SeaWiFS Technical Report Series

Two issues regarding primary productivity, as it pertains to the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Program and the National Aeronautics and Space Administration (NASA) Mission to Planet Earth (MTPE) are presented in this volume. Chapter 1 describes the development of a science plan for deriving primary production for the world ocean using satellite measurements, by the Ocean Primary Productivity Working Group (OPPWG). Chapter 2 presents discussions by the same group, of algorithm classification, algorithm parameterization and data availability, algorithm testing and validation, and the benefits of a consensus primary productivity algorithm

The Influence of cis-Regulatory Elements on DNA Methylation Fidelity

It is now established that, as compared to normal cells, the cancer cell genome has an overall inverse distribution of DNA methylation (“methylome”), i.e., predominant hypomethylation and localized hypermethylation, within “CpG islands” (CGIs). Moreover, although cancer cells have reduced methylation “fidelity” and genomic instability, accurate maintenance of aberrant methylomes that underlie malignant phenotypes remains necessary. However, the mechanism(s) of cancer methylome maintenance remains largely unknown. Here, we assessed CGI methylation patterns propagated over 1, 3, and 5 divisions of A2780 ovarian cancer cells, concurrent with exposure to the DNA cross-linking chemotherapeutic cisplatin, and observed cell generation-successive increases in total hyper- and hypo-methylated CGIs. Empirical Bayesian modeling revealed five distinct modes of methylation propagation: (1) heritable (i.e., unchanged) high- methylation (1186 probe loci in CGI microarray); (2) heritable (i.e., unchanged) low-methylation (286 loci); (3) stochastic hypermethylation (i.e., progressively increased, 243 loci); (4) stochastic hypomethylation (i.e., progressively decreased, 247 loci); and (5) considerable “random” methylation (582 loci). These results support a “stochastic model” of DNA methylation equilibrium deriving from the efficiency of two distinct processes, methylation maintenance and de novo methylation. A role for cis-regulatory elements in methylation fidelity was also demonstrated by highly significant (p<2.2×10−5) enrichment of transcription factor binding sites in CGI probe loci showing heritably high (118 elements) and low (47 elements) methylation, and also in loci demonstrating stochastic hyper-(30 elements) and hypo-(31 elements) methylation. Notably, loci having “random” methylation heritability displayed nearly no enrichment. These results demonstrate an influence of cis-regulatory elements on the nonrandom propagation of both strictly heritable and stochastically heritable CGIs

Vapochromic Behaviour of M[Au(CN)2]2-Based Coordination Polymers (M = Co, Ni)

A series of M[Au(CN)2]2(analyte)x coordination polymers (M = Co, Ni; analyte = dimethylsulfoxide (DMSO), N,N-dimethylformamide (DMF), pyridine; x = 2 or 4) was prepared and characterized. Addition of analyte vapours to solid M(μ-OH2)[Au(CN)2]2 yielded visible vapochromic responses for M = Co but not M = Ni; the IR νCN spectral region changed in every case. A single crystal structure of Zn[Au(CN)2]2(DMSO)2 revealed a corrugated 2-D layer structure with cis-DMSO units. Reacting a Ni(II) salt and K[Au(CN)2] in DMSO yielded the isostructural Ni[Au(CN)2]2(DMSO)2 product. Co[Au(CN)2]2(DMSO)2 and M[Au(CN)2]2(DMF)2 (M = Co, Ni) complexes have flat 2-D square-grid layer structures with trans-bound DMSO or DMF units; they are formed via vapour absorption by solid M(μ-OH2)[Au(CN)2]2 and from DMSO or DMF solution synthesis. Co[Au(CN)2]2(pyridine)4 is generated via vapour absorption by Co(μ-OH2)[Au(CN)2]2; the analogous Ni complex is synthesized by immersion of Ni(μ-OH2)[Au(CN)2]2 in 4% aqueous pyridine. Similar immersion of Co(μ-OH2)[Au(CN)2]2 yielded Co[Au(CN)2]2(pyridine)2, which has a flat 2-D square-grid structure with trans-pyridine units. Absorption of pyridine vapour by solid Ni(μ-OH2)[Au(CN)2]2 was incomplete, generating a mixture of pyridine-bound complexes. Analyte-free Co[Au(CN)2]2 was prepared by dehydration of Co(μ-OH2)[Au(CN)2]2 at 145 °C; it has a 3-D diamondoid-type structure and absorbs DMSO, DMF and pyridine to give the same materials as by vapour absorption from the hydrate

Fire as a fundamental ecological process: Research advances and frontiers

© 2020 The Authors. Journal of Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society Fire is a powerful ecological and evolutionary force that regulates organismal traits, population sizes, species interactions, community composition, carbon and nutrient cycling and ecosystem function. It also presents a rapidly growing societal challenge, due to both increasingly destructive wildfires and fire exclusion in fire-dependent ecosystems. As an ecological process, fire integrates complex feedbacks among biological, social and geophysical processes, requiring coordination across several fields and scales of study. Here, we describe the diversity of ways in which fire operates as a fundamental ecological and evolutionary process on Earth. We explore research priorities in six categories of fire ecology: (a) characteristics of fire regimes, (b) changing fire regimes, (c) fire effects on above-ground ecology, (d) fire effects on below-ground ecology, (e) fire behaviour and (f) fire ecology modelling. We identify three emergent themes: the need to study fire across temporal scales, to assess the mechanisms underlying a variety of ecological feedbacks involving fire and to improve representation of fire in a range of modelling contexts. Synthesis: As fire regimes and our relationships with fire continue to change, prioritizing these research areas will facilitate understanding of the ecological causes and consequences of future fires and rethinking fire management alternatives

