8,376 research outputs found
Impact of plant growth promoting rhizobacteria (PGPR) on stress resistance of winter wheat (Triticum aestivum L.)
Wheat is one of the worldwide most cultivated crop and highly contribute to secure food production in different world regions. Although, it grows almost ubiquitous, its production is severely vulnerable to drought. Soil and rhizosphere microbial communities associated to plants come more and more into the focus of modern agrobiology research, as a solution to maintain productivity under drought, and reinforce sustainable production. Whereas numerous studies on wheat production and the beneficial influence of the soil microbiome under drought have been performed in arid and semiarid regions of the world, comparable studies in Central Europe are rare. This might change due to the ongoing climate crisis and expected less frequent precipitations during the vegetation season. So far, most studies that focus on acclimatization of the wheat rhizobiome to water deficit mostly consider, at best, two interacting factors, and lack to consider other biotic or abiotic drivers of rhizosphere microbial communities structure and function. Therefore, the aim of this thesis was to combine complementary analytical approaches to investigate drought-induced structural and functional changes in wheat rhizosphere bacterial communities and individual species in dependency of soil type, farming system, wheat cultivar and plant development stage, and to determine how these changes affect wheat performance as a consequence of possible climate change scenarios in Central Germany.
The presented thesis starts with a general introduction and presentation of the project, followed by three consecutive chapters containing the main findings published in peer-reviewed articles. Starting with an experiment performed in the greenhouse (Chapter 1) and then moving to a realistic climate scenario under field conditions (Chapter 2 and 3), the three chapters demonstrate the sole and interacting effects of drought and farming system (Chapter 1-3), soil type and wheat cultivar (Chapter 1), as well as plant growth stages (Chapter 2 and 3) on bacterial communities and individual taxa of the wheat rhizobiome. The methods used reach from traditional cultivation and in-vitro bioassays (Chapter 3), over extracellular enzyme activity potentials (Chapter 1 and 2) to more advanced technologies such as metabarcoding (Chapter 1 and 2) and computational tools (Chapter 1 and 2), addressing single bacterial taxa as well as community level. Finalizing the thesis, a concluding synopsis compiles and critically reviews the gained results and formulates future study perspectives.
In Chapter 1, we evaluated the impact of soil type (loamy vs. sandy), farming management (conventional vs. organic), wheat cultivar (non-demanding vs. demanding), and the interacting effects of these factors on wheat rhizobacterial community composition and function under extreme drought conditions. Water deficit exerted a strong pressure on rhizobacterial communities, and interacted with soil type and farming management, but not with the wheat cultivar types. In the sandy soil, we observed a strong drought-induced shift in community composition, with a decrease in species diversity and extracellulare enzyme production, while changes by drought were less prominent in the fertile loamy soil. A particular exception from this pattern was found for enzyme activities involved in carbon cycling in the sandy soil suggesting a positive plant-soil-feedback on enzyme activities by drought conditioning.
In Chapter 2, two individual, but interrelated aims were pursued. First, we used the platform of the Global Change Experimental Facility (GCEF) to explore the impact of two farming practices (conventional vs. organic) and two climate treatments (ambient vs. future) on bacterial community composition and activity profiles of extracellulare enzymes involved in C,N and P cycles in the wheat rhizosphere at two different plant growth stages. The climate treatment in the GCEF had no effect on the rhizobacterial communities. Rhizobacterial community composition and functions significantly differed between vegetative and mature growth stages of the plants, in both conventional and organic farming. In a second step, we reused the data to explore further the accuracy of computational approaches, like Tax4Fun and PanFP, to predict functional profiles of bacterial communities based on 16S rDNA abundance data. To this end, we compared the measured enzyme activities with respective gene abundances in the community under different climate and farming treatments, and at the two plant development stages. This analysis revealed qualitative, but not necessarily quantitative concordances, i.e. we found effects of the different treatments on the measured enzyme activities reflected in the gene abundances.
Chapter 3 is a complementary approach to Chapter 2 with a focus on individual bacterial species level. Culture-dependent methods were used to specifically isolate strong P-solubilizing bacteria from the rhizosphere of wheat, which were tested for their in-vitro drought tolerance. Among the more than 800 isolated species, Phyllobacterium, Pseudomonas and Streptomyces species dominated. While farming management and climate treatment had only minor effects on composition and functions of the isolates, the wheat growth stages had an impact, whereby a dominance of Pseudomonas species at the vegetative growth phase was replaced by dominance of Phyllobacterium species at the mature growth phase. Since P-solubilizing potential was paralleled by a high in vitro drought tolerance, Phyllobacterium species were characterized as promising plant growth promoting rhizobacteria (PGPR) of wheat under future drought conditions.
