Sea anemone model has a single Toll-like receptor that can function in pathogen detection, NF-κB signal transduction, and development

In organisms from insects to vertebrates, Toll-like receptors (TLRs) are primary pathogen detectors that activate downstream pathways, specifically those that direct expression of innate immune effector genes. TLRs also have roles in development in many species. The sea anemone Nematostella vectensis is a useful cnidarian model to study the origins of TLR signaling because its genome encodes a single TLR and homologs of many downstream signaling components, including the NF-κB pathway. We have characterized the single N. vectensis TLR (Nv-TLR) and demonstrated that it can activate canonical NF-κB signaling in human cells. Furthermore, we show that the intracellular Toll/IL-1 receptor (TIR) domain of Nv-TLR can interact with the human TLR adapter proteins MAL and MYD88. We demonstrate that the coral pathogen Vibrio coralliilyticus causes a rapidly lethal disease in N. vectensis and that heat-inactivated V. coralliilyticus and bacterial flagellin can activate a reconstituted Nv-TLR–to–NF-κB pathway in human cells. By immunostaining of anemones, we show that Nv-TLR is expressed in a subset of cnidocytes and that many of these Nv-TLR–expressing cells also express Nv-NF-κB. Additionally, the nematosome, which is a Nematostella-specific multicellular structure, expresses Nv-TLR and many innate immune pathway homologs and can engulf V. coralliilyticus. Morpholino knockdown indicates that Nv-TLR also has an essential role during early embryonic development. Our characterization of this primitive TLR and identification of a bacterial pathogen for N. vectensis reveal ancient TLR functions and provide a model for studying the molecular basis of cnidarian disease and immunity.IOS-1354935 - National Science Foundation (NSF); GRFP - National Science Foundation (NSF); GRFP - National Science Foundation (NSF); 1262934 - National Science Foundation (NSF); 2014-BSP - Arnold and Mabel Beckman Foundatio

Testing for knowledge: Application of machine learning techniques for prediction of flashover in a 1/5 scale ISO 13784‐1 enclosure

A machine learning algorithm was applied to predict the onset of flashover in archival experiments in a 1/5 scale ISO 13784‐1 enclosure constructed with sandwich panels. The experiments were performed to assess whether a small‐scale model could provide a better full‐scale correlation than the single burning item test. To predict the binary output, a regularized logistic regression model was chosen as ML environment, for which lasso‐regression significantly reduced the amount of variance at a negligible increase in bias. With the regularized model, it was possible to discern the predictive variables and determine the decision boundary. In addition, a methodology was put forward on how to use the to update the learning algorithm iteratively. As a result, it was shown how a learning algorithm can be used to facilitate ongoing experimentation. At first as a crude guideline, and in later stages, as an accurate prediction algorithm. It is foreseen that, by iteratively updating the algorithm, by compiling existing and new experiments in databases, and by applying fire safety knowledge, the final learned algorithm will be able to make accurate predictions for unseen samples and test conditions

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

The ACOS CO_2 retrieval algorithm – Part II: Global X_(CO_2) data characterization

