Climate fluctuations and the spring invasion of the North Sea by Calanus finmarchicus

The population of Calanus finmarchicus in the North Sea is replenished each spring by invasion from an overwintering stock located beyond the shelf edge. A combincation of field observations, statistical analysis of Continuous Plankton Recorder (CPR) data, and particle tracking model simulations, was used to investigate the processes involved in the cross-shelf invasion. The results showed that the main source of overwintering animals entering the North Sea in the spring is at depths of greater than 600m in the Faroe Shetland Channel, where concentrations of up to 620m -3 are found in association with the overflow of Norwegian Sea Deep Water (NSDW) across the Iceland Scotland Ridge. The input of this water mass to the Faroe Shetland Channel, and hence the supply of overwintering C. finmarchicus, has declined since the late 1960s due to changes in convective processes in the Greenland Sea. Beginning in February, animals start to emerge from the overwintering state and migrate to the surface waters, where their transport into the North Sea is mainly determined by the incidence of north-westerly winds that have declined since the 1960s. Together, these two factors explain a high proportion of the 30-year trends in spring abundance in the North Sea as measured by the CPR survey. Both the regional winds and the NSDW overflow are connected to the North Atlantic Oscillation Index (NAO), which is an atmospheric climate index, but with different time scales of response. Thus, interannual fluctuations in the NAO can cause immediate changes in the incidence of north-westerly winds without leading to corresponding changes in C. finmarchicus abundance in the North Sea, because the NSDW overflow responds over longer (decadal) time scales

Comparative analysis of Calanus finmarchicus demography at locations around the Northeast Atlantic

Standardized time-series sampling was carried out throughout 1997 at seven locations around the Northeast Atlantic to investigate regional variations in the seasonal demography of Calanus finmarchicus. Sites ranged from an inshore location in the North Sea, where C. finmarchicus formed only a small component of the zooplankton (2000 mgC m-2 during spring and summer). The internal consistency of the demographic time-series from each site was investigated by three partial models of life-cycle processes. In general, the demography of late copepodites could be accounted for by a relatively simple forecast model of stage development and diapause. However, there was a large discrepancy between nowcast estimates of egg production based on female abundance, temperature, and chlorophyll, and hindcast simulations of the egg production required to account for the observed abundance of early copepodite stages. The results point to a gap in our understanding of seasonal variations in rates of egg production and/or survival of nauplii. Overall, the population sampled at Weathership M appeared to be reasonably self-contained, but all other sites were reliant on invasion of overwintered stock in spring. At least two generations were observed at all but one site, but the extent to which these were generated by discrete bursts of egg production varied between sites and seemed to be partly dependent on the proximity to an overwintering location

Mesophyll porosity is modulated by the presence of functional stomata

The formation of stomata and leaf mesophyll airspace must be coordinated to establish an efficient and robust network that facilitates gas exchange for photosynthesis, however the mechanism by which this coordinated development occurs remains unclear. Here, we combine microCT and gas exchange analyses with measures of stomatal size and patterning in a range of wild, domesticated and transgenic lines of wheat and Arabidopsis to show that mesophyll airspace formation is linked to stomatal function in both monocots and eudicots. Our results support the hypothesis that gas flux via stomatal pores influences the degree and spatial patterning of mesophyll airspace formation, and indicate that this relationship has been selected for during the evolution of modern wheat. We propose that the coordination of stomata and mesophyll airspace pattern underpins water use efficiency in crops, providing a target for future improvement

Low Complexity Regularization of Linear Inverse Problems

Inverse problems and regularization theory is a central theme in contemporary signal processing, where the goal is to reconstruct an unknown signal from partial indirect, and possibly noisy, measurements of it. A now standard method for recovering the unknown signal is to solve a convex optimization problem that enforces some prior knowledge about its structure. This has proved efficient in many problems routinely encountered in imaging sciences, statistics and machine learning. This chapter delivers a review of recent advances in the field where the regularization prior promotes solutions conforming to some notion of simplicity/low-complexity. These priors encompass as popular examples sparsity and group sparsity (to capture the compressibility of natural signals and images), total variation and analysis sparsity (to promote piecewise regularity), and low-rank (as natural extension of sparsity to matrix-valued data). Our aim is to provide a unified treatment of all these regularizations under a single umbrella, namely the theory of partial smoothness. This framework is very general and accommodates all low-complexity regularizers just mentioned, as well as many others. Partial smoothness turns out to be the canonical way to encode low-dimensional models that can be linear spaces or more general smooth manifolds. This review is intended to serve as a one stop shop toward the understanding of the theoretical properties of the so-regularized solutions. It covers a large spectrum including: (i) recovery guarantees and stability to noise, both in terms of $\ell^2$-stability and model (manifold) identification; (ii) sensitivity analysis to perturbations of the parameters involved (in particular the observations), with applications to unbiased risk estimation ; (iii) convergence properties of the forward-backward proximal splitting scheme, that is particularly well suited to solve the corresponding large-scale regularized optimization problem

