@phdthesis{Somody2023,
  author    = {Somody, Joseph Christian Campbell},
  title     = {Leveraging deep learning for identification and structural determination of novel protein complexes from \(in\) \(situ\) electron cryotomography of \(Mycoplasma\) \(pneumoniae\)},
  doi       = {10.25972/OPUS-31344},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-313447},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2023},
  abstract  = {The holy grail of structural biology is to study a protein in situ, and this goal has been fast approaching since the resolution revolution and the achievement of atomic resolution. A cell's interior is not a dilute environment, and proteins have evolved to fold and function as needed in that environment; as such, an investigation of a cellular component should ideally include the full complexity of the cellular environment. Imaging whole cells in three dimensions using electron cryotomography is the best method to accomplish this goal, but it comes with a limitation on sample thickness and produces noisy data unamenable to direct analysis. This thesis establishes a novel workflow to systematically analyse whole-cell electron cryotomography data in three dimensions and to find and identify instances of protein complexes in the data to set up a determination of their structure and identity for success. Mycoplasma pneumoniae is a very small parasitic bacterium with fewer than 700 protein-coding genes, is thin enough and small enough to be imaged in large quantities by electron cryotomography, and can grow directly on the grids used for imaging, making it ideal for exploratory studies in structural proteomics. As part of the workflow, a methodology for training deep-learning-based particle-picking models is established. As a proof of principle, a dataset of whole-cell Mycoplasma pneumoniae tomograms is used with this workflow to characterize a novel membrane-associated complex observed in the data. Ultimately, 25431 such particles are picked from 353 tomograms and refined to a density map with a resolution of 11 {\AA}. Making good use of orthogonal datasets to filter search space and verify results, structures were predicted for candidate proteins and checked for suitable fit in the density map. In the end, with this approach, nine proteins were found to be part of the complex, which appears to be associated with chaperone activity and interact with translocon machinery. Visual proteomics refers to the ultimate potential of in situ electron cryotomography: the comprehensive interpretation of tomograms. The workflow presented here is demonstrated to help in reaching that potential.},
  subject      = {Kryoelektronenmikroskopie},
  language  = {en}
}
@techreport{ElsayedRizk2022,
  type      = {Working Paper},
  author    = {Elsayed, Karim and Rizk, Amr},
  title     = {Response Times in Time-to-Live Caching Hierarchies under Random Network Delays},
  series = {W{\"u}rzburg Workshop on Next-Generation Communication Networks (WueWoWas'22)},
  journal   = {W{\"u}rzburg Workshop on Next-Generation Communication Networks (WueWoWas'22)},
  doi       = {10.25972/OPUS-28084},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-280843},
  pages     = {4},
  year      = {2022},
  abstract  = {Time-to-Live (TTL) caches decouple the occupancy of objects in cache through object-specific validity timers. Stateof- the art techniques provide exact methods for the calculation of object-specific hit probabilities given entire cache hierarchies with random inter-cache network delays. The system hit probability is a provider-centric metric as it relates to the origin offload, i.e., the decrease in the number of requests that are served by the content origin server. In this paper we consider a user-centric metric, i.e., the response time, which is shown to be structurally different from the system hit probability. Equipped with the state-of-theart exact modeling technique using Markov-arrival processes we derive expressions for the expected object response time and pave a way for its optimization under network delays.},
  subject      = {Datennetz},
  language  = {en}
}
@techreport{AlfredssonKasslerVestinetal.2022,
  type      = {Working Paper},
  author    = {Alfredsson, Rebecka and Kassler, Andreas and Vestin, Jonathan and Pieska, Marcus and Amend, Markus},
  title     = {Accelerating a Transport Layer based 5G Multi-Access Proxy on SmartNIC},
  series = {W{\"u}rzburg Workshop on Next-Generation Communication Networks (WueWoWas'22)},
  journal   = {W{\"u}rzburg Workshop on Next-Generation Communication Networks (WueWoWas'22)},
  doi       = {10.25972/OPUS-28079},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-280798},
  pages     = {4},
  year      = {2022},
