Young mangrove stands produce a large and high quality litter input to aquatic systems

Mangrove swamps are key ecosystems along the Vietnam coast. Although mangrove litter is thought to represent an important input of organic matter and nutrients to the coastal aquatic systems, the factors determining the quality and size of this litter flux have not been studied so far. We monitored leaf, stipule, twig, and reproductive litter monthly in monocultures of Rhizophora apiculata mangrove forests of 7, 11, 17 and 24 years old in the Camau province, Mekong Delta, Vietnam. Litter traps were used to measure litter fall production from June 2001 till May 2002. Total litter fall was in the range of 8.86-14.16 t DW ha-1 year-1. Leaves were the main component, and represented 70% of litter fall production in all stands. Total litter fall was lower in the older stands but the amount of reproductive litter was significantly higher in these stands (17 and 24 years). Biomass of leaf litter was highest between the end of the wet season and the beginning of the dry season. Phosphorus and nitrogen levels in leaf litter were significantly higher in younger than in older stands. Overall, our study indicated that young stands produced the highest input of litter and particularly of nitrogen and phosphorus to the surrounding aquatic system. Consequently, these stands contribute significantly to the fisherie

Program Management Integrated with Data and Decision Sciences

Program management (PM) complexity depends on the budget size and program types. In general, the program types can be classified into three categories, namely, defense, commercial, and civilian types. This chapter presents and discusses an approach for integrating the PM discipline areas with emerging data science and decision science1 (DDS) for any program type. Additionally, we describe the key PM areas and present a corresponding generalized model consists of a list of multiple PM discipline areas that can be tailored for any program types. To demonstrate the PM-DDS integration approach, we focus on three key PM areas and corresponding PM discipline areas related to schedule, cost, and risk management. These three discipline areas are analyzed to identify appropriate program elements that can be enhanced using existing DDS technology enablers (TEs). We also propose a flexible PM-DSS integration framework by leveraging existing machine learning operations (MLOps) framework. The proposed integration framework is expected to allow for enhancing the program planning and execution by reducing the program risk using a wide range of DDS TEs, including big data analytics, artificial intelligence, machine learning, deep learning, neural networks, and artificial intelligent

Platelet-activating factor (PAF) mediates NLRP3-NEK7 inflammasome induction independently of PAFR

The role of lipids in inflammasome activation remains underappreciated. The phospholipid, platelet-activating factor (PAF), exerts multiple physiological functions by binding to a G protein-coupled seven-transmembrane receptor (PAFR). PAF is associated with a number of inflammatory disorders, yet the molecular mechanism underlying its proinflammatory function remains to be fully elucidated. We show that multiple PAF isoforms and PAF-like lipids can activate the inflammasome, resulting in IL-1β and IL-18 maturation. This is dependent on NLRP3, ASC, caspase-1, and NEK7, but not on NLRC4, NLRP1, NLRP6, AIM2, caspase-11, or GSDMD. Inflammasome activation by PAF also requires potassium efflux and calcium influx but not lysosomal cathepsin or mitochondrial reactive oxygen species. PAF exacerbates peritonitis partly through inflammasome activation, but PAFR is dispensable for PAF-induced inflammasome activation in vivo or in vitro. These findings reveal that PAF represents a damage-associated signal that activates the canonical inflammasome independently of PAFR and provides an explanation for the ineffectiveness of PAFR antagonist in blocking PAF-mediated inflammation in the clinic

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

The Earth: Plasma Sources, Losses, and Transport Processes

This paper reviews the state of knowledge concerning the source of magnetospheric plasma at Earth. Source of plasma, its acceleration and transport throughout the system, its consequences on system dynamics, and its loss are all discussed. Both observational and modeling advances since the last time this subject was covered in detail (Hultqvist et al., Magnetospheric Plasma Sources and Losses, 1999) are addressed

Search for strong gravity in multijet final states produced in pp collisions at √s=13 TeV using the ATLAS detector at the LHC

A search is conducted for new physics in multijet final states using 3.6 inverse femtobarns of data from proton-proton collisions at √s = 13TeV taken at the CERN Large Hadron Collider with the ATLAS detector. Events are selected containing at least three jets with scalar sum of jet transverse momenta (HT) greater than 1TeV. No excess is seen at large HT and limits are presented on new physics: models which produce final states containing at least three jets and having cross sections larger than 1.6 fb with HT > 5.8 TeV are excluded. Limits are also given in terms of new physics models of strong gravity that hypothesize additional space-time dimensions

Operation and performance of the ATLAS semiconductor tracker

The semiconductor tracker is a silicon microstrip detector forming part of the inner tracking system of the ATLAS experiment at the LHC. The operation and performance of the semiconductor tracker during the first years of LHC running are described. More than 99% of the detector modules were operational during this period, with an average intrinsic hit efficiency of (99.74±0.04)%. The evolution of the noise occupancy is discussed, and measurements of the Lorentz angle, δ-ray production and energy loss presented. The alignment of the detector is found to be stable at the few-micron level over long periods of time. Radiation damage measurements, which include the evolution of detector leakage currents, are found to be consistent with predictions and are used in the verification of radiation background simulations

Measurement of the correlation between flow harmonics of different order in lead-lead collisions at √sNN = 2.76 TeV with the ATLAS detector

Correlations between the elliptic or triangular flow coefficients vm (m=2 or 3) and other flow harmonics vn (n=2 to 5) are measured using √sNN=2.76 TeV Pb+Pb collision data collected in 2010 by the ATLAS experiment at the LHC, corresponding to an integrated luminosity of 7 μb−1. The vm−vn correlations are measured in midrapidity as a function of centrality, and, for events within the same centrality interval, as a function of event ellipticity or triangularity defined in a forward rapidity region. For events within the same centrality interval, v3 is found to be anticorrelated with v2 and this anticorrelation is consistent with similar anticorrelations between the corresponding eccentricities, ε2 and ε3. However, it is observed that v4 increases strongly with v2, and v5 increases strongly with both v2 and v3. The trend and strength of the vm−vn correlations for n=4 and 5 are found to disagree with εm−εn correlations predicted by initial-geometry models. Instead, these correlations are found to be consistent with the combined effects of a linear contribution to vn and a nonlinear term that is a function of v22 or of v2v3, as predicted by hydrodynamic models. A simple two-component fit is used to separate these two contributions. The extracted linear and nonlinear contributions to v4 and v5 are found to be consistent with previously measured event-plane correlations

Search for H→γγ produced in association with top quarks and constraints on the Yukawa coupling between the top quark and the Higgs boson using data taken at 7 TeV and 8 TeV with the ATLAS detector

A search is performed for Higgs bosons produced in association with top quarks using the diphoton decay mode of the Higgs boson. Selection requirements are optimized separately for leptonic and fully hadronic final states from the top quark decays. The dataset used corresponds to an integrated luminosity of 4.5 fb−14.5 fb−1 of proton–proton collisions at a center-of-mass energy of 7 TeV and 20.3 fb−1 at 8 TeV recorded by the ATLAS detector at the CERN Large Hadron Collider. No significant excess over the background prediction is observed and upper limits are set on the tt¯H production cross section. The observed exclusion upper limit at 95% confidence level is 6.7 times the predicted Standard Model cross section value. In addition, limits are set on the strength of the Yukawa coupling between the top quark and the Higgs boson, taking into account the dependence of the tt¯H and tH cross sections as well as the H→γγ branching fraction on the Yukawa coupling. Lower and upper limits at 95% confidence level are set at −1.3 and +8.0 times the Yukawa coupling strength in the Standard Model