66 research outputs found
Exploring genetic associations with ceRNA regulation in the human genome
Get PDFabstract: Competing endogenous RNAs (ceRNAs) are RNA molecules that sequester shared microRNAs (miRNAs) thereby affecting the expression of other targets of the miRNAs. Whether genetic variants in ceRNA can affect its biological function and disease development is still an open question. Here we identified a large number of genetic variants that are associated with ceRNA's function using Geuvaids RNA-seq data for 462 individuals from the 1000 Genomes Project. We call these loci competing endogenous RNA expression quantitative trait loci or ‘cerQTL’, and found that a large number of them were unexplored in conventional eQTL mapping. We identified many cerQTLs that have undergone recent positive selection in different human populations, and showed that single nucleotide polymorphisms in gene 3΄UTRs at the miRNA seed binding regions can simultaneously regulate gene expression changes in both cis and trans by the ceRNA mechanism. We also discovered that cerQTLs are significantly enriched in traits/diseases associated variants reported from genome-wide association studies in the miRNA binding sites, suggesting that disease susceptibilities could be attributed to ceRNA regulation. Further in vitro functional experiments demonstrated that a cerQTL rs11540855 can regulate ceRNA function. These results provide a comprehensive catalog of functional non-coding regulatory variants that may be responsible for ceRNA crosstalk at the post-transcriptional level.The final version of this article, as published in Nucleic Acids Research, can be viewed online at: https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkx33
Constraining sleptons at the LHC in a supersymmetric low-scale seesaw scenario
Get PDFThe discovery of the Higgs boson in the 8 TeV run of the LHC [1, 2] marks one of the
most important milestones in particle physics. Its mass is already known rather precisely:
mh = 125.09 ± 0.21 (stat.) ±0.11 (syst.) GeV [3], and the signal strength of various LHC
searches has been found consistent with the SM predictions. While this completes the
Standard Model (SM) particle-wise, several questions still remain open, for example: (i) Is
it possible to include the SM in a grand unified theory where all gauge forces unify? (ii) Is
there a particle physics explanation of the observed dark matter relic density? (iii) What
causes the hierarchy in the fermion mass spectrum and why are neutrinos so much lighter
than the other fermions? What causes the observed mixing patterns in the fermion sector?
(iv) What stabilizes the Higgs mass at the electroweak scale?
Supersymmetric model address several of these questions and consequently the search for
supersymmetry (SUSY) is among the main priorities of the LHC collaborations. Up to now
no significant sign for physics beyond SM has been found. The combination of the Higgs
discovery with the (yet) unsuccessful searches has led to the introduction of a model class
called ‘natural SUSY’ [4–15]. Here, the basic idea is to give electroweak-scale masses only
to those SUSY particles giving a sizeable contribution to the mass of the Higgs boson, such
that a too large tuning of parameters is avoided. All other particle masses are taken at the
multi-TeV scale. In particular, masses of the order of a few hundred GeV up to about one
TeV are assigned to the higgsinos (the partners of the Higgs bosons), the lightest stop (the
partner of the top-quark) and, if the latter is mainly a left-stop, also to the light sbottom In
addition the gluino and the heavier stop masses should also be close to at most a few TeV.
Neutrino oscillation experiments confirm that at least two neutrinos have a non-zero mass.
The exact mass generation mechanism for these particles is unknown, and both the SM and
the MSSM remain agnostic on this topic. Although many ways to generate neutrino mass
exist, perhaps the most popular one is the seesaw mechanism [16–21]. The main problem
with the usual seesaw mechanisms lies on the difficulty in testing its validity. In general, if
Yukawa couplings are sizeable, the seesaw relations require Majorana neutrino masses to be
very large, such that the new heavy states cannot be produced at colliders. In contrast, if
one requires the masses to be light, then the Yukawas need to be small, making production
cross-sections and decay rates to vanish. A possible way out of this dilemma lies on what
3
is called the inverse seesaw [22], which is based on having specific structures on the mass
matrix (generally motivated by symmetry arguments) to generate small neutrino masses.
This, at the same time, allows Yukawa couplings to be large, and sterile masses to be light.
We consider here a supersymmetric model where neutrino data are explained via a minimal
inverse seesaw scenario where the gauge-singlet neutrinos have masses in the range
O(keV) to O(100 GeV). We explore this with a parametrization built for the standard seesaw,
and go to the limit where the inverse seesaw emerges, such that Yukawas and mixings
become sizeable. Although non-SUSY versions of this scenario can solve the dark matter
and matter-antimatter asymmetry problems [23–25], we shall make no claim on these issues
in our model.
