Edge gradients provide evidence of ecological interactions in planktonic thin layers

Physical, optical, and acoustical data from Monterey Bay, California, USA, describing the distributions of phytoplankton, zooplankton, and small fish as well as gradients in the physical habitat were used to examine the interactions between vertically compressed plankton structures (thin layers), their consumers, and the local physical forces they experience. The relationship between upper and lower edge gradients of a layer were used to define layer ‘shape’. The steepness of the vertical gradient on the top vs. the bottom of the plankton layer was correlated to the difference in the relative abundance of consumers above and below the layer. Phytoplankton layer gradients were steeper when more zooplankton were present on one side of the layer vs. the other while zooplankton layers were more diffuse when a greater number of fish were adjacent to one side of the layer than the other. Both layer types showed nearly symmetrical gradients when predators were in low abundance or absent. Predator-associated differences in phytoplankton and zooplankton layer shape were not correlated with vertical gradients in shear or mixing potential surrounding layers. In the absence of strong physical gradients, grazers can play an important role in structuring plankton thin layers. These observations likely represent one end of a continuum of biological and physical forcing responsible for formation and maintenance of thin plankton layers

OOI Biogeochemical Sensor Data: Best Practices and User Guide. Version 1.0.0.

The OOI Biogeochemical Sensor Data Best Practices and User Guide is intended to provide current and prospective users of data generated by biogeochemical sensors deployed on the Ocean Observatories Initiative (OOI) arrays with the information and guidance needed for them to ensure that the data is science-ready. This guide is aimed at researchers with an interest or some experience in ocean biogeochemical processes. We expect that users of this guide will have some background in oceanography, however we do not assume any prior experience working with biogeochemical sensors or their data. While initially envisioned as a “cookbook” for end users seeking to work with OOI biogeochemical (BGC) sensor data, our Working Group and Beta Testers realized that the processing required to meet the specific needs of all end users across a wide range of potential scientific applications and combinations of OOI BGC data from different sensors and platforms couldn’t be synthesized into a single “recipe”. We therefore provide here the background information and principles needed for the end user to successfully identify and understand all the available “ingredients” (data), the types of “cooking” (end user processing) that are recommended to prepare them, and a few sample “recipes” (worked examples) to support end users in developing their own “recipes” consistent with the best practices presented here. This is not intended to be an exhaustive guide to each of these sensors, but rather a synthesis of the key information to support OOI BGC sensor data users in preparing science-ready data products. In instances when more in-depth information might be helpful, references and links have been provided both within each chapter and in the Appendix

Cardiovascular comorbidities among public health clinic patients with diabetes: the Urban Diabetics Study

BACKGROUND: We sought to determine the frequency and distribution of cardiovascular comorbidities in a large cohort of low-income patients with diabetes who had received primary care for diabetes at municipal health clinics. METHODS: Outpatient data from the Philadelphia Health Care Centers was linked with hospital discharge data from all Pennsylvania hospitals and death certificates. RESULTS: Among 10,095 primary care patients with diabetes, with a mean observation period of 4.6 years (2.8 after diabetes diagnosis), 2,693 (14.3%) were diagnosed with heart disease, including 270 (1.4%) with myocardial infarction and 912 (4.8%) with congestive heart failure. Cerebrovascular disease was diagnosed in 588 patients (3.1%). Over 77% of diabetic patients were diagnosed with hypertension. Incidence rates of new complications ranged from 0.6 per 100 person years for myocardial infarction to 26.5 per 100 person years for hypertension. Non-Hispanic whites had higher rates of myocardial infarction, and Hispanics and Asians had fewer comorbid conditions than African Americans and non-Hispanic whites. CONCLUSION: Cardiovascular comorbidities were common both before and after diabetes diagnosis in this low-income cohort, but not substantially different from mixed-income managed care populations, perhaps as a consequence of access to primary care and pharmacy services

A Synoptical Classification of the Bivalvia (Mollusca)

