Bioinformatics approaches to study antibiotics resistance emergence across levels of biological organization.

The Review on Antimicrobial Resistance predicts that in thirty years infections with antibiotic-resistant microorganisms will become one of the leading causes of death. The discovery of new antibiotics has so far been too slow to ensure continuous use of antibiotics in the face of growing resistance. Therefore, efforts to curb resistance emergence gain in importance. These efforts comprise two complementary strategies. The first focuses on the mechanisms of resistance emergence, in the hope that it would enable development of pharmacological agents constraining resistance emergence. The second aims at improving antibiotic use practices, based on studies of the impact of antibiotics on resistance emergence within patient populations. Antibiotic resistance emerges in bacterial cells, negatively influences the human gut microbiome, and transfers between people. Hence, antibiotic resistance has impacts across several levels of biological organization. This thesis describes four projects, which concerned various aspects of antibiotics resistance. The first two projects deal with basic resistance emergence mechanisms, on the level of bacterial strains and bacterial consortia, whereas the other two deal with finding better practices for antibiotic use on a population level. During the first project, I analyzed changes in genomes of MRSA strains isolated from several patients throughout antibiotic therapies and developing MRSA infections. I observed changes in number and types of virulence factors responsible for interacting with the human body, which are attributed to mobile genetic elements. In the second project, I showed that, prompted by antibiotic therapy, within the human gut microbiome resistance transfers from bacterial genomes onto plasmids, prophages, and free phages. Hence, resistance emergence depends not only on the antibiotic therapy but also on the state of the gut microbiome, which again results from the patients’ overall health and previous antibiotic therapies. The third project, SATURN, employed machine learning methods for a large set of data regarding patients’ demographics, comorbidities, antibiotic therapies, surgeries, and colonization with multi-drug resistant bacteria. The final classifiers were made available on the AskSaturn website where the doctors can compare antibiotic therapies based on the probability of colonization with multi-drug resistant bacteria. The fourth project, Tübiom, focused on the antibiotic-influenced gut microbiomes of the healthy population. The first two projects rely on genome and metagenome sequencing data. For them, I designed specialized bioinformatics analysis pipelines. The latter two projects use mixed data, which were analyzed with machine learning algorithms. These projects also involved web development and data visualization. Although each of the projects requires different data and methods, each of them provides a crucial part in a pipeline aiming at utilizing gut microbiome information in medical practice to constrain resistance emergence

Body size trends in response to climate and urbanization in the widespread North American deer mouse, Peromyscus maniculatus

© 2020, The Author(s). Body size decline is hypothesized to be a key response to climate warming, including warming driven by urban heat islands. However, urbanization may also generate selective gradients for body size increases in smaller endotherms via habitat fragmentation. Here we utilize a densely sampled, multi-source dataset to examine how climate and urbanization affect body size of Peromyscus maniculatus (PEMA), an abundant rodent found across North America. We predicted PEMA would conform to Bergmann’s Rule, e.g. larger individuals in colder climates, spatially and temporally. Hypotheses regarding body size in relation to urbanization are less clear; however, with increased food resources due to greater anthropogenic activity, we expected an increase in PEMA size. Spatial mixed-models showed that PEMA conform to Bergmann’s Rule and that PEMA were shorter in more urbanized areas. With the inclusion of decade in mixed-models, we found PEMA mass, but not length, is decreasing over time irrespective of climate or population density. We also unexpectedly found that, over time, smaller-bodied populations of PEMA are getting larger, while larger-bodied populations are getting smaller. Our work highlights the importance of using dense spatiotemporal datasets, and modeling frameworks that account for bias, to better disentangle broad-scale climatic and urbanization effects on body size

Proceedings of the 21st Conference on Formal Methods in Computer-Aided Design – FMCAD 2021

