1,720,976 research outputs found
Heavy domain wall fermions: The RBC and UKQCD charm physics program
No full textWe review the domain wall charm physics program of the RBC and UKQCD collaborations based on simulations including ensembles with physical pion mass. We summarise our current set-up and present a status update on the decay constants fD, fD s, the charm quark mass, heavy-light and heavy-strange bag parameters and the ratio ξ</p
Planar cell polarity pathway as a master regulator of biliary morphogenesis
No full textThe biliary tree is an intricate network of ducts within the liver, responsible for
transporting bile to the intestine. The development of a functional biliary system during
organogenesis is indispensable to mammalian health and abnormal bile duct
formation leads to paediatric cholestatic diseases such as biliary atresia and Alagille
syndrome. While such human genetic conditions and animal research have identified
the role of key molecular regulators, such as the Notch and TGFβ signaling pathways,
in determining biliary lineage, the mechanisms driving the morphological
transformation of a simple ductal plate into a complex tubular network remain largely
elusive. Here, I aimed to identify candidate regulators of terminal morphogenesis in
bile duct development and functionally define the molecular mechanisms that
underpin this process. Analysis of a single-cell RNA-sequencing dataset of embryonic
mouse livers revealed increased transcriptional expression of Planar Cell Polarity
(PCP) pathway components during terminal biliary development. This increase in
expression coincides with the timeframe during which the ductal plate undergoes
significant morphological reorganisation. Using a combination of transgenic and
mutant mouse lines and 3D organoid cultures, I demonstrated that PCP signaling is
essential for proper bile duct morphogenesis. The use of whole-mount
immunofluorescence and proteomics revealed that VANGL2, a core PCP component,
localised to cell-cell junctions of ductular cells and interacts with the desmosome
complex. Consequently, the genetic loss of Vangl2 function led to the universal
disruption of cell-adhesions between ductular cells suggesting a novel role for PCP in
supporting the functional assembly of cell-cell junctions. Moreover, this also resulted
in failure to establish a continuous biliary network, with mutant animals displaying
fewer bile duct branches that remained disconnected, along with defects in terminal
bile duct differentiation. Additionally, mutant bile ducts demonstrated aberrant apical-basal cell polarity as evidenced by: (i) disrupted cytoskeletal arrangements, and (ii)
impaired efflux pump activity, thus highlighting the essential link between normal
tissue morphology and bile duct physiology. In this thesis I provide one of the first
mammalian descriptions of the biomechanical regulators of bile duct morphogenesis
Higher-order interactions in single-cell gene expression: towards a cybergenetic semantics of cell state
Full text linkFinding and understanding patterns in gene expression guides our understanding of living organisms, their development, and diseases, but is a challenging and high-dimensional problem as there are many molecules involved. One way to learn about the structure of a gene regulatory network is by studying the interdependencies among its constituents in transcriptomic data sets. These interdependencies could be arbitrarily complex, but almost all current models of gene regulation contain pairwise interactions only, despite experimental evidence existing for higher-order regulation that cannot be decomposed into pairwise mechanisms. I set out to capture these higher-order dependencies in single-cell RNA-seq data using two different approaches. First, I fitted maximum entropy (or Ising) models to expression data by training restricted Boltzmann machines (RBMs). On simulated data, RBMs faithfully reproduced both pairwise and third-order interactions. I then trained RBMs on 37 genes from a scRNA-seq data set of 70k astrocytes from an embryonic mouse. While pairwise and third-order interactions were revealed, the estimates contained a strong omitted variable bias, and there was no statistically sound and tractable way to quantify the uncertainty in the estimates. As a result I next adopted a model-free approach. Estimating model-free interactions (MFIs) in single-cell gene expression data required a quasi-causal graph of conditional dependencies among the genes, which I inferred with an MCMC graph-optimisation algorithm on an initial estimate found by the Peter-Clark algorithm. As the estimates are model-free, MFIs can be interpreted either as mechanistic relationships between the genes, or as substructures in the cell population. On simulated data, MFIs revealed synergy and higher-order mechanisms in various logical and causal dynamics more accurately than any correlation- or information-based quantities. I then estimated MFIs among 1,000 genes, at up to seventh-order, in 20k neurons and 20k astrocytes from two different mouse brain scRNA-seq data sets: one developmental, and one adolescent. I found strong evidence for up to fifth-order interactions, and the MFIs mostly disambiguated direct from indirect regulation by preferentially coupling causally connected genes, whereas correlations persisted across causal chains. Validating the predicted interactions against the Pathway Commons database, gene ontology annotations, and semantic similarity, I found that pairwise MFIs contained different but a similar amount of mechanistic information relative to networks based on correlation. Furthermore, third-order interactions provided evidence of combinatorial regulation by transcription factors and immediate early genes.
