This study sought to determine if the 2012 Eighth Grade Summer Reading Camp curriculum was aligned with the students’ needs. To determine if curriculum alignment existed, the researcher completed a qualitative and quantitative study. The qualitative study consisted of interviewing the school district program development team to ascertain how the curriculum was designed. The quantitative segment involved running descriptive statistics for student performance on the Pre-program Benchmark Examination. The determined student knowledge voids were compared to the amount of instructional time spent taught teaching those individual benchmarks to ascertain if the curriculum was aligned with student need. The curriculum was determined to not be aligned with the performance deficiencies of the students.

Cyber-physical systems (CPS) connect the physical and digital domains and are often realized as spatially distributed. CPS is built on the Internet of Things (IoT) and Internet of Services, which use cloud architecture to link a swarm of devices over a decentralized network. Modern CPSs are undergoing a foundational shift as Industry 4.0 is continually expanding its boundaries of digitization. From automating the industrial manufacturing process to interconnecting sensor devices within buildings, Industry 4.0 is about developing solutions for the digitized industry. An extensive amount of engineering efforts are put to design dynamically scalable and robust automation solutions that have the capacity to integrate heterogeneous CPS. Such heterogeneous systems must be able to communicate and exchange information with each other in real-time even if they are based on different underlying technologies, protocols, or semantic definitions in the form of ontologies. This development is subject to interoperability challenges and knowledge gaps that are addressed by engineers and researchers, in particular, machine learning approaches are considered to automate costly engineering processes. For example, challenges related to predictive maintenance operations and automatic translation of messages transmitted between heterogeneous devices are investigated using supervised and unsupervised machine learning approaches. In this thesis, a machine learning-based collaboration and automation-oriented IIoT framework named Cloud-based Collaborative Learning (CCL) is developed. CCL is based on a service-oriented architecture (SOA) offering a scalable CPS framework that provides machine learning-as-a-Service (MLaaS). Furthermore, interoperability in the context of the IIoT is investigated. I consider the ontology of an IoT device to be its language, and the structure of that ontology to be its grammar. In particular, the use of aggregated language and structural encoders is investigated to improve the alignment of entities in heterogeneous ontologies. Existing techniques of entity alignment are based on different approaches to integrating structural information, which overlook the fact that even if a node pair has similar entity labels, they may not belong to the same ontological context, and vice versa. To address these challenges, a model based on a modification of the BERT_INT model on graph triples is developed. The developed model is an iterative model for alignment of heterogeneous IIoT ontologies enabling alignments within nodes as well as relations. When compared to the state-of-the-art BERT_INT, on DBPK15 language dataset the developed model exceeds the baseline model by (HR@1/10, MRR) of 2.1%. This motivated the development of a proof-of-concept for conducting an empirical investigation of the developed model for alignment between heterogeneous IIoT ontologies. For this purpose, a dataset was generated from smart building systems and SOSA and SSN ontologies graphs. Experiments and analysis including an ablation study on the proposed language and structural encoders demonstrate the effectiveness of the model. The suggested approach, on the other hand, highlights prospective future studies that may extend beyond the scope of a single thesis. For instance, to strengthen the ablation study, a generalized IIoT ontology that is designed for any type of IoT devices (beyond sensors), such as SAREF can be tested for ontology alignment. Next potential future work is to conduct a crowdsourcing process for generating a validation dataset for IIoT ontology alignment and annotations. Lastly, this work can be considered as a step towards enabling translation between heterogeneous IoT sensor devices, therefore, the proposed model can be extended to a translation module in which based on the ontology graphs of any device, the model can interpret the messages transmitted from that device. This idea is at an abstract level as of now and needs extensive efforts and empirical study for full maturity.