Planktonic Microbes in the Gulf of Maine Area

In the Gulf of Maine area (GoMA), as elsewhere in the ocean, the organisms of greatest numerical abundance are microbes. Viruses in GoMA are largely cyanophages and bacteriophages, including podoviruses which lack tails. There is also evidence of Mimivirus and Chlorovirus in the metagenome. Bacteria in GoMA comprise the dominant SAR11 phylotype cluster, and other abundant phylotypes such as SAR86-like cluster, SAR116-like cluster, Roseobacter, Rhodospirillaceae, Acidomicrobidae, Flavobacteriales, Cytophaga, and unclassified Alphaproteobacteria and Gammaproteobacteria clusters. Bacterial epibionts of the dinoflagellate Alexandrium fundyense include Rhodobacteraceae, Flavobacteriaceae, Cytophaga spp., Sulfitobacter spp., Sphingomonas spp., and unclassified Bacteroidetes. Phototrophic prokaryotes in GoMA include cyanobacteria that contain chlorophyll (mainly Synechococcus), aerobic anoxygenic phototrophs that contain bacteriochlorophyll, and bacteria that contain proteorhodopsin. Eukaryotic microalgae in GoMA include Bacillariophyceae, Dinophyceae, Prymnesiophyceae, Prasinophyceae, Trebouxiophyceae, Cryptophyceae, Dictyochophyceae, Chrysophyceae, Eustigmatophyceae, Pelagophyceae, Synurophyceae, and Xanthophyceae. There are no records of Bolidophyceae, Aurearenophyceae, Raphidophyceae, and Synchromophyceae in GoMA. In total, there are records for 665 names and 229 genera of microalgae. Heterotrophic eukaryotic protists in GoMA include Dinophyceae, Alveolata, Apicomplexa, amoeboid organisms, Labrynthulida, and heterotrophic marine stramenopiles (MAST). Ciliates include Strombidium, Lohmaniella, Tontonia, Strobilidium, Strombidinopsis and the mixotrophs Laboea strobila and Myrionecta rubrum (ex Mesodinium rubra). An inventory of selected microbial groups in each of 14 physiographic regions in GoMA is made by combining information on the depth-dependent variation of cell density and the depth-dependent variation of water volume. Across the entire GoMA, an estimate for the minimum abundance of cell-based microbes is 1.7×1025 organisms. By one account, this number of microbes implies a richness of 105 to 106 taxa in the entire water volume of GoMA. Morphological diversity in microplankton is well-described but the true extent of taxonomic diversity, especially in the femtoplankton, picoplankton and nanoplankton – whether autotrophic, heterotrophic, or mixotrophic, is unknown

An adaptive signaling network in melanoma inflammatory niches confers tolerance to MAPK signaling inhibition

Mitogen-activated protein kinase (MAPK) pathway antagonists induce profound clinical responses in advanced cutaneous melanoma, but complete remissions are frustrated by the development of acquired resistance. Before resistance emerges, adaptive responses establish a mutation-independent drug tolerance. Antagonizing these adaptive responses could improve drug effects, thereby thwarting the emergence of acquired resistance. In this study, we reveal that inflammatory niches consisting of tumor-associated macrophages and fibroblasts contribute to treatment tolerance through a cytokine-signaling network that involves macrophage-derived IL-1β and fibroblast-derived CXCR2 ligands. Fibroblasts require IL-1β to produce CXCR2 ligands, and loss of host IL-1R signaling in vivo reduces melanoma growth. In tumors from patients on treatment, signaling from inflammatory niches is amplified in the presence of MAPK inhibitors. Signaling from inflammatory niches counteracts combined BRAF/MEK (MAPK/extracellular signal–regulated kinase kinase) inhibitor treatment, and consequently, inhibiting IL-1R or CXCR2 signaling in vivo enhanced the efficacy of MAPK inhibitors. We conclude that melanoma inflammatory niches adapt to and confer drug tolerance toward BRAF and MEK inhibitors early during treatmen

Ten simple rules for working with high resolution remote sensing data

Researchers in Earth and environmental science can extract incredible value from high- resolution (sub-meter, sub-hourly or hyper-spectral) remote sensing data, but these data can be difficult to use. Correct, appropriate and competent use of such data requires skills from remote sensing and the data sciences that are rarely taught together. In practice, many researchers teach themselves how to use high-resolution remote sensing data with ad hoc trial and error processes, often resulting in wasted effort and resources. In order to implement a consistent strategy, we outline ten rules with examples from Earth and environmental science to help academic researchers and professionals in industry work more effectively and competently with high-resolution data

Recent Advances in Melanoma Staging and Therapy

Background: Recent advances in the staging and treatment of melanoma were reviewed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/41420/1/10434_1999_Article_467.pd