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Tardigrades are multicellular organisms, resistant to extreme environmental changes such as heat, drought, radiation and freezing. They outlast these conditions in an inactive form (tun) to escape damage to cellular structures and cell death. Tardigrades are apparently able to prevent or repair such damage and are therefore a crucial model organism for stress tolerance. Cultures of the tardigrade Milnesium tardigradum were dehydrated by removing the surrounding water to induce tun formation. During this process and the subsequent rehydration, metabolites were measured in a time series by GC-MS. Additionally expressed sequence tags are available, especially libraries generated from the active and inactive state. The aim of this integrated analysis is to trace changes in tardigrade metabolism and identify pathways responsible for their extreme resistance against physical stress.
The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.
Reliable personal identification remains a major challenge in forensic medicine, particularly in cases involving decomposition, thermal injury, or absence of DNA and dental records. In this context, anatomically protected and morphologically unique structures such as the sphenoid sinus may serve as valuable biometric markers. The aim of this study was to evaluate the forensic applicability of sphenoid sinus morphology using computed tomography (CT), automated segmentation, and three-dimensional (3D) computational analysis. The proposed framework integrates CT-based image preprocessing, automated 3D segmentation using nnU-Net architecture, and geometric comparison of reconstructed sphenoid sinus models through point cloud analysis and deep learning approaches. Morphological variability, spatial configuration, and structural stability of the sphenoid sinus were analyzed as discriminative biometric features. The GSA-Net–based identification model demonstrated high recognition performance, achieving Top-1 accuracies exceeding 97.8% and Top-3 accuracies up to 100% in controlled datasets. The results support the concept that the sphenoid sinus possesses sufficient individuality and anatomical preservation to enable reliable ante-mortem and post-mortem identification. The study highlights the potential of integrating automated segmentation and AI-driven 3D analysis into forensic workflows and emphasizes the importance of standardized imaging protocols and larger annotated datasets for future clinical and forensic implementation.
Background: Human identification in forensic contexts becomes challenging when fingerprints, dental records, or DNA are unavailable. The sphenoid sinus, owing to its protected anatomical location and high inter-individual variability, offers potential as a biometric marker.
Methods: We first replicated a YOLOv8-nano segmentation experiment using annotated CT scans in DICOM data format. Images were preprocessed with soft-tissue windowing and converted into high-quality PNGs for training. Model performance was evaluated with standard segmentation metrics. Based on observed limitations, we decided for 3D modelling approaches involving manual, semi-automatic, and normalization-based segmentation pipelines.
Results: The YOLOv8 model achieved high validation performance (mAP50 = 0.921; true positive rate = 91%; true negative rate = 100%), which is promising for the future implementation. However, its reliability for consistent sphenoid sinus segmentation was limited by anatomical complexity and heterogeneous datasets. In contrast, 3D modelling methods produced more robust and accurate reconstructions of the sphenoid sinus.
Conclusion: While deep learning-based 2D segmentation provides a strong baseline for sinus analysis, 3D modelling approaches currently offer greater reliability for forensic applications. Combining both strategies may pave the way toward fully automated, scalable identification frameworks based on sphenoid sinus morphology.
Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action
(2023)
The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish “gold standard” protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory ‘omics’ features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.
Die Auswertung komplexer anatomischer Strukturen auf Basis dreidimensionaler Bilddaten stellt eine zentrale Aufgabe der medizinisch-technischen Bildverarbeitung dar. Insbesondere Strukturen mit hoher interindividueller Variabilität erfordern robuste Segmentierungs- und Modellierungsverfahren, um reproduzierbare morphologische Analysen zu ermöglichen. Der Sinus sphenoidalis nimmt hierbei eine besondere Stellung ein, da er aufgrund seiner tiefen Lage an der Schädelbasis weitgehend vor äußeren Einflüssen geschützt ist und zugleich ausgeprägte individuelle Unterschiede aufweist.
In dieser Arbeit wird ein durchgängiger volumetrischer Workflow zur Segmentierung, dreidimensionalen Rekonstruktion und geometrischen Analyse des Sinus sphenoidalis auf Basis kranialer CT-Daten vorgestellt. Der methodische Schwerpunkt liegt auf standardisierter Vorverarbeitung, semi-automatischer Segmentierung sowie Deep-Learning-basierter Volumensegmentierung unter Einsatz des nnU-Net-Frameworks.
Die Ergebnisse zeigen, dass durch eine Kombination aus intensitätsbasierter Normalisierung, geometrischer Vereinheitlichung und datenadaptiver Segmentierung konsistente und vergleichbare 3D-Modelle erzeugt werden können. Auch bei deutlich heterogenen Akquisitionsparametern ließ sich eine stabile Segmentierungsleistung erzielen. Der vorgestellte Ansatz bietet damit eine belastbare Grundlage für weiterführende automatisierte morphologische Vergleichsverfahren.