I then switched focus from mechanism to population structure. Each significant MFI can be assigned a set of single cells that most influence its value. Hierarchical clustering of the MFIs by cell assignment revealed substructures in the cell population corresponding to diverse cell states. This offered a new, purely data-driven view on cell states because the inferred states are not required to localise in gene expression space. Across the four data sets, I found 69 significant and biologically interpretable cell states, where only 9 could be obtained by standard approaches. I identified immature neurons among developing astrocytes and radial glial cells, D1 and D2 medium spiny neurons, D1 MSN subtypes, and cell-cycle related states present across four data sets. I further found evidence for states defined by genes associated to neuropeptide signalling, neuronal activity, myelin metabolism, and genomic imprinting. MFIs thus provide a new, statistically sound method to detect substructure in single-cell gene expression data, identifying cell types, subtypes, or states that can be delocalised in gene expression space and whose hierarchical structure provides a new view on the semantics of cell state. The estimation of the quasi-causal graph, the MFIs, and inference of the associated states is implemented as a publicly available Nextflow pipeline called Stator
Integrating functional genomics and semi-parametric estimation to identify binding variants likely causal for altering human traits
Full text linkUnderstanding the genetic architecture of complex human traits is a central challenge
in modern genetics with applications in drug development and precision
medicine. This thesis presents methodological advancements for the discovery
of causal variants affecting human traits. These advancements are grounded in
mathematical statistics and functional genomics and supported by extensive simulations and real-world data studies using the UK Biobank.
In the first part of this body of work we introduce a comprehensive mathematical
framework for the analysis of genetic effects on traits or disease, including single
variant effects, non-linear allelic effects, and higher-order interactions. Genetic
effects are formally defined as causal estimands, yet remain difficult to identify,
reasons for which are discussed. We then construct semi-parametric estimators
for asymptotically unbiased and efficient estimation of associated statistical estimands.
Finally, we propose a network approach, based on genetic relatedness
to account for non-independent individuals. This statistical advancement is delivered
within state-of-the-art software called TarGene. TarGene is designed to
provide performant and reproducible semi-parametric estimation routines, scaling
to biobank-scale datasets, and compatible with modern high-performance
computing platforms.
In the second part, we investigate the empirical performance of these semiparametric
estimators in the context of population genetics, using UK Biobank
data. Firstly, this is done via an extensive simulation study, leveraging flexible
generative models that can adequately represent the data generating process.
Practical violations of theoretical assumptions are illustrated as well as strategies
for their mitigation. Secondly, we contrast semi-parametric estimates to published
data produced by conventional parametric models. To this end, we perform
a phenome-wide association study (768 traits) for a well-established variant
with large effect size on the body-mass index (BMI). We observe that p-values obtained
via parametric models are substantially smaller than those originating from
semi-parametric methods. The absence of overlap between some semi-parametric
confidence intervals and those originating from parametric models highlight inflated
false discovery rates due to model misspecification. In addition, for 39 traits
our method reveals non-linear allelic effects which are commonly overlooked by
current practices in linear modelling.
Finally, we propose a paradigm based on functional genetics for the discovery
of probable causal variants and the mechanism through which they act on human
traits. These variants are likely to be causal for two main reasons: (i) they are
experimentally shown to disrupt the binding of a specific transcription factor and
are thus biologically active; and, (ii) their effect on traits is modulated via transacting
variants that were associated with the same mechanism. As a pilot study,
we use TarGene to discover putative causal variants acting through the vitamin
D receptor. For these variants, a post-analysis is performed to gain more insight
into the mechanism of action.
Overall, this thesis advances the field of population genetics in three ways.
First, it provides a robust mathematical framework within which the main challenges
in the field are formally defined. Second, it addresses the statistical estimation
challenge by removing the need for parametric assumptions and delivers
an open-source state-of-the-art software. Third, it proposes a paradigm based on
functional genomics for the discovery of putative causal variants as well as the
mechanism through which they act on human traits
Lattice phenomenology of heavy quarks using dynamical fermions
Full text linkThe Standard Model of particle physics is believed to be only the low energy
limit of a more fundamental theory. In order to determine its range of validity,
a major part of theoretical and experimental efforts in physics is dedicated to
precision tests of the Standard Model. Lattice QCD is a non-perturbative, first-principles
approach to Quantum Field Theory. It plays an important role in
flavor physics by providing calculations of non-perturbative strong interaction
contributions to weak processes involving quarks. Measurements of hadronic
quantities can be used to constrain the Standard Model as well as theories Beyond
the Standard Model.
The first part of this thesis contains theoretical developments regarding non-perturbative
renormalization. A new renormalization scheme, RI/mSMOM, for
fermion bilinear operators in QCD at non-vanishing quark mass is presented.
In order to investigate the properties of the mSMOM scheme, an explicit one-loop
computation in perturbation theory using dimensional regularization is
performed. Numerically, vertex functions are generated on the lattice, with an
appropriate projector, based on the RI/SMOM scheme and the renormalization
factors are extracted. Quantities measured include renormalization of the axial
current ZA, required to renormalize the axial current entering the computation
of the decay constant and the renormalization of the bag parameter.
The second part of this report focuses on flavor physics phenomenology on
the lattice. It presents results of the first run of the RBC/UKQCD charm project
with (2+1)-flavor Domain Wall fermions. Observables and matrix elements are
measured on lattices with Iwasaki gauge action. There are two ensembles at the
physical point with inverse lattice spacings 1.73 and 2.36 GeV and a third finer
ensemble at 2.76 GeV as well as four other auxiliary ensembles with smaller
volumes and heavier pion masses which are used to perform the continuum
extrapolations. The quantities measured in the region of the charm quark mass
are meson masses, decay constants, the matrix element of the OV V +AA operator,
the neutral D-meson mixing parameter B and the SU(3) breaking ratio ξ
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Identifying unique cell states in early liver oncogenesis with transcription coupled repair
No full textMany cancers, such as the main form of primary liver cancer hepatocellular
carcinoma (HCC), typically arise in tissues with a high number of genetic mutations which
drive cell growth aberrantly. These mutations appear to precede tumour initiation, as DNA
sequencing has shown that cancer driver mutations can be found in otherwise-healthy
tissues at high levels. Though an increase in mutational burden increases the risk of cancer,
not all cells with driver mutations will form tumours. There is a growing body of evidence that
suggests tumour-initiating cells preferentially enter specific transcriptional states that interact
with driver mutations to promote tumourigenesis. Characterising the aberrant transcriptional
pathways of initiating cells could help elucidate the mechanisms for determining exactly
which mutated cells will go on to form cancer.
Here I aim to identify transcriptional cell states which can predispose hepatocytes to
tumorigenesis in the diethylnitrosamine (DEN) mouse model of HCC using a novel method
of transcriptomic profiling. DEN introduces adducts onto DNA that lead to mutations, and the
principal adduct on thymine can only be removed by the DNA repair process transcription
coupled repair (TCR). TCR is closely linked to transcription and only transcribed sections of
DNA are repaired, resulting in varying levels of mutations across the genome, with highly
transcribed sections having low mutation burdens, while the inverse is true for non-transcribed
sections.
By assessing the mutational burden across the genomes of established tumours
from the DEN model, I infer historical transcriptional activity from tumour initiating cells
immediately after DEN treatment. This analysis uncovered multiple genes associated with
cilium assembly and cilium organisation preferentially upregulated in response to DEN.
Genes associated with cell migration and adhesion were identified as downregulated after
DEN treatment. I then validated these findings using bulk RNA sequencing of whole liver
tissue post-DEN treatment, demonstrating that mutational foot printing as a result of TCR
can be used as a robust technique to infer historical transcriptional changes in cells that form
tumours.
To identify specific liver cell populations that are predisposed to tumour formation, I
conducted single nuclei RNA sequencing post-DEN treatment and compared the
transcriptional profiles of groups and of single nuclei to the mutational data. I show that a
subset of DEN-damaged Cyp2e1 expressing hepatocytes has the best expression fit with
the mutational data, suggesting these may represent a subset of hepatocytes at the earliest
stages of tumorigenesis.
In conclusion, this work identifies a subset of DEN-damaged hepatocytes with a
transcriptional profile suspected to convey preference for tumour formation over other
damaged and mutated cells and serves as the first description of using mutational data to
retroactively define the transcriptional state of cells that form tumours
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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