Generation, description and storage of dendritic morphology data

It is generally assumed that the variability of neuronal morphology has an important effect on both the connectivity and the activity of the nervous system, but this effect has not been thoroughly investigated. Neuroanatomical archives represent a crucial tool to explore structure–function relationships in the brain. We are developing computational tools to describe, generate, store and render large sets of three–dimensional neuronal structures in a format that is compact, quantitative, accurate and readily accessible to the neuroscientist. Single–cell neuroanatomy can be characterized quantitatively at several levels. In computer–aided neuronal tracing files, a dendritic tree is described as a series of cylinders, each represented by diameter, spatial coordinates and the connectivity to other cylinders in the tree. This ‘Cartesian’ description constitutes a completely accurate mapping of dendritic morphology but it bears little intuitive information for the neuroscientist. In contrast, a classical neuroanatomical analysis characterizes neuronal dendrites on the basis of the statistical distributions of morphological parameters, e.g. maximum branching order or bifurcation asymmetry. This description is intuitively more accessible, but it only yields information on the collective anatomy of a group of dendrites, i.e. it is not complete enough to provide a precise ‘blueprint’ of the original data. We are adopting a third, intermediate level of description, which consists of the algorithmic generation of neuronal structures within a certain morphological class based on a set of ‘fundamental’, measured parameters. This description is as intuitive as a classical neuroanatomical analysis (parameters have an intuitive interpretation), and as complete as a Cartesian file (the algorithms generate and display complete neurons). The advantages of the algorithmic description of neuronal structure are immense. If an algorithm can measure the values of a handful of parameters from an experimental database and generate virtual neurons whose anatomy is statistically indistinguishable from that of their real counterparts, a great deal of data compression and amplification can be achieved. Data compression results from the quantitative and complete description of thousands of neurons with a handful of statistical distributions of parameters. Data amplification is possible because, from a set of experimental neurons, many more virtual analogues can be generated. This approach could allow one, in principle, to create and store a neuroanatomical database containing data for an entire human brain in a personal computer. We are using two programs, L–NEURON and ARBORVITAE, to investigate systematically the potential of several different algorithms for the generation of virtual neurons. Using these programs, we have generated anatomically plausible virtual neurons for several morphological classes, including guinea pig cerebellar Purkinje cells and cat spinal cord motor neurons. These virtual neurons are stored in an online electronic archive of dendritic morphology. This process highlights the potential and the limitations of the ‘computational neuroanatomy’ strategy for neuroscience databases.

Planetary-wave-induced transport in the stratosphere

In the stratosphere, chemical tracers are drawn systematically from the equator to the pole. This observed Brewer–Dobson circulation is driven by wave drag, which in the stratosphere arises mainly from the breaking and dissipation of planetary-scale Rossby waves. While the overall sense of the circulation follows from fundamental physical principles, a quantitative theoretical understanding of the connection between wave drag and Lagrangian transport is limited to linear, small-amplitude waves. However, planetary waves in the stratosphere generally grow to a large amplitude and break in a strongly nonlinear fashion. This paper addresses the connection between stratospheric wave drag and Lagrangian transport in the presence of strong nonlinearity, using a mechanistic three-dimensional primitive equations model together with offline particle advection. Attention is deliberately focused on a weak forcing regime, such that sudden warmings do not occur and a quasi-steady state is reached, in order to examine this question in the cleanest possible context. Wave drag is directly linked to the transformed Eulerian mean (TEM) circulation, which is often used as a surrogate for mean Lagrangian motion. The results show that the correspondence between the TEM and mean Lagrangian velocities is quantitatively excellent in regions of linear, nonbreaking waves (i.e., outside the surf zone), where streamlines are not closed. Within the surf zone, where streamlines are closed and meridional particle displacements are large, the agreement between the vertical components of the two velocity fields is still remarkably good, especially wherever particle paths are coherent so that diabatic dispersion is minimized. However, in this region the meridional mean Lagrangian velocity bears little relation to the meridional TEM velocity, and reflects more the kinematics of mixing within and across the edges of the surf zone. The results from the mechanistic model are compared with those from the Canadian Middle Atmosphere Model to test the robustness of the conclusions.

The ModFOLD4 server for the quality assessment of 3D protein models

Once you have generated a 3D model of a protein, how do you know whether it bears any resemblance to the actual structure? To determine the usefulness of 3D models of proteins, they must be assessed in terms of their quality by methods that predict their similarity to the native structure. The ModFOLD4 server is the latest version of our leading independent server for the estimation of both the global and local (per-residue) quality of 3D protein models. The server produces both machine readable and graphical output, providing users with intuitive visual reports on the quality of predicted protein tertiary structures. The ModFOLD4 server is freely available to all at: http://www.reading.ac.uk/bioinf/ModFOLD/.