The following classification summarizes the suprageneric taxono-my of the Bivalvia for the upcoming revision of the Bivalvia volumes of the Treatise on Invertebrate Paleontology, Part N. The development of this classification began with Carter (1990a), Campbell, Hoeks-tra, and Carter (1995, 1998), Campbell (2000, 2003), and Carter, Campbell, and Campbell (2000, 2006), who, with assistance from the United States National Science Foundation, conducted large-scale morphological phylogenetic analyses of mostly Paleozoic bivalves, as well as molecular phylogenetic analyses of living bivalves. Dur-ing the past several years, their initial phylogenetic framework has been revised and greatly expanded through collaboration with many students of bivalve biology and paleontology, many of whom are coauthors. During this process, all available sources of phylogenetic information, including molecular, anatomical, shell morphological, shell microstructural, bio- and paleobiogeographic as well as strati-graphic, have been integrated into the classification. The more recent sources of phylogenetic information include, but are not limited to, Carter (1990a), Malchus (1990), J. Schneider (1995, 1998a, 1998b, 2002), T. Waller (1998), Hautmann (1999, 2001a, 2001b), Giribet and Wheeler (2002), Giribet and Distel (2003), Dreyer, Steiner, and Harper (2003), Matsumoto (2003), Harper, Dreyer, and Steiner (2006), Kappner and Bieler (2006), Mikkelsen and others (2006), Neulinger and others (2006), Taylor and Glover (2006), Kříž (2007), B. Morton (2007), Taylor, Williams, and Glover (2007), Taylor and others (2007), Giribet (2008), and Kirkendale (2009). This work has also benefited from the nomenclator of bivalve families by Bouchet and Rocroi (2010) and its accompanying classification by Bieler, Carter, and Coan (2010).This classification strives to indicate the most likely phylogenetic position for each taxon. Uncertainty is indicated by a question mark before the name of the taxon. Many of the higher taxa continue to undergo major taxonomic revision. This is especially true for the superfamilies Sphaerioidea and Veneroidea, and the orders Pectinida and Unionida. Because of this state of flux, some parts of the clas-sification represent a compromise between opposing points of view. Placement of the Trigonioidoidea is especially problematic. This Mesozoic superfamily has traditionally been placed in the order Unionida, as a possible derivative of the superfamily Unionoidea (see Cox, 1952; Sha, 1992, 1993; Gu, 1998; Guo, 1998; Bieler, Carter, & Coan, 2010). However, Chen Jin-hua (2009) summarized evi-dence that Trigonioidoidea was derived instead from the superfamily Trigonioidea. Arguments for these alternatives appear equally strong, so we presently list the Trigonioidoidea, with question, under both the Trigoniida and Unionida, with the contents of the superfamily indicated under the Trigoniida.Fil: Carter, Joseph G.. University of North Carolina; Estados UnidosFil: Altaba, Cristian R.. Universidad de las Islas Baleares; EspañaFil: Anderson, Laurie C.. South Dakota School of Mines and Technology; Estados UnidosFil: Araujo, Rafael. Consejo Superior de Investigaciones Cientificas. Museo Nacional de Ciencias Naturales; EspañaFil: Biakov, Alexander S.. Russian Academy of Sciences; RusiaFil: Bogan, Arthur E.. North Carolina State Museum of Natural Sciences; Estados UnidosFil: Campbell, David. Paleontological Research Institution; Estados UnidosFil: Campbell, Matthew. Charleston Southern University; Estados UnidosFil: Chen, Jin Hua. Chinese Academy of Sciences. Nanjing Institute of Geology and Palaeontology; República de ChinaFil: Cope, John C. W.. National Museum of Wales. Department of Geology; Reino UnidoFil: Delvene, Graciela. Instituto Geológico y Minero de España; EspañaFil: Dijkstra, Henk H.. Netherlands Centre for Biodiversity; Países BajosFil: Fang, Zong Jie. Chinese Academy of Sciences; República de ChinaFil: Gardner, Ronald N.. No especifica;Fil: Gavrilova, Vera A.. Russian Geological Research Institute; RusiaFil: Goncharova, Irina A.. Russian Academy of Sciences; RusiaFil: Harries, Peter J.. University of South Florida; Estados UnidosFil: Hartman, Joseph H.. University of North Dakota; Estados UnidosFil: Hautmann, Michael. Paläontologisches Institut und Museum; SuizaFil: Hoeh, Walter R.. Kent State University; Estados UnidosFil: Hylleberg, Jorgen. Institute of Biology; DinamarcaFil: Jiang, Bao Yu. Nanjing University; República de ChinaFil: Johnston, Paul. Mount Royal University; CanadáFil: Kirkendale, Lisa. University Of Wollongong; AustraliaFil: Kleemann, Karl. Universidad de Viena; AustriaFil: Koppka, Jens. Office de la Culture. Section d’Archéologie et Paléontologie; SuizaFil: Kříž, Jiří. Czech Geological Survey. Department of Sedimentary Formations. Lower Palaeozoic Section; República ChecaFil: Machado, Deusana. Universidade Federal do Rio de Janeiro; BrasilFil: Malchus, Nikolaus. Institut Català de Paleontologia; EspañaFil: Márquez Aliaga, Ana. Universidad de Valencia; EspañaFil: Masse, Jean Pierre. Universite de Provence; FranciaFil: McRoberts, Christopher A.. State University of New York at Cortland. Department of Geology; Estados UnidosFil: Middelfart, Peter U.. Australian Museum; AustraliaFil: Mitchell, Simon. The University of the West Indies at Mona; JamaicaFil: Nevesskaja, Lidiya A.. Russian Academy of Sciences; RusiaFil: Özer, Sacit. Dokuz Eylül University; TurquíaFil: Pojeta, John Jr.. National Museum of Natural History; Estados UnidosFil: Polubotko, Inga V.. Russian Geological Research Institute; RusiaFil: Pons, Jose Maria. Universitat Autònoma de Barcelona; EspañaFil: Popov, Sergey. Russian Academy of Sciences; RusiaFil: Sanchez, Teresa Maria. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Córdoba; ArgentinaFil: Sartori, André F.. Field Museum of National History; Estados UnidosFil: Scott, Robert W.. Precision Stratigraphy Associates; Estados UnidosFil: Sey, Irina I.. Russian Geological Research Institute; RusiaFil: Signorelli, Javier Hernan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico; ArgentinaFil: Silantiev, Vladimir V.. Kazan Federal University; RusiaFil: Skelton, Peter W.. Open University. Department of Earth and Environmental Sciences; Reino UnidoFil: Steuber, Thomas. The Petroleum Institute; Emiratos Arabes UnidosFil: Waterhouse, J. Bruce. No especifica;Fil: Wingard, G. Lynn. United States Geological Survey; Estados UnidosFil: Yancey, Thomas. Texas A&M University; Estados Unido