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Die Untersuchung der antimikrobiellen Fähigkeiten von Bodenisolaten ist von großer Bedeutung, um neue antibiotische Substanzen zu entdecken. Im Rahmen eines mikrobiologischen Praktikums wurden von Studierenden Bodenproben gesammelt und daraus 32 Streptomyces-Isolate gewonnen. Diese Isolate wurden auf ihre antimikrobiellen Leistungen untersucht und taxonomisch eingeordnet. Mittels Multi-Locus-Sequenz-Typisierung (MLST) wurden die 32 Bodenisolate der Gattung Streptomyces zugeordnet. Es gelang, alle Isolate erfolgreich dieser Gattung zuzuordnen. Allerdings konnte nicht für jedes Isolat eine eindeutige Spezieszugehörigkeit durch MLST festgestellt werden. Der Fokus lag insbesondere auf der detaillierten Analyse und Charakterisierung der Bodenisolate #4I1 und #12I2. Mittels Baumrekonstruktionen, basierend auf 16S-rRNA, MLST- und Gesamtgenomanalysen, wurden diese Stämme der Spezies Streptomyces silvae For3T zugeordnet. Das Genom des endophytischen Streptomyces sp. M3, welches aus der NCBI-Datenbank entnommen wurde, konnte ebenfalls dieser Spezies zugeordnet werden. Weiterhin wurde eine vergleichende Genomanalyse durchgeführt, um die genetische Struktur und die metabolischen Eigenschaften der S. silvae-Stämme zu erforschen. Diese umfassenden genetischen Untersuchungen trugen erheblich zum Verständnis der innerartlichen Variabilität und der adaptiven Merkmale der untersuchten Streptomyces-Stämme bei. Die physiologischen Profile der S. silvae-Stämme wurden mittels verschiedener Tests auf ihre antimikrobiellen Eigenschaften und ihre Anpassungsfähigkeit an verschiedene Umweltbedingungen untersucht. Besondere Aufmerksamkeit wurde dem Stamm S. silvae #4I1 gewidmet. Dieser wurde ausführlichen physiologischen Tests unterzogen, um eine präzise taxonomische Beschreibung als Repräsentant seiner Spezies zu ermöglichen. Die Ergebnisse der Studie zeigten, dass die S. silvae-Stämme ein breites Spektrum antimikrobieller Aktivitäten aufweisen. Die Ergebnisse der vergleichenden Genomanalyse verdeutlichten eine enge evolutionäre Verwandtschaft zwischen den S. silvae-Stämmen und offenbarten das Potenzial dieser Stämme zur Produktion von Sekundärmetaboliten, die zur Bekämpfung von pathogenen Mikroorganismen beitragen könnten.:Inhaltsverzeichnis___________________________________________________ I Abkürzungsverzeichnis______________________________________________ VI Zusammenfassung________________________________________________ VIII Summary_________________________________________________________ X Einleitung________________________________________________________ 1 Antibiotika________________________________________________________ 1 Innovative Lösungen zur Bekämpfung von Antibiotikaresistenzen_____________ 3 Identifizierung von Antibiotikaklassen mittels Ganzzell-Biosensoren____________ 4 Gattung Streptomyces______________________________________________ 6 1.4.1 Merkmale und Lebensweise von Streptomyces spp.___________________ 8 1.4.2 Genomstruktur von Streptomyces: Charakteristika und Besonderheiten___ 13 1.4.3 Genomische Organisation der Gene für Sekundärmetaboliten in Streptomyces spp. ____ 15 Zielstellung______________________________________________________ 17 2 Material und Methoden___________________________________________ 18 Verwendete Laborgeräte____________________________________________ 18 Materialien______________________________________________________ 19 2.2.1 Verbrauchsmaterialien_________________________________________ 19 2.2.2 Chemikalien_________________________________________________ 19 2.2.3 Medien_____________________________________________________ 21 2.2.4 Verwendete Bakterienstämme und Oligonukleotide___________________ 29 Physiogische Methoden____________________________________________ 31 2.3.1 Isolierung und Herstellung der Sporensuspensionen von Streptomyces spp. aus Bodenproben 31 2.3.2 Kultivierung der Streptomyces-Stämme____________________________ 32 2.3.3 Kultivierung der Biosensoren und Testorganismen___________________ 33 Methodische Herangehensweisen zur Evaluierung der antimikrobiellen Aktivitäten von Streptomyces spp. 34 2.4.1 Evaluierung des Hemmpotenzials von Streptomyces-Stämmen gegenüber ausgewählten Testorganismen 34 2.4.2 Biosensor-basierte Identifikation antibiotischer Substanzklassen_________ 36 Vergleichende Morphologie der S. silvae-Stämme 37 Physiologische Charakterisierung von S. silvae #4I1______________________ 37 2.6.1 Morphologie