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Computational modelling of membrane viscosity for immersed boundary simulations of red blood cell dynamics
No full textAlthough tremendous efforts have been devoted to modelling various membrane properties, few studies considered the membrane viscous effects. Meanwhile, immersed boundary method (IBM) has been a popular choice for simulating the motion of deformable cells in flow for the convenience of incorporating the flow-membrane interaction. Unfortunately, the direct implementation of membrane viscosity in IBM suffers severe numerical instability. In this thesis, three numerical schemes for implementing membrane viscosity in IBM
are developed. Furthermore, the effects of membrane viscosity on the capsule dynamics in shear flow have been examined in detail.
In Chapter 1, the biomechanical properties of red blood cells (RBCs) are introduced followed with a literature review. Also, the motivations and objectives, the structure of this thesis, and the contributions of the candidate are described. In Chapter 2, a finite-difference approach is proposed for implementing membrane viscosity in IBM. To improve the simulation stability, an artificial elastic element is added in series to the viscous component in the membrane mechanics. The detailed mathematical description and key steps for its implementation in immersed boundary programs are provided. Validation tests show a good agreement with analytical solutions and previous calculations. The accuracy dependence on membrane mesh resolution and simulation time
step is also examined.
In Chapter 3, two other schemes are proposed based on the convolution integral expression
of the Maxwell viscoelastic element. Several carefully designed tests are conducted and
the results show that the three schemes have nearly identical performances in accuracy,Doctor of Philosophy (PhD) in Engineering Scienc
Sentiment analysis on Citizenship Amendment Act of India 2019 using Twitter data
No full textFor the perspective of the latest happing news or some events occurring around the world, social
media is widely used. The reaction given by the people’s opinion comes in the way of raw natural
data in different languages and environments. All those written views have some kind of
unbalanced statement, i.e., some sensitive information or some slang words and uneven words.
This makes the researcher or data analyst to extract information and pattern from the dataset
available. This makes opinion mining and taking strategic decision useful in future market.
For sentiment analysis, Natural Language Processing (NLP) and Data Mining techniques are used
to structure an unbalanced data. Using machine learning techniques, the built method analyses
Twitter data to detect sentiment of views from people all around the world. For research purposes
of this study, the dataset was taken from Twitter for Citizenship Amendment Act 2019, India.
Throughout that time many people had given their opinions, views about this new Citizenship
Amendment Act. The sentiment polarity is measured using VADER (Valence Aware Dictionary
and Sentiment Reasoner), which purifies and analyses the data using natural language processing
techniques. The dataset was normalized and prepared using natural language processing
techniques such as Word Tokenization, Stemming and lemmatization, Part of Speech Tagging in
order to be used by machine learning algorithms. All the input variables are converted in the form
of vectors by using “term frequency-inverse document frequency” (TF-IDF). The python
programming language was used to implement this process. Classifiers such as Naïve Bayes, SVM
(support vector machine), k-nearest neighbor (KNN), neural network, Logistic Regression,
Random Forest, and a LSTM (Long-short Term Memory) based RNN (Recurrent Neural Network)
deep learning method were used to obtain evaluation parameters such as accuracy, precision, recall and F-score. On the mean values of performance metrics, a One-way Analysis of Variance
(ANOVA) test was performed on all the methods.Master of Science (MSc) in Computational Science
Intraspecific variation in life history traits of the panamanian electric fish Brachyhypopomus Occidentalis
Get PDFThe purpose of this thesis was to investigate intraspecific variation of life history traits in the
electric knifefish Brachyhypopomus occidentalis under natural conditions, and to explore how
individuals optimize their reproduction and brain size under varying predation risks. In the first
chapter, I describe the reproductive biology of B. occidentalis, using several reproductive traits
selected in both females and males to provide insight on the reproductive effort of mature
knifefish. I provide field evidence supporting the hypothesis that predation risk environment
and geographical isolation drive variation in female reproductive strategies. In the second
chapter, I explore whether predation risk and drainage contributed to brain mass variation B.
