Bump Hunting Research Articles

Genome-wide DNA methylation analysis in female veterans with military sexual trauma and comorbid PTSD/MDD

BackgroundMilitary sexual trauma (MST) is a prevalent issue within the U.S. military. Victims are more likely to develop comorbid diseases such as posttraumatic stress disorder (PTSD) and major depressive disorder (MDD). Nonetheless, not everyone who suffers from MST develops PTSD and/or MDD. DNA methylation, which can regulate gene expression, might give us insight into the molecular mechanisms behind this discrepancy. Therefore, we sought to identify genomic loci and enriched biological pathways that differ between patients with and without MST, PTSD, and MDD. MethodsSaliva samples were collected from 113 female veterans. Following DNA extraction and processing, DNA methylation levels were measured through the Infinium HumanMethylationEPIC BeadChip array. We used limma and bump hunting methods to generate the differentially methylated positions and differentially methylated regions (DMRs), respectively. Concurrently, we used Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome to find enriched pathways. ResultsA DMR close to the transcription start site of ZFP57 was differentially methylated between subjects with and without PTSD, replicating previous findings and emphasizing the potential role of ZFP57 in PTSD susceptibility. In the pathway analyses, none survived multiple correction, although top GO terms included some potentially relevant to MST, PTSD, and MDD etiology. ConclusionWe conducted one of the first DNA methylation analyses investigating MST along with PTSD and MDD. In addition, we found one DMR near ZFP57 to be associated with PTSD. The replication of this finding indicates further investigation of ZFP57 in PTSD may be warranted.

Review of dark matter and detect dark matter using collider

This review study will give a brief introduction to dark matter, Large Hadron colliders (LHC), and circular electron-positron colliders(CEPC). The first part of the paper will discuss the fundamental properties of dark matter and the evidence for its existence. There will be a brief discussion of the theories used to explain dark matter. There will be three dark matter profiles and dark halo introductions. The basic configuration, ideas, and dark matter detection of LHC will then be covered in the study. The detection process includes missing momentum signals, bump hunting, and limiting the WIMP zone. The final section will describe CEPC's basic setup and its benefits for locating dark matter.

PyBumpHunter: A model independent bump hunting tool in Python for high energy physics analyses

The BumpHunter algorithm is widely used in the search for new particles in High Energy Physics analysis. This algorithm offers the advantage of evaluating the local and global p-values of a localized deviation in the observed data without making any hypothesis on the supposed signal. The increasing popularity of the Python programming language motivated the development of a new public implementation of this algorithm in Python, called pyBumpHunter, together with several improvements and additional features. It is the first public implementation of the BumpHunter algorithm to be added to Scikit-HEP. This paper presents in detail the BumpHunter algorithm as well as all the features proposed in this implementation. All these features have been tested in order to demonstrate their behaviour and performance.

Resonant anomaly detection without background sculpting

We introduce a new technique named Latent CATHODE (LaCATHODE) for performing "enhanced bump hunts", a type of resonant anomaly search that combines conventional one-dimensional bump hunts with a model-agnostic anomaly score in an auxiliary feature space where potential signals could also be localized. The main advantage of LaCATHODE over existing methods is that it provides an anomaly score that is well behaved when evaluating it beyond the signal region, which is essential to prevent the sculpting of background distributions in the bump hunt. LaCATHODE accomplishes this by constructing the anomaly score directly in the latent space learned by a conditional normalizing flow trained on sideband regions. We demonstrate the superior stability and comparable performance of LaCATHODE for enhanced bump hunting in an illustrative toy example as well as on the LHC Olympics R&D dataset.

Hunting for bumps in the margins

Data driven modelling is vital to many analyses at collider experiments, however the derived inference of physical properties becomes subject to details of the model fitting procedure. This work brings a principled Bayesian picture — based on the marginal likelihood — of both data modelling and signal extraction to a common collider physics scenario. First the marginal likelihood based method is used to propose a more principled construction of the background process, systematically exploring a variety of candidate shapes. Second the picture is extended to propose the marginal likelihood as a useful tool for anomaly detection challenges in particle physics. This proposal offers insight into both precise background model determination and demonstrates a flexible method to extend signal determination beyond a simple bump hunt.