In the synopsis part, we evaluated the multifactorial and multidisciplinary approaches and investigated to what extent the adaptations of bacterial communities in field and pot experiments coincided or differed.
Overall, we found common and distinct adaptation processes of bacterial communities and individual species in the rhizosphere of wheat to drought, whereby single factors, but also interacting effects exerted a strong impact on these processes. This study underlines the importance of multifactorial approaches to reveal community- or species-specific plant-soil-feedbacks.:Contents 3
Preface 5
Bibliographic description 6
Zusammenfassung 9
Summary 13
Introduction 16
When extreme events become the new normal 17
Feedback to agricultural production and need for management adaptation 20
Difficulties in exploring the soil microbiome and identification of plant beneficial microbial taxa 22
Our approach with wheat 24
Bibliography 27
Ö Chapter 1 31
Interactions Between Soil Properties, Agricultural Management and Cultivar Type Drive Structural and Functional Adaptations of the Wheat Rhizosphere Microbiome To Drought 31
Supplemental Tables 51
Supplemental Figures 55
⏠Chapter 2 59
Can We Estimate Functionality of Soil Microbial Communities from Structure-Derived Predictions? A Reality Test in Agricultural Soils 59
Supplementary Tables 79
Supplemental Figures 84
Supplemental Material 1: 87
Variation in edaphic parameters according to experimental factors 87
Supplemental Material 2 88
Effect of abiotic soil parameters on bacterial community structure and function 88
Supplemental Material 3 90
Indicator species analysis 90
Û Chapter 3 95
Shifts Between and Among Populations of Wheat Rhizosphere Pseudomonas, Streptomyces and Phyllobacterium Suggest Consistent Phosphate Mobilization at Different Wheat Growth Stages Under Abiotic Stress 95
Supplementary Figures 112
Supplementary Tables 117
Synopsis 152
Multidisciplinary approaches combine advantages of cultivation-based and high throughput community-based methods 155
Multifactorial approaches to gain a more holistic understanding of plant-microbe interactions in pot experiments 157
Transferability of findings gained in the pot experiment to field conditions 159
Towards a wheat core microbiome? 161
Study limitations and outlook 163
Bibliography 164
Acknowledgements 169
Curriculum Vitae 171
Personal details 171
Education 171
Work experience 172
Research and Mentoring experience 172
Extracurricular activities 173
List of publications and Presentations 174
Publications in peer-reviewed journals: 174
Oral Presentations: 175
Poster Presentations: 175
Statutory declaration 176
Eidesstattliche ErklÀrung 177
Author contributions 178Weizen ist eine der weltweit am hĂ€ufigsten angebauten Kulturpflanzen und trĂ€gt zur Sicherung der Nahrungsmittelproduktion in verschiedenen Regionen der Welt bei. Obwohl er fast ĂŒberall angebaut werden kann, ist die Produktion durch Trockenheit limitiert. Daher rĂŒcken mehr und mehr die mikrobiellen Gemeinschaften im Boden und in der RhizosphĂ€re in den Mittelpunkt der modernen agrarbiologischen Forschung, um die ProduktivitĂ€t bei Trockenheit aufrechtzuerhalten und eine nachhaltige Produktion zu fördern. WĂ€hrend bereits zahlreiche Studien ĂŒber die Weizenproduktion und den positiven Einfluss des Bodenmikrobioms in ariden und semiariden Regionen der Welt durchgefĂŒhrt wurden, sind vergleichbare Studien in Mitteleuropa selten. Dies könnte sich aufgrund der anhaltenden Klimakrise und der zu erwartenden ausbleibenden SommerniederschlĂ€ge Ă€ndern. Dabei haben die meisten Studien, die sich mit der Akklimatisierung des Weizenrhizobioms an Wasserdefizite befasst haben, bestenfalls den Einfluss von Trockenheit und ein oder zwei weiteren biotischen oder abiotischen Einflussfaktoren, die zudem miteinander interagieren können, auf die Struktur und Funktion der mikrobiellen Gemeinschaften in der RhizosphĂ€re untersucht. Ziel dieser Arbeit war es daher, verschiedene komplementĂ€re Analysemethoden zu kombinieren, um trockenheitsbedingte strukturelle und funktionelle VerĂ€nderungen in den bakteriellen Gemeinschaften und auch einzelner Arten in der WeizenrhizosphĂ€re, in AbhĂ€ngigkeit von Bodentyp, Landnutzungssystem, Weizensorte und Pflanzenentwicklungsstadium zu untersuchen, und zu ermitteln, wie sich diese VerĂ€nderungen auf die ProduktivitĂ€t des Weizens als Folge möglicher Szenarien des Klimawandels in Mitteldeutschland auswirken.
Die vorliegende Arbeit leitet mit einer allgemeinen EinfĂŒhrung und Vorstellung des Projekts ein, gefolgt von drei aufeinanderfolgenden Kapiteln, die die wichtigsten Ergebnisse enthalten, die in von Fachleuten begutachteten Artikeln veröffentlicht wurden. Beginnend mit einem Experiment im GewĂ€chshaus (Kapitel 1) und weiterfĂŒhrend zu einem realistischen Klimaszenario unter Feldbedingungen (Kapitel 2 und 3), beschreiben die drei Kapitel die alleinigen und interagierenden Auswirkungen von Trockenheit und Anbausystem (Kapitel 1-3), Bodentyp und Weizensorte (Kapitel 1), sowie Pflanzenwachstumsstadien (Kapitel 2 und 3) auf Bakteriengemeinschaften und einzelne Taxa des Weizenrhizobioms. Die verwendeten Methoden reichen dabei von der traditionellen Kultivierung und In-vitro-Bioassays (Kapitel 3), ĂŒber extrazellulĂ€re EnzymaktivitĂ€tspotenziale (Kapitel 1 und 2), bis hin zu fortschrittlicheren Technologien, wie Metabarcoding (Kapitel 1 und 2) und computergestĂŒtzten Vorhersagen (Kapitel 1 und 2). Zum Abschluss der Arbeit werden in einer abschlieĂenden Synopsis die gewonnenen Ergebnisse zusammengetragen und kritisch betrachtet, sowie Ideen fĂŒr zukĂŒnftige Studien formuliert.
In Kapitel 1 untersuchten wir die Auswirkungen des Bodentyps (lehmig vs. sandig), der Bewirtschaftung (konventionell vs. ökologisch), der Weizensorte (anspruchslos vs. anspruchsvoll) und die Wechselwirkungen zwischen diesen Faktoren auf die Zusammensetzung und Funktion der Bakteriengemeinschaft in der RhizosphĂ€re von Weizen unter extremen Trockenheitsbedingungen. Das Wasserdefizit ĂŒbte einen starken Druck auf die RhizosphĂ€renbakteriengemeinschaften aus und stand in Wechselwirkung mit dem Bodentyp und der Bewirtschaftung, nicht aber mit den Weizensorten. In den Sandböden beobachteten wir eine starke trockenheitsbedingte VerĂ€nderung der Zusammensetzung der Gemeinschaft mit einem RĂŒckgang der Artenvielfalt und der extrazellulĂ€ren Enzymproduktion, wĂ€hrend die VerĂ€nderungen durch die Trockenheit in den fruchtbaren Lehmböden weniger stark ausgeprĂ€gt waren. Eine besondere Ausnahme von diesem Muster wurde fĂŒr EnzymaktivitĂ€ten gefunden, die am Kohlenstoffkreislauf im Sandboden beteiligt sind, was auf eine positive RĂŒckkopplung zwischen Pflanze und Bodengemeinschaften unter Trockenheit hindeutet.
