@article{LandwehrKenzelZobelHoffmannetal.2018, author = {Landwehr-Kenzel, Sybille and Zobel, Anne and Hoffmann, Henrike and Landwehr, Niels and Schmueck-Henneresse, Michael and Schachtner, Thomas and Roemhild, Andy and Reinke, Petra}, title = {Ex vivo expanded natural regulatory T cells from patients with end-stage renal disease or kidney transplantation are useful for autologous cell therapy}, series = {Kidney international : official journal of the International Society of Nephrology}, volume = {93}, journal = {Kidney international : official journal of the International Society of Nephrology}, number = {6}, publisher = {Elsevier}, address = {New York}, issn = {0085-2538}, doi = {10.1016/j.kint.2018.01.021}, pages = {1452 -- 1464}, year = {2018}, abstract = {Novel concepts employing autologous, ex vivo expanded natural regulatory T cells (nTreg) for adoptive transfer has potential to prevent organ rejection after kidney transplantation. However, the impact of dialysis and maintenance immunosuppression on the nTreg phenotype and peripheral survival is not well understood, but essential when assessing patient eligibility. The current study investigates regulatory T-cells in dialysis and kidney transplanted patients and the feasibility of generating a clinically useful nTreg product from these patients. Heparinized blood from 200 individuals including healthy controls, dialysis patients with end stage renal disease and patients 1, 5, 10, 15, 20 years after kidney transplantation were analyzed. Differentiation and maturation of nTregs were studied by flow cytometry in order to compare dialysis patients and kidney transplanted patients under maintenance immunosuppression to healthy controls. CD127 expressing CD4(+)CD25(high)FoxP3(+) nTregs were detectable at increased frequencies in dialysis patients with no negative impact on the nTreg end product quality and therapeutic usefulness of the ex vivo expanded nTregs. Further, despite that immunosuppression mildly altered nTreg maturation, neither dialysis nor pharmacological immunosuppression or previous acute rejection episodes impeded nTreg survival in vivo. Accordingly, the generation of autologous, highly pure nTreg products is feasible and qualifies patients awaiting or having received allogenic kidney transplantation for adoptive nTreg therapy. Thus, our novel treatment approach may enable us to reduce the incidence of organ rejection and reduce the need of long-term immunosuppression.}, language = {en} } @article{BussasSawadeKuhnetal.2017, author = {Bussas, Matthias and Sawade, Christoph and Kuhn, Nicolas and Scheffer, Tobias and Landwehr, Niels}, title = {Varying-coefficient models for geospatial transfer learning}, series = {Machine learning}, volume = {106}, journal = {Machine learning}, publisher = {Springer}, address = {Dordrecht}, issn = {0885-6125}, doi = {10.1007/s10994-017-5639-3}, pages = {1419 -- 1440}, year = {2017}, abstract = {We study prediction problems in which the conditional distribution of the output given the input varies as a function of task variables which, in our applications, represent space and time. In varying-coefficient models, the coefficients of this conditional are allowed to change smoothly in space and time; the strength of the correlations between neighboring points is determined by the data. This is achieved by placing a Gaussian process (GP) prior on the coefficients. Bayesian inference in varying-coefficient models is generally intractable. We show that with an isotropic GP prior, inference in varying-coefficient models resolves to standard inference for a GP that can be solved efficiently. MAP inference in this model resolves to multitask learning using task and instance kernels. We clarify the relationship between varying-coefficient models and the hierarchical Bayesian multitask model and show that inference for hierarchical Bayesian multitask models can be carried out efficiently using graph-Laplacian kernels. We explore the model empirically for the problems of predicting rent and real-estate prices, and predicting the ground motion during seismic events. We find that varying-coefficient models with GP priors excel at predicting rents and real-estate prices. The ground-motion model predicts seismic hazards in the State of California more accurately than the previous state of the art.}, language = {en} } @article{DrimallaLandwehrHessetal.2019, author = {Drimalla, Hanna and Landwehr, Niels and Hess, Ursula and Dziobek, Isabel}, title = {From face to face}, series = {Cognition and Emotion}, volume = {33}, journal = {Cognition and Emotion}, number = {8}, publisher = {Routledge, Taylor \& Francis Group}, address = {Abingdon}, issn = {0269-9931}, doi = {10.1080/02699931.2019.1596068}, pages = {1672 -- 1686}, year = {2019}, abstract = {Despite advances in the conceptualisation of facial mimicry, its role in the processing of social information is a matter of debate. In the present study, we investigated the relationship between mimicry and cognitive and emotional empathy. To assess mimicry, facial electromyography was recorded for 70 participants while they completed the Multifaceted Empathy Test, which presents complex context-embedded emotional expressions. As predicted, inter-individual differences in emotional and cognitive empathy were associated with the level of facial mimicry. For positive emotions, the intensity of the mimicry response scaled with the level of state emotional empathy. Mimicry was stronger for the emotional empathy task compared to the cognitive empathy task. The specific empathy condition could be successfully detected from facial muscle activity at the level of single individuals using machine learning techniques. These results support the view that mimicry occurs depending on the social context as a tool to affiliate and it is involved in cognitive as well as emotional empathy.}, language = {en} } @article{SchirrmannLandwehrGiebeletal.2021, author = {Schirrmann, Michael and Landwehr, Niels and Giebel, Antje and Garz, Andreas and Dammer, Karl-Heinz}, title = {Early detection of stripe rust in winter wheat using deep residual neural networks}, series = {Frontiers in plant science : FPLS}, volume = {12}, journal = {Frontiers in plant science : FPLS}, publisher = {Frontiers Media}, address = {Lausanne}, issn = {1664-462X}, doi = {10.3389/fpls.2021.469689}, pages = {14}, year = {2021}, abstract = {Stripe rust (Pst) is a major disease of wheat crops leading untreated to severe yield losses. The use of fungicides is often essential to control Pst when sudden outbreaks are imminent. Sensors capable of detecting Pst in wheat crops could optimize the use of fungicides and improve disease monitoring in high-throughput field phenotyping. Now, deep learning provides new tools for image recognition and may pave the way for new camera based sensors that can identify symptoms in early stages of a disease outbreak within the field. The aim of this study was to teach an image classifier to detect Pst symptoms in winter wheat canopies based on a deep residual neural network (ResNet). For this purpose, a large annotation database was created from images taken by a standard RGB camera that was mounted on a platform at a height of 2 m. Images were acquired while the platform was moved over a randomized field experiment with Pst-inoculated and Pst-free plots of winter wheat. The image classifier was trained with 224 x 224 px patches tiled from the original, unprocessed camera images. The image classifier was tested on different stages of the disease outbreak. At patch level the image classifier reached a total accuracy of 90\%. To test the image classifier on image level, the image classifier was evaluated with a sliding window using a large striding length of 224 px allowing for fast test performance. At image level, the image classifier reached a total accuracy of 77\%. Even in a stage with very low disease spreading (0.5\%) at the very beginning of the Pst outbreak, a detection accuracy of 57\% was obtained. Still in the initial phase of the Pst outbreak with 2 to 4\% of Pst disease spreading, detection accuracy with 76\% could be attained. With further optimizations, the image classifier could be implemented in embedded systems and deployed on drones, vehicles or scanning systems for fast mapping of Pst outbreaks.}, language = {en} } @article{HempelAdolphsLandwehretal.2020, author = {Hempel, Sabrina and Adolphs, Julian and Landwehr, Niels and Janke, David and Amon, Thomas}, title = {How the selection of training data and modeling approach affects the estimation of ammonia emissions from a naturally ventilated dairy barn—classical statistics versus machine learning}, series = {Sustainability}, volume = {12}, journal = {Sustainability}, number = {3}, publisher = {MDPI}, address = {Basel}, issn = {2071-1050}, doi = {10.3390/su12031030}, pages = {18}, year = {2020}, abstract = {Environmental protection efforts can only be effective in the long term with a reliable quantification of pollutant gas emissions as a first step to mitigation. Measurement and analysis strategies must permit the accurate extrapolation of emission values. We systematically analyzed the added value of applying modern machine learning methods in the process of monitoring emissions from naturally ventilated livestock buildings to the atmosphere. We considered almost 40 weeks of hourly emission values from a naturally ventilated dairy cattle barn in Northern Germany. We compared model predictions using 27 different scenarios of temporal sampling, multiple measures of model accuracy, and eight different regression approaches. The error of the predicted emission values with the tested measurement protocols was, on average, well below 20\%. The sensitivity of the prediction to the selected training dataset was worse for the ordinary multilinear regression. Gradient boosting and random forests provided the most accurate and robust emission value predictions, accompanied by the second-smallest model errors. Most of the highly ranked scenarios involved six measurement periods, while the scenario with the best overall performance was: One measurement period in summer and three in the transition periods, each lasting for 14 days.}, language = {en} } @article{ThonLandwehrDeRaedt2011, author = {Thon, Ingo and Landwehr, Niels and De Raedt, Luc}, title = {Stochastic relational processes efficient inference and applications}, series = {Machine learning}, volume = {82}, journal = {Machine learning}, number = {2}, publisher = {Springer}, address = {Dordrecht}, issn = {0885-6125}, doi = {10.1007/s10994-010-5213-8}, pages = {239 -- 272}, year = {2011}, abstract = {One of the goals of artificial intelligence is to develop agents that learn and act in complex environments. Realistic environments typically feature a variable number of objects, relations amongst them, and non-deterministic transition behavior. While standard probabilistic sequence models provide efficient inference and learning techniques for sequential data, they typically cannot fully capture the relational complexity. On the other hand, statistical relational learning techniques are often too inefficient to cope with complex sequential data. In this paper, we introduce a simple model that occupies an intermediate position in this expressiveness/efficiency trade-off. It is based on CP-logic (Causal Probabilistic Logic), an expressive probabilistic logic for modeling causality. However, by specializing CP-logic to represent a probability distribution over sequences of relational state descriptions and employing a Markov assumption, inference and learning become more tractable and effective. Specifically, we show how to solve part of the inference and learning problems directly at the first-order level, while transforming the remaining part into the problem of computing all satisfying assignments for a Boolean formula in a binary decision diagram. We experimentally validate that the resulting technique is able to handle probabilistic relational domains with a substantial number of objects and relations.}, language = {en} } @article{CiliaLandwehrPasserini2011, author = {Cilia, Elisa and Landwehr, Niels and Passerini, Andrea}, title = {Relational feature mining with hierarchical multitask kFOIL}, series = {Fundamenta informaticae}, volume = {113}, journal = {Fundamenta informaticae}, number = {2}, publisher = {IOS Press}, address = {Amsterdam}, issn = {0169-2968}, doi = {10.3233/FI-2011-604}, pages = {151 -- 177}, year = {2011}, abstract = {We introduce hierarchical kFOIL as a simple extension of the multitask kFOIL learning algorithm. The algorithm first learns a core logic representation common to all tasks, and then refines it by specialization on a per-task basis. The approach can be easily generalized to a deeper hierarchy of tasks. A task clustering algorithm is also proposed in order to automatically generate the task hierarchy. The approach is validated on problems of drug-resistance mutation prediction and protein structural classification. Experimental results show the advantage of the hierarchical version over both single and multi task alternatives and its potential usefulness in providing explanatory features for the domain. Task clustering allows to further improve performance when a deeper hierarchy is considered.}, language = {en} } @article{SawadeBickelvonOertzenetal.2013, author = {Sawade, Christoph and Bickel, Steffen and von Oertzen, Timo and Scheffer, Tobias and Landwehr, Niels}, title = {Active evaluation of ranking functions based on graded relevance}, series = {Machine learning}, volume = {92}, journal = {Machine learning}, number = {1}, publisher = {Springer}, address = {Dordrecht}, issn = {0885-6125}, doi = {10.1007/s10994-013-5372-5}, pages = {41 -- 64}, year = {2013}, abstract = {Evaluating the quality of ranking functions is a core task in web search and other information retrieval domains. Because query distributions and item relevance change over time, ranking models often cannot be evaluated accurately on held-out training data. Instead, considerable effort is spent on manually labeling the relevance of query results for test queries in order to track ranking performance. We address the problem of estimating ranking performance as accurately as possible on a fixed labeling budget. Estimates are based on a set of most informative test queries selected by an active sampling distribution. Query labeling costs depend on the number of result items as well as item-specific attributes such as document length. We derive cost-optimal sampling distributions for the commonly used performance measures Discounted Cumulative Gain and Expected Reciprocal Rank. Experiments on web search engine data illustrate significant reductions in labeling costs.}, language = {en} } @article{HempelAdolphsLandwehretal.2020, author = {Hempel, Sabrina and Adolphs, Julian and Landwehr, Niels and Willink, Dilya and Janke, David and Amon, Thomas}, title = {Supervised machine learning to assess methane emissions of a dairy building with natural ventilation}, series = {Applied Sciences}, volume = {10}, journal = {Applied Sciences}, number = {19}, publisher = {MDPI}, address = {Basel}, issn = {2076-3417}, doi = {10.3390/app10196938}, pages = {21}, year = {2020}, abstract = {A reliable quantification of greenhouse gas emissions is a basis for the development of adequate mitigation measures. Protocols for emission measurements and data analysis approaches to extrapolate to accurate annual emission values are a substantial prerequisite in this context. We systematically analyzed the benefit of supervised machine learning methods to project methane emissions from a naturally ventilated cattle building with a concrete solid floor and manure scraper located in Northern Germany. We took into account approximately 40 weeks of hourly emission measurements and compared model predictions using eight regression approaches, 27 different sampling scenarios and four measures of model accuracy. Data normalization was applied based on median and quartile range. A correlation analysis was performed to evaluate the influence of individual features. This indicated only a very weak linear relation between the methane emission and features that are typically used to predict methane emission values of naturally ventilated barns. It further highlighted the added value of including day-time and squared ambient temperature as features. The error of the predicted emission values was in general below 10\%. The results from Gaussian processes, ordinary multilinear regression and neural networks were least robust. More robust results were obtained with multilinear regression with regularization, support vector machines and particularly the ensemble methods gradient boosting and random forest. The latter had the added value to be rather insensitive against the normalization procedure. In the case of multilinear regression, also the removal of not significantly linearly related variables (i.e., keeping only the day-time component) led to robust modeling results. We concluded that measurement protocols with 7 days and six measurement periods can be considered sufficient to model methane emissions from the dairy barn with solid floor with manure scraper, particularly when periods are distributed over the year with a preference for transition periods. Features should be normalized according to median and quartile range and must be carefully selected depending on the modeling approach.}, language = {en} } @article{GautamZhangLandwehretal.2021, author = {Gautam, Khem Raj and Zhang, Guoqiang and Landwehr, Niels and Adolphs, Julian}, title = {Machine learning for improvement of thermal conditions inside a hybrid ventilated animal building}, series = {Computers and electronics in agriculture : COMPAG online ; an international journal}, volume = {187}, journal = {Computers and electronics in agriculture : COMPAG online ; an international journal}, publisher = {Elsevier Science}, address = {Amsterdam [u.a.]}, issn = {0168-1699}, doi = {10.1016/j.compag.2021.106259}, pages = {10}, year = {2021}, abstract = {In buildings with hybrid ventilation, natural ventilation opening positions (windows), mechanical ventilation rates, heating, and cooling are manipulated to maintain desired thermal conditions. The indoor temperature is regulated solely by ventilation (natural and mechanical) when the external conditions are favorable to save external heating and cooling energy. The ventilation parameters are determined by a rule-based control scheme, which is not optimal. This study proposes a methodology to enable real-time optimum control of ventilation parameters. We developed offline prediction models to estimate future thermal conditions from the data collected from building in operation. The developed offline model is then used to find the optimal controllable ventilation parameters in real-time to minimize the setpoint deviation in the building. With the proposed methodology, the experimental building's setpoint deviation improved for 87\% of time, on average, by 0.53 degrees C compared to the current deviations.}, language = {en} }