The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing

Computer Aided Verification

This open access two-volume set LNCS 10980 and 10981 constitutes the refereed proceedings of the 30th International Conference on Computer Aided Verification, CAV 2018, held in Oxford, UK, in July 2018. The 52 full and 13 tool papers presented together with 3 invited papers and 2 tutorials were carefully reviewed and selected from 215 submissions. The papers cover a wide range of topics and techniques, from algorithmic and logical foundations of verification to practical applications in distributed, networked, cyber-physical, and autonomous systems. They are organized in topical sections on model checking, program analysis using polyhedra, synthesis, learning, runtime verification, hybrid and timed systems, tools, probabilistic systems, static analysis, theory and security, SAT, SMT and decisions procedures, concurrency, and CPS, hardware, industrial applications

Channel parameter tuning in a hybrid Wi-Fi-Dynamic Spectrum Access Wireless Mesh Network

This work addresses Channel Assignment in a multi-radio multi-channel (MRMC) Wireless Mesh Network (WMN) using both Wi-Fi and Dynamic Spectrum Access (DSA) spectrum bands and standards. This scenario poses new challenges because nodes are spread out geographically so may have differing allowed channels and experience different levels of external interference in different channels. A solution must meet two conflicting requirements simultaneously: 1) avoid or minimise interference within the network and from external interference sources, and 2) maintain connectivity within the network. These two requirements must be met while staying within the link constraints and the radio interface constraints, such as only assigning as many channels to a node as it has radios. This work's original contribution to the field is a unified framework for channel optimisation and assignment in a WMN that uses both DSA and traditional Wi-Fi channels for interconnectivity. This contribution is realised by providing and analysing the performance of near-optimal Channel Assignment (CA) solutions using metaheuristic algorithms for the MRMC WMNs using DSA bands. We have created a simulation framework for evaluating the algorithms. The performance of Simulated Annealing, Genetic Algorithm, Differential Evolution, and Particle Swarm Optimisation algorithms have been analysed and compared for the CA optimisation problem. We introduce a novel algorithm, used alongside the metaheuristic optimisation algorithms, to generate feasible candidate CA solutions. Unlike previous studies, this sensing and CA work takes into account the requirement to use a Geolocation Spectrum Database (GLSD) to get the allowed channels, in addition to using spectrum sensing to identify and estimate the cumulative severity of both internal and external interference sources. External interference may be caused by other secondary users (SUs) in the vicinity or by primary transmitters of the DSA band whose emissions leak into adjacent channels, next-toadjacent, or even into further channels. We use signal-to-interference-plus-noise ratio (SINR) as the optimisation objective. This incorporates any possible source or type of interference and makes our method agnostic to the protocol or technology of the interfering devices while ensuring that the received signal level is high enough for connectivity to be maintained on as many links as possible. To support our assertion that SINR is a reasonable criterion on which to base the optimisation, we have carried out extensive outdoor measurements in both line-of-sight and wooded conditions in the television white space (TVWS) DSA band and the 5 GHz Wi-Fi band. These measurements show that SINR is useful as a performance measure, especially when the interference experienced on a link is high. Our statistical analysis shows that SINR effectively differentiates the performance of different channels and that SINR is well correlated with throughput and is thus a good predictor of end-user experience, despite varying conditions. We also identify and analyse the idle times created by Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) contention-based Medium Access Control (MAC) operations and propose the use of these idle times for spectrum sensing to measure the SINR on possible channels. This means we can perform spectrum sensing with zero spectrum sensing delay experienced by the end user. Unlike previous work, this spectrum sensing is transparent and can be performed without causing any disruption to the normal data transmission of the network. We conduct Markov chain analysis to find the expected length of time of a sensing window. We also derive an efficient minimum variance unbiased estimator of the interference plus noise and show how the SINR can be found using this estimate. Our estimation is more granular, accurate, and appropriate to the problem of Secondary User (SU)-SU coexistence than the binary hypothesis testing methods that are most common in the literature. Furthermore, we construct confidence intervals based on the probability density function derived for the observations. This leads to finding and showing the relationships between the number of sampling windows and sampling time, the interference power, and the achievable confidence interval width. While our results coincide with (and thus are confirmed by) some key previous recommendations, ours are more precise, granular, and accurate and allow for application to a wider range of operating conditions. Finally, we present alterations to the IEEE 802.11k protocol to enable the reporting of spectrum sensing results to the fusion or gateway node and algorithms for distributing the Channel Assignment once computed. We analyse the convergence rate of the proposed procedures and find that high network availability can be maintained despite the temporary loss of connectivity caused by the channel switching procedure. This dissertation consolidates the different activities required to improve the channel parameter settings of a multi-radio multi-channel DSA-WMN. The work facilitates the extension of Internet connectivity to the unconnected or unreliably connected in rural or peri-urban areas in a more cost-effective way, enabling more meaningful and affordable access technologies. It also empowers smaller players to construct better community networks for sharing local content. This technology can have knock-on effects of improved socio-economic conditions for the communities that use it

