Machine learning combining multi-omics data and network algorithms identifies adrenocortical carcinoma prognostic biomarkers

Background: Rare endocrine cancers such as Adrenocortical Carcinoma (ACC) present a serious diagnostic and prognostication challenge. The knowledge about ACC pathogenesis is incomplete, and patients have limited therapeutic options. Identification of molecular drivers and effective biomarkers is required for timely diagnosis of the disease and stratify patients to offer the most beneficial treatments. In this study we demonstrate how machine learning methods integrating multi-omics data, in combination with system biology tools, can contribute to the identification of new prognostic biomarkers for ACC.Methods: ACC gene expression and DNA methylation datasets were downloaded from the Xena Browser (GDC TCGA Adrenocortical Carcinoma cohort). A highly correlated multi-omics signature discriminating groups of samples was identified with the data integration analysis for biomarker discovery using latent components (DIABLO) method. Additional regulators of the identified signature were discovered using Clarivate CBDD (Computational Biology for Drug Discovery) network propagation and hidden nodes algorithms on a curated network of molecular interactions (MetaBase™). The discriminative power of the multi-omics signature and their regulators was delineated by training a random forest classifier using 55 samples, by employing a 10-fold cross validation with five iterations. The prognostic value of the identified biomarkers was further assessed on an external ACC dataset obtained from GEO (GSE49280) using the Kaplan-Meier estimator method. An optimal prognostic signature was finally derived using the stepwise Akaike Information Criterion (AIC) that allowed categorization of samples into high and low-risk groups.Results: A multi-omics signature including genes, micro RNA's and methylation sites was generated. Systems biology tools identified additional genes regulating the features included in the multi-omics signature. RNA-seq, miRNA-seq and DNA methylation sets of features revealed a high power to classify patients from stages I-II and stages III-IV, outperforming previously identified prognostic biomarkers. Using an independent dataset, associations of the genes included in the signature with Overall Survival (OS) data demonstrated that patients with differential expression levels of 8 genes and 4 micro RNA's showed a statistically significant decrease in OS. We also found an independent prognostic signature for ACC with potential use in clinical practice, combining 9-gene/micro RNA features, that successfully predicted high-risk ACC cancer patients.Conclusion: Machine learning and integrative analysis of multi-omics data, in combination with Clarivate CBDD systems biology tools, identified a set of biomarkers with high prognostic value for ACC disease. Multi-omics data is a promising resource for the identification of drivers and new prognostic biomarkers in rare diseases that could be used in clinical practice

Single-cell multi-omic analysis profiles defective genome activation and epigenetic reprogramming associated with human pre-implantation embryo arrest

During pre-implantation stages of mammalian development, maternally stored material promotes both the erasure of the sperm and oocyte epigenetic profiles and is responsible for concomitant genome activation. Here, we have utilized single-cell methylome and transcriptome sequencing (scM&T-seq) to quantify both mRNA expression and DNA methylation in oocytes and a developmental series of human embryos at single-cell resolution. We fully characterize embryonic genome activation and maternal transcript degradation and map key epigenetic reprogramming events in developmentally high-quality embryos. By comparing these signatures with early embryos that have undergone spontaneous cleavage-stage arrest, as determined by time-lapse imaging, we identify embryos that fail to appropriately activate their genomes or undergo epigenetic reprogramming. Our results indicate that a failure to successfully accomplish these essential milestones impedes the developmental potential of pre-implantation embryos and is likely to have important implications, similar to aneuploidy, for the success of assisted reproductive cycles

Differences in expression rather than methylation at placenta-specific imprinted loci is associated with intrauterine growth restriction

