ALBERT

Description: To understand the biology and evolution of ruminants, the cattle genome was sequenced to about sevenfold coverage. The cattle genome contains a minimum of 22,000 genes, with a core set of 14,345 orthologs shared among seven mammalian species of which 1217 are absent or undetected in noneutherian (marsupial or monotreme) genomes. Cattle-specific evolutionary breakpoint regions in chromosomes have a higher density of segmental duplications, enrichment of repetitive elements, and species-specific variations in genes associated with lactation and immune responsiveness. Genes involved in metabolism are generally highly conserved, although five metabolic genes are deleted or extensively diverged from their human orthologs. The cattle genome sequence thus provides a resource for understanding mammalian evolution and accelerating livestock genetic improvement for milk and meat production.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2943200/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2943200/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Bovine Genome Sequencing and Analysis Consortium -- Elsik, Christine G -- Tellam, Ross L -- Worley, Kim C -- Gibbs, Richard A -- Muzny, Donna M -- Weinstock, George M -- Adelson, David L -- Eichler, Evan E -- Elnitski, Laura -- Guigo, Roderic -- Hamernik, Debora L -- Kappes, Steve M -- Lewin, Harris A -- Lynn, David J -- Nicholas, Frank W -- Reymond, Alexandre -- Rijnkels, Monique -- Skow, Loren C -- Zdobnov, Evgeny M -- Schook, Lawrence -- Womack, James -- Alioto, Tyler -- Antonarakis, Stylianos E -- Astashyn, Alex -- Chapple, Charles E -- Chen, Hsiu-Chuan -- Chrast, Jacqueline -- Camara, Francisco -- Ermolaeva, Olga -- Henrichsen, Charlotte N -- Hlavina, Wratko -- Kapustin, Yuri -- Kiryutin, Boris -- Kitts, Paul -- Kokocinski, Felix -- Landrum, Melissa -- Maglott, Donna -- Pruitt, Kim -- Sapojnikov, Victor -- Searle, Stephen M -- Solovyev, Victor -- Souvorov, Alexandre -- Ucla, Catherine -- Wyss, Carine -- Anzola, Juan M -- Gerlach, Daniel -- Elhaik, Eran -- Graur, Dan -- Reese, Justin T -- Edgar, Robert C -- McEwan, John C -- Payne, Gemma M -- Raison, Joy M -- Junier, Thomas -- Kriventseva, Evgenia V -- Eyras, Eduardo -- Plass, Mireya -- Donthu, Ravikiran -- Larkin, Denis M -- Reecy, James -- Yang, Mary Q -- Chen, Lin -- Cheng, Ze -- Chitko-McKown, Carol G -- Liu, George E -- Matukumalli, Lakshmi K -- Song, Jiuzhou -- Zhu, Bin -- Bradley, Daniel G -- Brinkman, Fiona S L -- Lau, Lilian P L -- Whiteside, Matthew D -- Walker, Angela -- Wheeler, Thomas T -- Casey, Theresa -- German, J Bruce -- Lemay, Danielle G -- Maqbool, Nauman J -- Molenaar, Adrian J -- Seo, Seongwon -- Stothard, Paul -- Baldwin, Cynthia L -- Baxter, Rebecca -- Brinkmeyer-Langford, Candice L -- Brown, Wendy C -- Childers, Christopher P -- Connelley, Timothy -- Ellis, Shirley A -- Fritz, Krista -- Glass, Elizabeth J -- Herzig, Carolyn T A -- Iivanainen, Antti -- Lahmers, Kevin K -- Bennett, Anna K -- Dickens, C Michael -- Gilbert, James G R -- Hagen, Darren E -- Salih, Hanni -- Aerts, Jan -- Caetano, Alexandre R -- Dalrymple, Brian -- Garcia, Jose Fernando -- Gill, Clare A -- Hiendleder, Stefan G -- Memili, Erdogan -- Spurlock, Diane -- Williams, John L -- Alexander, Lee -- Brownstein, Michael J -- Guan, Leluo -- Holt, Robert A -- Jones, Steven J M -- Marra, Marco A -- Moore, Richard -- Moore, Stephen S -- Roberts, Andy -- Taniguchi, Masaaki -- Waterman, Richard C -- Chacko, Joseph -- Chandrabose, Mimi M -- Cree, Andy -- Dao, Marvin Diep -- Dinh, Huyen H -- Gabisi, Ramatu Ayiesha -- Hines, Sandra -- Hume, Jennifer -- Jhangiani, Shalini N -- Joshi, Vandita -- Kovar, Christie L -- Lewis, Lora R -- Liu, Yih-Shin -- Lopez, John -- Morgan, Margaret B -- Nguyen, Ngoc Bich -- Okwuonu, Geoffrey O -- Ruiz, San Juana -- Santibanez, Jireh -- Wright, Rita A -- Buhay, Christian -- Ding, Yan -- Dugan-Rocha, Shannon -- Herdandez, Judith -- Holder, Michael -- Sabo, Aniko -- Egan, Amy -- Goodell, Jason -- Wilczek-Boney, Katarzyna -- Fowler, Gerald R -- Hitchens, Matthew Edward -- Lozado, Ryan J -- Moen, Charles -- Steffen, David -- Warren, James T -- Zhang, Jingkun -- Chiu, Readman -- Schein, Jacqueline E -- Durbin, K James -- Havlak, Paul -- Jiang, Huaiyang -- Liu, Yue -- Qin, Xiang -- Ren, Yanru -- Shen, Yufeng -- Song, Henry -- Bell, Stephanie Nicole -- Davis, Clay -- Johnson, Angela Jolivet -- Lee, Sandra -- Nazareth, Lynne V -- Patel, Bella Mayurkumar -- Pu, Ling-Ling -- Vattathil, Selina -- Williams, Rex Lee Jr -- Curry, Stacey -- Hamilton, Cerissa -- Sodergren, Erica -- Wheeler, David A -- Barris, Wes -- Bennett, Gary L -- Eggen, Andre -- Green, Ronnie D -- Harhay, Gregory P -- Hobbs, Matthew -- Jann, Oliver -- Keele, John W -- Kent, Matthew P -- Lien, Sigbjorn -- McKay, Stephanie D -- McWilliam, Sean -- Ratnakumar, Abhirami -- Schnabel, Robert D -- Smith, Timothy -- Snelling, Warren M -- Sonstegard, Tad S -- Stone, Roger T -- Sugimoto, Yoshikazu -- Takasuga, Akiko -- Taylor, Jeremy F -- Van Tassell, Curtis P -- Macneil, Michael D -- Abatepaulo, Antonio R R -- Abbey, Colette A -- Ahola, Virpi -- Almeida, Iassudara G -- Amadio, Ariel F -- Anatriello, Elen -- Bahadue, Suria M -- Biase, Fernando H -- Boldt, Clayton R -- Carroll, Jeffery A -- Carvalho, Wanessa A -- Cervelatti, Eliane P -- Chacko, Elsa -- Chapin, Jennifer E -- Cheng, Ye -- Choi, Jungwoo -- Colley, Adam J -- de Campos, Tatiana A -- De Donato, Marcos -- Santos, Isabel K F de Miranda -- de Oliveira, Carlo J F -- Deobald, Heather -- Devinoy, Eve -- Donohue, Kaitlin E -- Dovc, Peter -- Eberlein, Annett -- Fitzsimmons, Carolyn J -- Franzin, Alessandra M -- Garcia, Gustavo R -- Genini, Sem -- Gladney, Cody J -- Grant, Jason R -- Greaser, Marion L -- Green, Jonathan A -- Hadsell, Darryl L -- Hakimov, Hatam A -- Halgren, Rob -- Harrow, Jennifer L -- Hart, Elizabeth A -- Hastings, Nicola -- Hernandez, Marta -- Hu, Zhi-Liang -- Ingham, Aaron -- Iso-Touru, Terhi -- Jamis, Catherine -- Jensen, Kirsty -- Kapetis, Dimos -- Kerr, Tovah -- Khalil, Sari S -- Khatib, Hasan -- Kolbehdari, Davood -- Kumar, Charu G -- Kumar, Dinesh -- Leach, Richard -- Lee, Justin C-M -- Li, Changxi -- Logan, Krystin M -- Malinverni, Roberto -- Marques, Elisa -- Martin, William F -- Martins, Natalia F -- Maruyama, Sandra R -- Mazza, Raffaele -- McLean, Kim L -- Medrano, Juan F -- Moreno, Barbara T -- More, Daniela D -- Muntean, Carl T -- Nandakumar, Hari P -- Nogueira, Marcelo F G -- Olsaker, Ingrid -- Pant, Sameer D -- Panzitta, Francesca -- Pastor, Rosemeire C P -- Poli, Mario A -- Poslusny, Nathan -- Rachagani, Satyanarayana -- Ranganathan, Shoba -- Razpet, Andrej -- Riggs, Penny K -- Rincon, Gonzalo -- Rodriguez-Osorio, Nelida -- Rodriguez-Zas, Sandra L -- Romero, Natasha E -- Rosenwald, Anne -- Sando, Lillian -- Schmutz, Sheila M -- Shen, Libing -- Sherman, Laura -- Southey, Bruce R -- Lutzow, Ylva Strandberg -- Sweedler, Jonathan V -- Tammen, Imke -- Telugu, Bhanu Prakash V L -- Urbanski, Jennifer M -- Utsunomiya, Yuri T -- Verschoor, Chris P -- Waardenberg, Ashley J -- Wang, Zhiquan -- Ward, Robert -- Weikard, Rosemarie -- Welsh, Thomas H Jr -- White, Stephen N -- Wilming, Laurens G -- Wunderlich, Kris R -- Yang, Jianqi -- Zhao, Feng-Qi -- 062023/Wellcome Trust/United Kingdom -- 077198/Wellcome Trust/United Kingdom -- BBS/B/13438/Biotechnology and Biological Sciences Research Council/United Kingdom -- BBS/B/13446/Biotechnology and Biological Sciences Research Council/United Kingdom -- P30 DA018310/DA/NIDA NIH HHS/ -- U54 HG003273/HG/NHGRI NIH HHS/ -- U54 HG003273-04/HG/NHGRI NIH HHS/ -- U54 HG003273-04S1/HG/NHGRI NIH HHS/ -- U54 HG003273-05/HG/NHGRI NIH HHS/ -- U54 HG003273-05S1/HG/NHGRI NIH HHS/ -- U54 HG003273-05S2/HG/NHGRI NIH HHS/ -- U54 HG003273-06/HG/NHGRI NIH HHS/ -- U54 HG003273-06S1/HG/NHGRI NIH HHS/ -- U54 HG003273-06S2/HG/NHGRI NIH HHS/ -- U54 HG003273-07/HG/NHGRI NIH HHS/ -- U54 HG003273-08/HG/NHGRI NIH HHS/ -- New York, N.Y. -- Science. 2009 Apr 24;324(5926):522-8. doi: 10.1126/science.1169588.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/19390049" target="_blank"〉PubMed〈/a〉

Description: Human skin is a large, heterogeneous organ that protects the body from pathogens while sustaining microorganisms that influence human health and disease. Our analysis of 16S ribosomal RNA gene sequences obtained from 20 distinct skin sites of healthy humans revealed that physiologically comparable sites harbor similar bacterial communities. The complexity and stability of the microbial community are dependent on the specific characteristics of the skin site. This topographical and temporal survey provides a baseline for studies that examine the role of bacterial communities in disease states and the microbial interdependencies required to maintain healthy skin.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2805064/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2805064/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Grice, Elizabeth A -- Kong, Heidi H -- Conlan, Sean -- Deming, Clayton B -- Davis, Joie -- Young, Alice C -- NISC Comparative Sequencing Program -- Bouffard, Gerard G -- Blakesley, Robert W -- Murray, Patrick R -- Green, Eric D -- Turner, Maria L -- Segre, Julia A -- Z01 HG000180-08/Intramural NIH HHS/ -- ZIA BC010938-02/Intramural NIH HHS/ -- ZIA HG000180-09/Intramural NIH HHS/ -- New York, N.Y. -- Science. 2009 May 29;324(5931):1190-2. doi: 10.1126/science.1171700.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Genetics and Molecular Biology Branch, National Human Genome Research Institute, Bethesda, MD 20892, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/19478181" target="_blank"〉PubMed〈/a〉

Description: Wearable sensor technologies are essential to the realization of personalized medicine through continuously monitoring an individual's state of health. Sampling human sweat, which is rich in physiological information, could enable non-invasive monitoring. Previously reported sweat-based and other non-invasive biosensors either can only monitor a single analyte at a time or lack on-site signal processing circuitry and sensor calibration mechanisms for accurate analysis of the physiological state. Given the complexity of sweat secretion, simultaneous and multiplexed screening of target biomarkers is critical and requires full system integration to ensure the accuracy of measurements. Here we present a mechanically flexible and fully integrated (that is, no external analysis is needed) sensor array for multiplexed in situ perspiration analysis, which simultaneously and selectively measures sweat metabolites (such as glucose and lactate) and electrolytes (such as sodium and potassium ions), as well as the skin temperature (to calibrate the response of the sensors). Our work bridges the technological gap between signal