often results in changes in gene expression via networks of transcription factors (TFs). Mediator is a highly conserved co-regulator complex and an essential component of the transcriptional machinery in eukaryotes. Some Mediator subunits have been implicated in stress-responsive signaling pathways; however, much remains unknown regarding the role of plant Mediator in abiotic stress responses. Here, we use RNA-seq to analyze the transcriptional response of Arabidopsis thaliana to heat, cold and salt stress conditions. We identify a set of common abiotic stress regulons and describe the sequential and combinatorial nature of TFs involved in their transcriptional regulation. Furthermore, we identify stress-specific roles for the Mediator subunits MED9, MED16, MED18 and CDK8, and putative TFs connecting them to different stress signaling pathways. Our data also indicate different modes of action for subunits or modules of Mediator at the same gene loci, including a co-repressor function for MED16 prior to stress. These results illuminate a poorly understood but important player in the transcriptional response of plants to abiotic stress and identify target genes and mechanisms as a prelude to further biochemical characterization.
The layered dichalcogenide MoS2 is relevant for electrochemical Li adsorption/intercalation, in the course of which the material undergoes a concomitant structural phase transition from semiconducting 2H-MoS2 to metallic 1T-LixMoS2. With the core hole clock approach at the S L1 X-ray absorption edge we quantify the ultrafast directional charge transfer of excited S3p electrons in-plane () and out-of-plane (perpendicular to) for 2H-MoS2 as tau 2H,=0.38 +/- 0.08 fs and tau 2H,perpendicular to =0.33 +/- 0.06 fs and for 1T-LixMoS2 as tau 1T,=0.32 +/- 0.12 fs and tau 1T,perpendicular to =0.09 +/- 0.07 fs. The isotropic charge delocalization of S3p electrons in the semiconducting 2H phase within the S-Mo-S sheets is assigned to the specific symmetry of the Mo-S bonding arrangement. Formation of 1T-LixMoS2 by lithiation accelerates the in-plane charge transfer by a factor of similar to 1.2 due to electron injection to the Mo-S covalent bonds and concomitant structural repositioning of S atoms within the S-Mo-S sheets. For excitation into out-of-plane orbitals, an accelerated charge transfer by a factor of similar to 3.7 upon lithiation occurs due to S-Li coupling.
In this paper, the phenomenon of light-driven diffusioosmotic (DO) long-range attractive and repulsive interactions between micro-sized objects trapped near a solid wall is investigated. The range of the DO flow extends several times the size of microparticles and can be adjusted to point towards or away from the particle by varying irradiation parameters such as intensity or wavelength of light. The "fuel" of the light-driven DO flow is a photosensitive surfactant which can be photo-isomerized between trans and cis-states. The trans-isomer tends to accumulate at the interface, while the cis-isomer prefers to stay in solution. In combination with a dissimilar photo-isomerization rate at the interface and in bulk, this yields a concentration gradient of the isomers around single particles resulting in local light-driven diffusioosmotic (l-LDDO) flow. Here, the extended analysis of the l-LDDO flow as a function of irradiation parameters by introducing time-dependent development of the concentration excess of isomers near the particle surface is presented. It is also demonstrated that the l-LDDO can be generated at any solid/liquid interface being more pronounced in the case of strongly absorbing material. This phenomenon has plenty of potential applications since it makes any type of surface act as a micropump.
Signaling pathways in biological systems rely on specific interactions between multiple biomolecules. Fluorescence fluctuation spectroscopy provides a powerful toolbox to quantify such interactions directly in living cells. Cross-correlation analysis of spectrally separated fluctuations provides information about intermolecular interactions but is usually limited to two fluorophore species. Here, we present scanning fluorescence spectral correlation spectroscopy (SFSCS), a versatile approach that can be implemented on commercial confocal microscopes, allowing the investigation of interactions between multiple protein species at the plasma membrane. We demonstrate that SFSCS enables cross-talk-free cross-correlation, diffusion, and oligomerization analysis of up to four protein species labeled with strongly overlapping fluorophores. As an example, we investigate the interactions of influenza A virus (IAV) matrix protein 2 with two cellular host factors simultaneously. We furthermore apply raster spectral image correlation spectroscopy for the simultaneous analysis of up to four species and determine the stoichiometry of ternary IAV polymerase complexes in the cell nucleus.