The analysis of polar clouds from AVHRR satellite data using pattern recognition techniques

The cloud cover in a set of summertime and wintertime AVHRR data from the Arctic and Antarctic regions was analyzed using a pattern recognition algorithm. The data were collected by the NOAA-7 satellite on 6 to 13 Jan. and 1 to 7 Jul. 1984 between 60 deg and 90 deg north and south latitude in 5 spectral channels, at the Global Area Coverage (GAC) resolution of approximately 4 km. This data embodied a Polar Cloud Pilot Data Set which was analyzed by a number of research groups as part of a polar cloud algorithm intercomparison study. This study was intended to determine whether the additional information contained in the AVHRR channels (beyond the standard visible and infrared bands on geostationary satellites) could be effectively utilized in cloud algorithms to resolve some of the cloud detection problems caused by low visible and thermal contrasts in the polar regions. The analysis described makes use of a pattern recognition algorithm which estimates the surface and cloud classification, cloud fraction, and surface and cloudy visible (channel 1) albedo and infrared (channel 4) brightness temperatures on a 2.5 x 2.5 deg latitude-longitude grid. In each grid box several spectral and textural features were computed from the calibrated pixel values in the multispectral imagery, then used to classify the region into one of eighteen surface and/or cloud types using the maximum likelihood decision rule. A slightly different version of the algorithm was used for each season and hemisphere because of differences in categories and because of the lack of visible imagery during winter. The classification of the scene is used to specify the optimal AVHRR channel for separating clear and cloudy pixels using a hybrid histogram-spatial coherence method. This method estimates values for cloud fraction, clear and cloudy albedos and brightness temperatures in each grid box. The choice of a class-dependent AVHRR channel allows for better separation of clear and cloudy pixels than does a global choice of a visible and/or infrared threshold. The classification also prevents erroneous estimates of large fractional cloudiness in areas of cloudfree snow and sea ice. The hybrid histogram-spatial coherence technique and the advantages of first classifying a scene in the polar regions are detailed. The complete Polar Cloud Pilot Data Set was analyzed and the results are presented and discussed

Spectroscopy of z ~ 7 candidate galaxies: using Lyman α to constrain the neutral fraction of hydrogen in the high-redshift universe

Following our previous spectroscopic observations of z > 7 galaxies with Gemini/Gemini Near Infra-Red Spectrograph (GNIRS) and Very Large Telescope (VLT)/XSHOOTER, which targeted a total of eight objects, we present here our results from a deeper and larger VLT/FOcal Reducer and Spectrograph (FORS2) spectroscopic sample of Wide Field Camera 3 selected z > 7 candidate galaxies. With our FORS2 setup we cover the 737–1070 nm wavelength range, enabling a search for Lyman α in the redshift range spanning 5.06–7.80. We target 22 z-band dropouts and find no evidence of Lyman α emission, with the exception of a tentative detection (<5σ, which is our adopted criterion for a secure detection) for one object. The upper limits on Lyman α flux and the broad-band magnitudes are used to constrain the rest-frame equivalent widths for this line emission. We analyse our FORS2 observations in combination with our previous GNIRS and XSHOOTER observations, and suggest that a simple model where the fraction of high rest-frame equivalent width emitters follows the trend seen at z = 3-6.5 is inconsistent with our non-detections at z ∼ 7.8 at the 96 per cent confidence level. This may indicate that a significant neutral H I fraction in the intergalactic medium suppresses Lyman α, with an estimated neutral fraction χHI∼0.5, in agreement with other estimates