und Bildung melanoider Pigmente______________________ 37 2.6.2 Bestimmung der Nutzung verschiedener Kohlenstoffquellen sowie der Natriumchloridtoleranz und Lysozymresistenz_ 37 2.6.3 Evaluierung der pH-Präferenzen_________________________________ 38 2.6.4 Bestimmung der Hämolyseaktivität________________________________ 39 2.6.5 Bestimmung des Temperaturoptimums_____________________________ 39 2.6.6 Analyse der Mikromorphologie mittels Elektronenmikroskopie___________ 39 Molekularbiologische und genetische Methoden__ 40 2.7.1 Isolation genomischer DNA aus Bodenisolaten______________________ 40 2.7.2 Messung der Konzentration genomischer DNA______________________ 40 2.7.3 Gesamt-Genom-Sequenzierung__________________________________ 40 2.7.4 MLST (Multi-Locus-Sequenz-Typisierung)__________________________ 41 2.7.4.1 PCR (Polymerasekettenreaktion)_______________________________ 41 2.7.4.2 Gelektrophorese____________________________________________ 43 2.7.4.3 Extraktion und Aufreinigung der PCR-Produkte____________________ 43 2.7.4.4 Sequenzierung der PCR-Produkte______________________________ 43 Bioinformatische Methoden__________________________________________ 44 2.8.1 Anwendung der MLST-Methode bei den Bodenisolaten________________ 44 2.8.2 Bioinformatische Methoden zur vergleichenden Genomik der vier S. silvae-Stämme ___ 45 2.8.3 Bioinformatische Strategien zum Genomassembling__________________ 46 2.8.3.1 Optimierung der Sequenzdaten aus der Gesamtgenomsequenzierung__ 47 2.8.3.2 de-novo-Genomassembling___________________________________ 48 2.8.3.3 Mapping__________________________________________________ 48 2.8.3.4 Extraktion der Genomsequenz_________________________________ 50 2.8.3.5 Auswahl der Referenzsequenzen für die Contig-Verschmelzung (Scaffolding) _ 50 2.8.3.6 Genomrekonstruktion durch Scaffolding__________________________ 51 2.8.4 Bioinformatische Ansätze zur Ermittlung genomischer Kennziffern________ 52 2.8.4.1 Berechnung des ANI-Wertes___________________________________ 53 2.8.4.2 Berechnung des genomischen GC (Guanin-Cytosin)- und AT (Adenin-Thymin)-Gehaltes 53 2.8.4.3 Berechnung des GC-Versatzes_________________________________ 54 2.8.5 16S-rRNA-Analyse____________________________________________ 55 2.8.5.1 16S-rRNA-Sequenzextraktion__________________________________ 55 2.8.5.2 16S-rRNA-Prozessierung und Alignment__________________________ 56 2.8.5.3 Generierung des 16S-rRNA-Baums_____________________________ 57 2.8.6 AutoMLST_______________________________________________________ 59 2.8.7 Gesamtgenom-basierte Konstruktion eines phylogenetischen Baumes____ 60 2.8.8 Genomische Charakterisierung und Identifizierung relevanter Gencluster in den Genomen von S.silvae__ 61 2.8.8.1 Genannotation___________________________________________________ 61 2.8.8.2 Identifikation von BGCs_______________________________________ 62 2.8.8.3 Identifikation von Antibiotikaresistenzgenen_______________________ 63 2.8.9 Visualisierung____________________________________________________ 63 2.8.9.1 Erstellung zirkulärer Genomkarten______________________________ 64 2.8.9.2 MAUVE-Alignement__________________________________________ 64 3 Ergebnisse_____________________________________________________ 66 Systematische Taxonomie von Streptomyces-Bodenisolaten mittels MLST__ 66 Vielfalt und Spezifität von Streptomyces-Isolaten: Inhibitorische Kapazitäten und biosensorische Charakterisierungen_ 69 3.2.1 Bestimmung der inhibitorischen Kapazität von Streptomyces-Isolaten gegenüber ausgewählten Testorganismen__70 3.2.2 Systematische Evaluierung der biosensorischen Profile von 36 Streptomyces-Stämmen _ 73 Komparative Genomanalyse von S. silvae______ 78 3.3.1 Optimierung der Genomassemblierung der Streptomyces-Isolate #12I2 und #4I1 ______ 79 3.3.2 Phylogenetische Einordnung von S. silvae-Stämmen mittels 16S-rRNA-Analyse und MLST 81 3.3.3 GBDP-gestützte Analyse zur Aufklärung der Phylogenie von S. silvae-Stämmen_______ 82 3.3.4 Komparative Genomkartografie zur Visualisierung der genetischen Diversität in der S. silvae-Klade_ 84 3.3.5 Identifikation homologer Genombereiche___________________________ 88 3.3.6 Vergleichende Analyse der BGCs für Sekundärmetabolite in den vier S. silvae-Genomen 91 3.3.7 Komparative Analyse der Antibiotikaresistenz-assoziierten Gentypen in den S. silvae-Stämmen__ 95 Vergleichende physiologische Untersuchungen von S. silvae-Stämmen________ 99 3.4.1 Differenzierte Analyse antimikrobieller Aktivitäten