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Veröffentlichungen der HSWT

Die chronologische Liste zeigt aktuelle Veröffentlichungen aus dem Forschungsbetrieb der Hochschule Weihenstephan-Triesdorf. Zuständig ist das Zentrum für Forschung und Wissenstransfer (ZFW).

8 Ergebnisse

  • Jana Englmeier, Christian von Hoermann, Daniel Rieker, M. Eric Benbow, Caryl S. Benjamin, Ute Fricke, Cristina Ganuza, Maria Haensel, Dr. Tomás Lackner, Oliver Mitesser, Sarah Redlich, Rebekka Riebl, Sandra Rojas-Botero, Thomas Rummler, Jörg-Alfred Salamon, David Sommer, Prof. Dr. Ingolf Steffan-Dewenter, Dr. Cynthia Tobisch, Johannes Uhler, Lars Uphus, Jie Zhang, Prof. Dr. Jörg Müller

    • Berechtigungen:  Open Access

    Climate and land-use effects on dung beetle assemblages (2022) DRYAD . DOI: 10.5061/dryad.6hdr7sr2c

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  • Prof. Dr. Michael Tröster

    Der Substitutions-Wert von Gärrückständen (2022) Vortrag auf der 5. Bayrischen Biogasfachtagung, Technologie- und Förderzentrum im Kompetenzzentrum für Nachwachsende Rohstoffe, Straubing, 28.09.2022 .

  • Patrizia Eben

    Bepflanzung von Regenwasserversickerungsanlagen. Aktuelle Forschungen (2022) Vortrag bei den bdla-Pflanzplanertagen, online .

  • Richa Bharti, Daniel Siebert, Bastian Blombach, Prof. Dr. Dominik Grimm

    • Berechtigungen:  Open Access
    • Berechtigungen:  Peer Reviewed

    Systematic analysis of the underlying genomic architecture for transcriptional-translational coupling in prokaryotes (2022) NAR Genomics and Bioinformatics 4 (3). DOI: 10.1093/nargab/lqac074

    Transcriptional-translational coupling is accepted to be a fundamental mechanism of gene expression in prokaryotes and therefore has been analyzed in detail. However, the underlying genomic architecture of the expression machinery has not been well investigated so far. In this study, we established a bioinformatics pipeline to systematically investigated >1800 bacterial genomes for the abundance of transcriptional and translational associated genes clustered in distinct gene cassettes. We identified three highly frequent cassettes containing transcriptional and translational genes, i.e. rplk-nusG (gene cassette 1; in 553 genomes), rpoA-rplQ-rpsD-rpsK-rpsM (gene cassette 2; in 656 genomes) and nusA-infB (gene cassette 3; in 877 genomes). Interestingly, each of the three cassettes harbors a gene (nusG, rpsD and nusA) encoding a protein which links transcription and translation in bacteria. The analyses suggest an enrichment of these cassettes in pathogenic bacterial phyla with >70% for cassette 3 (i.e. Neisseria, Salmonella and Escherichia) and >50% for cassette 1 (i.e. Treponema, Prevotella, Leptospira and Fusobacterium) and cassette 2 (i.e. Helicobacter, Campylobacter, Treponema and Prevotella). These insights form the basis to analyze the transcriptional regulatory mechanisms orchestrating transcriptional–translational coupling and might open novel avenues for future biotechnological approaches.
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  • Jan D Hüwel, Prof. Dr. Florian Haselbeck, Prof. Dr. Dominik Grimm, Christian Beecks

    Dynamically Self-Adjusting Gaussian Processes for Data Stream Modelling (2022) Vortrag auf der 45th German Conference on Artificial Intelligence, Lecture Notes in Artificial Intelligence am 22.09.2022, virtuell in Trier .

    One of the major challenges in time series analysis are changing data distributions, especially when processing data streams. To ensure an up-to-date model delivering useful predictions at all times, model reconfigurations are required to adapt to such evolving streams. For Gaussian processes, this might require the adaptation of the internal kernel expression. In this paper, we present dynamically self-adjusting Gaussian processes by introducing Event Triggered Kernel Adjustments in Gaussian process modelling (ETKA), a novel data stream modelling algorithm that can handle evolving and changing data distributions. To this end, we enhance the recently introduced Adjusting Kernel Search with a novel online change point detection method. Our experiments on simulated data with varying change point patterns suggest a broad applicability of ETKA. On real-world data, ETKA outperforms comparison partners that differ regarding the model adjustment and its refitting trigger in nine respective ten out of 14 cases. These results confirm ETKA's ability to enable a more accurate and, in some settings, also more efficient data stream processing via Gaussian processes.Code availability: https://github.com/JanHuewel/ETKA
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Zentrum für Forschung und Wissenstransfer - Lageplan in Weihenstephan an der HSWT

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Zentrum für Forschung und Wissenstransfer
Gebäude H21
Am Staudengarten 9
85354 Freising

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