In Kapitel 2 wurden zwei einzelne, jedoch miteinander verknĂŒpfte Ziele verfolgt. Erstens nutzten wir die Plattform der Global Change Experimental Facility (GCEF), um die Auswirkungen von zwei Anbaupraktiken (konventionell vs. ökologisch) und zwei Klimabehandlungen (ambient vs. zukĂŒnftig) auf die Zusammensetzung der Bakteriengemeinschaft und die AktivitĂ€tsprofile extrazellulĂ€rer Enzyme, die an den C-, N- und P-Zyklen in der RhizosphĂ€re von Weizen beteiligt sind, in zwei verschiedenen Pflanzenwachstumsstadien zu untersuchen. Die Klimabehandlung in der GCEF hatte keinen Einfluss auf die RhizosphĂ€renbakteriengemeinschaften. Die Zusammensetzung und die Funktionen der RhizosphĂ€renbakteriengemeinschaften unterschieden sich signifikant zwischen dem vegetativen und dem generativen Wachstumsstadium der Pflanzen, sowohl im konventionellen als auch im ökologischen Landbau. In einem zweiten Schritt nutzten wir die gewonnenen Daten, um die Genauigkeit rechnerischer AnsĂ€tze wie Tax4Fun und PanFP zur Vorhersage funktioneller Profile von Bakteriengemeinschaften auf der Grundlage von 16S rDNA-Daten zu ĂŒberprĂŒfen. Zu diesem Zweck verglichen wir die gemessenen EnzymaktivitĂ€ten mit den jeweiligen GenhĂ€ufigkeiten in der Gemeinschaft unter verschiedenen Klima- und Anbaubedingungen und in den beiden Entwicklungsstadien der Pflanzen. Diese Analyse ergab qualitative, aber nicht unbedingt quantitative Ăbereinstimmungen, d. h. wir fanden Auswirkungen der verschiedenen Behandlungen auf die gemessenen EnzymaktivitĂ€ten, die sich auch in den GenhĂ€ufigkeiten widerspiegeln.
Kapitel 3 stellt einen ergĂ€nzenden Ansatz zu Kapitel 2 dar, wobei der Schwerpunkt auf einzelnen Bakterienarten liegt. Mit kulturabhĂ€ngigen Methoden wurden gezielt stark Phosphat-solubilisierende Bakterien aus der RhizosphĂ€re von Weizen isoliert und auf ihre In-vitro-Trockenheitstoleranz getestet. Unter den mehr als 800 isolierten Arten dominierten Phyllobacterium-, Pseudomonas- und Streptomyces-Arten. WĂ€hrend Anbaumanagement und Klimabehandlung nur geringe Auswirkungen hatten, wirkten sich die Wachstumsstadien des Weizens signifikant auf die Zusammensetzung und Funktionen der Isolate aus, wobei eine Dominanz von Pseudomonas-Arten in der vegetativen Wachstumsphase durch eine Dominanz von Phyllobacterium-Arten in der generativen Wachstumsphase ersetzt wurde. Da das Potenzial zur P-Solubilisierung mit einer hohen in vitro-Trockenheitstoleranz einherging, wurden Phyllobacterium-Arten als vielversprechende pflanzenwachstumsfördernde Rhizobakterien (PGPR) fĂŒr Weizen unter zukĂŒnftigen Trockenheitsbedingungen charakterisiert.
In der Synopsis dieser Arbeit bewerteten wir die multifaktoriellen und multidisziplinĂ€ren AnsĂ€tze, und untersuchten, inwieweit die Anpassungen der Bakteriengemeinschaften in Feld- und Topfversuchen ĂŒbereinstimmen oder sich unterscheiden.
Insgesamt fanden wir allgemeine, aber auch differenzielle Anpassungsprozesse von Bakteriengemeinschaften und einzelnen Arten in der RhizosphĂ€re von Weizen an die Trockenheit, wobei einzelne Faktoren, aber auch interagierende Effekte einen starken Einfluss auf diese Prozesse ausĂŒbten. Diese Studie unterstreicht damit die Bedeutung multifaktorieller AnsĂ€tze, um gemeinschafts- oder artspezifische RĂŒckkopplungen zwischen Pflanze und Boden zu untersuchen.:Contents 3
Preface 5
Bibliographic description 6
Zusammenfassung 9
Summary 13
Introduction 16
When extreme events become the new normal 17
Feedback to agricultural production and need for management adaptation 20
Difficulties in exploring the soil microbiome and identification of plant beneficial microbial taxa 22
Our approach with wheat 24
Bibliography 27
Ö Chapter 1 31
Interactions Between Soil Properties, Agricultural Management and Cultivar Type Drive Structural and Functional Adaptations of the Wheat Rhizosphere Microbiome To Drought 31
Supplemental Tables 51
Supplemental Figures 55
⏠Chapter 2 59
Can We Estimate Functionality of Soil Microbial Communities from Structure-Derived Predictions? A Reality Test in Agricultural Soils 59
Supplementary Tables 79
Supplemental Figures 84
Supplemental Material 1: 87
Variation in edaphic parameters according to experimental factors 87
Supplemental Material 2 88
Effect of abiotic soil parameters on bacterial community structure and function 88
Supplemental Material 3 90
Indicator species analysis 90
Û Chapter 3 95
Shifts Between and Among Populations of Wheat Rhizosphere Pseudomonas, Streptomyces and Phyllobacterium Suggest Consistent Phosphate Mobilization at Different Wheat Growth Stages Under Abiotic Stress 95
Supplementary Figures 112
Supplementary Tables 117
Synopsis 152
Multidisciplinary approaches combine advantages of cultivation-based and high throughput community-based methods 155
