VERITAS and Fermi-LAT Observations of TeV Gamma-Ray Sources Discovered by HAWC in the 2HWC Catalog
(2018)
The High Altitude Water Cherenkov (HAWC) collaboration recently published their 2HWC catalog, listing 39 very high energy (VHE; >100 GeV) gamma-ray sources based on 507 days of observation. Among these, 19 sources are not associated with previously known teraelectronvolt (TeV) gamma-ray sources. We have studied 14 of these sources without known counterparts with VERITAS and Fermi-LAT. VERITAS detected weak gamma-ray emission in the 1 TeV-30 TeV band in the region of DA 495, a pulsar wind nebula coinciding with 2HWC J1953+294, confirming the discovery of the source by HAWC. We did not find any counterpart for the selected 14 new HAWC sources from our analysis of Fermi-LAT data for energies higher than 10 GeV. During the search, we detected gigaelectronvolt (GeV) gamma-ray emission coincident with a known TeV pulsar wind nebula, SNR G54.1+0.3 (VER J1930+188), and a 2HWC source, 2HWC J1930+188. The fluxes for isolated, steady sources in the 2HWC catalog are generally in good agreement with those measured by imaging atmospheric Cherenkov telescopes. However, the VERITAS fluxes for SNR G54.1+0.3, DA 495, and TeV J2032+4130 are lower than those measured by HAWC, and several new HAWC sources are not detected by VERITAS. This is likely due to a change in spectral shape, source extension, or the influence of diffuse emission in the source region.
Background: For heterogeneous tissues, such as blood, measurements of gene expression are confounded by relative proportions of cell types involved. Conclusions have to rely on estimation of gene expression signals for homogeneous cell populations, e.g. by applying micro-dissection, fluorescence activated cell sorting, or in-silico deconfounding. We studied feasibility and validity of a non-negative matrix decomposition algorithm using experimental gene expression data for blood and sorted cells from the same donor samples. Our objective was to optimize the algorithm regarding detection of differentially expressed genes and to enable its use for classification in the difficult scenario of reversely regulated genes. This would be of importance for the identification of candidate biomarkers in heterogeneous tissues.
Results: Experimental data and simulation studies involving noise parameters estimated from these data revealed that for valid detection of differential gene expression, quantile normalization and use of non-log data are optimal. We demonstrate the feasibility of predicting proportions of constituting cell types from gene expression data of single samples, as a prerequisite for a deconfounding-based classification approach. Classification cross-validation errors with and without using deconfounding results are reported as well as sample-size dependencies. Implementation of the algorithm, simulation and analysis scripts are available.
Conclusions: The deconfounding algorithm without decorrelation using quantile normalization on non-log data is proposed for biomarkers that are difficult to detect, and for cases where confounding by varying proportions of cell types is the suspected reason. In this case, a deconfounding ranking approach can be used as a powerful alternative to, or complement of, other statistical learning approaches to define candidate biomarkers for molecular diagnosis and prediction in biomedicine, in realistically noisy conditions and with moderate sample sizes.