RMD-QOSM: The NSIS Quality-of-Service Model for Resource Management in Diffserv

This document describes a Next Steps in Signaling (NSIS) Quality-of- Service (QoS) Model for networks that use the Resource Management in Diffserv (RMD) concept. RMD is a technique for adding admission control and preemption function to Differentiated Services (Diffserv) networks. The RMD QoS Model allows devices external to the RMD network to signal reservation requests to Edge nodes in the RMD network. The RMD Ingress Edge nodes classify the incoming flows into traffic classes and signals resource requests for the corresponding traffic class along the data path to the Egress Edge nodes for each flow. Egress nodes reconstitute the original requests and continue forwarding them along the data path towards the final destination. In addition, RMD defines notification functions to indicate overload situations within the domain to the Edge nodes

Cg-Rel, the first Rel/NF-κB homolog characterized in a mollusk, the Pacific oyster Crassostrea gigas

AbstractWe report here the identification and functional characterization of Cg-Rel, a gene encoding the Crassostrea gigas homolog of Rel/NF-κB transcription factors found in insects and mammals. Sequence and phylogenetic analysis showed that Cg-Rel shares the structural organization of Rel/NF-κB transcription factors of class II. It includes a Rel homology domain as well as a C-terminal transactivation domain (TD). Overexpression of Cg-Rel in the Drosophila S2 cell line activated the expression of a NF-κB-dependent reporter gene, whereas transfection with a Cg-Rel construct containing a C-terminal deletion of the TD or using a reporter gene with mutated κB binding sites failed to activate expression. These results suggest that Cg-Rel is a functional member of the Rel family of transcription factors, making this the sixth structurally homologous component of the Rel/NF-κB pathway characterized in C. gigas. Based on homology to other invertebrates’ Rel/NF-κB cascade, the function of the oyster pathway may serve to regulate genes involved in innate defense and/or development. These findings serve to highlight a potentially important regulatory pathway to the study of oyster immunology, hence allowing comparison of the immune system in vertebrates and invertebrates, an important key issue to understand its evolution

Microbial Iron(II) Oxidation in Littoral Freshwater Lake Sediment: The Potential for Competition between Phototrophic vs. Nitrate-Reducing Iron(II)-Oxidizers

The distribution of neutrophilic microbial iron oxidation is mainly determined by local gradients of oxygen, light, nitrate and ferrous iron. In the anoxic top part of littoral freshwater lake sediment, nitrate-reducing and phototrophic Fe(II)-oxidizers compete for the same e− donor; reduced iron. It is not yet understood how these microbes co-exist in the sediment and what role they play in the Fe cycle. We show that both metabolic types of anaerobic Fe(II)-oxidizing microorganisms are present in the same sediment layer directly beneath the oxic-anoxic sediment interface. The photoferrotrophic most probable number counted 3.4·105 cells·g−1 and the autotrophic and mixotrophic nitrate-reducing Fe(II)-oxidizers totaled 1.8·104 and 4.5·104 cells·g−1 dry weight sediment, respectively. To distinguish between the two microbial Fe(II) oxidation processes and assess their individual contribution to the sedimentary Fe cycle, littoral lake sediment was incubated in microcosm experiments. Nitrate-reducing Fe(II)-oxidizing bacteria exhibited a higher maximum Fe(II) oxidation rate per cell, in both pure cultures and microcosms, than photoferrotrophs. In microcosms, photoferrotrophs instantly started oxidizing Fe(II), whilst nitrate-reducing Fe(II)-oxidizers showed a significant lag-phase during which they probably use organics as e− donor before initiating Fe(II) oxidation. This suggests that they will be outcompeted by phototrophic Fe(II)-oxidizers during optimal light conditions; as phototrophs deplete Fe(II) before nitrate-reducing Fe(II)-oxidizers start Fe(II) oxidation. Thus, the co-existence of the two anaerobic Fe(II)-oxidizers may be possible due to a niche space separation in time by the day-night cycle, where nitrate-reducing Fe(II)-oxidizers oxidize Fe(II) during darkness and phototrophs play a dominant role in Fe(II) oxidation during daylight. Furthermore, metabolic flexibility of Fe(II)-oxidizing microbes may play a paramount role in the conservation of the sedimentary Fe cycle