Multifactorial approaches to gain a more holistic understanding of plant-microbe interactions in pot experiments 157
Transferability of findings gained in the pot experiment to field conditions 159
Towards a wheat core microbiome? 161
Study limitations and outlook 163
Bibliography 164
Acknowledgements 169
Curriculum Vitae 171
Personal details 171
Education 171
Work experience 172
Research and Mentoring experience 172
Extracurricular activities 173
List of publications and Presentations 174
Publications in peer-reviewed journals: 174
Oral Presentations: 175
Poster Presentations: 175
Statutory declaration 176
Eidesstattliche ErklÀrung 177
Author contributions 17
Multitenant Containers as a Service (CaaS) for Clouds and Edge Clouds
Cloud computing, offering on-demand access to computing resources through the
Internet and the pay-as-you-go model, has marked the last decade with its three
main service models; Infrastructure as a Service (IaaS), Platform as a Service
(PaaS), and Software as a Service (SaaS). The lightweight nature of containers
compared to virtual machines has led to the rapid uptake of another in recent
years, called Containers as a Service (CaaS), which falls between IaaS and PaaS
regarding control abstraction. However, when CaaS is offered to multiple
independent users, or tenants, a multi-instance approach is used, in which each
tenant receives its own separate cluster, which reimposes significant overhead
due to employing virtual machines for isolation. If CaaS is to be offered not
just at the cloud, but also at the edge cloud, where resources are limited,
another solution is required. We introduce a native CaaS multitenancy
framework, meaning that tenants share a cluster, which is more efficient than
the one tenant per cluster model. Whenever there are shared resources,
isolation of multitenant workloads is an issue. Such workloads can be isolated
by Kata Containers today. Besides, our framework esteems the application
requirements that compel complete isolation and a fully customized environment.
Node-level slicing empowers tenants to programmatically reserve isolated
subclusters where they can choose the container runtime that suits application
needs. The framework is publicly available as liberally-licensed, free,
open-source software that extends Kubernetes, the de facto standard container
orchestration system. It is in production use within the EdgeNet testbed for
researchers
Educating Sub-Saharan Africa:Assessing Mobile Application Use in a Higher Learning Engineering Programme
In the institution where I teach, insufficient laboratory equipment for engineering education pushed students to learn via mobile phones or devices. Using mobile technologies to learn and practice is not the issue, but the more important question lies in finding out where and how they use mobile tools for learning. Through the lens of Kearney et al.âs (2012) pedagogical model, using authenticity, personalisation, and collaboration as constructs, this case study adopts a mixed-method approach to investigate the mobile learning activities of students and find out their experiences of what works and what does not work. Four questions are borne out of the over-arching research question, âHow do students studying at a University in Nigeria perceive mobile learning in electrical and electronic engineering education?â The first three questions are answered from qualitative, interview data analysed using thematic analysis. The fourth question investigates their collaborations on two mobile social networks using social network and message analysis. The study found how studentsâ mobile learning relates to the real-world practice of engineering and explained ways of adapting and overcoming the mobile toolsâ limitations, and the nature of the collaborations that the students adopted, naturally, when they learn in mobile social networks. It found that mobile engineering learning can be possibly located in an offline mobile zone. It also demonstrates that investigating the effectiveness of mobile learning in the mobile social environment is possible by examining usersâ interactions. The study shows how mobile learning personalisation that leads to impactful engineering learning can be achieved. The study shows how to manage most interface and technical challenges associated with mobile engineering learning and provides a new guide for educators on where and how mobile learning can be harnessed. And it revealed how engineering education can be successfully implemented through mobile tools
Innovative Hybrid Approaches for Vehicle Routing Problems
This thesis deals with the efficient resolution of Vehicle Routing Problems (VRPs).
