A very brief description of LOFAR - the Low Frequency Array

LOFAR (Low Frequency Array) is an innovative radio telescope optimized for the frequency range 30-240 MHz. The telescope is realized as a phased aperture array without any moving parts. Digital beam forming allows the telescope to point to any part of the sky within a second. Transient buffering makes retrospective imaging of explosive short-term events possible. The scientific focus of LOFAR will initially be on four key science projects (KSPs): 1) detection of the formation of the very first stars and galaxies in the universe during the so-called epoch of reionization by measuring the power spectrum of the neutral hydrogen 21-cm line (Shaver et al. 1999) on the ~5' scale; 2) low-frequency surveys of the sky with of order $10^8$ expected new sources; 3) all-sky monitoring and detection of transient radio sources such as gamma-ray bursts, x-ray binaries, and exo-planets (Farrell et al. 2004); and 4) radio detection of ultra-high energy cosmic rays and neutrinos (Falcke & Gorham 2003) allowing for the first time access to particles beyond 10^21 eV (Scholten et al. 2006). Apart from the KSPs open access for smaller projects is also planned. Here we give a brief description of the telescope.Comment: 2 pages, IAU GA 2006, Highlights of Astronomy, Volume 14, K.A. van der Hucht, e

Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy.

MotivationMultiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia.ResultsWe performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified.Availability and implementationDatasets and scripts for reproduction of results are available through: https://nalab.stanford.edu/multiomics-pregnancy/.Supplementary informationSupplementary data are available at Bioinformatics online

Evidence of marine ice-cliff instability in Pine Island Bay from iceberg-keel plough marks.

Marine ice-cliff instability (MICI) processes could accelerate future retreat of the Antarctic Ice Sheet if ice shelves that buttress grounding lines more than 800 metres below sea level are lost. The present-day grounding zones of the Pine Island and Thwaites glaciers in West Antarctica need to retreat only short distances before they reach extensive retrograde slopes. When grounding zones of glaciers retreat onto such slopes, theoretical considerations and modelling results indicate that the retreat becomes unstable (marine ice-sheet instability) and thus accelerates. It is thought that MICI is triggered when this retreat produces ice cliffs above the water line with heights approaching about 90 metres. However, observational evidence confirming the action of MICI has not previously been reported. Here we present observational evidence that rapid deglacial ice-sheet retreat into Pine Island Bay proceeded in a similar manner to that simulated in a recent modelling study, driven by MICI. Iceberg-keel plough marks on the sea-floor provide geological evidence of past and present iceberg morphology, keel depth and drift direction. From the planform shape and cross-sectional morphologies of iceberg-keel plough marks, we find that iceberg calving during the most recent deglaciation was not characterized by small numbers of large, tabular icebergs as is observed today, which would produce wide, flat-based plough marks or toothcomb-like multi-keeled plough marks. Instead, it was characterized by large numbers of smaller icebergs with V-shaped keels. Geological evidence of the form and water-depth distribution of the plough marks indicates calving-margin thicknesses equivalent to the threshold that is predicted to trigger ice-cliff structural collapse as a result of MICI. We infer rapid and sustained ice-sheet retreat driven by MICI, commencing around 12,300 years ago and terminating before about 11,200 years ago, which produced large numbers of icebergs smaller than the typical tabular icebergs produced today. Our findings demonstrate the effective operation of MICI in the past, and highlight its potential contribution to accelerated future retreat of the Antarctic Ice Sheet

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts