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Neutrophilic asthma is associated with airway microbiology that is significantly different from that seen in patients with other inflammatory phenotypes, particularly eosinophilic asthma. Differences in microbiota composition might influence the response to antimicrobial and steroid therapies and the risk of lung infection.

Molecular automata are mixtures of molecules that undergo precisely defined structural changes in response to sequential interactions with inputs1–4. Previously studied nucleic acid-based-automata include game-playing molecular devices (MAYA automata3,5) and finite-state automata for analysis of nucleic acids6 with the latter inspiring circuits for the analysis of RNA species inside cells7,8. Here, we describe automata based on strand-displacement9,10 cascades directed by antibodies that can analyze cells by using their surface markers as inputs. The final output of a molecular automaton that successfully completes its analysis is the presence of a unique molecular tag on the cell surface of a specific subpopulation of lymphocytes within human blood cells.

We describe the algorithm that has been applied to develop a 42 yr record of total ozone and ozone profiles from eight Solar Backscatter UV (SBUV) instruments launched on NASA and NOAA satellites since April 1970. The Version 8 (V8) algorithm was released more than a decade ago and has been in use since then at NOAA to produce their operational ozone products. The current algorithm (V8.6) is basically the same as V8, except for updates to instrument calibration, incorporation of new ozone absorption cross-sections, and new ozone and cloud height climatologies. Since the V8 algorithm has been optimized for deriving monthly zonal mean (MZM) anomalies for ozone assessment and model comparisons, our emphasis in this paper is primarily on characterizing the sources of errors that are relevant for such studies. When data are analyzed this way the effect of some errors, such as vertical smoothing of short-term variability, and noise due to clouds and aerosols diminish in importance, while the importance of others, such as errors due to vertical smoothing of the quasi-biennial oscillation (QBO) and other periodic and aperiodic variations, become more important. With V8.6 zonal mean data we now provide smoothing kernels that can be used to compare anomalies in SBUV profile and partial ozone columns with models. In this paper we show how to use these kernels to compare SBUV data with Microwave Limb Sounder (MLS) ozone profiles. These kernels are particularly useful for comparisons in the lower stratosphere where SBUV profiles have poor vertical resolution but partial column ozone values have high accuracy. We also provide our best estimate of the smoothing errors associated with SBUV MZM profiles. Since smoothing errors are the largest source of uncertainty in these profiles, they can be treated as error bars in deriving interannual variability and trends using SBUV data and for comparing with other measurements. In the V8 and V8.6 algorithms we derive total column ozone by integrating the SBUV profiles, rather than from a separate set of wavelengths, as was done in previous algorithm versions. This allows us to extend the total ozone retrieval to 88° solar zenith angle (SZA). Since the quality of total column data is affected by reduced sensitivity to ozone in the lower atmosphere by cloud and Rayleigh attenuation, which gets worse with increasing SZA, we provide our best estimate of these errors, as well as the kernels that can be used to test the sensitivity of the derived columns to long-term changes in ozone in the lower atmosphere

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[1] This paper presents statistical analysis of arctic auroral oval ionospheric scintillation events during the current solar maximum based on high-rate Global Positioning System data collected in Gakona, Alaska (62.39°N, 145.15°W) from August 2010 to March 2013. The objective is to gain a better understanding of the climatology and morphology of ionospheric scintillation in high-latitude regions. A scintillation event filter, multipath identification procedures, and other processes are applied to exclude nonscintillation related signal intensity and phase fluctuation and to extract scintillation events with S 4 index above 0.12 and phase sigma above 6°from over 657 days of data. A total of over 5800 scintillation events were identified; most of them show phase fluctuations, only 10% of the phase fluctuations are accompanied by weak amplitude scintillation. Based on the occurrence time, signal direction of arrival, intensity, and duration of these scintillation events and the solar and geomagnetic activities associated with these events, diurnal, seasonal, spatial, and solar activity dependencies are derived and presented in the paper.

Abstract. Past studies have suggested that ozone in the troposphere has increased globally throughout much of the 20th century due to increases in anthropogenic emissions and transport. We show, by combining satellite measurements with a chemical transport model, that during the last four decades tropospheric ozone does indeed indicate increases that are global in nature, yet still highly regional. Satellite ozone measurements from Nimbus-7 and Earth Probe Total Ozone Mapping Spectrometer (TOMS) are merged with ozone measurements from the Aura Ozone Monitoring Instrument/Microwave Limb Sounder (OMI/MLS) to determine trends in tropospheric ozone for 1979–2016. Both TOMS (1979–2005) and OMI/MLS (2005–2016) depict large increases in tropospheric ozone from the Near East to India and East Asia and further eastward over the Pacific Ocean. The 38-year merged satellite record shows total net change over this region of about +6 to +7 Dobson units (DU) (i.e., ∼15 %–20 % of average background ozone), with the largest increase (∼4 DU) occurring during the 2005–2016 Aura period. The Global Modeling Initiative (GMI) chemical transport model with time-varying emissions is used to aid in the interpretation of tropospheric ozone trends for 1980–2016. The GMI simulation for the combined record also depicts the greatest increases of +6 to +7 DU over India and East Asia, very similar to the satellite measurements. In regions of significant increases in tropospheric column ozone (TCO) the trends are a factor of 2–2.5 larger for the Aura record when compared to the earlier TOMS record; for India and East Asia the trends in TCO for both GMI and satellite measurements are ∼+3 DU decade−1 or greater during 2005–2016 compared to about +1.2 to +1.4 DU decade−1 for 1979–2005. The GMI simulation and satellite data also reveal a tropospheric ozone increases in ∼+4 to +5 DU for the 38-year record over central Africa and the tropical Atlantic Ocean. Both the GMI simulation and satellite-measured tropospheric ozone during the latter Aura time period show increases of ∼+3 DU decade−1 over the N Atlantic and NE Pacific.

We describe the algorithm that has been applied to develop a 41 yr time series of total ozone and ozone profiles from eight solar-backscatter UV (sbuv) instruments launched on NASA and NOAA satellites since April 1970. Although the basic algorithm is similar to the V8 algorithm that was released about a decade ago and has been in use since then at NOAA, the details of the V8 algorithm have never been published. The current version (V8.6) incorporates several changes including the use of new ozone absorption cross-sections and new ozone and cloud height climatologies. A particular emphasis in this paper is on characterizing the sources of errors that are relevant for deriving trends from monthly mean anomalies and for estimating biases between different types of ozone sensors. We show that variations in the local time of the measurement due to drifting NOAA satellite orbits can complicate the analysis of trends in the upper stratosphere. Such variations not only increase instrumental and algorithmic uncertainties but also require correction for true local time variations of ozone in the upper stratosphere and lower mesosphere for trend analysis. We find that the monthly zonal anomalies derived from the SBUV data have high precision, sufficient to track year-to-year changes in ozone over a broad range of altitudes. However, because of poor vertical resolution the data are less well suited to track short-term variability of ozone at lower altitudes