Bump hunting in the diffuse flux of high-energy cosmic neutrinos

The origin of the bulk of the high-energy astrophysical neutrinos seen by IceCube, with TeV--PeV energies, is unknown. If they are made in photohadronic, i.e., proton-photon, interactions in astrophysical sources, this may manifest as a bump-like feature in their diffuse flux, centered around a characteristic energy. We search for evidence of this feature, allowing for variety in its shape and size, in 7.5 years of High-Energy Starting Events (HESE) collected by the IceCube neutrino telescope, and make forecasts using larger data samples from upcoming neutrino telescopes. Present-day data reveals no evidence of bump-like features, which allows us to constrain candidate populations of photohadronic neutrino sources. Near-future forecasts show promising potential for stringent constraints or decisive discovery of bump-like features. Our results provide new insight into the origins of high-energy astrophysical neutrinos, complementing those from point-source searches.

CURTAINs for your sliding window: Constructing unobserved regions by transforming adjacent intervals.

We propose a new model independent technique for constructing background data templates for use in searches for new physics processes at the LHC. This method, called Curtains, uses invertible neural networks to parameterise the distribution of side band data as a function of the resonant observable. The network learns a transformation to map any data point from its value of the resonant observable to another chosen value. Using Curtains, a template for the background data in the signal window is constructed by mapping the data from the side-bands into the signal region. We perform anomaly detection using the Curtains background template to enhance the sensitivity to new physics in a bump hunt. We demonstrate its performance in a sliding window search across a wide range of mass values. Using the LHC Olympics dataset, we demonstrate that Curtains matches the performance of other leading approaches which aim to improve the sensitivity of bump hunts, can be trained on a much smaller range of the invariant mass, and is fully data driven.

A goodness-of-fit test based on a recursive product of spacings

We introduce a new statistical test based on the observed spacings of ordered data. The statistic is sensitive to detect non-uniformity in random samples, or short-lived features in event time series. Under some conditions, this new test can outperform existing ones, such as the well known Kolmogorov-Smirnov or Anderson-Darling tests, in particular when the number of samples is small and differences occur over a small quantile of the null hypothesis distribution. A detailed description of the test statistic is provided including a detailed discussion of the parameterization of its distribution via asymptotic bootstrapping as well as a novel per-quantile error estimation of the empirical cumulative distribution. Two example applications are provided, using the test to boost the sensitivity in generic “bump hunting”, and employing the test to detect supernovae. The article is rounded off with an extended performance comparison to other, established goodness-of-fit tests.

Significance of modes in the torus by topological data analysis

AbstractThis paper addresses the problem of identifying modes or density bumps in multivariate angular or circular data, which have diverse applications in fields like medicine, biology and physics. We focus on the use of topological data analysis and persistent homology for this task. Specifically, we extend the methods for uncertainty quantification in the context of a torus sample space, where circular data lie. To achieve this, we employ two types of density estimators, namely, the von Mises kernel density estimator and the von Mises mixture model, to compute persistent homology, and propose a scale‐space view for searching significant bumps in the density. The results of bump hunting are summarised and visualised through a scale‐space diagram. Our approach using the mixture model for persistent homology offers advantages over conventional methods, allowing for dendrogram visualisation of components and identification of mode locations. For testing whether a detected mode is really there, we propose several inference tools based on bootstrap resampling and concentration inequalities, establishing their theoretical applicability. Experimental results on SARS‐CoV‐2 spike glycoprotein torsion angle data demonstrate the effectiveness of our proposed methods in practice.

Ephemeral Learning - Augmenting Triggers with Online-Trained Normalizing Flows

The large data rates at the LHC require an online trigger system to select relevant collisions. Rather than compressing individual events, we propose to compress an entire data set at once. We use a normalizing flow as a deep generative model to learn the probability density of the data online. The events are then represented by the generative neural network and can be inspected offline for anomalies or used for other analysis purposes. We demonstrate our new approach for a toy model and a correlation-enhanced bump hunt.

