Proof of Gravity and Yang-Mills Amplitude Relations

Using BCFW on-shell recursion techniques, we prove a sequence of explicit n-point Kawai-Lewellen-Tye relations between gravity and Yang-Mills amplitudes at tree level.Comment: 17 pages, no figures, JHE

Modelled ocean changes at the Plio-Pleistocene transition driven by Antarctic ice advance

The Earth underwent a major transition from the warm climates of the Pliocene to the Pleistocene ice ages between 3.2 and 2.6 million years ago. The intensification of Northern Hemisphere Glaciation is the most obvious result of the Plio-Pleistocene transition. However, recent data show that the ocean also underwent a significant change, with the convergence of deep water mass properties in the North Pacific and North Atlantic Ocean. Here we show that the lack of coastal ice in the Pacific sector of Antarctica leads to major reductions in Pacific Ocean overturning and the loss of the modern North Pacific Deep Water (NPDW) mass in climate models of the warmest periods of the Pliocene. These results potentially explain the convergence of global deep water mass properties at the Plio-Pleistocene transition, as Circumpolar Deep Water (CDW) became the common source

QCD and strongly coupled gauge theories : challenges and perspectives

We highlight the progress, current status, and open challenges of QCD-driven physics, in theory and in experiment. We discuss how the strong interaction is intimately connected to a broad sweep of physical problems, in settings ranging from astrophysics and cosmology to strongly coupled, complex systems in particle and condensed-matter physics, as well as to searches for physics beyond the Standard Model. We also discuss how success in describing the strong interaction impacts other fields, and, in turn, how such subjects can impact studies of the strong interaction. In the course of the work we offer a perspective on the many research streams which flow into and out of QCD, as well as a vision for future developments.Peer reviewe

Atiprimod blocks STAT3 phosphorylation and induces apoptosis in multiple myeloma cells

Multiple myeloma (MM) accounts for 1 % of all cancer deaths. Although treated aggressively, almost all myelomas eventually recur and become resistant to treatment. Atiprimod (2-(3-Diethylaminopropyl)-8,8-dipropyl-2-azaspiro[4,5] decane dimaleate) has exerted anti-inflammatory activities and inhibited oeteoclast-induced bone resorption in animal models and been well tolerated in patients with rheumatoid arthritis in phase I clinical trials. Therefore, we investigated its activity in MM cells and its mechanism of action. We found that Atiprimod inhibited proliferation of the myeloma cell lines U266-B1, OCI-MY5, MM-1, and MM-1R in a time- and dose-dependent manner. Atiprimod blocked U266-B1 myeloma cells in the G0/G1 phase, preventing cell cycle progression. Furthermore, Atiprimod inhibited signal transducer and activator of transcription (STAT) 3 activation, blocking the signalling pathway of interleukin-6, which contributes to myeloma cell proliferation and survival, and downregulated the antiapoptotic proteins Bcl-2, Bcl-XL, and Mcl-1. Incubation of U266-B1 myeloma cells with Atiprimod induced apoptosis through the activation of caspase 3 and subsequent cleavage of the DNA repair enzyme poly(adenosine diphosphate-ribose) polymerase. Finally, Atiprimod suppressed myeloma colony-forming cell proliferation in fresh marrow cells from five patients with newly diagnosed MM in a dose-dependent fashion. These data suggest that Atiprimod has a role in future therapies for MM

Predicting Prokaryotic Ecological Niches Using Genome Sequence Analysis

Automated DNA sequencing technology is so rapid that analysis has become the rate-limiting step. Hundreds of prokaryotic genome sequences are publicly available, with new genomes uploaded at the rate of approximately 20 per month. As a result, this growing body of genome sequences will include microorganisms not previously identified, isolated, or observed. We hypothesize that evolutionary pressure exerted by an ecological niche selects for a similar genetic repertoire in those prokaryotes that occupy the same niche, and that this is due to both vertical and horizontal transmission. To test this, we have developed a novel method to classify prokaryotes, by calculating their Pfam protein domain distributions and clustering them with all other sequenced prokaryotic species. Clusters of organisms are visualized in two dimensions as ‘mountains’ on a topological map. When compared to a phylogenetic map constructed using 16S rRNA, this map more accurately clusters prokaryotes according to functional and environmental attributes. We demonstrate the ability of this map, which we term a “niche map”, to cluster according to ecological niche both quantitatively and qualitatively, and propose that this method be used to associate uncharacterized prokaryotes with their ecological niche as a means of predicting their functional role directly from their genome sequence

Genomic Characterization of Methanomicrobiales Reveals Three Classes of Methanogens

BACKGROUND:Methanomicrobiales is the least studied order of methanogens. While these organisms appear to be more closely related to the Methanosarcinales in ribosomal-based phylogenetic analyses, they are metabolically more similar to Class I methanogens. METHODOLOGY/PRINCIPAL FINDINGS:In order to improve our understanding of this lineage, we have completely sequenced the genomes of two members of this order, Methanocorpusculum labreanum Z and Methanoculleus marisnigri JR1, and compared them with the genome of a third, Methanospirillum hungatei JF-1. Similar to Class I methanogens, Methanomicrobiales use a partial reductive citric acid cycle for 2-oxoglutarate biosynthesis, and they have the Eha energy-converting hydrogenase. In common with Methanosarcinales, Methanomicrobiales possess the Ech hydrogenase and at least some of them may couple formylmethanofuran formation and heterodisulfide reduction to transmembrane ion gradients. Uniquely, M. labreanum and M. hungatei contain hydrogenases similar to the Pyrococcus furiosus Mbh hydrogenase, and all three Methanomicrobiales have anti-sigma factor and anti-anti-sigma factor regulatory proteins not found in other methanogens. Phylogenetic analysis based on seven core proteins of methanogenesis and cofactor biosynthesis places the Methanomicrobiales equidistant from Class I methanogens and Methanosarcinales. CONCLUSIONS/SIGNIFICANCE:Our results indicate that Methanomicrobiales, rather than being similar to Class I methanogens or Methanomicrobiales, share some features of both and have some unique properties. We find that there are three distinct classes of methanogens: the Class I methanogens, the Methanomicrobiales (Class II), and the Methanosarcinales (Class III)