transduction, conditioning (amplification and filtering), processing and wireless transmission in wearable biosensors by merging plastic-based sensors that interface with the skin with silicon integrated circuits consolidated on a flexible circuit board for complex signal processing. This application could not have been realized using either of these technologies alone owing to their respective inherent limitations. The wearable system is used to measure the detailed sweat profile of human subjects engaged in prolonged indoor and outdoor physical activities, and to make a real-time assessment of the physiological state of the subjects. This platform enables a wide range of personalized diagnostic and physiological monitoring applications.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Gao, Wei -- Emaminejad, Sam -- Nyein, Hnin Yin Yin -- Challa, Samyuktha -- Chen, Kevin -- Peck, Austin -- Fahad, Hossain M -- Ota, Hiroki -- Shiraki, Hiroshi -- Kiriya, Daisuke -- Lien, Der-Hsien -- Brooks, George A -- Davis, Ronald W -- Javey, Ali -- P01 HG000205/HG/NHGRI NIH HHS/ -- England -- Nature. 2016 Jan 28;529(7587):509-14. doi: 10.1038/nature16521.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California 94720, USA. ; Berkeley Sensor and Actuator Center, University of California, Berkeley, California 94720, USA. ; Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA. ; Stanford Genome Technology Center, Stanford School of Medicine, Palo Alto, California 94304, USA. ; Integrative Biology, University of California, Berkeley, California 94720, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26819044" target="_blank"〉PubMed〈/a〉

Description: Laser beams emitted from the Geoscience Laser Altimeter System (GLAS), as well as other space-borne laser instruments, can only penetrate clouds to a limit of a few optical depths. As a result, only optical depths of thinner clouds (〈 about 3 for GLAS) are retrieved from the reflected lidar signal. This paper presents a comprehensive study of possible retrievals of optical depth of thick clouds using solar background light and treating GLAS as a solar radiometer. To do so we first calibrate the reflected solar radiation received by the photon-counting detectors of GLAS' 532 nm channel, which is the primary channel for atmospheric products. The solar background radiation is regarded as a noise to be subtracted in the retrieval process of the lidar products. However, once calibrated, it becomes a signal that can be used in studying the properties of optically thick clouds. In this paper, three calibration methods are presented: (I) calibration with coincident airborne and GLAS observations; (2) calibration with coincident Geostationary Operational Environmental Satellite (GOES) and GLAS observations of deep convective clouds; (3) calibration from the first principles using optical depth of thin water clouds over ocean retrieved by GLAS active remote sensing. Results from the three methods agree well with each other. Cloud optical depth (COD) is retrieved from the calibrated solar background signal using a one-channel retrieval. Comparison with COD retrieved from GOES during GLAS overpasses shows that the average difference between the two retrievals is 24%. As an example, the COD values retrieved from GLAS solar background are illustrated for a marine stratocumulus cloud field that is too thick to be penetrated by the GLAS laser. Based on this study, optical depths for thick clouds will be provided as a supplementary product to the existing operational GLAS cloud products in future GLAS data releases.