In view of the naturalness arguments, we further assume that the higgsinos have masses of
O(100 GeV), whereas the gaugino masses lie at the multi-TeV scale (see [26] for an example
of such a scenario). In addition, we assume all squarks are heavy enough such that LHC
bounds are avoided, and play no role in the phenomenology within this work1. In contrast
we allow for fairly light sleptons and investigate the extent to which current LHC data can
constrain such scenarios.
This paper is organized as follows: in the next section we present the model. Section
III summarizes the numerical tools used and gives an overview of the LHC analysis used
for these investigations. In Section IV we present our findings for the two generic scenarios
which differ in the nature of the lighest supersymmetric particle (LSP): a Higgsino LSP
and a sneutrino LSP. In Section V we draw our conclusions. Appendices A and B give the
complete formulae for the neutrino and sneutrino masses
Constraining sleptons at the LHC in a supersymmetric low-scale seesaw scenario
No full textThe discovery of the Higgs boson in the 8 TeV run of the LHC [1, 2] marks one of the
most important milestones in particle physics. Its mass is already known rather precisely:
mh = 125.09 ± 0.21 (stat.) ±0.11 (syst.) GeV [3], and the signal strength of various LHC
searches has been found consistent with the SM predictions. While this completes the
Standard Model (SM) particle-wise, several questions still remain open, for example: (i) Is
it possible to include the SM in a grand unified theory where all gauge forces unify? (ii) Is
there a particle physics explanation of the observed dark matter relic density? (iii) What
causes the hierarchy in the fermion mass spectrum and why are neutrinos so much lighter
than the other fermions? What causes the observed mixing patterns in the fermion sector?
(iv) What stabilizes the Higgs mass at the electroweak scale?
Supersymmetric model address several of these questions and consequently the search for
supersymmetry (SUSY) is among the main priorities of the LHC collaborations. Up to now
no significant sign for physics beyond SM has been found. The combination of the Higgs
discovery with the (yet) unsuccessful searches has led to the introduction of a model class
called ‘natural SUSY’ [4–15]. Here, the basic idea is to give electroweak-scale masses only
to those SUSY particles giving a sizeable contribution to the mass of the Higgs boson, such
that a too large tuning of parameters is avoided. All other particle masses are taken at the
multi-TeV scale. In particular, masses of the order of a few hundred GeV up to about one
TeV are assigned to the higgsinos (the partners of the Higgs bosons), the lightest stop (the
partner of the top-quark) and, if the latter is mainly a left-stop, also to the light sbottom In
addition the gluino and the heavier stop masses should also be close to at most a few TeV.
Neutrino oscillation experiments confirm that at least two neutrinos have a non-zero mass.
The exact mass generation mechanism for these particles is unknown, and both the SM and
the MSSM remain agnostic on this topic. Although many ways to generate neutrino mass
exist, perhaps the most popular one is the seesaw mechanism [16–21]. The main problem
with the usual seesaw mechanisms lies on the difficulty in testing its validity. In general, if
Yukawa couplings are sizeable, the seesaw relations require Majorana neutrino masses to be
very large, such that the new heavy states cannot be produced at colliders. In contrast, if
one requires the masses to be light, then the Yukawas need to be small, making production
cross-sections and decay rates to vanish. A possible way out of this dilemma lies on what
3
is called the inverse seesaw [22], which is based on having specific structures on the mass
matrix (generally motivated by symmetry arguments) to generate small neutrino masses.
This, at the same time, allows Yukawa couplings to be large, and sterile masses to be light.
We consider here a supersymmetric model where neutrino data are explained via a minimal
inverse seesaw scenario where the gauge-singlet neutrinos have masses in the range
O(keV) to O(100 GeV). We explore this with a parametrization built for the standard seesaw,
and go to the limit where the inverse seesaw emerges, such that Yukawas and mixings
become sizeable. Although non-SUSY versions of this scenario can solve the dark matter
and matter-antimatter asymmetry problems [23–25], we shall make no claim on these issues
in our model.
