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Publication36P A point mutation replacing cysteine with arginine at position 382 (C382R) in the transmembrane domain of FGFR2 leads to response to FGF2-inhibitor pemigatinib in chemo-refractory intrahepatic cholangiocarcinoma( 2022)Background: To date, targeted tyrosine kinase inhibitors have been approved for FGFR2 and FGFR3 fusions (pemigatinib and erdafitinib, respectively), but the importance of FGFR2 mutations for transformation activity and as a druggable gene variant with response to different FGFR inhibitors is poorly understood. FGFR2 inhibitors present a mainstay of treatment for locally advanced or metastatic intrahepatic cholangiocellular carcinoma (iCCA). Methods: A 74-year-old male was diagnosed with iCCA in liver segments seven and eight with infiltration of the hepatic veins and inferior vena cava revealed a C382R mutation of the intramembrane domain of FGRR2 receptor. We performed an in-silico study to understand the potential mode-of-action of the mutant FGFR2 targets. Based on experimentally determined structures we then used a structure generated by AlphaFold2 as the variation in question is located at a position not determined well in the experiments. This revealed that the C382R mutation is located in the trans-membranal domain at a position crucial for signal transduction, both for activation and inhibition of downstream-signaling. The Molecular Tumor Board decided to start the treatment with 13.5 mg pemigatinib once daily for 14 days, followed by 7 days of free therapy interval resulting in a sustained partial response. The patient continues to be treated of 13.5 mg as described above. Results: In our case report, we were able to show that the patient in whom an C382R mutation was detected responded to the therapy with pemigatinib. This shows that real-world scenarios differ from the data of the approval studies, thereby illustrating how complex data on patients with FGFR mutations is. One of the main problems of large approval studies is that the functionality of the respective alterations is often disregarded. Conclusions: Our results suggest that respective mutation may be successfully targeted by FGFR-selective tyrosine-kinase inhibitors, demonstrating the importance of the functional characterization of mutations. Legal entity responsible for the study: Louisa Hempel. Funding: Has not received any funding. Disclosure: All authors have declared no conflicts of interest.
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PublicationConfocal fluorescence microscopy with high-NA diffractive lens arrays( 2022)Traditionally, there is a trade-off between the numerical aperture and field of view for a microscope objective. Diffractive lens arrays (DLAs) with overlapping apertures are used to overcome such a problem. A spot array with an NA up to 0.83 and a pitch of 75 m is produced by the proposed DLA at a wavelength of 488 nm. By measurement of the fluorescence beads, the DLA-based confocal setup shows the capability of high-resolution measurement over an area of 3mm 3mm with a 2.5 0.07 NA objective. Further, the proposed fluorescence microscope is insensitive to optical aberrations, which has been demonstrated by imaging with a simple doublet lens.
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PublicationMultiagent Self-Redundancy Identification and Tuned Greedy-Exploration( 2022)The constant development of sensing applications using innovative and affordable measurement devices has increased the amount of data transmitted through networks, carrying in many cases, redundant information that requires more time to be analyzed or larger storage centers. This redundancy is mainly present because the network nodes do not recognize environmental variations requiring exploration, which causes a repetitive data collection in a set of limited locations. In this work, we propose a multiagent learning framework that uses the Gaussian process regression (GPR) to allow the agents to predict the environmental behavior by means of the neighborhood measurements, and the rate distortion function to establish a border in which the environmental information is neither misunderstood nor redundant. We apply this framework to a mobile sensor network and demonstrate that the nodes can tune the parameter s of the Blahut-Arimoto algorithm in order to adjust the gathered environment information and to become more or less exploratory within a sensing area.
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PublicationValidation of XAI Explanations for Multivariate Time Series Classification in the Maritime Domain( 2022)Due to the lack of explanation towards their internal mechanism, state-of-the-art deep learning-based classifiers are often considered as black-box models. For instance, in the maritime domain, models that classify the types of ships based on their trajectories and other features perform well, but give no further explanation for their predictions. To gain the trust of human operators responsible for critical decisions, the reason behind the classification is crucial. In this paper, we introduce explainable artificial intelligence (XAI) approaches to the task of classification of ship types. This supports decision-making by providing explanations in terms of the features contributing the most towards the prediction, along with their corresponding time intervals. In the case of the LIME explainer, we adapt the time-slice mapping technique (LimeforTime), while for Shapley additive explanations (SHAP) and path integrated gradient (PIG), we represent the relevance of each input variable to generate a heatmap as an explanation. In order to validate the XAI results, the existing perturbation and sequence analyses for classifiers of univariate time series data is employed for testing and evaluating the XAI explanations on multivariate time series. Furthermore, we introduce a novel evaluation technique to assess the quality of explanations yielded by the chosen XAI method.