The first chapter faces the archetype of all VRPs: the Capacitated Vehicle Routing Problem (CVRP). Despite having being introduced more than 60 years ago, it still remains an extremely challenging problem. In this chapter I design a Fast Iterated-Local-Search Localized Optimization algorithm for the CVRP, shortened to FILO. The simplicity of the CVRP definition allowed me to experiment with advanced local search acceleration and pruning techniques that have eventually became the core optimization engine of FILO. FILO experimentally shown to be extremely scalable and able to solve very large scale instances of the CVRP in a fraction of the computing time compared to existing state-of-the-art methods, still obtaining competitive solutions in terms of their quality.
The second chapter deals with an extension of the CVRP called the Extended Single Truck and Trailer Vehicle Routing Problem, or simply XSTTRP. The XSTTRP models a broad class of VRPs in which a single vehicle, composed of a truck and a detachable trailer, has to serve a set of customers with accessibility constraints making some of them not reachable by using the entire vehicle. This problem moves towards VRPs including more realistic constraints and it models scenarios such as parcel deliveries in crowded city centers or rural areas, where maneuvering a large vehicle is forbidden or dangerous. The XSTTRP generalizes several well known VRPs such as the Multiple Depot VRP and the Location Routing Problem. For its solution I developed an hybrid metaheuristic which combines a fast heuristic optimization with a polishing phase based on the resolution of a limited set partitioning problem. Finally, the thesis includes a final chapter aimed at guiding the computational evaluation of new approaches to VRPs proposed by the machine learning community
Growth trends and site productivity in boreal forests under management and environmental change: insights from long-term surveys and experiments in Sweden
Under a changing climate, current tree and stand growth information is indispensable to the carbon sink strength of boreal forests. Important questions regarding tree growth are to what extent have management and environmental change influenced it, and how it might respond in the future. In this thesis, results from five studies (Papers I-V) covering growth trends, site productivity, heterogeneity in managed forests and potentials for carbon storage in forests and harvested wood products via differing management strategies are presented. The studies were based on observations from national forest inventories and long-term experiments in Sweden. The annual height growth of Scots pine (Pinus sylvestris) and Norway spruce (Picea abies) had increased, especially after the millennium shift, while the basal area growth remains stable during the last 40 years (Papers I-II). A positive response on height growth with increasing temperature was observed. The results generally imply a changing growing condition and stand composition. In Paper III, yield capacity of conifers was analysed and compared with existing functions. The results showed that there is a bias in site productivity estimates and the new functions give better prediction of the yield capacity in Sweden. In Paper IV, the variability in stand composition was modelled as indices of heterogeneity to calibrate the relationship between basal area and leaf area index in managed stands of Norway spruce and Scots pine. The results obtained show that the stand structural heterogeneity effects here are of such a magnitude that they cannot be neglected in the implementation of hybrid growth models, especially those based on light interception and light-use efficiency. In the long-term, the net climate benefits in Swedish forests may be maximized through active forest management with high harvest levels and efficient product utilization, compared to increasing carbon storage in standing forests through land set-asides for nature conservation (Paper V). In conclusion, this thesis offers support for the development of evidence-based policy recommendations for site-adapted and sustainable management of Swedish forests in a changing climate
Optimizing transcriptomics to study the evolutionary effect of FOXP2
The field of genomics was established with the sequencing of the human genome, a pivotal achievement that has allowed us to address various questions in biology from a unique perspective. One question in particular, that of the evolution of human speech, has gripped philosophers, evolutionary biologists, and now genomicists. However, little is known of the genetic basis that allowed humans to evolve the ability to speak. Of the few genes implicated in human speech, one of the most studied is FOXP2, which encodes for the transcription factor Forkhead box protein P2 (FOXP2). FOXP2 is essential for proper speech development and two mutations in the human lineage are believed to have contributed to the evolution of human speech. To address the effect of FOXP2 and investigate its evolutionary contribution to human speech, one can utilize the power of genomics, more specifically gene expression analysis via ribonucleic acid sequencing (RNA-seq).