Indicator patterns of forced change learned by an artificial neural network

Many problems in climate science require the identification of signals obscured by both the "noise" of internal climate variability and differences across models. Following previous work, we train an artificial neural network (ANN) to identify the year of input maps of temperature and precipitation from forced climate model simulations. This prediction task requires the ANN to learn forced patterns of change amidst a background of climate noise and model differences. We then apply a neural network visualization technique (layerwise relevance propagation) to visualize the spatial patterns that lead the ANN to successfully predict the year. These spatial patterns thus serve as "reliable indicators" of the forced change. The architecture of the ANN is chosen such that these indicators vary in time, thus capturing the evolving nature of regional signals of change. Results are compared to those of more standard approaches like signal-to-noise ratios and multi-linear regression in order to gain intuition about the reliable indicators identified by the ANN. We then apply an additional visualization tool (backward optimization) to highlight where disagreements in simulated and observed patterns of change are most important for the prediction of the year. This work demonstrates that ANNs and their visualization tools make a powerful pair for extracting climate patterns of forced change.Comment: The first version of this manuscript has been submitted to the Journal of Advances in Modeling Earth Systems (JAMES), 202

Precipitation from Space: Advancing Earth System Science

Of the three primary sources of spatially contiguous precipitation observations (surface networks, ground-based radar, and satellite-based radar/radiometers), only the last is a viable source over ocean and much of the Earth's land. As recently as 15 years ago, users needing quantitative detail of precipitation on anything under a monthly time scale relied upon products derived from geostationary satellite thermal infrared (IR) indices. The Special Sensor Microwave Imager (SSMI) passive microwave (PMW) imagers originated in 1987 and continue today with the SSMI sounder (SSMIS) sensor. The fortunate longevity of the joint National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA) Tropical Rainfall Measuring Mission (TRMM) is providing the environmental science community a nearly unbroken data record (as of April 2012, over 14 years) of tropical and sub-tropical precipitation processes. TRMM was originally conceived in the mid-1980s as a climate mission with relatively modest goals, including monthly averaged precipitation. TRMM data were quickly exploited for model data assimilation and, beginning in 1999 with the availability of near real time data, for tropical cyclone warnings. To overcome the intermittently spaced revisit from these and other low Earth-orbiting satellites, many methods to merge PMW-based precipitation data and geostationary satellite observations have been developed, such as the TRMM Multisatellite Precipitation Product and the Climate Prediction Center (CPC) morphing method (CMORPH. The purpose of this article is not to provide a survey or assessment of these and other satellite-based precipitation datasets, which are well summarized in several recent articles. Rather, the intent is to demonstrate how the availability and continuity of satellite-based precipitation data records is transforming the ways that scientific and societal issues related to precipitation are addressed, in ways that would not be otherwise possible. These developments have taken place in parallel with the growth of an increasingly interconnected scientific environment. Scientists from different disciplines can easily interact with each other via information and materials they encounter online, and collaborate remotely without ever meeting each other in person. Likewise, these precipitation datasets are quickly and easily available via various data portals and are widely used. Within the framework of the NASA/JAXA Global Precipitation Measurement (GPM mission, these applications will become increasingly interconnected. We emphasize that precipitation observations by themselves provide an incomplete picture of the state of the atmosphere. For example, it is unlikely that a richer understanding of the global water cycle will be possible by standalone missions and algorithms, but must also involve some component of data, where model analyses of the physical state are constrained alongside multiple observations (e.g., precipitation, evaporation, radiation). The next section provides examples extracted from the many applications that use various high-resolution precipitation products. The final section summarizes the future system for global precipitation processing

The hepatitis B virus pre-core protein p22 activates Wnt sgnaling

An emerging theme for Wnt-addicted cancers is that the pathway is regulated at multiple steps via various mechanisms. Infection with hepatitis B virus (HBV) is a major risk factor for liver cancer, as is deregulated Wnt signaling, however, the interaction between these two causes is poorly understood. To investigate this interaction, we screened the effect of the various HBV proteins for their effect on Wnt/β-catenin signaling and identified the pre-core protein p22 as a novel and potent activator of TCF/β-catenin transcription. The effect of p22 on TCF/β-catenin transcription was dose dependent and inhibited by dominant-negative TCF4. HBV p22 activated synthetic and native Wnt target gene promoter reporters, and TCF/β-catenin target gene expression in vivo. Importantly, HBV p22 activated Wnt signaling on its own and in addition to Wnt or β-catenin induced Wnt signaling. Furthermore, HBV p22 elevated TCF/β-catenin transcription above constitutive activation in colon cancer cells due to mutations in downstream genes of the Wnt pathway, namely APC and CTNNB1. Collectively, our data identifies a previously unappreciated role for the HBV pre-core protein p22 in elevating Wnt signaling. Understanding the molecular mechanisms of p22 activity will provide insight into how Wnt signaling is fine-tuned in cancer