Species 2000 & ITIS Catalogue of Life, 2013 Annual Checklist

This release of the Catalogue of Life contains contributions from 132 databases with information on 1,352,112 species, 114,069 infraspecific taxa and also includes 928,147 synonyms and 408,689 common names covering the following groups: Viruses • Viruses and Subviral agents from ICTV_MSL UPDATED! Bacteria and Archaea from BIOS Chromista • Chromistan fungi from Species Fungorum Protozoa • Major groups from ITIS Regional, • Ciliates from CilCat, • Polycystines from WoRMS Polycystina UPDATED!, • Protozoan fungi from Species Fungorum and Trichomycetes database • Slime moulds from Nomen.eumycetozoa.com Fungi • Various taxa in whole or in part from CABI Bioservices databases (Species Fungorum, Phyllachorales, Rhytismatales, Saccharomycetes and Zygomycetes databases) and from three other databases covering Xylariaceae, Glomeromycota, Trichomycetes, Dothideomycetes • Lichens from LIAS UPDATED! Plantae (Plants) • Mosses from MOST • Liverworts and hornworts from ELPT • Conifers from Conifer Database • Cycads and 6 flowering plant families from IOPI-GPC, and 99 families from WCSP • Plus individual flowering plants families from AnnonBase, Brassicaceae, ChenoBase, Droseraceae Database, EbenaBase, GCC UPDATED!, ILDIS UPDATED!, LecyPages, LHD, MELnet UPDATED!, RJB Geranium, Solanaceae Source, Umbellifers. Animalia (Animals) • Marine groups from URMO, ITIS Global, Hexacorals, ETI WBD (Euphausiacea), WoRMS: WoRMS Asteroidea UPDATED!, WoRMS Bochusacea UPDATED!, WoRMS Brachiopoda UPDATED!, WoRMS Brachypoda UPDATED!, WoRMS Brachyura UPDATED!, WoRMS Bryozoa UPDATED!, WoRMS Cestoda NEW!, WoRMS Chaetognatha UPDATED!, WoRMS Cumacea UPDATED!, WoRMS Echinoidea UPDATED!, WoRMS Gastrotricha NEW!, WoRMS Gnathostomulida NEW!, WoRMS Holothuroidea UPDATED!, WoRMS Hydrozoa UPDATED!, WoRMS Isopoda UPDATED!, WoRMS Leptostraca UPDATED!, WoRMS Monogenea NEW!, WoRMS Mystacocarida UPDATED!, WoRMS Myxozoa NEW!, WoRMS Nemertea UPDATED!, WoRMS Oligochaeta UPDATED!, WoRMS Ophiuroidea UPDATED!, WoRMS Phoronida UPDATED!, WoRMS Placozoa NEW!, WoRMS Polychaeta UPDATED!, WoRMS Polycystina UPDATED!, WoRMS Porifera UPDATED!, WoRMS Priapulida NEW!, WoRMS Proseriata and Kalyptorhynchia UPDATED!, WoRMS Remipedia UPDATED!, WoRMS Scaphopoda UPDATED!, WoRMS Tanaidacea UPDATED!, WoRMS Tantulocarida UPDATED!, WoRMS Thermosbaenacea UPDATED!, WoRMS Trematoda NEW!, WoRMS Xenoturbellida UPDATED! • Rotifers, mayflies, freshwater hairworms, planarians from FADA databases: FADA Rotifera UPDATED!, FADA Ephemeroptera NEW!, FADA Nematomorpha NEW! & FADA Turbellaria NEW! • Entoprocts, water bears from ITIS Global • Spiders, scorpions, ticks & mites from SpidCat via ITIS UPDATED!, SalticidDB , ITIS Global, TicksBase, SpmWeb BdelloideaBase UPDATED! & Mites GSDs: OlogamasidBase, PhytoseiidBase, RhodacaridBase & TenuipalpidBase • Diplopods, centipedes, pauropods and symphylans from SysMyr UPDATED! & ChiloBase • Dragonflies and damselflies from Odonata database • Stoneflies from PlecopteraSF UPDATED! • Cockroaches from BlattodeaSF UPDATED! • Praying mantids from MantodeaSF UPDATED! • Stick and leaf insects from PhasmidaSF UPDATED! • Grasshoppers, locusts, katydids and crickets from OrthopteraSF UPDATED! • Webspinners from EmbiopteraSF UPDATED! • Bark & parasitic lices from PsocodeaSF NEW! • Some groups of true bugs from ScaleNet, FLOW, COOL, Psyllist, AphidSF UPDATED! , MBB, 3i Cicadellinae, 3i Typhlocybinae, MOWD & CoreoideaSF NEW!• Twisted-wing parasites from Strepsiptera Database UPDATED! • Lacewings, antlions, owlflies, fishflies, dobsonflies & snakeflies from LDL Neuropterida • Some beetle groups from the Scarabs UPDATED!, TITAN, WTaxa & ITIS Global • Fleas from Parhost • Flies, mosquitoes, bots, midges and gnats from Systema Dipterorum, CCW & CIPA • Butterflies and moths from LepIndex UPDATED!, GloBIS (GART) UPDATED!, Tineidae NHM, World Gracillariidae • Bees & wasps from ITIS Bees, Taxapad Ichneumonoidea, UCD, ZOBODAT Vespoidea & HymIS Rhopalosomatidae NEW!• Molluscs from WoRMS Mollusca NEW!, FADA Bivalvia NEW!, MolluscaFW NEW! & AFD (Pulmonata) • Fishes from FishBase UPDATED! • Reptiles from TIGR Reptiles • Amphibians, birds and mammals from ITIS Global PLUS additional species of many groups from ITIS Regional, NZIB and CoL China NEW!

