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The human gut microbiota harbors untapped potential for biotechnological applications. Within the phylum of Bacteroidota, Phocaeicola vulgatus stands out as a promising candidate for sustainable production of key platform chemicals like succinate. However, genetic engineering of Phocaeicola sp. remains challenging due to its intricate molecular landscape. This study lays the groundwork for manipulating the central carbon pathways in Phocaeicola vulgatus, offering insights into overcoming genetic hurdles for increased succinate yields.
A Fourier scatterometry setup is evaluated to recover the key parameters of optical phase gratings. Based on these parameters, systematic errors in the printing process of two-photon polymerization (TPP) gray-scale lithography three-dimensional printers can be compensated, namely tilt and curvature deviations. The proposed setup is significantly cheaper than a confocal microscope, which is usually used to determine calibration parameters for compensation of the TPP printing process. The grating parameters recovered this way are compared to those obtained with a confocal microscope. A clear correlation between confocal and scatterometric measurements is first shown for structures containing either tilt or curvature. The correlation is also shown for structures containing a mixture of tilt and curvature errors (squared Pearson coefficient r2 = 0.92). This compensation method is demonstrated on a TPP printer: a diffractive optical element printed with correction parameters obtained from Fourier scatterometry shows a significant reduction in noise as compared to the uncompensated system. This verifies the successful reduction of tilt and curvature errors. Further improvements of the method are proposed, which may enable the measurements to become more precise than confocal measurements in the future, since scatterometry is not affected by the diffraction limit.
Microorganisms not only contribute to the spoilage of food but can also cause illnesses through consumption. Consumer concerns and doubts about the shelf life of the products and the resulting enormous amounts of food waste have led to a demand for a rapid, robust, and non-destructive method for the detection of microorganisms, especially in the food sector. Therefore, a rapid and simple sampling method for the Raman- and infrared (IR)-microspectroscopic study of microorganisms associated with spoilage processes was developed. For subsequent evaluation pre-processing routines, as well as chemometric models for classification of spoilage microorganisms were developed. The microbiological samples are taken using a disinfectable sampling stamp and measured by microspectroscopy without the usual pre-treatments such as purification separation, washing, and centrifugation. The resulting complex multivariate data sets were pre-processed, reduced by principal component analysis, and classified by discriminant analysis. Classification of independent unlabeled test data showed that microorganisms could be classified at genus, species, and strain levels with an accuracy of 96.5 % (Raman) and 94.5 % (IR), respectively, despite large biological differences and novel sampling strategies. As bacteria are exposed to constantly changing conditions and their adaptation mechanisms may make them inaccessible to conventional measurement methods, the methods and models developed were investigated for their suitability for microorganisms exposed to stress. Compared to normal growth conditions, spectral changes in lipids, polysaccharides, nucleic acids, and proteins were observed in microorganisms exposed to stress. Models were developed to discriminate microorganisms, independent of the involvement of various stress factors and storage times. Classification of the investigated bacteria yielded accuracies of 97.6 % (Raman) and 96.6 % (IR), respectively, and a robust and meaningful model was developed to discriminate different microorganisms at the genus, species, and strain levels. The obtained results are very promising and show that the methods and models developed for the discrimination of microorganisms as well as the investigation of stress factors on microorganisms by means of Raman- and IR-microspectroscopy have the potential to be used, for example, in the food sector for the rapid determination of surface contamination.
The development of whole-genome amplification (WGA) techniques has opened up new avenues for genetic analysis and genome research, in particular by facilitating the genome-wide analysis of few or even single copies of genomic DNA, such as from single cells (prokaryotic or eukaryotic) or virions. Using WGA, the few copies of genomic DNA obtained from such entities are unspecifically amplified using PCR or PCR-related processes in order to obtain higher DNA quantities that can then be successfully analysed further.
P30 - Das Elektrospinnen von halbleitenden Zinndioxidfasern für die Detektion von Wasserstoff
(2022)
Das Ziel dieser Arbeit ist die Entwicklung von dünnen keramischen Fasern als halbleitendes Sensormaterial zum Nachweis von Wasserstoff, möglichst bei Zimmertemperatur. Die elektrische Leitfähigkeit halbleitender Metalloxide ändert sich durch die Einwirkung von oxidierenden und reduzierenden Gasen auf die Oberfläche des Metalloxids. Dieser Effekt kann zur Messung der Gaskonzentration genutzt werden. Die Reaktion von Zinn(IV)-oxid mit Wasserstoff basiert auf der Reduktion des Zinn(IV)-oxids zum Zinn, wobei die Elektronen des Zinn(IV)-oxids im metallischen Zinn verbleiben und dort im nicht gebundenen Zustand zu einer Leitfähigkeitserhöhung beitragen. Die Reaktion des Wasserstoffes kann sowohl mit den Sauerstoffatomen des Oxids als auch mit adsorbierten Sauerstoffatomen an der Oxidoberfläche stattfinden.[ 6] Da die Reaktionen an der Oberfläche des Oxids stattfinden, sollten Sensoren mit einer großen Oberfläche im Vergleich zu metalloxidischen Bulkmaterialien eine höhere Empfindlichkeit aufweisen. [3] Die Verwendung von Fasern anstelle von Dünn- oder Dickschichten führt dabei zu einer besseren Sensitivität gegenüber Gasen.