Self-supervised anomaly detection for new physics

We investigate a method of model-agnostic anomaly detection through studying jets, collimated sprays of particles produced in high-energy collisions. We train a transformer neural network to encode simulated QCD ``event space'' dijets into a low-dimensional ``latent space'' representation. We optimize the network using the self-supervised contrastive loss, which encourages the preservation of known physical symmetries of the dijets. We then train a binary classifier to discriminate a beyond the standard model resonant dijet signal from a QCD dijet background both in the event space and the latent space representations. We find the classifier performances on the event and latent spaces to be comparable. We finally perform an anomaly detection search using a weakly supervised bump hunt on the latent space dijets, finding again a comparable performance to a search run on the physical space dijets. This opens the door to using low-dimensional latent representations as a computationally efficient space for resonant anomaly detection in generic particle collision events.

Classifying anomalies through outer density estimation

We propose a new model-agnostic search strategy for physics beyond the standard model (BSM) at the LHC, based on a novel application of neural density estimation to anomaly detection. Our approach, which we call Classifying Anomalies THrough Outer Density Estimation (CATHODE), assumes the BSM signal is localized in a signal region (defined e.g. using invariant mass). By training a conditional density estimator on a collection of additional features outside the signal region, interpolating it into the signal region, and sampling from it, we produce a collection of events that follow the background model. We can then train a classifier to distinguish the data from the events sampled from the background model, thereby approaching the optimal anomaly detector. Using the LHC Olympics R&D dataset, we demonstrate that CATHODE nearly saturates the best possible performance, and significantly outperforms other approaches that aim to enhance the bump hunt (CWoLa Hunting and ANODE). Finally, we demonstrate that CATHODE is very robust against correlations between the features and maintains nearly-optimal performance even in this more challenging setting.

Bump hunting in latent space

Unsupervised anomaly detection could be crucial in future analyses searching for rare phenomena in large datasets, as for example collected at the LHC. To this end, we introduce a physics inspired variational autoencoder (VAE) architecture which performs competitively and robustly on the LHC Olympics Machine Learning Challenge datasets. We demonstrate how embedding some physical observables directly into the VAE latent space, while at the same time keeping the classifier manifestly agnostic to them, can help to identify and characterise features in measured spectra as caused by the presence of anomalies in a dataset.

Differences of DNA methylation patterns in the placenta of large for gestational age infant

To investigate the molecular mechanisms of later metabolic health changes in large for gestational age (LGA) newborns by analyzing deoxyribonucleic acid (DNA) methylation patterns in the placenta of LGA and appropriate for gestational age (AGA) newborns.A total of 6 placentas of LGA and 6 placentas of AGA newborns were enrolled as LGA group and AGA group. DNA methylation was analyzed using the Illumina Infinium Human MethylationEPIC BeadChip microarrays and verified via pyrosequencing and reverse transcription-quantitative real-time polymerase chain reaction. Functional enrichment analysis were constructed by gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis based on the differentially methylated regions between LGA and AGA groups.Clinical investigation showed that LGA newborns had significantly lower hemoglobin and blood glucose compared to AGA newborns. Birth weight was negatively correlated to hemoglobin and blood glucose. Genome-wide DNA methylation analysis identified 17 244 methylation variable positions achieving genome-wide significance (adjusted P < .05). 34% methylation variable positions were located in the gene promoter region. A total of 117 differentially methylated regions were revealed by bump hunting analysis, which mapped to 107 genes. Function analysis showed 13 genes enriched in adhesion and infection process, endocrine and other factor-regulated calcium reabsorption, calcium signaling pathway and transmembrane transport. Four genes linked to type II diabetes mellitus. Among the 13 genes, we selected GNAS and calcium voltage-gated channel subunit alpha1 G for independent verification of pyrosequencing, and the messenger ribonucleic acid levels of guanine nucleotide binding protein, calcium voltage-gated channel subunit alpha1 G, DECR1, and FK506 binding protein 11 were verified by reverse transcription-quantitative real-time polymerase chain reaction.DNA methylation variation and gene expression differences in placental samples were associated with LGA newborns, which linking the effect of intrauterine environment to regulation of the offspring's gene expression. Furthermore, pathway analysis suggested that intrauterine environment affecting fetal growth might had a functional impact on multiple signaling pathways involved in fetal growth, metabolism, and inflammation. Further studies were required to understand the differences of methylation patterns.