von S. silvae-Stämmen in Abhängigkeit vom Nährmedium__ 99 3.4.2 Klassifikation der antimikrobiellen Sekundärmetaboliten von S. silvae-Stämmen unter Einsatz lumineszierender Biosensoren_101 3.4.3 Vergleichende Analyse der Makromorphologie von S. silvae-Stämmen auf diversen Nährmedien_ 103 Physiologische Charakterisierung von S. silvae #4I1_____________________ 106 3.5.1 REM-basierte Untersuchung der Mikromorphologie von S. silvae #4I1___ 107 3.5.2 Morphologische Analyse und Melanoidpigmentsynthese von S. silvae #4I1 auf ISP-Medien__ 108 3.5.3 Analyse des Verwertungsspektrums von Kohlenstoffquellen durch S. silvae #4I1 _____ 110 3.5.4 Bestimmung der Salztoleranz von S. silvae #4I1_____________________ 112 3.5.5 Einfluss des pH-Wertes auf das Wachstum von S. silvae #4I1__________ 113 3.5.6 Bestimmung der Lysozymresistenz bei S. silvae #4I1_________________ 114 3.5.7 Untersuchung der hämolytischen Aktivität von S. silvae #4I1___________ 116 3.5.8 Bestimmung der Temperaturpräferenz von S. silvae #4I1______________ 117 4 Diskussion____________________________________________________ 118 Genetische und Phänotypische Diversität der Streptomyces-Isolate__________ 119 4.1.1 MLST-basierte Einordnung und genetische Diversität der Streptomyces-Isolate _______ 119 4.1.2 Kultivierbarkeit der Streptomyces-Isolate__________________________ 119 4.1.3 Hemmeffekte von Streptomyces-Isolaten__________________________ 120 4.1.4 Biosensorische Profile und antimikrobielle Fähigkeiten von Streptomyces-Bodensolaten 123 Fokussierte Untersuchungen der S. silvae-Stämme_______ 125 4.2.1 Signifikanz der 16S-rRNA-Sequenzidentität in S. silvae_______________ 125 4.2.2 Etablierung von S. silvae als monophyletischen Gruppierung__________ 126 4.2.3 Sekundärmetabolismus und der Einfluss variabler Kulturbedingungen auf die antimikrobielle Aktivität und Biosensor-Induktion der S. silvae-Stämme__130 5 Ausblick______________________________________________________ 139 Literaturverzeichnis_______________________________________________ 142 Anhang________________________________________________________ 173

The focus of this research was the spontaneous magnetic alignment responses of animals. We show that snapping turtles (Chelydra serpentina) and crayfish (Cambarus sciotensis) spontaneously align their body axes relative to the magnetic field. In snapping turtles, this response is sensitive to low-level radio frequency fields, consistent with a mechanism involving a light-dependent radical pair mechanism. Findings from the turtle experiments also suggest that the Earth's magnetic field plays an important role in encoding spatial information in novel surroundings, and may help to organize multiple locales into a 'mental map' of familiar space. Given the importance of magnetic input in many aspects of spatial behavior, another important finding was that magnetic alignment of yearling turtles was disrupted by high levels of maternally transferred mercury, an industrial waste product found at high levels in some fresh water ecosystems. In crayfish, we investigated the effects of ectosymbionts (Annelida: Branchiobdellida) on magnetic alignment responses. Interestingly, the response of crayfish to magnetic cues parallels the complex symbiotic interaction between crayfish and their ectosymbiotic worms, which changes from mutualistic to parasitic with increasing worm density. Our working hypothesis was that these changes in spatial behavior may increase or decrease contact to other crayfish, and therefore increase or decrease transmission rates. Next, to address the ontogeny of the SMA, we attempted to replicate an earlier study showing a possible magnetic alignment response in chicken embryos. Although chicken embryos did show non-random alignment, we were not able to find a magnetic effect. Alignment is also an important feature of animal constructions and is very likely to have fitness consequences, which we explored in woodpecker cavity alignments in a meta-analysis of available global data. The latitudinal and continental pattern in 23 species of woodpeckers suggests that an alignment response can have the proximate function to regulate microclimate in the cavity and therefore, presumably, optimize incubation temperatures and increase hatching success. Overall, the presented findings show how experimental and observational studies of spontaneous alignment behavior can provide insight into the ecology and sensory biology of a wide range of animals. / Ph. D.