Integrative bioinformatics applications for complex human disease contexts

This thesis presents new methods for the analysis of high-throughput data from modern sources in the context of complex human diseases, at the example of a bioinformatics analysis workflow. New measurement techniques improve the resolution with which cellular and molecular processes can be monitored. While RNA sequencing (RNA-seq) measures mRNA expression, single-cell RNA-seq (scRNA-seq) resolves this on a per-cell basis. Long-read sequencing is increasingly used in genomics. With imaging mass spectrometry (IMS) the protein level in tissues is measured spatially resolved. All these techniques induce specific challenges, which need to be addressed with new computational methods. Collecting knowledge with contextual annotations is important for integrative data analyses. Such knowledge is available through large literature repositories, from which information, such as miRNA-gene interactions, can be extracted using text mining methods. After aggregating this information in new databases, specific questions can be answered with traceable evidence. The combination of experimental data with these databases offers new possibilities for data integrative methods and for answering questions relevant for complex human diseases. Several data sources are made available, such as literature for text mining miRNA-gene interactions (Chapter 2), next- and third-generation sequencing data for genomics and transcriptomics (Chapters 4.1, 5), and IMS for spatially resolved proteomics (Chapter 4.4). For these data sources new methods for information extraction and pre-processing are developed. For instance, third-generation sequencing runs can be monitored and evaluated using the poreSTAT and sequ-into methods. The integrative (down-stream) analyses make use of these (heterogeneous) data sources. The cPred method (Chapter 4.2) for cell type prediction from scRNA-seq data was successfully applied in the context of the SARS-CoV-2 pandemic. The robust differential expression (DE) analysis pipeline RoDE (Chapter 6.1) contains a large set of methods for (differential) data analysis, reporting and visualization of RNA-seq data. Topics of accessibility of bioinformatics software are discussed along practical applications (Chapter 3). The developed miRNA-gene interaction database gives valuable insights into atherosclerosis-relevant processes and serves as regulatory network for the prediction of active miRNA regulators in RoDE (Chapter 6.1). The cPred predictions, RoDE results, scRNA-seq and IMS data are unified as input for the 3D-index Aorta3D (Chapter 6.2), which makes atherosclerosis related datasets browsable. Finally, the scRNA-seq analysis with subsequent cPred cell type prediction, and the robust analysis of bulk-RNA-seq datasets, led to novel insights into COVID-19. Taken all discussed methods together, the integrative analysis methods for complex human disease contexts have been improved at essential positions.Die Dissertation beschreibt Methoden zur Prozessierung von aktuellen Hochdurchsatzdaten, sowie Verfahren zu deren weiterer integrativen Analyse. Diese findet Anwendung vor allem im Kontext von komplexen menschlichen Krankheiten. Neue Messtechniken erlauben eine detailliertere Beobachtung biomedizinischer Prozesse. Mit RNA-Sequenzierung (RNA-seq) wird mRNA-Expression gemessen, mit Hilfe von moderner single-cell-RNA-seq (scRNA-seq) sogar für (sehr viele) einzelne Zellen. Long-Read-Sequenzierung wird zunehmend zur Sequenzierung