Forensic DNA profiles are established by multiplex PCR amplification of a set of highly variable short tandem repeat (STR) loci followed by capillary electrophoresis (CE) as a means to assign alleles to PCR products of differential length. Recently, CE analysis of STR amplicons has been supplemented by high-throughput next generation sequencing (NGS) techniques that are able to detect isoalleles bearing sequence polymorphisms and allow for an improved analysis of degraded DNA. Several such assays have been commercialised and validated for forensic applications. However, these systems are cost-effective only when applied to high numbers of samples. We report here an alternative, cost-efficient shallow-sequence output NGS assay called maSTR assay that, in conjunction with a dedicated bioinformatics pipeline called SNiPSTR, can be implemented with standard NGS instrumentation. In a back-to-back comparison with a CE-based, commercial forensic STR kit, we find that for samples with low DNA content, with mixed DNA from different individuals, or containing PCR inhibitors, the maSTR assay performs equally well, and with degraded DNA is superior to CE-based analysis. Thus, the maSTR assay is a simple, robust and cost-efficient NGS-based STR typing method applicable for human identification in forensic and biomedical contexts.
Robust Identification and Segmentation of the Outer Skin Layers in Volumetric Fingerprint Data
(2022)
Despite the long history of fingerprint biometrics and its use to authenticate individuals, there are still some unsolved challenges with fingerprint acquisition and presentation attack detection (PAD). Currently available commercial fingerprint capture devices struggle with non-ideal skin conditions, including soft skin in infants. They are also susceptible to presentation attacks, which limits their applicability in unsupervised scenarios such as border control. Optical coherence tomography (OCT) could be a promising solution to these problems. In this work, we propose a digital signal processing chain for segmenting two complementary fingerprints from the same OCT fingertip scan: One fingerprint is captured as usual from the epidermis (“outer fingerprint”), whereas the other is taken from inside the skin, at the junction between the epidermis and the underlying dermis (“inner fingerprint”). The resulting 3D fingerprints are then converted to a conventional 2D grayscale representation from which minutiae points can be extracted using existing methods. Our approach is device-independent and has been proven to work with two different time domain OCT scanners. Using efficient GPGPU computing, it took less than a second to process an entire gigabyte of OCT data. To validate the results, we captured OCT fingerprints of 130 individual fingers and compared them with conventional 2D fingerprints of the same fingers. We found that both the outer and inner OCT fingerprints were backward compatible with conventional 2D fingerprints, with the inner fingerprint generally being less damaged and, therefore, more reliable.
The following work presents algorithms for semi-automatic validation, feature extraction and ranking of time series measurements acquired from MOX gas sensors. Semi-automatic measurement validation is accomplished by extending established curve similarity algorithms with a slope-based signature calculation. Furthermore, a feature-based ranking metric is introduced. It allows for individual prioritization of each feature and can be used to find the best performing sensors regarding multiple research questions. Finally, the functionality of the algorithms, as well as the developed software suite, are demonstrated with an exemplary scenario, illustrating how to find the most power-efficient MOX gas sensor in a data set collected during an extensive screening consisting of 16,320 measurements, all taken with different sensors at various temperatures and analytes.
Kollaborative Industrieroboter werden für produzierende Unternehmen immer kosteneffizienter. Während diese Systeme für den menschlichen Mitarbeiter eine große Hilfe sein können, stellen sie gleichzeitig ein ernstes Gesundheitsrisiko dar, wenn die zwingend notwendigen Sicherheitsmaßnahmen nur unzureichend umgesetzt werden. Herkömmliche Sicherheitseinrichtungen wie Zäune oder Lichtvorhänge bieten einen guten Schutz, aber solch statische Schutzvorrichtungen sind in neuen, hochdynamischen Arbeitsszenarien problematisch.
Im Forschungsprojekt BeyondSPAI wurde ein Funktionsmuster eines Multisensorsystems zur Absicherung solcher dynamischer Arbeitsszenarien entworfen, implementiert und im Feld getestet. Kern des Systems ist eine robuste optische Materialklassifikation, die mit Hilfe eines intelligenten InGaAs-Kamerasystems Haut von anderen typischen Werkstückoberflächen (z.B. Holz, Metalle od. Kunststoffe) unterscheiden kann. Diese einzigartige Eigenschaft wird genutzt, um menschliche Mitarbeiter zuverlässig zu erkennen, so dass ein konventioneller Roboter in Folge als personenbewusster Cobot arbeiten kann.
Das System ist modular und kann leicht mit weiteren Sensoren verschiedenster Art erweitert werden. Es kann an verschiedene Marken von Industrierobotern angepasst werden und lässt sich schnell an bestehenden Robotersystemen integrieren. Die vier vom System bereitgestellten Sicherheitsausgänge können dazu verwendet werden - abhängig von der durchdrungenen Überwachungszone - entweder eine Warnung auszugeben, die Bewegung des Roboters auf eine sichere Geschwindigkeit zu verlangsamen, oder den Roboter sicher anzuhalten. Sobald alle Zonen wieder als „eindeutig frei von Personen“ identifiziert sind, kann der Roboter wieder beschleunigen, seine ursprüngliche Bewegung wiederaufnehmen und die Arbeit fortsetzen.