The Interiors of Giant Planets: Models and Outstanding Questions

We know that giant planets played a crucial role in the making of our Solar System. The discovery of giant planets orbiting other stars is a formidable opportunity to learn more about these objects, what is their composition, how various processes influence their structure and evolution, and most importantly how they form. Jupiter, Saturn, Uranus and Neptune can be studied in detail, mostly from close spacecraft flybys. We can infer that they are all enriched in heavy elements compared to the Sun, with the relative global enrichments increasing with distance to the Sun. We can also infer that they possess dense cores of varied masses. The intercomparison of presently caracterised extrasolar giant planets show that they are also mainly made of hydrogen and helium, but that they either have significantly different amounts of heavy elements, or have had different orbital evolutions, or both. Hence, many questions remain and are to be answered for significant progresses on the origins of planets.Comment: 43 pages, 11 figures, 3 tables. To appear in Annual Review of Earth and Planetary Sciences, vol 33, (2005

Wind Energy Forecasting at Different Time Horizons with Individual and Global Models

This paper has been presented at: 14th IFIP International Conference on Artificial Intelligence Applications and InnovationsIn this work two different machine learning approaches have been studied to predict wind power for different time horizons: individual and global models. The individual approach constructs a model for each horizon while the global approach obtains a single model that can be used for all horizons. Both approaches have advantages and disadvantages. Each individual model is trained with data pertaining to a single horizon, thus it can be specific for that horizon, but can use fewer data for training than the global model, which is constructed with data belonging to all horizons. Support Vector Machines have been used for constructing the individual and global models. This study has been tested on energy production data obtained from the Sotavento wind farm and meteorological data from the European Centre for Medium-Range Weather Forecasts, for a 5 × 5 grid around Sotavento. Also, given the large amount of variables involved, a feature selection algorithm (Sequential Forward Selection) has been used in order to improve the performance of the models. Experimental results show that the global model is more accurate than the individual ones, specially when feature selection is used.The authors acknowledge financial support granted by the Spanish Ministry of Science under contract ENE2014-56126-C2-2-R

A Practical, Accurate, Information Criterion for Nth Order Markov Processes

The recent increase in the breath of computational methodologies has been matched with a corresponding increase in the difficulty of comparing the relative explanatory power of models from different methodological lineages. In order to help address this problem a Markovian information criterion (MIC) is developed that is analogous to the Akaike information criterion (AIC) in its theoretical derivation and yet can be applied to any model able to generate simulated or predicted data, regardless of its methodology. Both the AIC and proposed MIC rely on the Kullback–Leibler (KL) distance between model predictions and real data as a measure of prediction accuracy. Instead of using the maximum likelihood approach like the AIC, the proposed MIC relies instead on the literal interpretation of the KL distance as the inefficiency of compressing real data using modelled probabilities, and therefore uses the output of a universal compression algorithm to obtain an estimate of the KL distance. Several Monte Carlo tests are carried out in order to (a) confirm the performance of the algorithm and (b) evaluate the ability of the MIC to identify the true data-generating process from a set of alternative models

Long-Range Intra-Protein Communication Can Be Transmitted by Correlated Side-Chain Fluctuations Alone

Allosteric regulation is a key component of cellular communication, but the way in which information is passed from one site to another within a folded protein is not often clear. While backbone motions have long been considered essential for long-range information conveyance, side-chain motions have rarely been considered. In this work, we demonstrate their potential utility using Monte Carlo sampling of side-chain torsional angles on a fixed backbone to quantify correlations amongst side-chain inter-rotameric motions. Results indicate that long-range correlations of side-chain fluctuations can arise independently from several different types of interactions: steric repulsions, implicit solvent interactions, or hydrogen bonding and salt-bridge interactions. These robust correlations persist across the entire protein (up to 60 Å in the case of calmodulin) and can propagate long-range changes in side-chain variability in response to single residue perturbations

HtrA2/Omi Terminates Cytomegalovirus Infection and Is Controlled by the Viral Mitochondrial Inhibitor of Apoptosis (vMIA)

Viruses encode suppressors of cell death to block intrinsic and extrinsic host-initiated death pathways that reduce viral yield as well as control the termination of infection. Cytomegalovirus (CMV) infection terminates by a caspase-independent cell fragmentation process after an extended period of continuous virus production. The viral mitochondria-localized inhibitor of apoptosis (vMIA; a product of the UL37x1 gene) controls this fragmentation process. UL37x1 mutant virus-infected cells fragment three to four days earlier than cells infected with wt virus. Here, we demonstrate that infected cell death is dependent on serine proteases. We identify mitochondrial serine protease HtrA2/Omi as the initiator of this caspase-independent death pathway. Infected fibroblasts develop susceptibility to death as levels of mitochondria-resident HtrA2/Omi protease increase. Cell death is suppressed by the serine protease inhibitor TLCK as well as by the HtrA2-specific inhibitor UCF-101. Experimental overexpression of HtrA2/Omi, but not a catalytic site mutant of the enzyme, sensitizes infected cells to death that can be blocked by vMIA or protease inhibitors. Uninfected cells are completely resistant to HtrA2/Omi induced death. Thus, in addition to suppression of apoptosis and autophagy, vMIA naturally controls a novel serine protease-dependent CMV-infected cell-specific programmed cell death (cmvPCD) pathway that terminates the CMV replication cycle