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PublicationIntelligente Bild- und Videoauswertung für die Sicherheit( 2022)
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PublicationCluster Crash: Learning from Recent Vulnerabilities in Communication Stacks( 2022)To ensure functionality and security of network stacks in Industrial Devices, thorough testing is necessary. This includes blackbox network fuzzing, where fields in network packets are filled with unexpected values to test the device's behavior in edge cases. Due to resource constraints, the tests need to be efficient and such the input values need to be chosen intelligently. Previous solutions use heuristics based on vague knowledge from previous projects to make these decisions. We aim to structure existing knowledge by defining Vulnerability Anti-Patterns for network communication stacks based on an analysis of the recent vulnerability groups Ripple20, Amnesia:33, and Urgent/11. For our evaluation, we implement fuzzing test scripts based on the Vulnerability Anti-Patterns and run them against 8 Industrial Devices from 5 different device classes. We show (I) that similar vulnerabilities occur in implementations of the same protocol as well as in different protocols, (II) that similar vulnerabilities also spread over different device classes, and (III) that test scripts based on the Vulnerability Anti-Patterns help to identify these vulnerabilities.
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PublicationComposition and Symmetries - Computational Analysis of Fine-Art Aesthetics( 2022)This article deals with the problem of quantitative research of the aesthetic content of the fine-art object. The paper states that a fine-art object is a conceptually formed sequence of signs, and its composition is a structural form, that can be measured using mathematical models. The main approach is based on the perception of the formal order as a determinant of the aesthetic category of beauty. The composition of the image is directly related to the formation of aesthetic sensations and values, since it performs the function of controlling the viewer's perception of a work of art. The research is based on the studies of computational aesthetics by G. D. Birkhoff and M. Bense, as well as the studies of the receptive aesthetics of R. Ingarden, W. Iser, H. R. Jauss and Ya. Mukarzhovsky. The computational aesthetics methods, such as CNN-based object detectors, and gestalt-based symmetry analysis, are used to detect symmetry axes in fine-art images. Experimental analysis demonstrates that the applied computational approach is consistent with the philosophical analysis and the expert evaluations of the fine-art images, therefore it allows to obtain more detailed fine-art paintings description.
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PublicationFeasibility of artificial intelligence-supported assessment of bone marrow infiltration using dual-energy computed tomography in patients with evidence of monoclonal protein - a retrospective observational study( 2022)Objectives To demonstrate the feasibility of an automated, non-invasive approach to estimate bone marrow (BM) infiltration of multiple myeloma (MM) by dual-energy computed tomography (DECT) after virtual non-calcium (VNCa) post-processing. Methods Individuals with MM and monoclonal gammopathy of unknown significance (MGUS) with concurrent DECT and BM biopsy between May 2018 and July 2020 were included in this retrospective observational study. Two pathologists and three radiologists reported BM infiltration and presence of osteolytic bone lesions, respectively. Bone mineral density (BMD) was quantified CT-based by a CE-certified software. Automated spine segmentation was implemented by a pre-trained convolutional neural network. The non-fatty portion of BM was defined as voxels > 0 HU in VNCa. For statistical assessment, multivariate regression and receiver operating characteristic (ROC) were conducted. Results Thirty-five patients (mean age 65 ± 12 years; 18 female) were evaluated. The non-fatty portion of BM significantly predicted BM infiltration after adjusting for the covariable BMD (p = 0.007, r = 0.46). A non-fatty portion of BM > 0.93% could anticipate osteolytic lesions and the clinical diagnosis of MM with an area under the ROC curve of 0.70 [0.49-0.90] and 0.71 [0.54-0.89], respectively. Our approach identified MM-patients without osteolytic lesions on conventional CT with a sensitivity and specificity of 0.63 and 0.71, respectively. Conclusions Automated, AI-supported attenuation assessment of the spine in DECT VNCa is feasible to predict BM infiltration in MM. Further, the proposed method might allow for pre-selecting patients with higher pre-test probability of osteolytic bone lesions and support the clinical diagnosis of MM without pathognomonic lesions on conventional CT.
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PublicationTowards a Better Understanding of Machine Learning based Network Intrusion Detection Systems in Industrial Networks( 2022)It is crucial in an industrial network to understand how and why a intrusion detection system detects, classifies, and reports intrusions. With the ongoing introduction of machine learning into the research area of intrusion detection, this understanding gets even more important since the used systems often appear as a black-box for the user and are no longer understandable in an intuitive and comprehensible way. We propose a novel approach to understand the internal characteristics of a machine learning based network intrusion detection system. This approach includes methods to understand which data sources the system uses, to evaluate whether the system uses linear or non-linear classification approaches, and to find out which underlying machine learning model is implemented in the system. Our evaluation on two publicly available industrial datasets shows that the detection of the data source and the differentiation between linear and non-linear models is possible with our approach. In addition, the identification of the underlying machine learning model can be accomplished with statistical significance for non-linear models. The information made accessible by our approach helps to develop a deeper understanding of the functioning of a network intrusion detection system, and contributes towards developing transparent machine learning based intrusion detection approaches.
