Remote sensing of CO2 and CH4 using solar absorption spectrometry with a low resolution spectrometer

Throughout the last few years solar absorption Fourier Transform Spectrometry (FTS) has been further developed to measure the total columns of CO2 and CH4. The observations are performed at high spectral resolution, typically at 0.02 cm(-1). The precision currently achieved is generally better than 0.25%. However, these high resolution instruments are quite large and need a dedicated room or container for installation. We performed these observations using a smaller commercial interferometer at its maximum possible resolution of 0.11 cm(-1). The measurements have been performed at Bremen and have been compared to observations using our high resolution instrument also situated at the same location. The high resolution instrument has been successfully operated as part of the Total Carbon Column Observing Network (TCCON). The precision of the low resolution instrument is 0.32% for XCO2 and 0.46% for XCH4. A comparison of the measurements of both instruments yields an average deviation in the retrieved daily means of 0.2% for CO2. For CH4 an average bias between the instruments of 0.47% was observed. For test cases, spectra recorded by the high resolution instrument have been truncated to the resolution of 0.11 cm(-1). This study gives an offset of 0.03% for CO2 and 0.26% for CH4. These results indicate that for CH4 more than 50% of the difference between the instruments results from the resolution dependent retrieval. We tentatively assign the offset to an incorrect a-priori concentration profile or the effect of interfering gases, which may not be treated correctly

SigsPack, a package for cancer mutational signatures

BACKGROUND: Mutational signatures are specific patterns of somatic mutations introduced into the genome by oncogenic processes. Several mutational signatures have been identified and quantified from multiple cancer studies, and some of them have been linked to known oncogenic processes. Identification of the processes contributing to mutations observed in a sample is potentially informative to understand the cancer etiology. RESULTS: We present here SigsPack, a Bioconductor package to estimate a sample's exposure to mutational processes described by a set of mutational signatures. The package also provides functions to estimate stability of these exposures, using bootstrapping. The performance of exposure and exposure stability estimations have been validated using synthetic and real data. Finally, the package provides tools to normalize the mutation frequencies with respect to the tri-nucleotide contents of the regions probed in the experiment. The importance of this effect is illustrated in an example. CONCLUSION: SigsPack provides a complete set of tools for individual sample exposure estimation, and for mutation catalogue & mutational signatures normalization

Identification and ranking of recurrent neo-epitopes in cancer

BACKGROUND: Immune escape is one of the hallmarks of cancer and several new treatment approaches attempt to modulate and restore the immune system’s capability to target cancer cells. At the heart of the immune recognition process lies antigen presentation from somatic mutations. These neo-epitopes are emerging as attractive targets for cancer immunotherapy and new strategies for rapid identification of relevant candidates have become a priority. METHOS: We carefully screen TCGA data sets for recurrent somatic amino acid exchanges and apply MHC class I binding predictions. RESULTS: We propose a method for in silico selection and prioritization of candidates which have a high potential for neo-antigen generation and are likely to appear in multiple patients. While the percentage of patients carrying a specific neo-epitope and HLA-type combination is relatively small, the sheer number of new patients leads to surprisingly high reoccurence numbers. We identify 769 epitopes which are expected to occur in 77629 patients per year. CONCLUSION: While our candidate list will definitely contain false positives, the results provide an objective order for wet-lab testing of reusable neo-epitopes. Thus recurrent neo-epitopes may be suitable to supplement existing personalized T cell treatment approaches with precision treatment options

Identification and ranking of recurrent neo-epitopes in cancer

Neo-epitopes are emerging as attractive targets for cancer immunotherapy and new strategies for rapid identification of relevant candidates have become a priority. We propose a method for in silico selection of candidates which have a high potential for neo-antigen generation and are likely to appear in multiple patients. This is achieved by carefully screening 33 TCGA data sets for recurrent somatic amino acid exchanges and, for the 1,055 resulting recurrent variants, applying MHC class I binding prediction algorithms. A preliminary confirmation of epitope binding and recognition by CD8 T cells has been carried out for a couple of candidates in humanized mice. Recurrent neo-epitopes may be suitable to supplement existing personalized T cell treatment approaches with precision treatment options

Vegetation Type and Decomposition Priming Mediate Brackish Marsh Carbon Accumulation Under Interacting Facets of Global Change

Coastal wetland carbon pools are globally important, but their response to interacting facets of global change remain unclear. Numerical models neglect species-specific vegetation responses to sea level rise (SLR) and elevated CO2 (eCO2) that are observed in field experiments, while field experiments cannot address the long-term feedbacks between flooding and soil growth that models show are important. Here, we present a novel numerical model of marsh carbon accumulation parameterized with empirical observations from a long-running eCO2 experiment in an organic rich, brackish marsh. Model results indicate that eCO2 and SLR interact synergistically to increase soil carbon burial, driven by shifts in plant community composition and soil volume expansion. However, newly parameterized interactions between plant biomass and decomposition (i.e. soil priming) reduce the impact of eCO2 on marsh survival, and by inference, the impact of eCO2 on soil carbon accumulation

Toward accurate CO_2 and CH_4 observations from GOSAT

The column-average dry air mole fractions of atmospheric carbon dioxide and methane (X_(CO_2) and X_(CH_4)) are inferred from observations of backscattered sunlight conducted by the Greenhouse gases Observing SATellite (GOSAT). Comparing the first year of GOSAT retrievals over land with colocated ground-based observations of the Total Carbon Column Observing Network (TCCON), we find an average difference (bias) of −0.05% and −0.30% for X_(CO_2) and X_(CH_4) with a station-to-station variability (standard deviation of the bias) of 0.37% and 0.26% among the 6 considered TCCON sites. The root-mean square deviation of the bias-corrected satellite retrievals from colocated TCCON observations amounts to 2.8 ppm for X_(CO_2) and 0.015 ppm for X_(CH_4). Without any data averaging, the GOSAT records reproduce general source/sink patterns such as the seasonal cycle of X_(CO_2) suggesting the use of the satellite retrievals for constraining surface fluxes

Preliminary validation of column-averaged volume mixing ratios of carbon dioxide and methane retrieved from GOSAT short-wavelength infrared spectra

Column-averaged volume mixing ratios of carbon dioxide and methane retrieved from the Greenhouse gases Observing SATellite (GOSAT) Short-Wavelength InfraRed observation (GOSAT SWIR XCO2 and XCH4) were compared with the reference data ob- 5 tained by ground-based high-resolution Fourier Transform Spectrometers (g-b FTSs) participating in the Total Carbon Column Observing Network (TCCON). Through calibrations of g-b FTSs with airborne in-situ measurements, the uncertainty of XCO2 and XCH4 associated with the g-b FTS was determined to be 0.8 ppm (0.2%) and 4 ppb (0.2%), respectively. The GOSAT products are validated with 10 these calibrated g-b FTS data. Preliminary results are as follows: The GOSAT SWIR XCO2 and XCH4 (Version 01.xx) are biased low by 8.85±4.75 ppm (2.3±1.2%) and 20.4±18.9 ppb (1.2±1.1%), respectively. The precision of the GOSAT SWIR XCO2 and XCH4 is considered to be about 1%. The latitudinal distributions of zonal means of the GOSAT SWIR XCO2 and XCH4 show similar features to those of the g-b FTS data