Description: Spatiotemporal data from satellite remote sensing and surface meteorology networks have made it possible to continuously monitor global plant production, and to identify global trends associated with land cover/use and climate change. Gross primary production (GPP) and net primary production (NPP) are routinely derived from the MOderate Resolution Imaging Spectroradiometer (MODIS) onboard satellites Terra and Aqua, and estimates generally agree with independent measurements at validation sites across the globe. However, the accuracy of GPP and NPP estimates in some regions may be limited by the quality of model input variables and heterogeneity at fine spatial scales. We developed new methods for deriving model inputs (i.e., land cover, leaf area, and photosynthetically active radiation absorbed by plant canopies) from airborne laser altimetry (LiDAR) and Quickbird multispectral data at resolutions ranging from about 30 m to 1 km. In addition, LiDAR-derived biomass was used as a means for computing carbon-use efficiency. Spatial variables were used with temporal data from ground-based monitoring stations to compute a six-year GPP and NPP time series for a 3600 ha study site in the Great Lakes region of North America. Model results compared favorably with independent observations from a 400 m flux tower and a process-based ecosystem model (BIOME-BGC), but only after removing vapor pressure deficit as a constraint on photosynthesis from the MODIS global algorithm. Fine resolution inputs captured more of the spatial variability, but estimates were similar to coarse-resolution data when integrated across the entire vegetation structure, composition, and conversion efficiencies were similar to upland plant communities. Plant productivity estimates were noticeably improved using LiDAR-derived variables, while uncertainties associated with land cover generalizations and wetlands in this largely forested landscape were considered less important.

Description: Seasonal forecasts made by coupled atmosphere-ocean general circulation models (GCMs) are increasingly able to provide skillful forecasts of climate anomalies. At some centers, the capabilities of these models are being expanded to represent carbon-climate feedbacks including ocean biogeochemistry (OB), terrestrial biosphere (TB) interactions, and fires. These advances raise the question of whether such models can support skillful forecasts of carbon fluxes.Here, we examine whether land and ocean carbon flux anomalies associated with the 2015-16 El Nino could have been predicted months in advance. This El Nino was noteworthy for the magnitude of the ocean temperature perturbation, the skill with which this perturbation was predicted, and the extensive satellite observations that can be used to track its impact. We explore this topic using NASA's Goddard Earth Observing System (GEOS) model, which routinely produces an ensemble of seasonal climate forecasts, and a suite of offline dynamical and statistical models that estimate carbon flux processes. Using GEOS forecast fields from 2015-16 to force flux model hindcasts shows that these models are able to reproduce significant features observed by satellites. Specifically, OB hindcasts are able to predict anomalies in chlorophyll distributions with lead times of 3-4 months. The ability of TB hindcasts to reproduce NDVI anomalies is driven by the skill of the climate forecast, which is greatest at short lead times over tropical landmasses. Statistical fire forecasts driven by ocean climate indices are able to predict burned area in the tropics with lead times of 3-12 months. We also integrate the ocean and land hindcast fluxes into the GEOS GCM to examine the magnitude of the atmospheric carbon dioxide anomaly and compare with satellite and ground-based observations.While seasonal forecasting remains an active area of research, these results demonstrate that forecasts of carbon flux processes can support a variety of applications, potentially allowing scientists to understand carbon-climate feedbacks as they happen and to capitalize on more flexible satellite technologies that allow areas of interest to be targeted with lead times of weeks to months. We also provide a first glimpse at the spring 2019 carbon forecast using the GEOS-based forecasting system.