  abstract  = {Utilizing multiple access technologies such as 5G, 4G, and Wi-Fi within a coherent framework is currently standardized by 3GPP within 5G ATSSS. Indeed, distributing packets over multiple networks can lead to increased robustness, resiliency and capacity. A key part of such a framework is the multi-access proxy, which transparently distributes packets over multiple paths. As the proxy needs to serve thousands of customers, scalability and performance are crucial for operator deployments. In this paper, we leverage recent advancements in data plane programming, implement a multi-access proxy based on the MP-DCCP tunneling approach in P4 and hardware accelerate it by deploying the pipeline on a smartNIC. This is challenging due to the complex scheduling and congestion control operations involved. We present our pipeline and data structures design for congestion control and packet scheduling state management. Initial measurements in our testbed show that packet latency is in the range of 25 μs demonstrating the feasibility of our approach.},
  subject      = {Datennetz},
  language  = {en}
}
@article{BencurovaShityakovSchaacketal.2022,
  author    = {Bencurova, Elena and Shityakov, Sergey and Schaack, Dominik and Kaltdorf, Martin and Sarukhanyan, Edita and Hilgarth, Alexander and Rath, Christin and Montenegro, Sergio and Roth, G{\"u}nter and Lopez, Daniel and Dandekar, Thomas},
  title     = {Nanocellulose composites as smart devices with chassis, light-directed DNA Storage, engineered electronic properties, and chip integration},
  series = {Frontiers in Bioengineering and Biotechnology},
  volume    = {10},
  journal   = {Frontiers in Bioengineering and Biotechnology},
  issn      = {2296-4185},
  doi       = {10.3389/fbioe.2022.869111},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-283033},
  year      = {2022},
  abstract  = {The rapid development of green and sustainable materials opens up new possibilities in the field of applied research. Such materials include nanocellulose composites that can integrate many components into composites and provide a good chassis for smart devices. In our study, we evaluate four approaches for turning a nanocellulose composite into an information storage or processing device: 1) nanocellulose can be a suitable carrier material and protect information stored in DNA. 2) Nucleotide-processing enzymes (polymerase and exonuclease) can be controlled by light after fusing them with light-gating domains; nucleotide substrate specificity can be changed by mutation or pH change (read-in and read-out of the information). 3) Semiconductors and electronic capabilities can be achieved: we show that nanocellulose is rendered electronic by iodine treatment replacing silicon including microstructures. Nanocellulose semiconductor properties are measured, and the resulting potential including single-electron transistors (SET) and their properties are modeled. Electric current can also be transported by DNA through G-quadruplex DNA molecules; these as well as classical silicon semiconductors can easily be integrated into the nanocellulose composite. 4) To elaborate upon miniaturization and integration for a smart nanocellulose chip device, we demonstrate pH-sensitive dyes in nanocellulose, nanopore creation, and kinase micropatterning on bacterial membranes as well as digital PCR micro-wells. Future application potential includes nano-3D printing and fast molecular processors (e.g., SETs) integrated with DNA storage and conventional electronics. This would also lead to environment-friendly nanocellulose chips for information processing as well as smart nanocellulose composites for biomedical applications and nano-factories.},
  language  = {en}
}
@article{KrenzerMakowskiHekaloetal.2022,
  author    = {Krenzer, Adrian and Makowski, Kevin and Hekalo, Amar and Fitting, Daniel and Troya, Joel and Zoller, Wolfram G. and Hann, Alexander and Puppe, Frank},
  title     = {Fast machine learning annotation in the medical domain: a semi-automated video annotation tool for gastroenterologists},
  series = {BioMedical Engineering OnLine},
  volume    = {21},
  journal   = {BioMedical Engineering OnLine},
  number    = {1},
  doi       = {10.1186/s12938-022-01001-x},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-300231},
  year      = {2022},
  abstract  = {Background Machine learning, especially deep learning, is becoming more and more relevant in research and development in the medical domain. For all the supervised deep learning applications, data is the most critical factor in securing successful implementation and sustaining the progress of the machine learning model. Especially gastroenterological data, which often involves endoscopic videos, are cumbersome to annotate. Domain experts are needed to interpret and annotate the videos. To support those domain experts, we generated a framework. With this framework, instead of annotating every frame in the video sequence, experts are just performing key annotations at the beginning and the end of sequences with pathologies, e.g., visible polyps. Subsequently, non-expert annotators supported by machine learning add the missing annotations for the frames in-between. Methods In our framework, an expert reviews the video and annotates a few video frames to verify the object's annotations for the non-expert. In a second step, a non-expert has visual confirmation of the given object and can annotate all following and preceding frames with AI assistance. After the expert has finished, relevant frames will be selected and passed on to an AI model. This information allows the AI model to detect and mark the desired object on all following and preceding frames with an annotation. Therefore, the non-expert can adjust and modify the AI predictions and export the results, which can then be used to train the AI model. Results Using this framework, we were able to reduce workload of domain experts on average by a factor of 20 on our data. This is primarily due to the structure of the framework, which is designed to minimize the workload of the domain expert. Pairing this framework with a state-of-the-art semi-automated AI model enhances the annotation speed further. Through a prospective study with 10 participants, we show that semi-automated annotation using our tool doubles the annotation speed of non-expert annotators compared to a well-known state-of-the-art annotation tool. Conclusion In summary, we introduce a framework for fast expert annotation for gastroenterologists, which reduces the workload of the domain expert considerably while maintaining a very high annotation quality. The framework incorporates a semi-automated annotation system utilizing trained object detection models. The software and framework are open-source.},
  language  = {en}
}
@article{KaltdorfSchulzeHelmprobstetal.2017,
  author    = {Kaltdorf, Kristin Verena and Schulze, Katja and Helmprobst, Frederik and Kollmannsberger, Philip and Dandekar, Thomas and Stigloher, Christian},
  title     = {Fiji macro 3D ART VeSElecT: 3D automated reconstruction tool for vesicle structures of electron tomograms},
  series = {PLoS Computational Biology},
  volume    = {13},
  journal   = {PLoS Computational Biology},
  number    = {1},
  doi       = {10.1371/journal.pcbi.1005317},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-172112},
  year      = {2017},
  abstract  = {Automatic image reconstruction is critical to cope with steadily increasing data from advanced microscopy. We describe here the Fiji macro 3D ART VeSElecT which we developed to study synaptic vesicles in electron tomograms. We apply this tool to quantify vesicle properties (i) in embryonic Danio rerio 4 and 8 days past fertilization (dpf) and (ii) to compare Caenorhabditis elegans N2 neuromuscular junctions (NMJ) wild-type and its septin mutant (unc-59(e261)). We demonstrate development-specific and mutant-specific changes in synaptic vesicle pools in both models. We confirm the functionality of our macro by applying our 3D ART VeSElecT on zebrafish NMJ showing smaller vesicles in 8 dpf embryos then 4 dpf, which was validated by manual reconstruction of the vesicle pool. Furthermore, we analyze the impact of C. elegans septin mutant unc-59(e261) on vesicle pool formation and vesicle size. Automated vesicle registration and characterization was implemented in Fiji as two macros (registration and measurement). This flexible arrangement allows in particular reducing false positives by an optional manual revision step. Preprocessing and contrast enhancement work on image-stacks of 1nm/pixel in x and y direction. Semi-automated cell selection was integrated. 3D ART VeSElecT removes interfering components, detects vesicles by 3D segmentation and calculates vesicle volume and diameter (spherical approximation, inner/outer diameter). Results are collected in color using the RoiManager plugin including the possibility of manual removal of non-matching confounder vesicles. Detailed evaluation considered performance (detected vesicles) and specificity (true vesicles) as well as precision and recall. We furthermore show gain in segmentation and morphological filtering compared to learning based methods and a large time gain compared to manual segmentation. 3D ART VeSElecT shows small error rates and its speed gain can be up to 68 times faster in comparison to manual annotation. Both automatic and semi-automatic modes are explained including a tutorial.},
  language  = {en}
}
@techreport{MartinoDeutschmannHielscheretal.2023,
  type      = {Working Paper},
  author    = {Martino, Luigi and Deutschmann, J{\"o}rg and Hielscher, Kai-Steffen and German, Reinhard},
  title     = {Towards a 5G Satellite Communication Framework for V2X},
  series = {KuVS Fachgespr{\"a}ch - W{\"u}rzburg Workshop on Modeling, Analysis and Simulation of Next-Generation Communication Networks 2023 (WueWoWAS'23)},
  journal   = {KuVS Fachgespr{\"a}ch - W{\"u}rzburg Workshop on Modeling, Analysis and Simulation of Next-Generation Communication Networks 2023 (WueWoWAS'23)},
  doi       = {10.25972/OPUS-32214},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-322148},
  pages     = {5},
  year      = {2023},