Physics Analysis Expert PAX: First Applications

PAX (Physics Analysis Expert) is a novel, C++ based toolkit designed to assist teams in particle physics data analysis issues. The core of PAX are event interpretation containers, holding relevant information about and possible interpretations of a physics event. Providing this new level of abstraction beyond the results of the detector reconstruction programs, PAX facilitates the buildup and use of modern analysis factories. Class structure and user command syntax of PAX are set up to support expert teams as well as newcomers in preparing for the challenges expected to arise in the data analysis at future hadron colliders.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003, 7 pages, LaTeX, 10 eps figures. PSN THLT00

Concepts, Developments and Advanced Applications of the PAX Toolkit

The Physics Analysis eXpert (PAX) is an open source toolkit for high energy physics analysis. The C++ class collection provided by PAX is deployed in a number of analyses with complex event topologies at Tevatron and LHC. In this article, we summarize basic concepts and class structure of the PAX kernel. We report about the most recent developments of the kernel and introduce two new PAX accessories. The PaxFactory, that provides a class collection to facilitate event hypothesis evolution, and VisualPax, a Graphical User Interface for PAX objects

A preliminary checklist of fungi at the Boston Harbor Islands

Between December 2012 and May 2017, we conducted a fungal inventory at the Boston Harbor Islands National Recreation Area (BHI) in Massachusetts. We extensively sampled 4 sites (Grape Island, Peddocks Island, Thompson Island, and World's End peninsula) and occasionally visited 4 others for sampling (Calf Island, Great Brewster Island, Slate Island, and Webb Memorial State Park). We made over 900 collections, of which 313 have been identified. The survey yielded 172 species in 123 genera, 62 families, 24 orders, 11 classes, and 2 phyla. We report 4 species as new, but not formally described, in the genera Orbilia, Resupinatus, and Xylaria. Another collection in the genus Lactarius may be new to science, but further morphological and molecular work is needed to confirm this conclusion. Additionally, Orbilia aprilis is a new report for North America, Proliferodiscus earoleucus represents only the second report for the US, and Chrysosporium sulfureum, a common fungus of some cheeses, was discovered on woodlice (Crustacea: Malacostraca: Isopoda: Oniscidea). We discuss our findings in the light of DNA-based identifications using the ITS ribosomal DNA region, including the advantages and disadvantages of this approach, and stress the need for biodiversity studies in urbanized areas during all seasons

Heisenberg picture operators in the quantum state diffusion model

A stochastic simulation algorithm for the computation of multitime correlation functions which is based on the quantum state diffusion model of open systems is developed. The crucial point of the proposed scheme is a suitable extension of the quantum master equation to a doubled Hilbert space which is then unraveled by a stochastic differential equation.Comment: LaTeX2E, 6 pages, 3 figures, uses iopar

2D Potts Model Correlation Lengths: Numerical Evidence for ξo=ξd\xi_o = \xi_d at βt\beta_t

We have studied spin-spin correlation functions in the ordered phase of the two-dimensional $q$-state Potts model with $q=10$, 15, and 20 at the first-order transition point $\beta_t$. Through extensive Monte Carlo simulations we obtain strong numerical evidence that the correlation length in the ordered phase agrees with the exactly known and recently numerically confirmed correlation length in the disordered phase: $\xi_o(\beta_t) = \xi_d(\beta_t)$. As a byproduct we find the energy moments in the ordered phase at $\beta_t$ in very good agreement with a recent large $q$-expansion.Comment: 11 pages, PostScript. To appear in Europhys. Lett. (September 1995). See also http://www.cond-mat.physik.uni-mainz.de/~janke/doc/home_janke.htm