A Cloud Greenhouse Effect on Mars: Significant Climate Change in the Recent Past

The large variations in Mars orbit parameters are known to be significant drivers of climate change on the Red planet. The recent discovery of buried CO2 ice at the South Pole adds another dimension to climate change studies. In this paper we present results from the Ames GCM that show within the past million years it is possible that clouds from a greatly intensified Martian hydrological cycle may have produced a greenhouse effect strong enough to raise global mean surface temperatures by several tens of degrees Kelvin. It is made possible by the ability of the Martian atmosphere to transport water to high altitudes where cold clouds form, reduce the outgoing longwave radiation, and drive up surface temperatures to maintain global energy balance

Herbicides for grain sorghum 1984

"Federal regulations on the use of herbicides change frequently, so stay informed on the status of label registration. To the best of our knowledge, this guidesheet conforms to laws and regulations at the time of writing."--First page.James A. Schaeffer, Harold D. Kerr, David Guethle, O. Hale Fletchall, E.J. Peters, L.E. Anderson, and Zane R. Helsel (Department of Agronomy, College of AGriculture)Revised 1/84/10

Chemical weed control in grain sorghum

"Federal regulations on the use of herbicides change frequently, so stay informed on the status of label registration. To the best of our knowledge, this guidesheet conforms to laws and regulations at the time of writing."--First page.James A. Schaeffer, Harold D. Kerr, David Guethle, O. Hale Fletchall, E.J. Peters, L.E. Anderson, and Zane R. Helsel (Department of Agronomy, College of AGriculture)Revised 1/85/10

Effect of Hydrologic Restoration on the Habitat of The Cape Sable Seaside Sparrow, Annual Report of 2002-2003

After developing field sampling protocols and making a series of consultations with investigators involved in research in CSSS habitat, we determined that vegetationhydrology interactions within this landscape are best sampled at a combination of scales. At the finer scale, we decided to sample at 100 m intervals along transects that cross the range of habitats present, and at the coarser scale, to conduct an extensive survey of vegetation at sites of known sparrow density dispersed throughout the range of the CSSS. We initiated sampling in the first week of January 2003 and continued it through the last week of May. During this period, we established 6 transects, one in each CSSS subpopulation, completed topographic survey along the Transects A, C, D, and F, and sampled herb and shrub stratum vegetation, soil depth and periphyton along Transects A, and at 179 census points. We also conducted topographic surveys and completed vegetation and soil depth sampling along two of five transects used by ENP researchers for monitoring long-term vegetation change in Taylor Slough. We analyzed the data by summarizing the compositional and structural measures and by using cluster analysis, ordination, weighted averaging regression, and weighted averaging calibration. The mean elevation of transects decreased from north to south, and Transect F had greater variation than other transects. We identified eight vegetation assemblages that can be grouped into two broad categories, ‘wet prairie’ and ‘marsh’. In the 2003 survey, wet prairies were most dominant in the northeastern sub-populations, and had shorter inferred-hydroperiod, higher species richness and shallower soils than marshes, which were common in Subpopulations A, D, and the southernmost regions of Sub-population B. Most of the sites at which birds were observed during 2001 or 2002 had an inferred-hydroperiod of 120-150 days, while no birds were observed at sites with an inferred-hydroperiod less than 120 days or more than 300 days. Management-induced water level changes in Taylor Slought during the 1980’s and 1990’s appeared to elicit parallel changes in vegetation. The results described in detail in the following pages serve as a basis for evaluating and modifying, if necessary, the sampling design and analytical techniques to be used in the next three years of the project

Systematic Errors in the Hubble Constant Based on Measurement of the Sunyaev-Zeldovich Effect

Values of the Hubble constant reported to date which are based on measurement of the Sunyaev-Zeldovich (SZ) effect in clusters of galaxies are systematically lower than those derived by other methods (e.g., Cepheid variable stars, or the Tully-Fisher relation). We investigate the possibility that systematic errors may be introduced into the analysis by the generally adopted assumptions that observed clusters are in hydrostatic equilibrium, are spherically symmetric, and are isothermal. We construct self-consistent theoretical models of merging clusters of galaxies using hydrodynamical/N-body simulations. We then compute the magnitude of Ho derived from the SZ effect at different times and at different projection angles both from first principles, and by applying each of the standard assumptions used in the interpretation of observations. Our results indicate that the assumption of isothermality in the evolving clusters can result in Ho being underestimated by 10-30% depending on both epoch and projection angle. Moreover, use of the projected, emission-weighted temperature profile under the assumption of spherical symmetry does not significantly improve the situation except in the case of more extreme mergers (i.e., those involving relatively gas-rich subclusters).Comment: 31 pages, Latex, 2 tables, 10 postscript figures, Accepted for publication in ApJ, scheduled for June 20, 199

Characterization of the nighttime low‐latitude water ice deposits in the NASA Ames Mars General Circulation Model 2.1 under present‐day atmospheric conditions

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94673/1/jgre2617.pd

Genetic Determinants of Circulating Sphingolipid Concentrations in European Populations

Sphingolipids have essential roles as structural components of cell membranes and in cell signalling, and disruption of their metabolism causes several diseases, with diverse neurological, psychiatric, and metabolic consequences. Increasingly, variants within a few of the genes that encode enzymes involved in sphingolipid metabolism are being associated with complex disease phenotypes. Direct experimental evidence supports a role of specific sphingolipid species in several common complex chronic disease processes including atherosclerotic plaque formation, myocardial infarction (MI), cardiomyopathy, pancreatic beta-cell failure, insulin resistance, and type 2 diabetes mellitus. Therefore, sphingolipids represent novel and important intermediate phenotypes for genetic analysis, yet little is known about the major genetic variants that influence their circulating levels in the general population. We performed a genome-wide association study (GWAS) between 318,237 single-nucleotide polymorphisms (SNPs) and levels of circulating sphingomyelin (SM), dihydrosphingomyelin (Dih-SM), ceramide (Cer), and glucosylceramide (GluCer) single lipid species (33 traits); and 43 matched metabolite ratios measured in 4,400 subjects from five diverse European populations. Associated variants (32) in five genomic regions were identified with genome-wide significant corrected p-values ranging down to 9.08 x 10(-66). The strongest associations were observed in or near 7 genes functionally involved in ceramide biosynthesis and trafficking: SPTLC3, LASS4, SGPP1, ATP10D, and FADS1-3. Variants in 3 loci (ATP10D, FADS3, and SPTLC3) associate with MI in a series of three German MI studies. An additional 70 variants across 23 candidate genes involved in sphingolipid-metabolizing pathways also demonstrate association (p = 10(-4) or less). Circulating concentrations of several key components in sphingolipid metabolism are thus under strong genetic control, and variants in these loci can be tested for a role in the development of common cardiovascular, metabolic, neurological, and psychiatric diseases

A novel prostate cancer subtyping classifier based on luminal and basal phenotypes