Prostate cancer is one of the most common causes of death in men. In this work a siRNA screen of around 1500 cancer-relevant genes was performed using 3 different cell lines (VCaP, LAPC-4, RWPE-1). A novel technique, the living cell array, was initiated in order to obtain information about the biology of Androgeninduced growth in prostate tumour cell lines. This technique is based on the principal of reverse transfection [1] and genes are knocked down by siRNAs. The cells on the living cell array were set under stress by reduction of the androgens in the media while the proliferation and apoptosis were quantified. The statistical analysis of the data implicates the success of the screen and shows that this method is suitable for large-scale experiments.
Genomics technologies, especially transcriptional profiling, allows comparisons of gene expression within and across different organisms. By the use of the model group Tardigrada, also known as water bear, we try to understand the phenomenon of cryptobiosis. The dry organisms can survive for years without water. When re-exposed to water, the animals rehydrate and come back to life. The expression of genes in response to dehydratation and rehydratation is being examined in our laboratory through the generation of expressed sequence tags (ESTs), representational difference analysis (RDA) and subsequent microarray analysis. Molecular dissection of this complex phenomenom, including gene regulation, will allow the development of techniques for preservation and stabilisation of biological materials in a dried state.
Die simultane Bestimmung mehrerer Analyten und die Erstellung komplexer Parameterprofile erlangt immer größere Bedeutung in der heutigen Labordiagnostik. Die Bead-basierte Multiplexanalytik bietet hier eine flexible, schnelle und einfache Methode zur Erstellung individueller Analysen. Aufgrund der Vielzahl von Anwendungsmöglichkeiten, die diese moderne Nachweismethode im Bereich molekularbiologischer Fragestellungen bietet, ist die Etablierung der Beadbasierten Multiplexanalytik im Labor für Molekularbiologie und funktionelle Genomik der Technischen Hochschule Wildau von großem Nutzen. Zur Einarbeitung in das Testsystem wurden die Konzentrationen der Zytokine IL-2, IL-4, IFN-γ und TNF-α in Zellkulturüberständen mit kommerziellen Fertigsystemen gemessen und mit mRNA-Expressionsraten der gleichen Proben verglichen. Des Weiteren wurde ein Testsystem zum Nachweis von humanen Antikörpern der Klassen IgG und IgM sowie deren antigen-spezifischer Anteil in Zellkulturüberständen entwickelt. Außerdem konnte durch die erfolgreiche Detektion von DNA-gekoppelten Beads mittels markierter Oligonukleotidsequenz die Kopplung und die Anwendbarkeit der Methode auf Bindungsexperimente mit Nukleotidsequenzen gezeigt werden.
Einführung eines Qualitätsmanagement-Systems in einem molekularbiologischen Labor einer Hochschule
(2010)
Zertifizierte Qualitätsmanagementsysteme (QM-Systeme) sind in akademischen Forschungslaboren bisher kaum vertreten. Hier ist vor allem die Bedeutung eines zertifizierten Labors vor dem Hintergrund der Veröffentlichung wissenschaftlicher Publikationen scheinbar bedeutungslos. Im industriellen Bereich hingegen sind QM-Systeme weitaus häufiger eingeführt. Labore im akademischen Bereich stehen jedoch grundsätzlich in der Verantwortung, schonend mit den Ressourcen des Staates umzugehen, und haben darüber hinaus das Bestreben, Forschungs-/Industrieaufträge einzuwerben. Mit dieser Publikation wird gezeigt, wie durch die Einrichtung eines QM-Systems und der Einhaltung der normativen Vorgabe ein verbesserter schonender Umgang mit staatlichen Ressourcen möglich ist, die Ergebnissicherheit erhöht, Betriebsabläufe optimiert, die Chancen für Studenten auf dem Arbeitsmarkt verbessert werden und mehr Forschungs-/Industrieaufträge akquiriert werden können. Es wird dargestellt, wie ein QM-System in einem Labor eingerichtet werden kann. Dabei wird erläutert, welche QM-Regelwerke für ein Labor im Hochschulbereich in Betracht kommen. Zudem wird eine Methodik in Form eines Projektplans vorgestellt, mit dem die Anforderungen der Norm strukturiert umgesetzt werden können. Das QM-System wurde im Labor für Molekularbiologie und Funktionelle Genomik an der Technischen Hochschule Wildau [FH] eingeführt und im Rahmen der Zertifizierung der Hochschule in das Audit einbezogen.