This study presents the development and evaluation of an automated pipeline for genome comparison, leveraging four bioinformatics tools: alignment-based methods (pyANI, Fas- tANI) and k-mer-based methods (Sourmash, BinDash 2.0). The analysis focuses on high- quality genomic datasets characterized by 100% completeness, ensuring consistency and accuracy in the comparison process. The pipeline processes genomes under uniform con- ditions, recording key performance metrics such as execution time and rank correlations. Initial comparisons were conducted on a subset of five genomes, generating 10 unique pair- wise comparisons to establish baseline performance. This preliminary analysis identified k = 10 as the optimal k-mer size for Sourmash and BinDash, significantly improving their comparability with alignment-based methods. For the expanded dataset of 175 genomes, encompassing (175C2) = 15,225 unique comparisons, pyANI and FastANI demonstrated high similarity values, often exceeding 90% for closely related genomes. Rank correlations, calculated using Spearman's ρ and Kendall's τ , high- lighted strong agreement between pyANI and FastANI (ρ = 0.9630 , τ = 0.8625) due to their shared alignment-based methodology. Similarly, Sourmash and BinDash, both employing k-mer-based approaches, exhibited moderate-to-strong rank correlations (ρ = 0.6967, τ = 0.5290). In contrast, the rank correlations between alignment-based and k-mer-based tools were lower, underscoring methodological differences in genome similarity calculations. Execution times revealed significant contrasts between the tools. Alignment-based meth- ods required substantial computation time, with pyANI taking an average of 1.97 seconds per comparison and FastANI averaging 0.81 seconds per comparison. Conversely, k-mer- based methods demonstrated exceptional computational efficiency, with Sourmash complet- ing comparisons in 2.1 milliseconds and BinDash in just 0.25 milliseconds per comparison, reflecting a difference of nearly three orders of magnitude between the two categories. These results underscore the trade-offs between computational cost and methodological approaches in genome similarity estimation. This study provides valuable insights into the relative strengths and weaknesses of genome comparison tools, offering a comprehensive framework for selecting appropriate methods for diverse genomic research applications. The findings emphasize the importance of param- eter optimization for k-mer-based tools and highlight the scalability of these methods for large-scale genomic analyses. / Master of Science / This study explores the strengths and weaknesses of different tools used to compare genomes, which are the complete set of DNA in living organisms. Comparing genomes allows scientists to understand how different species are related, uncover shared traits, and identify what makes each species unique. The tools we examined fall into two main categories: detailed tools (called alignment-based methods) and faster, more approximate tools (called k-mer- based methods). The detailed tools, such as pyANI and FastANI, compare DNA sequences piece by piece, providing very accurate results. In contrast, the faster tools, such as Sourmash and BinDash, look for patterns in smaller sections of DNA, which makes them much quicker but sometimes less precise. To start, we tested these tools on a small group of genomes to see how they performed. By adjusting a setting in the faster tools, we found that their results became more similar to the detailed tools, improving their reliability. Encouraged by these findings, we expanded the comparison to a much larger dataset of 175 genomes. For this larger dataset, the detailed tools provided highly accurate results but required much more time and computational power. On the other hand, the faster tools completed the comparisons in a fraction of the time, making them ideal for larger datasets where quick results are needed. We also compared how the tools ranked genome similarities and found that tools using similar methods, like pyANI and FastANI, had very consistent rankings. Likewise, the faster tools, Sourmash and BinDash, also agreed with each other. However, the rankings between the two types of tools (detailed versus faster) were less consistent, reflecting their different approaches to genome comparison. This research provides a practical guide for scientists choosing tools to compare genomes. If accuracy and detail are most important, alignment-based tools are the best choice, though they take more time and computational resources. If speed is critical, such as when working with very large datasets, k-mer-based tools offer an excellent alternative. By understanding the strengths and trade-offs of each method, researchers can make informed decisions to suit their specific needs, whether focusing on small, detailed studies or large-scale genome analyses.