Comparison of Characteristics and Outcomes of Trial Participants and Nonparticipants: Example of Blood and Marrow Transplant Clinical Trials Network 0201 Trial

Controversy surrounds the question of whether clinical trial participants have better outcomes than comparable patients who are not treated on a trial. We explored this question using a recent large, randomized, multi-center study comparing peripheral blood (PB) with bone marrow (BM) transplantation from unrelated donors (URD), conducted by the Blood and Marrow Transplant Clinical Trials Network (BMT CTN)

Analysis of the Effect of Race, Socioeconomic Status, and Center Size on Unrelated National Marrow Donor Program Donor Outcomes: Donor Toxicities Are More Common at Low-Volume Bone Marrow Collection Centers

Previous studies have shown that risks of collection-related pain and symptoms are associated with sex, body mass index (BMI), and age in unrelated donors undergoing collection at National Marrow Donor Program (NMDP) centers. We hypothesized that other important factors (race, socioeconomic status (SES), and number of procedures at the collection center) might affect symptoms in donors. We assessed outcomes in 2,726 bone marrow (BM) and 6,768 peripheral blood stem cell (PBSC) donors collected between 2004 and 2009. Pain/symptoms are reported as maximum levels over mobilization and collection (PBSC) or within 2 days of collection (BM) and at 1 week after collection. For PBSC donors, race and center volumes were not associated with differences in pain/symptoms at any time. PBSC donors with high SES levels reported higher maximum symptom levels 1 week post donation (p=0.017). For BM donors, black males reported significantly higher levels of pain (OR=1.90, CI=1.14-3.19, p=0.015). No differences were noted by SES groups. BM donors from low volume centers reported more toxicity (OR=2.09, CI=1.26-3.46, p=0.006). In conclusion, race and SES have a minimal effect on donation associated symptoms. However, donors from centers performing ≤1 BM collection every 2 months have more symptoms following BM donation. Approaches should be developed by registries and low volume centers to address this issue

Better Regional Ocean Observing Through Cross-National Cooperation: A Case Study From the Northeast Pacific

The ocean knows no political borders. Ocean processes, like summertime wind-driven upwelling, stretch thousands of kilometers along the Northeast Pacific (NEP) coast. This upwelling drives marine ecosystem productivity and is modulated by weather systems and seasonal to interdecadal ocean-atmosphere variability. Major ocean currents in the NEP transport water properties such as heat, fresh water, nutrients, dissolved oxygen, pCO2, and pH close to the shore. The eastward North Pacific Current bifurcates offshore in the NEP, delivering open-ocean signals south into the California Current and north into the Gulf of Alaska. There is a large and growing number of NEP ocean observing elements operated by government agencies, Native American Tribes, First Nations groups, not-for-profit organizations, and private entities. Observing elements include moored and mobile platforms, shipboard repeat cruises, as well as land-based and estuarine stations. A wide range of multidisciplinary ocean sensors are deployed to track, for example, upwelling, downwelling, ocean productivity, harmful algal blooms, ocean acidification and hypoxia, seismic activity and tsunami wave propagation. Data delivery to shore and observatory controls are done through satellite and cell phone communication, and via seafloor cables. Remote sensing from satellites and land-based coastal radar provide broader spatial coverage, while numerical circulation and biogeochemical modeling complement ocean observing efforts. Models span from the deep ocean into the inland Salish Sea and estuaries. NEP ocean observing systems are used to understand regional processes and, together with numerical models, provide ocean forecasts. By sharing data, experiences and lessons learned, the regional ocean observatory is better than the sum of its parts