Toward a better understanding of fish‐based contribution to ocean carbon flux

Fishes are the dominant vertebrates in the ocean, yet we know little of their contribution to carbon export flux at regional to global scales. We synthesize the existing information on fish‐based carbon flux in coastal and pelagic waters, identify gaps and challenges in measuring this flux and approaches to address them, and recommend research priorities. Based on our synthesis of passive (fecal pellet sinking) and active (migratory) flux of fishes, we estimated that fishes contribute an average (± standard deviation) of about 16.1% (± 13%) to total carbon flux out of the euphotic zone. Using the mean value of model‐generated global carbon flux estimates, this equates to an annual flux of 1.5 ± 1.2 Pg C yr−1. High variability in estimations of the fish‐based contribution to total carbon flux among previous field studies and reported here highlight significant methodological variations and observational gaps in our present knowledge. Community‐adopted methodological standards, improved and more frequent measurements of biomass and passive and active fluxes of fishes, and stronger linkages between observations and models will decrease uncertainty, increase our confidence in the estimation of fish‐based carbon flux, and enable identification of controlling factors to account for spatial and temporal variability. Better constraints on this key component of the biological pump will provide a baseline for understanding how ongoing climate change and harvest will affect the role fishes play in carbon flux

H-ATLAS/GAMA: the nature and characteristics of optically red galaxies detected at submillimetre wavelengths

We combine Herschel/SPIRE sub-millimeter (submm) observations with existing multi-wavelength data to investigate the characteristics of low redshift, optically red galaxies detected in submm bands. We select a sample of galaxies in the redshift range 0.01≤z≤0.2, having >5σ detections in the SPIRE 250 micron submm waveband. Sources are then divided into two sub-samples of red and blue galaxies, based on their UV-optical colours. Galaxies in the red sample account for ≈4.2 per cent of the total number of sources with stellar masses M∗≳1010 Solar-mass. Following visual classification of the red galaxies, we find that ≳30 per cent of them are early-type galaxies and ≳40 per cent are spirals. The colour of the red-spiral galaxies could be the result of their highly inclined orientation and/or a strong contribution of the old stellar population. It is found that irrespective of their morphological types, red and blue sources occupy environments with more or less similar densities (i.e., the Σ5 parameter). From the analysis of the spectral energy distributions (SEDs) of galaxies in our samples based on MAGPHYS, we find that galaxies in the red sample (of any morphological type) have dust masses similar to those in the blue sample (i.e. normal spiral/star-forming systems). However, in comparison to the red-spirals and in particular blue systems, red-ellipticals have lower mean dust-to-stellar mass ratios. Besides galaxies in the red-elliptical sample have much lower mean star-formation/specific-star-formation rates in contrast to their counterparts in the blue sample. Our results support a scenario where dust in early-type systems is likely to be of an external origin

The interplay of microscopic and mesoscopic structure in complex networks

Not all nodes in a network are created equal. Differences and similarities exist at both individual node and group levels. Disentangling single node from group properties is crucial for network modeling and structural inference. Based on unbiased generative probabilistic exponential random graph models and employing distributive message passing techniques, we present an efficient algorithm that allows one to separate the contributions of individual nodes and groups of nodes to the network structure. This leads to improved detection accuracy of latent class structure in real world data sets compared to models that focus on group structure alone. Furthermore, the inclusion of hitherto neglected group specific effects in models used to assess the statistical significance of small subgraph (motif) distributions in networks may be sufficient to explain most of the observed statistics. We show the predictive power of such generative models in forecasting putative gene-disease associations in the Online Mendelian Inheritance in Man (OMIM) database. The approach is suitable for both directed and undirected uni-partite as well as for bipartite networks

A combined inverse finite element – elastoplastic modelling method to simulate the size-effect in nanoindentation and characterise materials from the nano to micro-scale

Material properties such as hardness can be dependent on the size of the indentation load when that load is small, a phenomenon known as the indentation size effect (ISE). In this work an inverse finite element method (IFEM) is used to investigate the ISE, with reference to experiments with a Berkovich indenter and an aluminium test material. It was found that the yield stress is highly dependent on indentation depth and in order to simulate this, an elastoplastic constitutive relation in which yielding varies with indentation depth/load was developed. It is shown that whereas Young's modulus and Poisson's ratio are not influenced by the length scale over the range tested, the amplitude portion of yield stress, which is independent of hardening and corresponds to the initial stress for a bulk material, changes radically at small indentation depths. Using the proposed material model and material parameters extracted using IFEM, the indentation depth-time and load-depth plots can be predicted at different loads with excellent agreement to experiment; the relative residual achieved between FE modelling displacement and experiment being less than 0.32%. An improved method of determining hardness from nanoindentation test data is also presented, which shows goof agreement with that determined using the IFEM

Circulating cytokine levels and antibody responses to human Schistosoma haematobium: IL-5 and IL-10 levels depend upon age and infection status

Experimental schistosome infections induce strong parasite-specific Th2 responses. This study aims to relate human systemic cytokine and antibody levels to schistosome infection levels and history. Levels of anti-Schistosoma haematobium antibodies (directed against crude cercariae, egg and adult worm antigens) and plasma cytokines (IFN-γ, IL-2, IL-4, IL-5, IL-10, IL-13, IL-17, IL-21, and IL-23) were measured by ELISA in 227 Zimbabweans (6–60 years old) in a schistosome-endemic area and related to age and infection status. Egg-positive people had significantly higher levels of specific antibodies, IL-2, IFN-γ and IL-23. In contrast, egg-negative individuals had significantly higher circulating IL-10, IL-4, IL-13 and IL-21 that were detected with high frequency in all participants. Subjects with detectable plasma IL-17 produced few or no eggs. When analyzed by age, IL-4 and IL-10 increased significantly, as did schistosome-specific antibodies. However, when age was combined with infection status, IL-5 declined over time in egg-positive people, while increased with age in the egg-negative group. Older, lifelong residents had significantly higher IL-4 and IL-5 levels than younger egg-negative people. Thus, a mixed Th1/Th2 systemic environment occurs in people with patent schistosome infection, while a stronger Th2-dominated suite of cytokines is evident in egg-negative individuals

Metabonomics and Intensive Care

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency medicine 2016. Other selected articles can be found online at http://www.biomedcentral.com/collections/annualupdate2016. Further information about the Annual Update in Intensive Care and Emergency Medicine is available from http://www.springer.com/series/8901

Theoretical studies of 31P NMR spectral properties of phosphanes and related compounds in solution

Selected theoretical methods, basis sets and solvation models have been tested in their ability to predict 31P NMR chemical shifts of large phosphorous-containing molecular systems in solution. The most efficient strategy was found to involve NMR shift calculations at the GIAO-MPW1K/6-311++G(2d,2p)//MPW1K/6-31G(d) level in combination with a dual solvation model including the explicit consideration of single solvent molecules and a continuum (PCM) solvation model. For larger systems it has also been established that reliable 31P shift predictions require Boltzmann averaging over all accessible conformations in solution

Hierarchy measure for complex networks

Nature, technology and society are full of complexity arising from the intricate web of the interactions among the units of the related systems (e.g., proteins, computers, people). Consequently, one of the most successful recent approaches to capturing the fundamental features of the structure and dynamics of complex systems has been the investigation of the networks associated with the above units (nodes) together with their relations (edges). Most complex systems have an inherently hierarchical organization and, correspondingly, the networks behind them also exhibit hierarchical features. Indeed, several papers have been devoted to describing this essential aspect of networks, however, without resulting in a widely accepted, converging concept concerning the quantitative characterization of the level of their hierarchy. Here we develop an approach and propose a quantity (measure) which is simple enough to be widely applicable, reveals a number of universal features of the organization of real-world networks and, as we demonstrate, is capable of capturing the essential features of the structure and the degree of hierarchy in a complex network. The measure we introduce is based on a generalization of the m-reach centrality, which we first extend to directed/partially directed graphs. Then, we define the global reaching centrality (GRC), which is the difference between the maximum and the average value of the generalized reach centralities over the network. We investigate the behavior of the GRC considering both a synthetic model with an adjustable level of hierarchy and real networks. Results for real networks show that our hierarchy measure is related to the controllability of the given system. We also propose a visualization procedure for large complex networks that can be used to obtain an overall qualitative picture about the nature of their hierarchical structure.Comment: 29 pages, 9 figures, 4 table