A standardisation framework for bio‐logging data to advance ecological research and conservation

Bio‐logging data obtained by tagging animals are key to addressing global conservation challenges. However, the many thousands of existing bio‐logging datasets are not easily discoverable, universally comparable, nor readily accessible through existing repositories and across platforms, slowing down ecological research and effective management. A set of universal standards is needed to ensure discoverability, interoperability and effective translation of bio‐logging data into research and management recommendations. We propose a standardisation framework adhering to existing data principles (FAIR: Findable, Accessible, Interoperable and Reusable; and TRUST: Transparency, Responsibility, User focus, Sustainability and Technology) and involving the use of simple templates to create a data flow from manufacturers and researchers to compliant repositories, where automated procedures should be in place to prepare data availability into four standardised levels: (a) decoded raw data, (b) curated data, (c) interpolated data and (d) gridded data. Our framework allows for integration of simple tabular arrays (e.g. csv files) and creation of sharable and interoperable network Common Data Form (netCDF) files containing all the needed information for accuracy‐of‐use, rightful attribution (ensuring data providers keep ownership through the entire process) and data preservation security. We show the standardisation benefits for all stakeholders involved, and illustrate the application of our framework by focusing on marine animals and by providing examples of the workflow across all data levels, including filled templates and code to process data between levels, as well as templates to prepare netCDF files ready for sharing. Adoption of our framework will facilitate collection of Essential Ocean Variables (EOVs) in support of the Global Ocean Observing System (GOOS) and inter‐governmental assessments (e.g. the World Ocean Assessment), and will provide a starting point for broader efforts to establish interoperable bio‐logging data formats across all fields in animal ecology

Genetic modifiers of lung disease in cystic fibrosis

BACKGROUND: Polymorphisms in genes other than the cystic fibrosis transmembrane conductance regulator (CFTR) gene may modify the severity of pulmonary disease in patients with cystic fibrosis. METHODS: We performed two studies with different patient samples. We first tested 808 patients who were homozygous for the ΔF508 mutation and were classified as having either severe or mild lung disease, as defined by the lowest or highest quartile of forced expiratory volume in one second (FEV 1), respectively, for age. We genotyped 16 polymorphisms in 10 genes reported by others as modifiers of disease severity in cystic fibrosis and tested for an association in patients with severe disease (263 patients) or mild disease (545). In the replication (second) study, we tested 498 patients, with various CFTR genotypes and a range of FEV 1 values, for an association of the TGFβ1 codon 10 CC genotype with low FEV 1. RESULTS: In the initial study, significant allelic and genotypic associations with phenotype were seen only for TGFβ1 (the gene encoding transforming growth factor β1), particularly the -509 and codon 10 polymorphisms (with P values obtained with the use of Fisher's exact test and logistic regression ranging from 0.006 to 0.0002). The odds ratio was about 2.2 for the highest-risk TGFβ1 genotype (codon 10 CC) in association with the phenotype for severe lung disease. The replication study confirmed the association of the TGFβ1 codon 10 CC genotype with more severe lung disease in comparisons with the use of dichotomized FEV 1 for severity status (P=0.0002) and FEV 1 values directly (P=0.02). CONCLUSIONS: Genetic variation in the 5′ end of TGFβ1 or a nearby upstream region modifies disease severity in cystic fibrosis

Scaling ozone responses of forest trees to the ecosystem level in a changing climate

Many uncertainties remain regarding how climate change will alter the structure and function of forest ecosystems. At the Aspen FACE experiment in northern Wisconsin, we are attempting to understand how an aspen/birch/maple forest ecosystem responds to long-term exposure to elevated carbon dioxide (CO 2 ) and ozone (O 3 ), alone and in combination, from establishment onward. We examine how O 3 affects the flow of carbon through the ecosystem from the leaf level through to the roots and into the soil micro-organisms in present and future atmospheric CO 2 conditions. We provide evidence of adverse effects of O 3 , with or without co-occurring elevated CO 2 , that cascade through the entire ecosystem impacting complex trophic interactions and food webs on all three species in the study: trembling aspen ( Populus tremuloides Michx . ), paper birch ( Betula papyrifera Marsh), and sugar maple ( Acer saccharum Marsh). Interestingly, the negative effect of O 3 on the growth of sugar maple did not become evident until 3 years into the study. The negative effect of O 3 effect was most noticeable on paper birch trees growing under elevated CO 2 . Our results demonstrate the importance of long-term studies to detect subtle effects of atmospheric change and of the need for studies of interacting stresses whose responses could not be predicted by studies of single factors. In biologically complex forest ecosystems, effects at one scale can be very different from those at another scale. For scaling purposes, then, linking process with canopy level models is essential if O 3 impacts are to be accurately predicted. Finally, we describe how outputs from our long-term multispecies Aspen FACE experiment are being used to develop simple, coupled models to estimate productivity gain/loss from changing O 3 .Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72464/1/j.1365-3040.2005.01362.x.pd

Clustering Algorithms: Their Application to Gene Expression Data

Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and iden-tify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure