85,148 research outputs found
Oncometabolites: tailoring our genes
Increased glucose metabolism in cancer cells is a phenomenon that has been known for over 90 years, allowing maximal cell growth through faster ATP production and redistribution of carbons towards nucleotide, protein and fatty acid synthesis. Recently, metabolites that can promote tumorigeneis by altering the epigenome have been identified. These ‘oncometabolites’ include the tricarboxylic acid cycle metabolites succinate and fumarate, whose levels are elevated in rare tumours with succinate dehydrogenase and fumarate hydratase mutations, respectively. 2-Hydroxyglutarate is another oncometabolite; it is produced de novo as a result of the mutation of isocitrate dehydrogenase, and is commonly found in gliomas and acute myeloid leukaemia. Interestingly, the structural similarity of these oncometabolites to their precursor metabolite, α-ketoglutarate, explains the tumorigenic potential of these metabolites, by competitive inhibition of a superfamily of enzymes called the α-ketoglutarate-dependent dioxygenases. These enzymes utilize α-ketoglutarate as a cosubstrate, and are involved in fatty acid metabolism, oxygen sensing, collagen biosynthesis, and modulation of the epigenome. They include enzymes that are involved in regulating gene expression via DNA and histone tail demethylation. In this review, we will focus on the link between metabolism and epigenetics, and how we may target oncometabolite-induced tumorigenesis in the future
Protecting migrant children’s rights
This report relates to the training workshop on the protection of migrant children’s rights which is part of an ESRC-funded research project currently led by Dr Ana Beduschi (https://migrantchildren.org/). The event was organised by the University of Exeter in partnership with the NGO Network for Children’s Rights Greece. The workshop was held at their headquarters in Athens on the 28 January 2017. Agenda and training materials (in English and in Greek) available here: https://migrantchildren.org/2016/10/25/upcoming-in-january-2017/ The event was attended by aid workers (frontline workers) providing services in three refugee camps in Athens and by refugee lawyers working for the Network for Children’s Rights and for ARSIS (Greek NGO providing support for children). This workshop emphasised training on legal aspects of the protection of migrant children. It also explored the possibility of developing a vulnerability and best interests tool which would be simple and accessible to frontline workers. Despite the absence of specific official procedure, frontline workers at the NGO Network for Children’s Rights are already conducting a form of best interests of the child determinations in relation to migrant children. Following the workshop and considering their input, we propose a vulnerability and best interests of the child determination tool, which can be used by frontline workers in their daily work.ESRC IAA Project Co-Creation Fun
On the massive gluon propagator, the PT-BFM scheme and the low-momentum behaviour of decoupling and scaling DSE solutions
We study the low-momentum behaviour of Yang-Mills propagators obtained from
Landau-gauge Dyson-Schwinger equations (DSE) in the PT-BFM scheme. We compare
the ghost propagator numerical results with the analytical ones obtained by
analyzing the low-momentum behaviour of the ghost propagator DSE in Landau
gauge, assuming for the truncation a constant ghost-gluon vertex and a simple
model for a massive gluon propagator. The asymptotic expression obtained for
the regular or decoupling ghost dressing function up to the order is proven to fit pretty well the numerical PT-BFM results.
Furthermore, when the size of the coupling renormalized at some scale
approaches some critical value, the numerical PT-BFM propagators tend to behave
as the scaling ones. We also show that the scaling solution, implying a
diverging ghost dressing function, cannot be a DSE solution in the PT-BFM
scheme but an unattainable limiting case.Comment: 16 pages, 2 figs., 2 tabs (updated version to be published in JHEP
Analisis Kepuasan Wajib Pajak: Pendekatan Terhadappenggunaan Teknologi Informasi Dan Self Assessment
The aim of this study is to investigate the influence of using Information technology and self assessment system to tax prayers' Satisfaction. The Hypothesis which have been formulated in this research are: there is direct and indirect influence of using Information technology and self assessment system to tax prayer's Satisfaction. The research is a survey to tax payers in Surakarta, Data have been collected with quesionaries as 100 respondents are processed with, validity and reliability test, the regression analysis, t test, F test and test of R2.
Keyword: using Information technology, self assessment system, tax prayers Satisfaction
Protecting migrant children’s rights
This report relates to the training workshop on the protection of migrant children’s rights which is part of an ESRC-funded research project currently led by Dr Ana Beduschi (https://migrantchildren.org/). The event was organised by the University of Exeter in partnership with the NGO Network for Children’s Rights Greece. The workshop was held at their headquarters in Athens on the 28 January 2017. Agenda and training materials (in English and in Greek) available here: https://migrantchildren.org/2016/10/25/upcoming-in-january-2017/ The event was attended by aid workers (frontline workers) providing services in three refugee camps in Athens and by refugee lawyers working for the Network for Children’s Rights and for ARSIS (Greek NGO providing support for children). This workshop emphasised training on legal aspects of the protection of migrant children. It also explored the possibility of developing a vulnerability and best interests tool which would be simple and accessible to frontline workers. Despite the absence of specific official procedure, frontline workers at the NGO Network for Children’s Rights are already conducting a form of best interests of the child determinations in relation to migrant children. Following the workshop and considering their input, we propose a vulnerability and best interests of the child determination tool, which can be used by frontline workers in their daily work.ESRC IAA Project Co-Creation Fun
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Seismological constraints on the down-dip shape of normal faults
We present a seismological technique for determining the down-dip shape of seismogenic normal faults. Synthetic models of non-planar source geometries reveal the important signals in teleseismic P and SH waveforms that are diagnostic of down-dip curvature. In particular, along-strike SH waveforms are the most sensitive to variations in source geometry, and have significantly more complex and larger-amplitude waveforms for curved source geometries than planar ones. We present the results of our forward-modelling technique for 13 earthquakes. Most continental normal-faulting earthquakes that rupture through the full seismogenic layer are planar and have dips of 30°–60°. There is evidence for faults with a listric shape from some of the earthquakes occurring in two regions; Tibet and East Africa. These ruptures occurred on antithetic faults, or minor faults within the hanging walls of the rifts affected, which may suggest a reason for the down-dip curvature. For these earthquakes, the change in dip across the seismogenic part of the fault plane is ≤30°.This work forms part of the NERC- and ESRC-funded project ‘Earthquakes without Frontiers’ and was partially supported by the NERC large grant ‘Looking inside the Continents from Space’
Bayesian network structure learning with causal effects in the presence of latent variables.
Latent variables may lead to spurious relationships that can be
misinterpreted as causal relationships. In Bayesian Networks (BNs), this
challenge is known as learning under causal insufficiency. Structure learning
algorithms that assume causal insufficiency tend to reconstruct the ancestral
graph of a BN, where bi-directed edges represent confounding and directed edges
represent direct or ancestral relationships. This paper describes a hybrid
structure learning algorithm, called CCHM, which combines the constraint-based
part of cFCI with hill-climbing score-based learning. The score-based process
incorporates Pearl s do-calculus to measure causal effects and orientate edges
that would otherwise remain undirected, under the assumption the BN is a linear
Structure Equation Model where data follow a multivariate Gaussian
distribution. Experiments based on both randomised and well-known networks show
that CCHM improves the state-of-the-art in terms of reconstructing the true
ancestral graph
Tuning structure learning algorithms with out-of-sample and resampling strategies
One of the challenges practitioners face when applying structure learning algorithms to their data involves determining a set of hyperparameters; otherwise, a set of hyperparameter defaults is assumed. The optimal hyperparameter configuration often depends on multiple factors, including the size and density of the usually unknown underlying true graph, the sample size of the input data, and the structure learning algorithm. We propose a novel hyperparameter tuning method, called the Out-of-sample Tuning for Structure Learning (OTSL), that employs out-of-sample and resampling strategies to estimate the optimal hyperparameter configuration for structure learning, given the input dataset and structure learning algorithm. Synthetic experiments show that employing OTSL to tune the hyperparameters of hybrid and score-based structure learning algorithms leads to improvements in graphical accuracy compared to the state-of-the-art. We also illustrate the applicability of this approach to real datasets from different disciplines
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