Dysregulated host responses to infection can lead to organ dysfunction and sepsis, causing millions of global deaths each year. To alleviate this burden, improved prognostication and biomarkers of response are urgently needed. We investigated the use of whole-blood transcriptomics for stratification of patients with severe infection by integrating data from 3149 samples from patients with sepsis due to community-acquired pneumonia or fecal peritonitis admitted to intensive care and healthy individuals into a gene expression reference map. We used this map to derive a quantitative sepsis response signature (SRSq) score reflective of immune dysfunction and predictive of clinical outcomes, which can be estimated using a 7- or 12-gene signature. Last, we built a machine learning framework, SepstratifieR, to deploy SRSq in adult and pediatric bacterial and viral sepsis, H1N1 influenza, and COVID-19, demonstrating clinically relevant stratification across diseases and revealing some of the physiological alterations linking immune dysregulation to mortality. Our method enables early identification of individuals with dysfunctional immune profiles, bringing us closer to precision medicine in infection.

Background: Flow cytometry is a powerful tool for identifying and quantifying various cell markers, such as viability, vitality, and individual cell age, at single-cell stages. However, cell autofluorescence and marker fluorophore signals overlap at low fluorescence intensities. Thus, these signals must be unmixed before determining the age fraction. Methods and Results:A comparison was made between principal component regression (PCR) and random forest (RF) to predict autofluorescence signals of Saccharomyces pastorianus var. carlsbergensis in a flow cytometer. RF provided better prediction results than the PCR and was therefore determined to be better suited for unmixing signals. In the subsequent application for unmixing the autofluorescence signal from the marker fluorophore signal, the Gaussian mixture analysis based on RF was in better agreement with the microscopy-determined replicative age distribution than the PCR-based method. Conclusion:The proposed approach of single-laser spectral unmixing and subsequent Gaussian mixture analysis showed that the microscopy data was consistent with the unmixed fluorescence spectra. The demonstrated approach enables fast and reliable unmixing of flow cytometric spectral data using a single-laser spectral unmixing method. This analysis method enables age determination of cells in industrial processes. This age determination allows for quantifying the yeast cell's age fractions, providing a detailed view of age-related changes. Additionally, the bud scar labeling technique can be used to determine age-related changes in Pichia pastoris yeast for biotechnological applications or recombinant protein expression.

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Der Prozess der Bierherstellung präsentiert sich seit jeher als Triebkraft und prädestiniertes Einsatzgebiet für Innovationen -von der Einführung der modernen Kältetechnik durch Carl von Linde bis zur Etablierung von Fertigungsphilosophien wie der Process Analytical Technology (PAT) Initiative. Die PAT ist ein innovatives Werkzeug zur Gestaltung einer optimalen Prozessführung mit dem Ziel einer gesicherten Produktqualität. Im Gegensatz zu der in der Praxis etablierten Produktfreigabe mittels aufwändiger Laboranalytik wird hierbei eine prozessorientierte Validierung und Freigabe in Echtzeit angestrebt.The beer production process has always been a driving force and a suitable application area for innovations -from the introduction of modern refrigeration by Carl von Linde to the establishment of new manufacturing philosophies such as the Process Analytical Technology (PAT) initiative. The PAT is an innovative tool for implementing an optimum process control with the objective of consistent product quality. In contrast to the common practice of product release by extensive laboratory analysis, a process-oriented validation and release in real time is desired.

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Classification of barley varieties is a crucial part of the control and assessment of barley seeds especially for the malting and brewing industry. The correct classification of barley is essential in that a majority of decisions made regarding process specifications, economic considerations, and the type of product produced with the cereal are made based on the barley variety itself. This fact combined with the need to promptly assess the cereal as it is delivered to a malt house or production facility creates the need for a technique to quickly identify a barley variety based on a sample. This work explores the feasibility of differentiating between barley varieties based on the protein spectrum of barley seeds. In order to produce a rapid analysis of the protein composition of the barley seeds, lab-on-a-chip micro fluid technology is used to analyze the protein composition. Classification of the barley variety is then made using disjoint principle component models. This work included 19 different barley varieties. The varieties consisted of both winter and summer barley types. In this work, it is demonstrated that this system can identify the most likely barley variety with an accuracy of 95.9% based on cross validation and can screen summer barley with an accuracy of 95.2% and a false positive rate of 0.0% based on cross validation. This demonstrates the feasibility of the method to provide a rapid and relatively inexpensive method to verify the heritage of barley seeds.