Description: A dual-photoelastic-modulator- (PEM-) based spectropolarimetric camera concept is presented as an approach for global aerosol monitoring from space. The most challenging performance objective is to measure degree of linear polarization (DOLP) with an uncertainty of less than 0.5% in multiple spectral bands, at moderately high spatial resolution, over a wide field of view, and for the duration of a multiyear mission. To achieve this, the tandem PEMs are operated as an electro-optic circular retardance modulator within a high-performance reflective imaging system. Operating the PEMs at slightly different resonant frequencies generates a beat signal that modulates the polarized component of the incident light at a much lower heterodyne frequency. The Stokes parameter ratio q = Q/I is obtained from measurements acquired from each pixel during a single frame, providing insensitivity to pixel responsivity drift and minimizing polarization artifacts that conventionally arise when this quantity is derived from differences in the signals from separate detectors. Similarly, u = U/I is obtained from a different pixel; q and u are then combined to form the DOLP. A detailed accuracy and tolerance analysis for this polarimeter is presented.

Description: The future trajectory of the stratospheric ozone recovery will be sensitive to greenhouse gas concentrations through thermal control of chemical loss and via stratospheric circulation changes. The latter in particular is subject to considerable uncertainty meriting continuing monitoring of the evolution of ozone throughout the depth of the stratosphere. Atmospheric reanalyses utilize the data assimilation methodology to obtain comprehensive representations of the state of the atmosphere, including its composition, on multidecadal scales by combining diverse measurements from satellite-borne and conventional data sources. Systematic biases among these various data types pose a challenge for assimilation by introducing spurious discontinuities that affect the utility of reanalyses for studies of long-term variability and trends.In this presentation we will outline an approach, developed at NASA's Global Modeling and Assimilation Office (GMAO), that allows joint assimilation of stratospheric ozone profiles from the Microwave Limb Sounder (MLS) on EOS Aura and the Ozone Mapping and Profiler Suite Limb Profiler (OMPS-LP) currently flying on the Suomi-NPP satellite with future missions projected into the 2030s. We will demonstrate that a simple offline correction significantly reduces biases between MLS and OMPS-LP ozone data providing a strategy for generating a long-term vertically resolved homogenized representation of stratospheric ozone in future reanalyses. One novel element of our approach compared to previous GMAO reanalysis is the use of a version of the Goddard Earth Observing System model with full stratospheric chemistry. We will show selected comparisons of MLS and OMPS-LP assimilation experiments with independent ozonesonde and satellite data as well as two examples of process-based evaluation focused on the 2016 QBO disruption and Arctic winter ozone loss focusing on the relative performance of the MLS and OMPS-LP analyses.

Description: Watersheds draining into the Gulf of Alaska (GoA) experience large seasonal and inter-annual variations of water in the form of rain, snow, and ice, but accurate constraints on these variations have been difficult to obtain. Over larger geographic regions, water variations can be inferred directly from the Gravity Recovery and Climate Experiment (GRACE) data. However, because GoA variations occur over such a small region, the inferred average value of water flux increases as the applied smoothing of the GRACE data decreases. We use this observed scaling together with scaling results obtained from forward models to infer a seasonal amplitude of 115 plus or minus 20 cubic kilometers of water and an average contribution to sea level rise over the two years of data of 0.31 plus or minus 0.09 millimeters per year. These results suggest that accelerated melting that began in the late 1990s, as inferred from altimetry, continues unabated.

Description: No abstract available