To this end, I first contributed in developing mcSCRB-seq, a highly sensitive, powerful, and efficient single cell RNA-seq (scRNA-seq) protocol. Previously having emerged as a central method for studying cellular heterogeneity and identifying cellular processes, scRNA-seq was a powerful genomic tool but lacked the sensitivity and cost-efficiency of more established protocols. By systematically evaluating each step of the process, I helped find that the addition of polyethylene glycol increased sensitivity by enhancing the cDNA synthesis reaction. This, along with other optimizations resulted in developing a sensitive and flexible protocol that is cost-efficient and ideal in many research settings.
A primary motivation driving the extensive optimizations surrounding single cell transcriptomics has been the generation of cellular atlases, which aim to identify and characterize all of the cells in an organism. As such efforts are carried out in a variety of research groups using a number of different RNA-seq protocols, I contributed in an effort to benchmark and standardize scRNA-seq methods. This not only identified methods which may be ideal for the purpose of cell atlas creation, but also highlighted optimizations that could be integrated into existing protocols.
Using mcSCRB-seq as a foundation as well as the findings from the scRNA-seq benchmarking, I helped develop prime-seq, a sensitive, robust, and most importantly, affordable bulk RNA-seq protocol. Bulk RNA-seq was frequently overlooked during the efforts to optimize and establish single-cell techniques, even though the method is still extensively used in analyzing gene expression. Introducing early barcoding and reducing library generation costs kept prime-seq cost-efficient, but basing it off of single-cell methods ensured that it would be a sensitive and powerful technique. I helped verify this by benchmarking it against TruSeq generated data and then helped test the robustness by generating prime-seq libraries from over seventeen species. These optimizations resulted in a final protocol that is well suited for investigating gene expression in comprehensive and high-throughput studies.
Finally, I utilized prime-seq in order to develop a comprehensive gene expression atlas to study the function of FOXP2 and its role in speech evolution. I used previously generated mouse models: a knockout model containing one non-functional Foxp2 allele and a humanized model, which has a variant Foxp2 allele with two human-specific mutations. To study the effect globally across the mouse, I helped harvest eighteen tissues which were previously identified to express FOXP2. By then comparing the mouse models to wild-type mice, I helped highlight the importance of FOXP2 within lung development and the importance of the human variant allele in the brain.
Both mcSCRB-seq and prime-seq have already been used and published in numerous studies to address a variety of biological and biomedical questions. Additionally, my work on FOXP2 not only provides a thorough expression atlas, but also provides a detailed and cost-efficient plan for undertaking a similar study on other genes of interest. Lastly, the studies on FOXP2 done within this work, lay the foundation for future studies investigating the role of FOXP2 in modulating learning behavior, and thereby affecting human speech
Unraveling the effect of sex on human genetic architecture
Sex is arguably the most important differentiating characteristic in most mammalian
species, separating populations into different groups, with varying behaviors, morphologies,
and physiologies based on their complement of sex chromosomes, amongst other factors. In
humans, despite males and females sharing nearly identical genomes, there are differences
between the sexes in complex traits and in the risk of a wide array of diseases. Sex provides
the genome with a distinct hormonal milieu, differential gene expression, and environmental
pressures arising from gender societal roles. This thus poses the possibility of observing
gene by sex (GxS) interactions between the sexes that may contribute to some of the
phenotypic differences observed. In recent years, there has been growing evidence of GxS,
with common genetic variation presenting different effects on males and females. These
studies have however been limited in regards to the number of traits studied and/or
statistical power. Understanding sex differences in genetic architecture is of great
importance as this could lead to improved understanding of potential differences in
underlying biological pathways and disease etiology between the sexes and in turn help
inform personalised treatments and precision medicine.
In this thesis we provide insights into both the scope and mechanism of GxS across the
genome of circa 450,000 individuals of European ancestry and 530 complex traits in the UK
Biobank. We found small yet widespread differences in genetic architecture across traits
through the calculation of sex-specific heritability, genetic correlations, and sex-stratified
genome-wide association studies (GWAS). We further investigated whether sex-agnostic
(non-stratified) efforts could potentially be missing information of interest, including sex-specific trait-relevant loci and increased phenotype prediction accuracies. Finally, we
studied the potential functional role of sex differences in genetic architecture through sex
biased expression quantitative trait loci (eQTL) and gene-level analyses.