occidentalis. I also explore how ontogenetic scaling relationships influence brain mass, and
how this association may have been affected by predation. I show that predation risk is an
important driver of brain mass variation and discuss the potential implications for the fish and
other highly encephalized vertebrates.Master of Science (M.Sc.) in Biolog
Developing an in vitro model to study trained immunity
Get PDFNovel discoveries have proven that the innate immune system has the capacity to adapt a
memory response, termed “trained immunity”, providing broad non-specific protection against
pathogens. To date, trained immunity has been studied in vivo, and ex vivo using human
peripheral blood mononuclear cells (PBMCs) and bone marrow derived macrophages (BMDMs)
from mice. However, we aim to develop and characterize an in vitro cell model to study trained
immunity. J774A.1 macrophages and THP-1 monocytes were evaluated as murine and human
models, respectively, to study trained immunity in vitro. THP-1 monocytes were differentiated
from monocytes to macrophages with 72-hour phorbol 12-myristate-13-acetate (PMA)
stimulation. Cells were trained for 24 hours with muramyl dipeptide (MDP), lipopolysaccharide
(LPS), glucopyranosyl lipid adjuvant (GLA), or a novel delivery system containing cationic
mannosylated liposome (CL) containing muramyl dipeptide (MDP) and glucopyranosyl lipid
adjuvant (GLA). After a 2 days’ rest, cells were restimulated with MDP, LPS, Dukoral or
influenza virus for 24 hours. Subsequently, trained immunity was evaluated in terms of tumor
necrosis factor D (TNFD) and interleukin-10 (IL-10) production, and cell surface expression of
complement domain 14 (CD14) and complement domain 16 (CD16). We demonstrated that both
J774A.1 cells and PMA differentiated THP-1 cells had a trained immunity cytokine profile, as
defined by an increase of TNFα but not IL-10, following restimulation with various non-specific
pathogens. Next, we developed a delivery system comprised of MDP and GLA entrapped in
cationic liposome (CL). The delivery system significantly increased TNFD production following
non-specific restimulation in both the murine and human in vitro models, indicating the ability to
train the cells. By way of flow cytometry, we discovered the delivery system increased the
expression of CD14 and decreased the expression of CD16 on PMA differentiated THP-1 cells To determine the method of training induced by the delivery system, we used two cell signaling
inhibitors, CLI-095 and Gefitinib, and deduced that the delivery system was inducing training
partially through toll-like receptor 4 (TLR4) and nucleotide-binding oligomerization domaincontaining protein 2 (NOD2), respectively. Finally, complement domain 3/ complement domain
4 (CD3/CD28) activated Jurkat T cells co-cultured with trained THP-1 macrophages produced
significantly higher levels of interleukin 2 (IL-2) upon subsequent non-specific restimulation,
suggesting trained innate immune cells have the potential to influence adaptive immune
responses. On the whole this research has contributed to the development of an in vitro bioassay
model that will allow scientists in all fields of immunology to further explore the phenomenon of
trained immunity. Additionally, these in vitro models can act as tools to demonstrate the
potential of trained immunity as a novel therapeutic strategy. At last, our delivery system proved
to be a promising trained immunity stimulant that warrants further investigation
Stories of athlete maltreatment and revictimization: media data from three elite gymnastics teams
Get PDFMedia headlines have recently brought to our attention a stark duality present in elite
athlete development. Although elite sport has often been portrayed as a positive developmental
context (Coakley, 2015), current media coverage is ripe with sinister stories of athlete
maltreatment. The outcries from elite athletes have encompassed a range of abuses including
sexual harassment and abuse, physical violence, emotional abuse, financial abuse, and neglect.
These stories have triggered researchers to hypothesize that athlete maltreatment is present
across all levels of sport, however most prominent within elite sport (Ljungqvist, et al., 2007).
Elite gymnasts have been one of the most vocal groups of athletes calling for change and
demanding protection from a sport culture that has sacrificed athletes’ physical and emotional
well-being (Weiss & Mohr, 2018).
Despite acknowledgements by athletes, media sources, and researchers that athlete
maltreatment is a pervasive issue, little is known about the long-term consequences that plague
athlete survivors. Researchers have identified that elite athletes do experience ongoing
challenges with their mental (Gouttabarge, et al., 2017; Schinke et al., 2017) as well as their
physical (Mountjoy et al., 2016) health resulting from elite athletics. Comparatively, the longterm consequences for survivors of maltreatment in non-athletic contexts are better understood.
Researchers from other disciplines know all to well that one of the most dubious outcomes for
survivors of maltreatment is their propensity for revictimization, a cyclical phenomenon wherein
survivors of maltreatment have a pervasive increased risk of future victimization compared to
others who have not experienced interpersonal trauma (Tseloni & Pease, 2003).
I engaged in this research project to answer three research questions: 1) How does athlete
maltreatment (including physical, sexual, emotional, and financial abuse and neglect) occur in elite gymnastics and why are elite gymnasts victimized as derived from media (re)presentations?