Risk allocation in a public–private partnership: a case study of construction and operation of kindergartens in Kazakhstan

In Kazakhstan, a transitional nation in Central Asia, the development of public–private partnerships (PPPs) is at its early stage and increasingly of strategic importance. This case study investigates risk allocation in an ongoing project: the construction and operation of 11 kindergartens in the city of Karaganda in the concession form for 14 years. Drawing on a conceptual framework of effective risk allocation, the study identifies principal PPP risks, provides a critical assessment of how and in what way each partner bears a certain risk, highlights the reasons underpinning risk allocation decisions and delineates the lessons learned. The findings show that the government has effectively transferred most risks to the private sector partner, whilst both partners share the demand risk of childcare services and the project default risk. The strong elements of risk allocation include clear assignment of parties’ responsibilities, streamlined financing schemes and incentives to complete the main project phases on time. However, risk allocation has missed an opportunity to create incentives for service quality improvements and take advantage of economies of scale. The most controversial element of risk allocation, as the study finds, is a revenue stream that an operator is supposed to receive from the provision of services unrelated to childcare, as neither partner is able to mitigate this revenue risk. The article concludes that in the kindergartens’ PPP, the government has achieved almost complete transfer of risks to the private sector partner. However, the costs of transfer are extensive government financial outlays that seriously compromise the PPP value for money.

Responsibility effects in decision making under risk

We systematically explore decision situations in which a decision maker bears responsibility for somebody else's outcomes as well as for her own in situations of payoff equality. In the gain domain we confirm the intuition that being responsible for somebody else's payoffs increases risk aversion. This is however not attributable to a 'cautious shift' as often thought. Indeed, looking at risk attitudes in the loss domain, we find an increase in risk seeking under responsibility. This raises issues about the nature of various decision biases under risk, and to what extent changed behavior under responsibility may depend on a social norm of caution in situations of responsibility versus naive corrections from perceived biases. To further explore this issue, we designed a second experiment to explore risk-taking behavior for gain prospects offering very small or very large probabilities of winning. For large probabilities, we find increased risk aversion, thus confirming our earlier finding. For small probabilities however, we find an increase of risk seeking under conditions of responsibility. The latter finding thus discredits hypotheses of a social rule dictating caution under responsibility, and can be explained through flexible self-correction models predicting an accentuation of the fourfold pattern of risk attitudes predicted by prospect theory. An additional accountability mechanism does not change risk behavior, except for mixed prospects, in which it reduces loss aversion. This indicates that loss aversion is of a fundamentally different nature than probability weighting or utility curvature. Implications for debiasing are discussed.

An Ensemble Kalman Smoother for Nonlinear Dynamics

It is for mally proved that the general smoother for nonlinear dynamics can be for mulated as a sequential method, that is, obser vations can be assimilated sequentially during a for ward integration. The general filter can be derived from the smoother and it is shown that the general smoother and filter solutions at the final time become identical, as is expected from linear theor y. Then, a new smoother algorithm based on ensemble statistics is presented and examined in an example with the Lorenz equations. The new smoother can be computed as a sequential algorithm using only for ward-in-time model integrations. It bears a strong resemblance with the ensemble Kalman filter . The difference is that ever y time a new dataset is available during the for ward integration, an analysis is computed for all previous times up to this time. Thus, the first guess for the smoother is the ensemble Kalman filter solution, and the smoother estimate provides an improvement of this, as one would expect a smoother to do. The method is demonstrated in this paper in an intercomparison with the ensemble Kalman filter and the ensemble smoother introduced by van Leeuwen and Evensen, and it is shown to be superior in an application with the Lorenz equations. Finally , a discussion is given regarding the properties of the analysis schemes when strongly non-Gaussian distributions are used. It is shown that in these cases more sophisticated analysis schemes based on Bayesian statistics must be used.