Food for pollinators: quantifying the nectar and pollen resources of urban flower meadows

Planted meadows are increasingly used to improve the biodiversity and aesthetic amenity value of urban areas. Although many ‘pollinator-friendly’ seed mixes are available, the floral resources these provide to flower-visiting insects, and how these change through time, are largely unknown. Such data are necessary to compare the resources provided by alternative meadow seed mixes to each other and to other flowering habitats. We used quantitative surveys of over 2 million flowers to estimate the nectar and pollen resources offered by two exemplar commercial seed mixes (one annual, one perennial) and associated weeds grown as 300m2 meadows across four UK cities, sampled at six time points between May and September 2013. Nectar sugar and pollen rewards per flower varied widely across 65 species surveyed, with native British weed species (including dandelion, Taraxacum agg.) contributing the top five nectar producers and two of the top ten pollen producers. Seed mix species yielding the highest rewards per flower included Leontodon hispidus, Centaurea cyanus and C. nigra for nectar, and Papaver rhoeas, Eschscholzia californica and Malva moschata for pollen. Perennial meadows produced up to 20x more nectar and up to 6x more pollen than annual meadows, which in turn produced far more than amenity grassland controls. Perennial meadows produced resources earlier in the year than annual meadows, but both seed mixes delivered very low resource levels early in the year and these were provided almost entirely by native weeds. Pollen volume per flower is well predicted statistically by floral morphology, and nectar sugar mass and pollen volume per unit area are correlated with flower counts, raising the possibility that resource levels can be estimated for species or habitats where they cannot be measured directly. Our approach does not incorporate resource quality information (for example, pollen protein or essential amino acid content), but can easily do so when suitable data exist. Our approach should inform the design of new seed mixes to ensure continuity in floral resource availability throughout the year, and to identify suitable species to fill resource gaps in established mixes

The role of DNA microarrays in Toxoplasma gondii research, the causative agent of ocular toxoplasmosis

Ocular toxoplasmosis, which is caused by the protozoan parasite Toxoplasma gondii, is the leading cause of retinochoroiditis. Toxoplasma is an obligate intracellular pathogen that replicates within a parasitophorous vacuole. Infections are initiated by digestion of parasites deposited in cat feces or in undercooked meat. Parasites then disseminate to target tissues that include the retina where they then develop into long-lived asymptomatic tissue cysts. Occasionally, cysts reactivate and growth of newly emerged parasites must be controlled by the host’s immune system or disease will occur. The mechanisms by which Toxoplasma grows within its host cell, encysts, and interacts with the host’s immune system are important questions. Here, we will discuss how the use of DNA microarrays in transcriptional profiling, genotyping, and epigenetic experiments has impacted our understanding of these processes. Finally, we will discuss how these advances relate to ocular toxoplasmosis and how future research on ocular toxoplasmosis can benefit from DNA microarrays

Computing Highly Correlated Positions Using Mutual Information and Graph Theory for G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) are a superfamily of seven transmembrane-spanning proteins involved in a wide array of physiological functions and are the most common targets of pharmaceuticals. This study aims to identify a cohort or clique of positions that share high mutual information. Using a multiple sequence alignment of the transmembrane (TM) domains, we calculated the mutual information between all inter-TM pairs of aligned positions and ranked the pairs by mutual information. A mutual information graph was constructed with vertices that corresponded to TM positions and edges between vertices were drawn if the mutual information exceeded a threshold of statistical significance. Positions with high degree (i.e. had significant mutual information with a large number of other positions) were found to line a well defined inter-TM ligand binding cavity for class A as well as class C GPCRs. Although the natural ligands of class C receptors bind to their extracellular N-terminal domains, the possibility of modulating their activity through ligands that bind to their helical bundle has been reported. Such positions were not found for class B GPCRs, in agreement with the observation that there are not known ligands that bind within their TM helical bundle. All identified key positions formed a clique within the MI graph of interest. For a subset of class A receptors we also considered the alignment of a portion of the second extracellular loop, and found that the two positions adjacent to the conserved Cys that bridges the loop with the TM3 qualified as key positions. Our algorithm may be useful for localizing topologically conserved regions in other protein families