2)What revictimization pathways are foreshadowed in the interpretation of media
(re)presentations of elite gymnasts’ stories of athlete maltreatment and why might this be the
case? And 3) Does the media change the framing of athlete’s stories of maltreatment in relation
to the characters (i.e., the victim or perpetrator gender), setting (i.e., country) and story line (i.e.,
type of abuse) and why does the media change their (re)presentations accordingly? Addressing
the research questions necessitated a novel application of media data to develop our
understanding of the phenomenon of athlete maltreatment. I explored media data of athlete
maltreatment narratives from three elite gymnastics teams across three unique cultural contexts:
the Brazilian Men’s Gymnastics Team, the Australian Women’s Artistic Gymnastics Team, and
the British Men’s and Women’s Gymnastics Teams. Each case study culminated in nuanced
interpretations that contributed to four overarching conclusions from this project. First, I present
athlete maltreatment as culturally constructed and recommend that researchers and practitioners
improve their understanding of culturally constituted risk factors for athlete maltreatment.
Second, I present athlete abuse as a chronic phenomenon that demands we elongate our
understanding of athlete abuse timelines and further consider abuses that both do and do not
cross the threshold for criminality. Third, I conclude that athlete maltreatment extends outside
athletes’ sport domain and recommend researchers and practitioners broaden their scope to
include non-sport mechanisms and consequences of abuse. Fourth, I interpret the media as an
active agent in the cycle of abuse and present considerations to protect survivor athletes through
carefully harnessing healing narratives.Doctor of Philosophy (PhD) in Human Studie
A metagenomic and metabolomic analysis of an Ecuadorian mine tailings microbial community
Get PDFMining produces an enormous quantity of waste material called tailings. Tailings are an
environmental liability when they oxidize and produce acid mine drainage (AMD), but also
contain residual metals with substantial economic value. Native microbial consortia catalyze AMD
production by a factor of 106 over abiotic oxidation rates. These oxidative capabilities can be used
in bioleaching heaps or stirred tank reactors to extract refractory metals from tailings and stabilize
toxic heavy metals. Currently, bioleaching has unpredictable yields because both the microbes
carrying out oxidation and accessory microbes are unidentified or poorly understood. The first
portion of this project uses a metagenomic approach to identify key players in the Ecuador Tailings
(ECT) community (including Acidithiobacillales, Bacillales, Capnodiales, Clostridiales,
Eurotiales, Nitrospirales, and Thermoplasmatales) and their respective roles in iron- and sulfuroxidation pathways, quorum sensing, and cold- and heat-shock response. The second portion of
the project developed methods for a metabolomic approach but did not produce data from ECT.
Using complementary ‘omic’ approaches will allow broader understanding of key ECT organisms
and help optimize bioleaching for maximum metal recovery
Distinguishing fake and real news of twitter data with the help of machine learning techniques
Get PDFNews articles have an influence on people's belief and views about various circumstances. In this
regard, some news publishers with political or ideological bias try to spread news which are
distorted or totally wrong. This thesis intends to develop a machine learning model that identifies
fake news and original news by taking aid from natural language processing. Natural language
processing was used to preprocess the text. Some general features like, number of words,
sentences, stopwords, non-alphabetic words, verbs, nouns, and adjectives were identified. The
stopwords and hyperlinks were removed to clean the text data. In the preprocessing step after
cleaning the data and removing the stopwords, the position of each word was concatenated with
the word itself. This procedure helps in distinguishing between a word as a noun, a pronoun, an
adjective or a verb in the sentences. After preprocessing, feature extraction methods were used
for converting the text of news to analyzable data. The frequency of the words in each article was
used for filtering out the non-informative words. Three feature extraction methods were used in
this study namely, count vectorizer, Term Frequency-Inverse Document Frequency (TF-IDF)
vectorizer and word2vec embedding. It was observed that the results obtained by TF-IDF feature
extraction method were superior compared with the other two methods. After feature extraction,
various machine learning models were used for training the model namely, Naive Bayes,
Logistic Regression, Random Forest, K-nearest neighbors (KNN) and Support Vector Machine
(SVM). The Recurrent Neural Network (RNN) was also used as a deep learning model. The
model was successfully tested on two datasets. On the first dataset, SVM achieved an accuracy
of 98.5% and RNN achieved an accuracy of 98.03% which is much improvement over the best
results of Agarwalla et al., 2019 (83.16 % accuracy). On the second dataset, SVM achieved an accuracy of 97.76%, RNN achieved 97.1% and Logistic Regression achieved 97.50% which is an
improvement over the best results of Vijayraghavan et al. 2020 (94.88% accuracy).MSc Computational Science
(Re)thinking public school architecture as a pedagogical tool
No full textThis thesis aims to rethink elementary public-school architecture by exploring its
ability to become an influential aspect of the pedagogical process in schools. As
educational paradigms have historically responded to social, political, and cultural
conditions, it appears that the development of educational paradigms has moved
faster than the educational buildings of the 21st century. Paradoxically, the spatial
conditions of educational architecture seem to be stuck in the 19th century.