Spitzer observations of a 24 micron shadow: Bok Globule CB190

We present Spitzer observations of the dark globule CB190 (L771). We observe a roughly circular 24 micron shadow with a 70 arcsec radius. The extinction profile of this shadow matches the profile derived from 2MASS photometry at the outer edges of the globule and reaches a maximum of ~32 visual magnitudes at the center. The corresponding mass of CB190 is ~10 Msun. Our 12CO and 13CO J = 2-1 data over a 10 arcmin X 10 arcmin region centered on the shadow show a temperature ~10 K. The thermal continuum indicates a similar temperature for the dust. The molecular data also show evidence of freezeout onto dust grains. We estimate a distance to CB190 of 400 pc using the spectroscopic parallax of a star associated with the globule. Bonnor-Ebert fits to the density profile, in conjunction with this distance, yield xi_max = 7.2, indicating that CB190 may be unstable. The high temperature (56 K) of the best fit Bonnor-Ebert model is in contradiction with the CO and thermal continuum data, leading to the conclusion that the thermal pressure is not enough to prevent free-fall collapse. We also find that the turbulence in the cloud is inadequate to support it. However, the cloud may be supported by the magnetic field, if this field is at the average level for dark globules. Since the magnetic field will eventually leak out through ambipolar diffusion, it is likely that CB190 is collapsing or in a late pre-collapse stage.Comment: 16 pages, 13 figures, accepted for publication in Ap

MPLW515L Is a Novel Somatic Activating Mutation in Myelofibrosis with Myeloid Metaplasia

BACKGROUND: The JAK2V617F allele has recently been identified in patients with polycythemia vera (PV), essential thrombocytosis (ET), and myelofibrosis with myeloid metaplasia (MF). Subsequent analysis has shown that constitutive activation of the JAK-STAT signal transduction pathway is an important pathogenetic event in these patients, and that enzymatic inhibition of JAK2V617F may be of therapeutic benefit in this context. However, a significant proportion of patients with ET or MF are JAK2V617F-negative. We hypothesized that activation of the JAK-STAT pathway might also occur as a consequence of activating mutations in certain hematopoietic-specific cytokine receptors, including the erythropoietin receptor (EPOR), the thrombopoietin receptor (MPL), or the granulocyte-colony stimulating factor receptor (GCSFR). METHODS AND FINDINGS: DNA sequence analysis of the exons encoding the transmembrane and juxtamembrane domains of EPOR, MPL, and GCSFR, and comparison with germline DNA derived from buccal swabs, identified a somatic activating mutation in the transmembrane domain of MPL (W515L) in 9% (4/45) of JAKV617F-negative MF. Expression of MPLW515L in 32D, UT7, or Ba/F3 cells conferred cytokine-independent growth and thrombopoietin hypersensitivity, and resulted in constitutive phosphorylation of JAK2, STAT3, STAT5, AKT, and ERK. Furthermore, a small molecule JAK kinase inhibitor inhibited MPLW515L-mediated proliferation and JAK-STAT signaling in vitro. In a murine bone marrow transplant assay, expression of MPLW515L, but not wild-type MPL, resulted in a fully penetrant myeloproliferative disorder characterized by marked thrombocytosis (Plt count 1.9–4.0 × 10 (12)/L), marked splenomegaly due to extramedullary hematopoiesis, and increased reticulin fibrosis. CONCLUSIONS: Activation of JAK-STAT signaling via MPLW515L is an important pathogenetic event in patients with JAK2V617F-negative MF. The bone marrow transplant model of MPLW515L-mediated myeloproliferative disorders (MPD) exhibits certain features of human MF, including extramedullary hematopoiesis, splenomegaly, and megakaryocytic proliferation. Further analysis of positive and negative regulators of the JAK-STAT pathway is warranted in JAK2V617F-negative MPD