Background: genome-wide studies have begun to link subtle variations in both allelic DNA methylation and parent-of-origin genetic effects with early development. Numerous reports have highlighted that the placenta plays a critical role in coordinating fetal growth, with many key functions regulated by genomic imprinting. With the recent description of wide-spread polymorphic placenta-specific imprinting, the molecular mechanisms leading to this curious polymorphic epigenetic phenomenon is unknown, as is their involvement in pregnancies complications. Results: profiling of 35 ubiquitous and 112 placenta-specific imprinted differentially methylated regions (DMRs) using high-density methylation arrays and pyrosequencing revealed isolated aberrant methylation at ubiquitous DMRs as well as abundant hypomethylation at placenta-specific DMRs. Analysis of the underlying chromatin state revealed that the polymorphic nature is not only evident at the level of allelic methylation, but DMRs can also adopt an unusual epigenetic signature where the underlying histones are biallelically enrichment of H3K4 methylation, a modification normally mutually exclusive with DNA methylation. Quantitative expression analysis in placenta identified two genes, GPR1-AS1 and ZDBF2, that were differentially expressed between IUGRs and control samples after adjusting for clinical factors, revealing coordinated deregulation at the chromosome 2q33 imprinted locus. Conclusions: DNA methylation is less stable at placenta-specific imprinted DMRs compared to ubiquitous DMRs and contributes to privileged state of the placenta epigenome. IUGR-associated expression differences were identified for several imprinted transcripts independent of allelic methylation. Further work is required to determine if these differences are the cause IUGR or reflect unique adaption by the placenta to developmental stresses

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis

Search for heavy resonances decaying to two Higgs bosons in final states containing four b quarks

A search is presented for narrow heavy resonances X decaying into pairs of Higgs bosons (H) in proton-proton collisions collected by the CMS experiment at the LHC at root s = 8 TeV. The data correspond to an integrated luminosity of 19.7 fb(-1). The search considers HH resonances with masses between 1 and 3 TeV, having final states of two b quark pairs. Each Higgs boson is produced with large momentum, and the hadronization products of the pair of b quarks can usually be reconstructed as single large jets. The background from multijet and t (t) over bar events is significantly reduced by applying requirements related to the flavor of the jet, its mass, and its substructure. The signal would be identified as a peak on top of the dijet invariant mass spectrum of the remaining background events. No evidence is observed for such a signal. Upper limits obtained at 95 confidence level for the product of the production cross section and branching fraction sigma(gg -> X) B(X -> HH -> b (b) over barb (b) over bar) range from 10 to 1.5 fb for the mass of X from 1.15 to 2.0 TeV, significantly extending previous searches. For a warped extra dimension theory with amass scale Lambda(R) = 1 TeV, the data exclude radion scalar masses between 1.15 and 1.55 TeV

Measurement of the top quark mass using charged particles in pp collisions at root s=8 TeV

Peer reviewe

Search for supersymmetry in events with one lepton and multiple jets in proton-proton collisions at root s=13 TeV

Peer reviewe

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Profiling of oxBS-450K 5-hydroxymethylcytosine in human placenta and brain reveals enrichment at imprinted loci

<p>DNA methylation (5-methylcytosine, 5 mC) is involved in many cellular processes and is an epigenetic mechanism primarily associated with transcriptional repression. The recent discovery that 5 mC can be oxidized to 5-hydromethylcytosine (5hmC) by TET proteins has revealed the “sixth base” of DNA and provides additional complexity to what was originally thought to be a stable repressive mark. However, our knowledge of the genome-wide distribution of 5hmC in different tissues is currently limited. Here, we sought to define loci enriched for 5hmC in the placenta genome by combining oxidative bisulphite (oxBS) treatment with high-density Illumina Infinium HumanMethylation450 methylation arrays and to compare our results with those obtained in brain. Despite identifying over 17,000 high-confidence CpG sites with consistent 5hmC enrichment, the distribution of this modification in placenta is relatively sparse when compared to cerebellum and frontal cortex. Supported by validation using allelic T4 β-glucosyltransferase assays we identify 5hmC at numerous imprinted loci, often overlapping regions associated with parent-of-origin allelic 5 mC in both placenta and brain samples. Furthermore, we observe tissue-specific monoallelic enrichment of 5hmC overlapping large clusters of imprinted snoRNAs-miRNAs processed from long noncoding RNAs (lncRNAs) within the <i>DLK1-DIO3</i> cluster on chromosome 14 and <i>SNRPN-UBE3A</i> domain on chromosome 15. Enrichment is observed solely on the transcribed alleles suggesting 5hmC is positively associated with transcription at these loci. Our study provides an extensive description of the 5hmC/5 mC landscape in placenta with our data available at <a href="http://www.humanimprints.net" target="_blank">www.humanimprints.net</a>, which represents the most comprehensive resource for exploring the epigenetic profiles associated with human imprinted genes.</p