Although there are notable school buildings that emerged from the 20th and
21st century that challenge a conventional school model, the existence of a gap
between school architecture and pedagogical paradigms is predominant in the North
American context.
Beginning with an investigation of the current spatial conditions of educational
architecture, specifically in North America, this thesis analyzes the relationship
between school buildings and pedagogical paradigms that draw upon the history
of education and its built institutions. As well, it examines the factors that prevent
such correlation. Relevant building typologies were studied through orthographic
drawings to create a visual comparison of school buildings from the 19th century
to today. This allows us to observe the major spatial transformations that occurred
between school models over time. Additionally, the analysis addresses how the
social, economic, and political factors influence the relationship between the design
of learning environments and the shift in educational paradigms, uncovering the
principles of school designs and identifying clear discontinuities between the built
forms and educational models. Undoubtedly, most of the contemporary educational
buildings present in the North American context manifest spatial traditions that bear
few relations to the current knowledge of the learning processes.
Considering the significant role of the learning environment in the support of critical
thinking, discovery, and creativity, this thesis explores this potential to overcome
century-old traditions of learning through memorization and subservience to the
authority of the teacher. We use the context of Markham, Ontario, in the Greater
Toronto Area, to create an elementary school based on the principles seen in
Montessori’s, Reggio Emilia Schools, and Lab Ecole projects, which respond to the
basis of the most actual theories of children education. The designs we see today
of newly constructed school buildings within the suburban context tend to be
an afterthought, prioritizing budget, and fast construction rates with little to no
consideration to how the built environment can aid in the learning process. As a
result, the suburbs provide an ideal setting to explore how the physical environment
can aid in the learning process. Ultimately, using architecture as a pedagogical
tool that prompts the physical environment to inspire, stimulate, and encourage
exploration and investigation of new ideas while supporting collaboration and the
development of connections beyond the typical school environment
Potential-field modelling of the prospective Chibougamau area (northeastern Abitibi subprovince, Quebec, Canada) using geological, geophysical, and petrophysical constraints
Get PDFThis paper focusses on obtaining a better understanding of the subsurface geology of the Chibougamau area, in
the northeast of the Abitibi greenstone belt (Superior craton), using geophysical data collected along a 128 km long traverse with a rough southwest–northeast orientation. We have constructed two-dimensional (2D) models of the study area that are consistent with newly collected gravity data and high-resolution magnetic data sets. The initial models were constrained at depth by an interpretation of a new seismic section and at surface by the bedrock geology and known geometry of lithological units. The attributes of the model were constrained using petrophysical measurements so that the final model is compatible with all available geological and geophysical data. The potential-field data modelling resolved the geometry of plutons and magnetic bodies that are transparent on seismic sections. The new model is consistent with the known structural geology, such as open folding, and provides an improvement in estimating the size, shape, and depth of the Barlow and Chibougamau plutons. The Chibougamau pluton is known to be associated with Cu–Au magmatic-hydrothermal mineralisation and, as the volume and geometry of intrusive bodies is paramount to the exploration of such mineralisation, the modelling presented here provides a scientific foundation to exploration models focused on such mineralisation
Re-defining Toronto's collective housing: an architectural model for floating communities in the Don River Watershed
No full textRe-defining Toronto’s Collective Housing: An Architectural Model for Floating Communities in the Don River Watershed is meant to be a critique of the existing typology of floating communities in Toronto, and a proposal for a new model focused around building a sustainable and intentional culture around the water.
Existing water-based communities in Toronto pose many issues in terms of sustainability, land use, community, stewardship and public access to the waterfront.
This thesis will address these issues and serve as a kick starter to the development
of similar communities in the future.
Toronto is a large waterfront urban centre, however, even given its history
surrounding the water, the current city is not very oriented around it. The idea of an
affordable community focused around the water has the opportunity to elevate this
connection between the city, its inhabitants and its watersheds. This thesis will analyze and take into consideration the current typology of floating communities in the
city and draw on the inspiration of global precedents to develop a program model
that values the importance of community, sustainability, financial accessibility and
local culture. This thesis aims to aid not only in the development of community, but
also in the remediation and conservation of Toronto watersheds, providing a place
for conservationists and eco-minded patrons to live closely with the ecosystems
they strive to protect. The final design will use a sensitive design approach to build a
program and building system that reflects the goals and ideals of this study.
Question:
How can a new floating community typology address the lack of balance and
attention to our watersheds through an affordable and sustainable community focused model