Simulation-assisted decorrelation for resonant anomaly detection

A growing number of weak and unsupervised machine learning approaches to anomaly detection are being proposed to significantly extend the search program at the Large Hadron Collider (LHC) and elsewhere. One of the prototypical examples for these methods is the search for resonant new physics, where a bump hunt can be performed in an invariant mass spectrum after applying a classifier to enhance the presence of a potential signal. A significant challenge to methods that rely entirely on data is that they are susceptible to sculpting artificial bumps from the dependence of the machine learning classifier on the invariant mass. We explore two solutions to this challenge by minimally incorporating simulation into the learning. In particular, we study the robustness of simulation assisted likelihood-free anomaly detection to correlations between the classifier and the invariant mass. Next, we propose a new approach that only uses the simulation for decorrelation but uses the classification without labels approach for achieving signal sensitivity. Both methods are compared using a full background fit analysis on simulated data from the LHC Olympics and are robust to correlations in the data.

Searching in 2-Dimensional mass space for final states with 2 invisible particles

A method to search for particles of unknown masses in final states with two invisible particles is presented. Searching for final states with missing energy is a challenging task usually performed in the tail of a missing energy related distribution. The search method proposed is based on a 2-Dimensional mass reconstruction of the final state with two invisible particles. Thus, a bump hunting is possible, allowing a stronger signal versus background discrimination. Parameters of the new theory can be extracted from the mass distributions, a valuable step towards understanding its true nature. The proof of principle is based on the existing SM top pairs in their dilepton final state. The method is applicable in many interesting searches at the LHC, including dark matter candidates or heavy top partners.

TMOD-05. GENOME-WIDE DNA METHYLATION PROFILE: A POWERFUL STRATEGY TO RECAPITULATE HETEROGENEITY OF PEDIATRIC BRAIN TUMORS IN PRIMARY CELL LINES

BackgroundDevelopment of in vitro models of pediatric brain tumors (pBT) is instrumental for both understanding the contributing oncogenic molecular mechanisms and identifying and testing new therapeutic strategies. Primary cell lines should be established and managed to prevent epigenetic and genetic alterations and thus recapitulating the original tumor. DNA methylation (DM) is a stable epigenetic modification, altered in cancer and recently used to classify tumors. We aim to apply DM and Copy Number Variation (CNV) profiling to characterize pBT primary cell lines and tumors. MethodsWe investigated 34 pBT tissues from different histology paired to 52 their derived primary cultures in both 2D and 3D conditions, as stem-cells or in serum-supplemented medium, and both short and long-terms in culture. We studied 18 additional pBT-derived cell-lines, 9 organoids, 5 commercial cell-lines, and 122 pBT tissues from the same histological categories, as controls, for a total of 240 genome-wide DM profiles. We analyzed DM and CNV profiles by using Illumina EPIC-arrays. By means of a bump hunting strategy, we identified differentially methylated regions in faithful vs unfaithful cell lines, and performed a functional characterization using over-representation analysis. Results The 69% (25/36) of cells at early passages retained genetic alteration and the same DM patterns of the original tumors, with no differences related to 2D/3D methods or the presence of serum in media. The 70% (24/34) of primary cell lines analyzed at later passages (>5 or >14 days in culture) diverged from the primary tumor, the totality of those cultured with serum. All divergent cells clustered together acquiring common deregulated epigenetic signature induced by serum culture media, 2D methods and longer time in culture. ConclusionsWe have shown that global DM profiles, along with CNV analysis are useful tools to detect the recapitulation of pBT-derived primary cell-lines from the original tumor. Whatever subgroups tested, our results suggest that in vitro models should be passaged as little as possible to retain the epigenetic and genetic alterations of the tumors and thus to be considered relevant for basic and translational biology.