In view of the naturalness arguments, we further assume that the higgsinos have masses of
O(100 GeV), whereas the gaugino masses lie at the multi-TeV scale (see [26] for an example
of such a scenario). In addition, we assume all squarks are heavy enough such that LHC
bounds are avoided, and play no role in the phenomenology within this work1. In contrast
we allow for fairly light sleptons and investigate the extent to which current LHC data can
constrain such scenarios.
This paper is organized as follows: in the next section we present the model. Section
III summarizes the numerical tools used and gives an overview of the LHC analysis used
for these investigations. In Section IV we present our findings for the two generic scenarios
which differ in the nature of the lighest supersymmetric particle (LSP): a Higgsino LSP
and a sneutrino LSP. In Section V we draw our conclusions. Appendices A and B give the
complete formulae for the neutrino and sneutrino masses
Role of the long non-coding RNA PVT1 in the dysregulation of the ceRNA-ceRNA network in human breast cancer
Get PDFRecent findings have identified competing endogenous RNAs (ceRNAs) as the drivers in many disease conditions, including cancers. The ceRNAs indirectly regulate each other by reducing the amount of microRNAs (miRNAs) available to target messenger RNAs (mRNAs). The ceRNA interactions mediated by miRNAs are modulated by a titration mechanism, i.e. large changes in the ceRNA expression levels either overcome, or relieve, the miRNA repression on competing RNAs; similarly, a very large miRNA overexpression may abolish competition. The ceRNAs are also called miRNA decoys or miRNA sponges and encompass different RNAs competing with each other to attract miRNAs for interactions: mRNA, long non-coding RNAs (lncRNAs), pseudogenes, or circular RNAs. Recently, we developed a computational method for identifying ceRNA-ceRNA interactions in breast invasive carcinoma. We were interested in unveiling which lncRNAs could exert the ceRNA activity. We found a drastic rewiring in the cross-talks between ceRNAs from the physiological to the pathological condition. The main actor of this dysregulated lncRNA-associated ceRNA network was the lncRNA PVT1, which revealed a net biding preference towards the miR-200 family members in normal breast tissues. Despite its up-regulation in breast cancer tissues, mimicked by the miR-200 family members, PVT1 stops working as ceRNA in the cancerous state. The specific conditions required for a ceRNA landscape to occur are still far from being determined. Here, we emphasized the importance of the relative concentration of the ceRNAs, and their related miRNAs. In particular, we focused on the withdrawal in breast cancer tissues of the PVT1 ceRNA activity and performed a gene expression and sequence analysis of its multiple isoforms. We found that the PVT1 isoform harbouring the binding site for a representative miRNA of the miR-200 family shows a drastic decrease in its relative concentration with respect to the miRNA abundance in breast cancer tissues, providing a plausibility argument to the breakdown of the sponge program orchestrated by the oncogene PVT1. © 2017 Conte et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Mapping the Landscape of Blockchain for Transparent and Sustainable Supply Chains: A Bibliometric and Thematic Analysis
No full textBackground: The increasing complexity of global supply chains has intensified the demand for transparency, traceability, security, and sustainability in logistics and operations. Blockchain technology enables decentralized, immutable frameworks that improve data integrity, automate transactions via smart contracts, and integrate seamlessly with the IoT and AI. Methods: This bibliometric review analyzes 559 peer-reviewed publications retrieved from Scopus and Web of Science using a PRISMA-guided protocol. Data were processed with Bibliometrix and Biblioshiny to examine scientific production, contributing institutions, author countries, collaboration patterns, thematic clusters, and keyword evolution. Results: The analysis reveals a 400% increase in publications after 2020, with China, India, and the USA leading in output but with limited international collaboration. Keyword co-occurrence and thematic mapping reveal dominant topics, including smart contracts, food supply chain traceability, and sustainability, as well as emerging themes such as decentralization, privacy, and the circular economy. Conclusions: The field is marked by interdisciplinary growth, yet it remains thematically and geographically fragmented. This review maps the intellectual structure of blockchain-enabled sustainable supply chains, offering insights for policymakers, developers, and industry leaders and outlining future research avenues centered on global cooperation, platform efficiency, and ethical and regulatory dimensions
A network-based matrix factorization framework for ceRNA co-modules recognition of cancer genomic data
No full text\ua9 The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected]. With the development of high-throughput technologies, the accumulation of large amounts of multidimensional genomic data provides an excellent opportunity to study the multilevel biological regulatory relationships in cancer. Based on the hypothesis of competitive endogenous ribonucleic acid (RNA) (ceRNA) network, lncRNAs can eliminate the inhibition of microRNAs (miRNAs) on their target genes by binding to intracellular miRNA sites so as to improve the expression level of these target genes. However, previous studies on cancer expression mechanism are mostly based on individual or two-dimensional data, and lack of integration and analysis of various RNA-seq data, making it difficult to verify the complex biological relationships involved. To explore RNA expression patterns and potential molecular mechanisms of cancer, a network-regularized sparse orthogonal-regularized joint non-negative matrix factorization (NSOJNMF) algorithm is proposed, which combines the interaction relations among RNA-seq data in the way of network regularization and effectively prevents multicollinearity through sparse constraints and orthogonal regularization constraints to generate good modular sparse solutions. NSOJNMF algorithm is performed on the datasets of liver cancer and colon cancer, then ceRNA co-modules of them are recognized. The enrichment analysis of these modules shows that >90% of them are closely related to the occurrence and development of cancer. In addition, the ceRNA networks constructed by the ceRNA co-modules not only accurately mine the known correlations of the three RNA molecules but also further discover their potential biological associations, which may contribute to the exploration of the competitive relationships among multiple RNAs and the molecular mechanisms affecting tumor development
Constraining sleptons at the LHC in a supersymmetric low-scale seesaw scenario
Get PDFAbstract We consider a scenario inspired by natural supersymmetry, where neutrino data is explained within a low-scale seesaw scenario. We extend the Minimal Supersymmetric Standard Model by adding light right-handed neutrinos and their superpartners, the R-sneutrinos, and consider the lightest neutralinos to be higgsino-like. We consider the possibilities of having either an R-sneutrino or a higgsino as lightest supersymmetric particle. Assuming that squarks and gauginos are heavy, we systematically evaluate the bounds on slepton masses due to existing LHC data
Advancements and Applications of Machine Learning in Detecting Radon Nuclear Tracks from 2001 to 2023: A Bibliometric Analysis
Get PDFWe present a bibliometric analysis of the advancements in machine learning for detecting radon nuclear tracks, using publications from 2001 to 2023 sourced from Scopus and Web of Science databases. We analyze the growth in research output, particularly highlighting contributions from China and the United States, and identify key themes such as "machine learning", "radon", "neural networks", and emerging methods like "xgboost" and "long short-term memory networks". Our findings underscore the collaborative efforts within the field, as evidenced by the global authorship networks. The research landscape is mapped out, revealing core and peripheral areas of study that define the current state and prospects of radon detection research. The present study encapsulates the evolution of the field and emphasizes the necessity for continued interdisciplinary collaboration to enhance radon risk assessment methods
INTERRELATIONSHIPS BETWEEN MACULAR CAROTENOIDS, RETINAL MORPHOMETRY, AND COGNITIVE FUNCTION IN PERSONS WITH MULTIPLE SCLEROSIS
Get PDFBackground: Multiple sclerosis (MS) is a neurodegenerative, autoimmune disease that affects oligodendrocyte-producing myelin cells and leads to cognitive, visual, affective, and motor problems, as well as fatigue, bowel dysfunction, among other symptoms. Although MS etiopathology is incompletely understood, it is known to be influenced by hereditary and environmental factors which can drastically affect the course of the disease. Amongst environmental factors, nutrition has gained significant attention. In 2015, The National Multiple Sclerosis Society published a report detailing that among various alternative approaches, diet was of the highest interest within the MS community, in part due to the limited dietary therapies as well as the poor efficacy of pharmacological approaches. Due to the health-promoting benefits that consumption of fruits and vegetables confer, their dietary components have gained substantial attention. Recently, lutein and zeaxanthin, dietary xanthophylls, have been shown to be associated with cognitive health. Although xanthophylls are not recognized to be essential, their lack of endogenous de novo synthesis necessitates regular dietary consumption to derive benefits. Macular xanthophylls are dietary carotenoids that comprise the macular pigment, serving as blue light filters and countering photooxidative damage. The robust antioxidant effects of carotenoids could support nerve health and protect against cognitive decline. However, macular xanthophyll status and its implications for markers of neuroaxonal degeneration, such as retinal morphometry, have not been examined among persons with MS. Specifically, the research presented herein aimed to explore the implications of macular and serum xanthophyll status for markers of retinal morphometry, and the associations between macular xanthophyll accumulation, retinal morphometry, and cognitive function in adults 18-64 years old with MS, with (MS-ON) and without ON (MS), and healthy controls (HC).
Methods: Adults 18-64 years (HC, n=42; MS, n=40-42) participated in a cross-sectional study. Macular pigment optical density (MPOD) was measured via heterochromatic flicker photometry using a macular densitometer. Retinal morphometry was measured via optical coherence tomography (OCT). Serum carotenoids were quantified using high-performance liquid chromatography. Dietary carotenoids were assessed using 7-day dietary records. Cognitive function was assessed using an Eriksen Flanker task for attentional control with event-related potentials (ERPs). One-way analysis of variance was conducted to determine group effects on macular, serum, and dietary carotenoids. Partial correlations examined the relationships between MPOD, OCT metrics, dietary variables, and serum carotenoids (with a false discovery rate [FDR] correction implemented for exploratory analyses with serum and dietary variables). Independent sample t-test/Mann-Whitney U-test was conducted to assess between-group differences, and Spearman’s Rank correlations were used to examine the relationships between MPOD and retinal morphometry with covariates and cognitive variables of interest. Linear regressions were used to explore the relationship between retinal and cognitive measures.
Results: Persons with MS-ON had lower MPOD and thickness and volume in OCT than HCs (i.e., optic disc retinal nerve fiber layer [odRNFL], macular retinal nerve fiber layer [mRNFL], total macular volume [TMV]). MS had significantly lower odRNFL thickness than HCs, and lower serum lutein than MS-ON subjects. Among MS, MPOD was positively correlated with odRNFL thickness and TMV, whereas odRNFL was negatively correlated with serum lutein and zeaxanthin. Between-group differences were observed in all cognitive outcomes showing lower attentional performance in persons with MS. After covariate adjustment, linear regressions showed that MPOD significantly predicted variance for incongruent P3 peak latency and odRNFL significantly predicted variance for congruent reaction time and congruent P3 peak latency. Interestingly, the interaction term between MPOD and odRNFL significantly predicted variance in all but one metric (congruent P3 peak latency).
Conclusion: These findings provide initial evidence that persons with MS-ON have poorer xanthophyll status in the macula and serum. Further, MPOD was positively associated with beneficial anatomical features in the MS group. These results suggest that MPOD might be protective of AVP structural integrity. Additionally, higher carotenoid accumulation in the macula and higher retinal and optic nerve thickness were associated with greater attentional control, using both behavioral and neuroelectric indices, among people with MS.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-05-01The student, Jonathan Cerna, accepted the attached license on 2021-04-22 at 11:21.The student, Jonathan Cerna, submitted this Thesis for approval on 2021-04-22 at 11:36.This Thesis was approved for publication on 2021-04-27 at 18:20.DSpace SAF Submission Ingestion Package generated from Vireo submission #16502 on 2021-09-16 at 17:04:51Made available in DSpace on 2021-09-17T02:34:45Z (GMT). No. of bitstreams: 3
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Construction of competing endogenous RNA networks from paired RNA-seq data sets by pointwise mutual information
Get PDF© 2019 The Author(s). Background: A long noncoding RNA (lncRNA) can act as a competing endogenous RNA (ceRNA) to compete with an mRNA for binding to the same miRNA. Such an interplay between the lncRNA, miRNA, and mRNA is called a ceRNA crosstalk. As an miRNA may have multiple lncRNA targets and multiple mRNA targets, connecting all the ceRNA crosstalks mediated by the same miRNA forms a ceRNA network. Methods have been developed to construct ceRNA networks in the literature. However, these methods have limits because they have not explored the expression characteristics of total RNAs. Results: We proposed a novel method for constructing ceRNA networks and applied it to a paired RNA-seq data set. The first step of the method takes a competition regulation mechanism to derive candidate ceRNA crosstalks. Second, the method combines a competition rule and pointwise mutual information to compute a competition score for each candidate ceRNA crosstalk. Then, ceRNA crosstalks which have significant competition scores are selected to construct the ceRNA network. The key idea, pointwise mutual information, is ideally suitable for measuring the complex point-to-point relationships embedded in the ceRNA networks. Conclusion: Computational experiments and results demonstrate that the ceRNA networks can capture important regulatory mechanism of breast cancer, and have also revealed new insights into the treatment of breast cancer. The proposed method can be directly applied to other RNA-seq data sets for deeper disease understanding
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