ganzer Genome eingesetzt. Mittels bildgebender Massenspektrometrie (IMS) können Proteine in Geweben räumlich aufgelöst quantifiziert werden. Diese Techniken bringen spezifische Herausforderungen mit sich, die mit neuen bioinformatischen Methoden angegangen werden müssen. Für die integrative Datenanalyse ist auch die Gewinnung von geeignetem Kontextwissen wichtig. Wissenschaftliche Erkenntnisse werden in Artikeln veröffentlicht, die über große Literaturdatenbanken zugänglich sind. Mittels Textmining können daraus Informationen extrahiert werden, z.B. miRNA-Gen-Interaktionen, die in eigenen Datenbank aggregiert werden um spezifische Fragen mit nachvollziehbaren Belegen zu beantworten. In Kombination mit experimentellen Daten bieten sich so neue Möglichkeiten für integrative Methoden. Durch die Extraktion von Rohdaten und deren Vorprozessierung werden mehrere Datenquellen erschlossen, wie z.B. Literatur für Textmining von miRNA-Gen-Interaktionen (Kapitel 2), Long-Read- und RNA-seq-Daten für Genomics und Transcriptomics (Kapitel 4.2, 5) und IMS für Protein-Messungen (Kapitel 4.4). So dienen z.B. die poreSTAT und sequ-into Methoden der Vorprozessierung und Auswertung von Long-Read-Sequenzierungen. In der integrativen (down-stream) Analyse werden diese (heterogenen) Datenquellen verwendet. Für die Bestimmung von Zelltypen in scRNA-seq-Experimenten wurde die cPred-Methode (Kapitel 4.2) erfolgreich im Kontext der SARS-CoV-2-Pandemie eingesetzt. Auch die robuste Pipeline RoDE fand dort Anwendung, die viele Methoden zur (differentiellen) Datenanalyse, zum Reporting und zur Visualisierung bereitstellt (Kapitel 6.1). Themen der Benutzbarkeit von (bioinformatischer) Software werden an Hand von praktischen Anwendungen diskutiert (Kapitel 3). Die entwickelte miRNA-Gen-Interaktionsdatenbank gibt wertvolle Einblicke in Atherosklerose-relevante Prozesse und dient als regulatorisches Netzwerk für die Vorhersage von aktiven miRNA-Regulatoren in RoDE (Kapitel 6.1). Die cPred-Methode, RoDE-Ergebnisse, scRNA-seq- und IMS-Daten werden im 3D-Index Aorta3D (Kapitel 6.2) zusammengeführt, der relevante Datensätze durchsuchbar macht. Die diskutierten Methoden führen zu erheblichen Verbesserungen für die integrative Datenanalyse in komplexen menschlichen Krankheitskontexten

Compiling Communication-Minimizing Query Plans

Because of the low arithmetic intensity of relational database operators, the performance of in-memory column stores ought to be bound by main-memory bandwidth, and in practice, highly-optimized operator implementations already achieve close to their peak theoretical performance. By itself, this would imply that hardware acceleration for analytics would be of limited utility, but I show that the emergence of full-query compilation presents new opportunities to reduce memory traffic and trade computation for communication, meaning that database-oriented processors may yet be worth designing.Moreover, the communication costs of queries on a given processor and memory hierarchy are determined by factors below the level of abstraction expressed in traditional query plans, such as how operators are (or are not) fused together, how execution is parallelized and cache-blocked, and how intermediate results are arranged in memory. I present a Scala- embedded programming language called Ressort that exposes these machine-level aspects of query compilation, and which emits parallel C++/OpenMP code as its target to express a greater range of algorithmic variants for each query than would be easy to study by hand