Here, we report preliminary estimates of the column averaged carbon dioxide (CO_2) dry air mole fraction, X_(CO_2), retrieved from spectra recorded over land by the Greenhouse gases Observing Satellite, GOSAT (nicknamed "Ibuki"), using retrieval methods originally developed for the NASA Orbiting Carbon Observatory (OCO) mission. After screening for clouds and other known error sources, these retrievals reproduce much of the expected structure in the global X_(CO_2) field, including its variation with latitude and season. However, low yields of retrieved X_(CO_2) over persistently cloudy areas and ice covered surfaces at high latitudes limit the coverage of some geographic regions, even on seasonal time scales. Comparisons of early GOSAT X_(CO_2) retrievals with X_(CO_2) estimates from the Total Carbon Column Observing Network (TCCON) revealed a global, −2% (7–8 parts per million, ppm, with respect to dry air) X_(CO_2) bias and 2 to 3 times more variance in the GOSAT retrievals. About half of the global X_(CO_2) bias is associated with a systematic, 1% overestimate in the retrieved air mass, first identified as a global +10 hPa bias in the retrieved surface pressure. This error has been attributed to errors in the O_2 A-band absorption cross sections. Much of the remaining bias and spurious variance in the GOSAT X_(CO_2) retrievals has been traced to uncertainties in the instrument's calibration, oversimplified methods for generating O_2 and CO_2 absorption cross sections, and other subtle errors in the implementation of the retrieval algorithm. Many of these deficiencies have been addressed in the most recent version (Build 2.9) of the retrieval algorithm, which produces negligible bias in X_(CO_2) on global scales as well as a ~30% reduction in variance. Comparisons with TCCON measurements indicate that regional scale biases remain, but these could be reduced by applying empirical corrections like those described by Wunch et al. (2011b). We recommend that such corrections be applied before these data are used in source sink inversion studies to minimize spurious fluxes associated with known biases. These and other lessons learned from the analysis of GOSAT data are expected to accelerate the delivery of high quality data products from the Orbiting Carbon Observatory-2 (OCO-2), once that satellite is successfully launched and inserted into orbit

Atmospheric greenhouse gases retrieved from SCIAMACHY: comparison to ground-based FTS measurements and model results

SCIAMACHY onboard ENVISAT (launched in 2002) enables the retrieval of global long-term column-averaged dry air mole fractions of the two most important anthropogenic greenhouse gases carbon dioxide and methane (denoted XCO_2 and XCH_4). In order to assess the quality of the greenhouse gas data obtained with the recently introduced v2 of the scientific retrieval algorithm WFM-DOAS, we present validations with ground-based Fourier Transform Spectrometer (FTS) measurements and comparisons with model results at eight Total Carbon Column Observing Network (TCCON) sites providing realistic error estimates of the satellite data. Such validation is a prerequisite to assess the suitability of data sets for their use in inverse modelling. It is shown that there are generally no significant differences between the carbon dioxide annual increases of SCIAMACHY and the assimilation system CarbonTracker (2.00 ± 0.16 ppm yr^(−1) compared to 1.94 ± 0.03 ppm yr−1 on global average). The XCO_2 seasonal cycle amplitudes derived from SCIAMACHY are typically larger than those from TCCON which are in turn larger than those from CarbonTracker. The absolute values of the northern hemispheric TCCON seasonal cycle amplitudes are closer to SCIAMACHY than to CarbonTracker and the corresponding differences are not significant when compared with SCIAMACHY, whereas they can be significant for a subset of the analysed TCCON sites when compared with CarbonTracker. At Darwin we find discrepancies of the seasonal cycle derived from SCIAMACHY compared to the other data sets which can probably be ascribed to occurrences of undetected thin clouds. Based on the comparison with the reference data, we conclude that the carbon dioxide data set can be characterised by a regional relative precision (mean standard deviation of the differences) of about 2.2 ppm and a relative accuracy (standard deviation of the mean differences) of 1.1–1.2 ppm for monthly average composites within a radius of 500 km. For methane, prior to November 2005, the regional relative precision amounts to 12 ppb and the relative accuracy is about 3 ppb for monthly composite averages within the same radius. The loss of some spectral detector pixels results in a degradation of performance thereafter in the spectral range currently used for the methane column retrieval. This leads to larger scatter and lower XCH_4 values are retrieved in the tropics for the subsequent time period degrading the relative accuracy. As a result, the overall relative precision is estimated to be 17 ppb and the relative accuracy is in the range of about 10–20 ppb for monthly averages within a radius of 500 km. The derived estimates show that the SCIAMACHY XCH_4 data set before November 2005 is suitable for regional source/sink determination and regional-scale flux uncertainty reduction via inverse modelling worldwide. In addition, the XCO2 monthly data potentially provide valuable information in continental regions, where there is sparse sampling by surface flask measurements

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