@article{BerryDammhahnBlaum2023, author = {Berry, Paul E. and Dammhahn, Melanie and Blaum, Niels}, title = {Keeping cool on hot days}, series = {Frontiers in ecology and evolution}, volume = {11}, journal = {Frontiers in ecology and evolution}, publisher = {Frontiers Media}, address = {Lausanne}, issn = {2296-701X}, doi = {10.3389/fevo.2023.1172303}, pages = {13}, year = {2023}, abstract = {Long-lived organisms are likely to respond to a rapidly changing climate with behavioral flexibility. Animals inhabiting the arid parts of southern Africa face a particularly rapid rise in temperature which in combination with food and water scarcity places substantial constraints on the ability of animals to tolerate heat. We investigated how three species of African antelope-springbok Antidorcas marsupialis, kudu Tragelaphus strepsiceros and eland T. oryx-differing in body size, habitat preference and movement ecology, change their activity in response to extreme heat in an arid savanna. Serving as a proxy for activity, dynamic body acceleration data recorded every five minutes were analyzed for seven to eight individuals per species for the three hottest months of the year. Activity responses to heat during the hottest time of day (the afternoons) were investigated and diel activity patterns were compared between hot and cool days. Springbok, which prefer open habitat, are highly mobile and the smallest of the species studied, showed the greatest decrease in activity with rising temperature. Furthermore, springbok showed reduced mean activity over the 24 h cycle on hot days compared to cool days. Large-bodied eland seemed less affected by afternoon heat than springbok. While eland also reduced diurnal activity on hot days compared to cool days, they compensated for this by increasing nocturnal activity, possibly because their predation risk is lower. Kudu, which are comparatively sedentary and typically occupy shady habitat, seemed least affected during the hottest time of day and showed no appreciable difference in diel activity patterns between hot and cool days. The interplay between habitat preference, body size, movement patterns, and other factors seems complex and even sub-lethal levels of heat stress have been shown to impact an animal's long-term survival and reproduction. Thus, differing heat tolerances among species could result in a shift in the composition of African herbivore communities as temperatures continue to rise, with significant implications for economically important wildlife-based land use and conservation.}, language = {en} } @article{StieglerPahlGuillenetal.2023, author = {Stiegler, Jonas and Pahl, Janice and Guillen, Rafael Arce and Ullmann, Wiebke and Blaum, Niels}, title = {The heat is on}, series = {Frontiers in Ecology and Evolution}, volume = {11}, journal = {Frontiers in Ecology and Evolution}, publisher = {Frontiers Media}, address = {Lausanne}, issn = {2296-701X}, doi = {10.3389/fevo.2023.1193861}, pages = {10}, year = {2023}, abstract = {Climate conditions severely impact the activity and, consequently, the fitness of wildlife species across the globe. Wildlife can respond to new climatic conditions, but the pace of human-induced change limits opportunities for adaptation or migration. Thus, how these changes affect behavior, movement patterns, and activity levels remains unclear. In this study, we investigate how extreme weather conditions affect the activity of European hares (Lepus europaeus) during their peak reproduction period. When hares must additionally invest energy in mating, prevailing against competitors, or lactating, we investigated their sensitivities to rising temperatures, wind speed, and humidity. To quantify their activity, we used the overall dynamic body acceleration (ODBA) calculated from tri-axial acceleration measurements of 33 GPS-collared hares. Our analysis revealed that temperature, humidity, and wind speed are important in explaining changes in activity, with a strong response for high temperatures above 25 \& DEG;C and the highest change in activity during temperature extremes of over 35 \& DEG;C during their inactive period. Further, we found a non-linear relationship between temperature and activity and an interaction of activity changes between day and night. Activity increased at higher temperatures during the inactive period (day) and decreased during the active period (night). This decrease was strongest during hot tropical nights. At a stage of life when mammals such as hares must substantially invest in reproduction, the sensitivity of females to extreme temperatures was particularly pronounced. Similarly, both sexes increased their activity at high humidity levels during the day and low wind speeds, irrespective of the time of day, while the effect of humidity was stronger for males. Our findings highlight the importance of understanding the complex relationships between extreme weather conditions and mammal behavior, critical for conservation and management. With ongoing climate change, extreme weather events such as heat waves and heavy rainfall are predicted to occur more often and last longer. These events will directly impact the fitness of hares and other wildlife species and hence the population dynamics of already declining populations across Europe.}, language = {en} } @article{CohenHershcovitchTarazetal.2023, author = {Cohen, Sarel and Hershcovitch, Moshik and Taraz, Martin and Kissig, Otto and Issac, Davis and Wood, Andrew and Waddington, Daniel and Chin, Peter and Friedrich, Tobias}, title = {Improved and optimized drug repurposing for the SARS-CoV-2 pandemic}, series = {PLoS one}, volume = {18}, journal = {PLoS one}, number = {3}, publisher = {PLoS}, address = {San Fransisco}, issn = {1932-6203}, doi = {10.1371/journal.pone.0266572}, pages = {13}, year = {2023}, abstract = {The active global SARS-CoV-2 pandemic caused more than 426 million cases and 5.8 million deaths worldwide. The development of completely new drugs for such a novel disease is a challenging, time intensive process. Despite researchers around the world working on this task, no effective treatments have been developed yet. This emphasizes the importance of drug repurposing, where treatments are found among existing drugs that are meant for different diseases. A common approach to this is based on knowledge graphs, that condense relationships between entities like drugs, diseases and genes. Graph neural networks (GNNs) can then be used for the task at hand by predicting links in such knowledge graphs. Expanding on state-of-the-art GNN research, Doshi et al. recently developed the Dr-COVID model. We further extend their work using additional output interpretation strategies. The best aggregation strategy derives a top-100 ranking of 8,070 candidate drugs, 32 of which are currently being tested in COVID-19-related clinical trials. Moreover, we present an alternative application for the model, the generation of additional candidates based on a given pre-selection of drug candidates using collaborative filtering. In addition, we improved the implementation of the Dr-COVID model by significantly shortening the inference and pre-processing time by exploiting data-parallelism. As drug repurposing is a task that requires high computation and memory resources, we further accelerate the post-processing phase using a new emerging hardware-we propose a new approach to leverage the use of high-capacity Non-Volatile Memory for aggregate drug ranking.}, language = {en} } @article{KappattanavarHeckerMoontahaetal.2023, author = {Kappattanavar, Arpita Mallikarjuna and Hecker, Pascal and Moontaha, Sidratul and Steckhan, Nico and Arnrich, Bert}, title = {Food choices after cognitive load}, series = {Sensors}, volume = {23}, journal = {Sensors}, number = {14}, publisher = {MDPI}, address = {Basel}, issn = {1424-8220}, doi = {10.3390/s23146597}, pages = {22}, year = {2023}, abstract = {Psychology and nutritional science research has highlighted the impact of negative emotions and cognitive load on calorie consumption behaviour using subjective questionnaires. Isolated studies in other domains objectively assess cognitive load without considering its effects on eating behaviour. This study aims to explore the potential for developing an integrated eating behaviour assistant system that incorporates cognitive load factors. Two experimental sessions were conducted using custom-developed experimentation software to induce different stimuli. During these sessions, we collected 30 h of physiological, food consumption, and affective states questionnaires data to automatically detect cognitive load and analyse its effect on food choice. Utilising grid search optimisation and leave-one-subject-out cross-validation, a support vector machine model achieved a mean classification accuracy of 85.12\% for the two cognitive load tasks using eight relevant features. Statistical analysis was performed on calorie consumption and questionnaire data. Furthermore, 75\% of the subjects with higher negative affect significantly increased consumption of specific foods after high-cognitive-load tasks. These findings offer insights into the intricate relationship between cognitive load, affective states, and food choice, paving the way for an eating behaviour assistant system to manage food choices during cognitive load. Future research should enhance system capabilities and explore real-world applications.}, language = {en} } @article{GarrelsKhodabakhshRenardetal.2023, author = {Garrels, Tim and Khodabakhsh, Athar and Renard, Bernhard Y. and Baum, Katharina}, title = {LazyFox: fast and parallelized overlapping community detection in large graphs}, series = {PEERJ Computer Science}, volume = {9}, journal = {PEERJ Computer Science}, publisher = {PeerJ Inc.}, address = {London}, issn = {2376-5992}, doi = {10.7717/peerj-cs.1291}, pages = {30}, year = {2023}, abstract = {The detection of communities in graph datasets provides insight about a graph's underlying structure and is an important tool for various domains such as social sciences, marketing, traffic forecast, and drug discovery. While most existing algorithms provide fast approaches for community detection, their results usually contain strictly separated communities. However, most datasets would semantically allow for or even require overlapping communities that can only be determined at much higher computational cost. We build on an efficient algorithm, FOX, that detects such overlapping communities. FOX measures the closeness of a node to a community by approximating the count of triangles which that node forms with that community. We propose LAZYFOX, a multi-threaded adaptation of the FOX algorithm, which provides even faster detection without an impact on community quality. This allows for the analyses of significantly larger and more complex datasets. LAZYFOX enables overlapping community detection on complex graph datasets with millions of nodes and billions of edges in days instead of weeks. As part of this work, LAZYFOX's implementation was published and is available as a tool under an MIT licence at https://github.com/TimGarrels/LazyFox.}, language = {en} } @article{GaertnerSchneiderArnrichetal.2023, author = {G{\"a}rtner, Thomas and Schneider, Juliana and Arnrich, Bert and Konigorski, Stefan}, title = {Comparison of Bayesian Networks, G-estimation and linear models to estimate causal treatment effects in aggregated N-of-1 trials with carry-over effects}, series = {BMC Medical Research Methodology}, volume = {23}, journal = {BMC Medical Research Methodology}, number = {1}, publisher = {BMC}, address = {London}, issn = {1471-2288}, doi = {10.1186/s12874-023-02012-5}, pages = {12}, year = {2023}, abstract = {Background The aggregation of a series of N-of-1 trials presents an innovative and efficient study design, as an alternative to traditional randomized clinical trials. Challenges for the statistical analysis arise when there is carry-over or complex dependencies of the treatment effect of interest. Methods In this study, we evaluate and compare methods for the analysis of aggregated N-of-1 trials in different scenarios with carry-over and complex dependencies of treatment effects on covariates. For this, we simulate data of a series of N-of-1 trials for Chronic Nonspecific Low Back Pain based on assumed causal relationships parameterized by directed acyclic graphs. In addition to existing statistical methods such as regression models, Bayesian Networks, and G-estimation, we introduce a carry-over adjusted parametric model (COAPM). Results The results show that all evaluated existing models have a good performance when there is no carry-over and no treatment dependence. When there is carry-over, COAPM yields unbiased and more efficient estimates while all other methods show some bias in the estimation. When there is known treatment dependence, all approaches that are capable to model it yield unbiased estimates. Finally, the efficiency of all methods decreases slightly when there are missing values, and the bias in the estimates can also increase. Conclusions This study presents a systematic evaluation of existing and novel approaches for the statistical analysis of a series of N-of-1 trials. We derive practical recommendations which methods may be best in which scenarios.}, language = {en} } @article{MoontahaSchumannArnrich2023, author = {Moontaha, Sidratul and Schumann, Franziska Elisabeth Friederike and Arnrich, Bert}, title = {Online learning for wearable EEG-Based emotion classification}, series = {Sensors}, volume = {23}, journal = {Sensors}, number = {5}, publisher = {MDPI}, address = {Basel}, issn = {1424-8220}, doi = {10.3390/s23052387}, pages = {23}, year = {2023}, abstract = {Giving emotional intelligence to machines can facilitate the early detection and prediction of mental diseases and symptoms. Electroencephalography (EEG)-based emotion recognition is widely applied because it measures electrical correlates directly from the brain rather than indirect measurement of other physiological responses initiated by the brain. Therefore, we used non-invasive and portable EEG sensors to develop a real-time emotion classification pipeline. The pipeline trains different binary classifiers for Valence and Arousal dimensions from an incoming EEG data stream achieving a 23.9\% (Arousal) and 25.8\% (Valence) higher F1-Score on the state-of-art AMIGOS dataset than previous work. Afterward, the pipeline was applied to the curated dataset from 15 participants using two consumer-grade EEG devices while watching 16 short emotional videos in a controlled environment. Mean F1-Scores of 87\% (Arousal) and 82\% (Valence) were achieved for an immediate label setting. Additionally, the pipeline proved to be fast enough to achieve predictions in real-time in a live scenario with delayed labels while continuously being updated. The significant discrepancy from the readily available labels on the classification scores leads to future work to include more data. Thereafter, the pipeline is ready to be used for real-time applications of emotion classification.}, language = {en} } @article{BoschDeCesareDemskeetal.2023, author = {Bosch, Sina and De Cesare, Ilaria and Demske, Ulrike and Felser, Claudia}, title = {Correction zu: Word-order variation and coherence in German infinitival complementation. - (The journal of comparative Germanic linguistics. - 26 (2023) 1) . - https://doi.org/10.1007/s10828-023-09140-8}, series = {The journal of comparative Germanic linguistics}, volume = {26}, journal = {The journal of comparative Germanic linguistics}, number = {1}, publisher = {Springer}, address = {New York}, issn = {1383-4924}, doi = {10.1007/s10828-023-09143-5}, pages = {2}, year = {2023}, language = {en} } @article{BereswillGatzMillerSuetal.2023, author = {Bereswill, Sarah and Gatz-Miller, Hannah and Su, Danyang and T{\"o}tzke, Christian and Kardjilov, Nikolay and Oswald, Sascha and Mayer, Klaus Ulrich}, title = {Coupling non-invasive imaging and reactive transport modeling to investigate water and oxygen dynamics in the root zone}, series = {Vadose zone journal}, volume = {22}, journal = {Vadose zone journal}, number = {5}, publisher = {Wiley}, address = {Hoboken}, issn = {1539-1663}, doi = {10.1002/vzj2.20268}, pages = {19}, year = {2023}, abstract = {Oxygen (O-2) availability in soils is vital for plant growth and productivity. The transport and consumption of O-2 in the root zone is closely linked to soil moisture content, the spatial distribution of roots, as well as structure and heterogeneity of the surrounding soil. In this study, we measure three-dimensional root system architecture and the spatiotemporal dynamics of soil moisture (\& theta;) and O-2 concentrations in the root zone of maize (Zea mays) via non-invasive imaging, and then construct and parameterize a reactive transport model based on the experimental data. The combination of three non-invasive imaging methods allowed for a direct comparison of simulation results with observations at high spatial and temporal resolution. In three different modeling scenarios, we investigated how the results obtained for different levels of conceptual complexity in the model were able to match measured \& theta; and O-2 concentration patterns. We found that the modeling scenario that considers heterogeneous soil structure and spatial variability of hydraulic parameters (permeability, porosity, and van Genuchten \& alpha; and n), better reproduced the measured \& theta; and O-2 patterns relative to a simple model with a homogenous soil domain. The results from our combined imaging and modeling analysis reveal that experimental O-2 and water dynamics can be reproduced quantitatively in a reactive transport model, and that O-2 and water dynamics are best characterized when conditions unique to the specific system beyond the distribution of roots, such as soil structure and its effect on water saturation and macroscopic gas transport pathways, are considered.}, language = {en} } @article{MatternReppertZeuschneretal.2023, author = {Mattern, Maximilian and Reppert, Alexander von and Zeuschner, Steffen Peer and Herzog, Marc and Pudell, Jan-Etienne and Bargheer, Matias}, title = {Concepts and use cases for picosecond ultrasonics with x-rays}, series = {Photoacoustics}, volume = {31}, journal = {Photoacoustics}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2213-5979}, doi = {10.1016/j.pacs.2023.100503}, pages = {22}, year = {2023}, abstract = {This review discusses picosecond ultrasonics experiments using ultrashort hard x-ray probe pulses to extract the transient strain response of laser-excited nanoscopic structures from Bragg-peak shifts. This method provides direct, layer-specific, and quantitative information on the picosecond strain response for structures down to few-nm thickness. We model the transient strain using the elastic wave equation and express the driving stress using Gruneisen parameters stating that the laser-induced stress is proportional to energy density changes in the microscopic subsystems of the solid, i.e., electrons, phonons and spins. The laser-driven strain response can thus serve as an ultrafast proxy for local energy-density and temperature changes, but we emphasize the importance of the nanoscale morphology for an accurate interpretation due to the Poisson effect. The presented experimental use cases encompass ultrathin and opaque metal-heterostructures, continuous and granular nanolayers as well as negative thermal expansion materials, that each pose a challenge to established all-optical techniques.}, language = {en} }