Overall, this study marks a broad examination of the genetics of sex differences. Our findings
parallel previous reports, suggesting the presence of sexual genetic heterogeneity across
complex traits of generally modest magnitude. Furthermore, our results suggest the need to
consider sex-stratified analyses in future studies in order to shed light into possible sex-specific molecular mechanisms
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Brain signal recognition using deep learning
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityBrain Computer Interface (BCI) has the potential to offer a new generation of applications independent of
muscular activity and controlled by the human brain. Brain imaging technologies are used to transfer the
cognitive tasks into control commands for a BCI system. The electroencephalography (EEG) technology
serves as the best available non-invasive solution for extracting signals from the brain. On the other hand,
speech is the primary means of communication, but for patients suffering from locked-in syndrome, there
is no easy way to communicate. Therefore, an ideal communication system for locked-in patients is a
thought-to-speech BCI system.
This research aims to investigate methods for the recognition of imagined speech from EEG signals
using deep learning techniques. In order to design an optimal imagined speech recognition BCI, variety
of issues have been solved. These include 1) proposing new feature extraction and classification
framework for recognition of imagined speech from EEG signals, 2) grammatical class recognition of
imagined words from EEG signals, 3) discriminating different cognitive tasks associated with speech in
the brain such as overt speech, covert speech, and visual imagery. In this work machine learning, deep
learning methods were used to analyze EEG signals.
For recognition of imagined speech from EEG signals, a new EEG database was collected while the
participants mentally spoke (imagined speech) the presented words. Along with imagined speech, EEG
data was recorded for visual imagery (imagining a scene or an image) and overt speech (verbal speech).
Spectro-temporal and spatio-temporal domain features were investigated for the classification of imagined
words from EEG signals. Further, a deep learning framework using the convolutional network
and attention mechanism was implemented for learning features in the spatial, temporal, and spectral
domains. The method achieved a recognition rate of 76.6% for three binary word pairs. These experiments
show that deep learning algorithms are ideal for imagined speech recognition from EEG signals
due to their ability to interpret features from non-linear and non-stationary signals. Grammatical classes
of imagined words from EEG signals were also recognized using a multi-channel convolution network
framework. This method was extended to a multi-level recognition system for multi-class classification
of imagined words which achieved an accuracy of 52.9% for 10 words, which is much better in
comparison to previous work.
In order to investigate the difference between imagined speech with verbal speech and visual imagery
from EEG signals, we used multivariate pattern analysis (MVPA). MVPA provided the time segments
when the neural oscillation for the different cognitive tasks was linearly separable. Further, frequencies
that result in most discrimination between the different cognitive tasks were also explored. A framework
was proposed to discriminate two cognitive tasks based on the spatio-temporal patterns in EEG signals.
The proposed method used the K-means clustering algorithm to find the best electrode combination and
convolutional-attention network for feature extraction and classification. The proposed method achieved
a high recognition rate of 82.9% and 77.7%.
The results in this research suggest that a communication based BCI system can be designed using
deep learning methods. Further, this work add knowledge to the existing work in the field of communication
based BCI system
A comprehensive review on laser powder bed fusion of steels : processing, microstructure, defects and control methods, mechanical properties, current challenges and future trends
Laser Powder Bed Fusion process is regarded as the most versatile metal additive manufacturing process, which has been proven to manufacture near net shape up to 99.9% relative density, with geometrically complex and high-performance metallic parts at reduced time. Steels and iron-based alloys are the most predominant engi-neering materials used for structural and sub-structural applications. Availability of steels in more than 3500 grades with their wide range of properties including high strength, corrosion resistance, good ductility, low cost, recyclability etc., have put them in forefront of other metallic materials. However, LPBF process of steels and iron-based alloys have not been completely established in industrial applications due to: (i) limited insight available in regards to the processing conditions, (ii) lack of specific materials standards, and (iii) inadequate knowledge to correlate the process parameters and other technical obstacles such as dimensional accuracy from a design model to actual component, part variability, limited feedstock materials, manual post-processing and etc. Continued efforts have been made to address these issues. This review aims to provide an overview of steels and iron-based alloys used in LPBF process by summarizing their key process parameters, describing thermophysical phenomena that is strongly linked to the phase transformation and microstructure evolution during solidifica-tion, highlighting metallurgical defects and their potential control methods, along with the impact of various post-process treatments; all of this have a direct impact on the mechanical performance. Finally, a summary of LPBF processed steels and iron-based alloys with functional properties and their application perspectives are presented. This review can provide a foundation of knowledge on LPBF process of steels by identifying missing information from the existing literature
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