Epigenome-wide analysis of long-term air pollution exposure and DNA methylation in monocytes: results from the Multi-Ethnic Study of Atherosclerosis

ABSTRACT Air pollution might affect atherosclerosis through DNA methylation changes in cells crucial to atherosclerosis, such as monocytes. We conducted an epigenome-wide study of DNA methylation in CD14+ monocytes and long-term ambient air pollution exposure in adults participating in the Multi-Ethnic Study of Atherosclerosis (MESA). We also assessed the association between differentially methylated signals and cis-gene expression. Using spatiotemporal models, one-year average concentrations of outdoor fine particulate matter (PM2.5) and oxides of nitrogen (NOX) were estimated at participants’ homes. We assessed DNA methylation and gene expression using Illumina 450k and HumanHT-12 v4 Expression BeadChips, respectively (n = 1,207). We used bump hunting and site-specific approaches to identify differentially methylated signals (false discovery rate of 0.05) and used linear models to assess associations between differentially methylated signals and cis-gene expression. Four differentially methylated regions (DMRs) located on chromosomes 5, 6, 7, and 16 (within or near SDHAP3, ZFP57, HOXA5, and PRM1, respectively) were associated with PM2.5. The DMRs on chromosomes 5 and 6 also associated with NOX. The DMR on chromosome 5 had the smallest p-value for both PM2.5 (p = 1.4×10−6) and NOX (p = 7.7×10−6). Three differentially methylated CpGs were identified for PM2.5, and cg05926640 (near TOMM20) had the smallest p-value (p = 5.6×10−8). NOX significantly associated with cg11756214 within ZNF347 (p = 5.6×10−8). Several differentially methylated signals were also associated with cis-gene expression. The DMR located on chromosome 7 was associated with the expression of HOXA5, HOXA9, and HOXA10. The DMRs located on chromosomes 5 and 16 were associated with expression of MRPL36 and DEXI, respectively. The CpG cg05926640 was associated with expression of ARID4B, IRF2BP2, and TOMM20. We identified differential DNA methylation in monocytes associated with long-term air pollution exposure. Methylation signals associated with gene expression might help explain how air pollution contributes to cardiovascular disease.

Gene Body Methylation and Transcriptional Activity in ASXL1-Mutant Chronic Myelomonocytic Leukemia