  abstract  = {In recent years, satellite communication has been expanding its field of application in the world of computer networks. This paper aims to provide an overview of how a typical scenario involving 5G Non-Terrestrial Networks (NTNs) for vehicle to everything (V2X) applications is characterized. In particular, a first implementation of a system that integrates them together will be described. Such a framework will later be used to evaluate the performance of applications such as Vehicle Monitoring (VM), Remote Driving (RD), Voice Over IP (VoIP), and others. Different configuration scenarios such as Low Earth Orbit and Geostationary Orbit will be considered.},
  language  = {en}
}
@techreport{RauberBrechtelSchotten2023,
  type      = {Working Paper},
  author    = {Rauber, Christof A. O. and Brechtel, Lukas and Schotten, Hans D.},
  title     = {JCAS-Enabled Sensing as a Service in 6th-Generation Mobile Communication Networks},
  series = {KuVS Fachgespr{\"a}ch - W{\"u}rzburg Workshop on Modeling, Analysis and Simulation of Next-Generation Communication Networks 2023 (WueWoWAS'23)},
  journal   = {KuVS Fachgespr{\"a}ch - W{\"u}rzburg Workshop on Modeling, Analysis and Simulation of Next-Generation Communication Networks 2023 (WueWoWAS'23)},
  doi       = {10.25972/OPUS-32213},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-322135},
  pages     = {4},
  year      = {2023},
  abstract  = {The introduction of new types of frequency spectrum in 6G technology facilitates the convergence of conventional mobile communications and radar functions. Thus, the mobile network itself becomes a versatile sensor system. This enables mobile network operators to offer a sensing service in addition to conventional data and telephony services. The potential benefits are expected to accrue to various stakeholders, including individuals, the environment, and society in general. The paper discusses technological development, possible integration, and use cases, as well as future development areas.},
  language  = {en}
}
@techreport{LohRaffeckGeissleretal.2023,
  type      = {Working Paper},
  author    = {Loh, Frank and Raffeck, Simon and Geißler, Stefan and Hoßfeld, Tobias},
  title     = {Paving the Way for an Energy Efficient and Sustainable Future Internet of Things},
  series = {KuVS Fachgespr{\"a}ch - W{\"u}rzburg Workshop on Modeling, Analysis and Simulation of Next-Generation Communication Networks 2023 (WueWoWAS'23)},
  journal   = {KuVS Fachgespr{\"a}ch - W{\"u}rzburg Workshop on Modeling, Analysis and Simulation of Next-Generation Communication Networks 2023 (WueWoWAS'23)},
  doi       = {10.25972/OPUS-32216},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-322161},
  pages     = {4},
  year      = {2023},
  abstract  = {In this work, we describe the network from data collection to data processing and storage as a system based on different layers. We outline the different layers and highlight major tasks and dependencies with regard to energy consumption and energy efficiency. With this view, we can outwork challenges and questions a future system architect must answer to provide a more sustainable, green, resource friendly, and energy efficient application or system. Therefore, all system layers must be considered individually but also altogether for future IoT solutions. This requires, in particular, novel sustainability metrics in addition to current Quality of Service and Quality of Experience metrics to provide a high power, user satisfying, and sustainable network.},
  language  = {en}
}
@techreport{FundaKonheiserGermanetal.2023,
  type      = {Working Paper},
  author    = {Funda, Christoph and Konheiser, Tobias and German, Reinhard and Hielscher, Kai-Steffen},
  title     = {How to Model and Predict the Scalability of a Hardware-In-The-Loop Test Bench for Data Re-Injection?},
  series = {KuVS Fachgespr{\"a}ch - W{\"u}rzburg Workshop on Modeling, Analysis and Simulation of Next-Generation Communication Networks 2023 (WueWoWAS'23)},
  journal   = {KuVS Fachgespr{\"a}ch - W{\"u}rzburg Workshop on Modeling, Analysis and Simulation of Next-Generation Communication Networks 2023 (WueWoWAS'23)},
  doi       = {10.25972/OPUS-32215},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-322150},
  pages     = {4},
  year      = {2023},
  abstract  = {This paper describes a novel application of an empirical network calculus model based on measurements of a hardware-in-the-loop (HIL) test system. The aim is to predict the performance of a HIL test bench for open-loop re-injection in the context of scalability. HIL test benches are distributed computer systems including software, hardware, and networking devices. They are used to validate complex technical systems, but have not yet been system under study themselves. Our approach is to use measurements from the HIL system to create an empirical model for arrival and service curves. We predict the performance and design the previously unknown parameters of the HIL simulator with network calculus (NC), namely the buffer sizes and the minimum needed pre-buffer time for the playback buffer. We furthermore show, that it is possible to estimate the CPU load from arrival and service-curves based on the utilization theorem, and hence estimate the scalability of the HIL system in the context of the number of sensor streams.},
  language  = {en}
}