Background: Prostate cancer (PCa) is a clinically heterogeneous disease. The creation of an expression-based subtyping model based on prostate-specific biological processes was sought. Methods: Unsupervised machine learning of gene expression profiles from prospectively collected primary prostate tumors (training, n = 32,000; evaluation, n = 68,547) was used to create a prostate subtyping classifier (PSC) based on basal versus luminal cell expression patterns and other gene signatures relevant to PCa biology. Subtype molecular pathways and clinical characteristics were explored in five other clinical cohorts. Results: Clustering derived four subtypes: luminal differentiated (LD), luminal proliferating (LP), basal immune (BI), and basal neuroendocrine (BN). LP and LD tumors both had higher androgen receptor activity. LP tumors also had a higher expression of cell proliferation genes, MYC activity, and characteristics of homologous recombination deficiency. BI tumors possessed significant interferon γactivity and immune infiltration on immunohistochemistry. BN tumors were characterized by lower androgen receptor activity expression, lower immune infiltration, and enrichment with neuroendocrine expression patterns. Patients with LD tumors had less aggressive tumor characteristics and the longest time to metastasis after surgery. Only patients with BI tumors derived benefit from radiotherapy after surgery in terms of time to metastasis (hazard ratio [HR], 0.09; 95% CI, 0.01–0.71; n = 855). In a phase 3 trial that randomized patients with metastatic PCa to androgen deprivation with or without docetaxel (n = 108), only patients with LP tumors derived survival benefit from docetaxel (HR, 0.21; 95% CI, 0.09–0.51). Conclusions: With the use of expression profiles from over 100,000 tumors, a PSC was developed that identified four subtypes with distinct biological and clinical features. Plain language summary: Prostate cancer can behave in an indolent or aggressive manner and vary in how it responds to certain treatments. To differentiate prostate cancer on the basis of biological features, we developed a novel RNA signature by using data from over 100,000 prostate tumors—the largest data set of its kind. This signature can inform patients and physicians on tumor aggressiveness and susceptibilities to treatments to help personalize cancer management

Reviews and syntheses: The promise of big diverse soil data, moving current practices towards future potential

In the age of big data, soil data are more available and richer than ever, but – outside of a few large soil survey resources – they remain largely unusable for informing soil management and understanding Earth system processes beyond the original study. Data science has promised a fully reusable research pipeline where data from past studies are used to contextualize new findings and reanalyzed for new insight. Yet synthesis projects encounter challenges at all steps of the data reuse pipeline, including unavailable data, labor-intensive transcription of datasets, incomplete metadata, and a lack of communication between collaborators. Here, using insights from a diversity of soil, data, and climate scientists, we summarize current practices in soil data synthesis across all stages of database creation: availability, input, harmonization, curation, and publication. We then suggest new soil-focused semantic tools to improve existing data pipelines, such as ontologies, vocabulary lists, and community practices. Our goal is to provide the soil data community with an overview of current practices in soil data and where we need to go to fully leverage big data to solve soil problems in the next century

Association of Estimated Glomerular Filtration Rate and Urinary Uromodulin Concentrations with Rare Variants Identified by UMOD Gene Region Sequencing

Background: Recent genome-wide association studies (GWAS) have identified common variants in the UMOD region associated with kidney function and disease in the general population. To identify novel rare variants as well as common variants that may account for this GWAS signal, the exons and 4 kb upstream region of UMOD were sequenced. Methodology/Principal Findings Individuals (n = 485) were selected based on presence of the GWAS risk haplotype and chronic kidney disease (CKD) in the ARIC Study and on the extremes of of the UMOD gene product, uromodulin, in urine (Tamm Horsfall protein, THP) in the Framingham Heart Study (FHS). Targeted sequencing was conducted using capillary based Sanger sequencing (3730 DNA Analyzer). Variants were tested for association with THP concentrations and estimated glomerular filtration rate (eGFR), and identified non-synonymous coding variants were genotyped in up to 22,546 follow-up samples. Twenty-four and 63 variants were identified in the 285 ARIC and 200 FHS participants, respectively. In both studies combined, there were 33 common and 54 rare (MAF<0.05) variants. Five non-synonymous rare variants were identified in FHS; borderline enrichment of rare variants was found in the extremes of THP (SKAT p-value = 0.08). Only V458L was associated with THP in the FHS general-population validation sample (p = 9*10$^{−3}$, n = 2,522), but did not show direction-consistent and significant association with eGFR in both the ARIC (n = 14,635) and FHS (n = 7,520) validation samples. Pooling all non-synonymous rare variants except V458L together showed non-significant associations with THP and eGFR in the FHS validation sample. Functional studies of V458L revealed no alternations in protein trafficking. Conclusions/Significance: Multiple novel rare variants in the UMOD region were identified, but none were consistently associated with eGFR in two independent study samples. Only V458L had modest association with THP levels in the general population and thus could not account for the observed GWAS signal