Introduction: Truncating mutations in Additional Sex Combs-Like 1 (ASXL1) are associated with a high-risk disease phenotype in myeloid malignancies. In chronic myelomonocytic leukemia (CMML), truncating ASXL1 mutations are known to increase transcriptional activity of leukemic driver genes and have been associated with gene body hypermethylation. We interrogated the transcriptome and methylome of patients with ASXL1-mutant (MT) and -wildtype (WT) CMML using a multi-omics approach to test the hypothesis that gene expression is mediated through gene body methylation. Methods: Bone marrow mononuclear cells from patients with ASXL1 WT (n=8) and MT (n=8) CMML were subjected to targeted NGS of DNA, whole transcriptome shotgun sequencing (RNA-seq), and immunoprecipitation of DNA methyl residues (DIP-seq). After quality control all samples were sequenced on an Illumina HiSeq 4000 before further processing and data analysis. Differential gene expression analysis was performed to identify genes up-regulated in MT CMML. The samples in the two groups were treated as biological replicates and subjected to a consensus peak calling strategy requiring an overlap of at least 30% between samples and an adjusted p-value &amp;lt; 5x10-5 for a methylation peak to be considered statistically significant. For validation purposes methylation analysis was performed on 3 ASXL1 MT and 3 WT CMML patients using Illumina Infinium MethylationEPIC microarrays and differentially methylated regions were identified using a bump hunting strategy. Gene body methylation was defined as methylation in gene bodies (outside the promoter region, i.e. transcription start site ±2kb). Gene body methylation was compared between WT and MT CMML for the up-regulated genes and correlated with expression of all genes in MT CMML. Results: Sixteen WHO-defined CMML patients were included, median age 69 years (48-77), 63% male, 50% had truncating ASXL1 frame shift mutations. Abnormal karyotypes were observed in the same number of patients and the burden of co-mutations was similar between the two groups (median number per group 3 vs. 3, p=0.508). This included several modulators of DNA methylation including TET2, DNMT3A, and IDH2 (median number per group 1 vs. 1, p=0.699). There was a predominant up-regulation of gene expression in MT CMML: 707 genes up- and 124 down-regulated (FDR&amp;lt;0.050) without evidence of differential methylation in promoter regions of the differentially expressed genes. There was no difference in the number of genes with consensus methylation peaks in the gene body or the extent of gene body methylation (area under the consensus peaks) between MT and WT CMML for the differentially expressed genes (Figure 1a). We further validated these results using methylation microarrays (Figure 1b). Among the MT CMML patients, there was a curvilinear relationship between gene body methylation and gene expression across all genes with intermediate gene body methylation being associated with the highest gene expression (Figure 1c). As an alternative unbiased approach, we identified 1595 differentially methylated regions (DMR, regions with FDR&amp;lt;0.05) in the validation data set. We mapped all hypermethylated regions to gene bodies requiring that there was no concurrent hypermethylation of the promoter region of the same gene. With this approach we identified one of the 707 up-regulated genes (0.14%) with evidence of isolated gene body hypermethylation (HBZ, log2-fold change in gene expression 4.25, FDR=0.0008, DMR area=1.61, DMR FDR=0.018). Representative examples of up-regulated mitotic kinases without evidence of differential gene body methylation are shown in Figure 1d (pie charts represent the mean β-values of microarray probes, p-values represent the comparisons of mean β-values between MT and WT CMML per gene). Conclusions: Gene body methylation was positively associated with gene expression in MT CMML. However, the lack of differential gene body methylation between WT and MT CMML for the up-regulated genes make it an unlikely explanation for the observed increase in transcriptional activity among patients with MT CMML. Figure 1 Disclosures Ordog: Millipore Sigma: Patents &amp; Royalties.

Differences of DNA methylation patterns in the placenta of large for gestational age infant

To investigate the molecular mechanisms of later metabolic health changes in large for gestational age (LGA) newborns by analyzing deoxyribonucleic acid (DNA) methylation patterns in the placenta of LGA and appropriate for gestational age (AGA) newborns.A total of 6 placentas of LGA and 6 placentas of AGA newborns were enrolled as LGA group and AGA group. DNA methylation was analyzed using the Illumina Infinium Human MethylationEPIC BeadChip microarrays and verified via pyrosequencing and reverse transcription-quantitative real-time polymerase chain reaction. Functional enrichment analysis were constructed by gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis based on the differentially methylated regions between LGA and AGA groups.Clinical investigation showed that LGA newborns had significantly lower hemoglobin and blood glucose compared to AGA newborns. Birth weight was negatively correlated to hemoglobin and blood glucose. Genome-wide DNA methylation analysis identified 17 244 methylation variable positions achieving genome-wide significance (adjusted P < .05). 34% methylation variable positions were located in the gene promoter region. A total of 117 differentially methylated regions were revealed by bump hunting analysis, which mapped to 107 genes. Function analysis showed 13 genes enriched in "adhesion and infection process, endocrine and other factor-regulated calcium reabsorption, calcium signaling pathway and transmembrane transport". Four genes linked to type II diabetes mellitus. Among the 13 genes, we selected GNAS and calcium voltage-gated channel subunit alpha1 G for independent verification of pyrosequencing, and the messenger ribonucleic acid levels of guanine nucleotide binding protein, calcium voltage-gated channel subunit alpha1 G, DECR1, and FK506 binding protein 11 were verified by reverse transcription-quantitative real-time polymerase chain reaction.DNA methylation variation and gene expression differences in placental samples were associated with LGA newborns, which linking the effect of intrauterine environment to regulation of the offspring's gene expression. Furthermore, pathway analysis suggested that intrauterine environment affecting fetal growth might had a functional impact on multiple signaling pathways involved in fetal growth, metabolism, and inflammation. Further studies were required to understand the differences of methylation patterns.