Age models, sedimentology and geochemistry from sediment cores in Drake Passage throughflow

The Drake Passage (DP) is the major geographic constriction for the Antarctic Circumpolar Current (ACC) and exerts a strong control on the exchange of physical, chemical, and biological properties between the Atlantic, Pacific, and Indian Ocean basins. Resolving changes in the flow of circumpolar water masses through this gateway is, therefore, crucial for advancing our understanding of the Southern Ocean's role in global ocean and climate variability. Here, we reconstruct changes in DP throughflow dynamics over the past 65,000 y based on grain size and geochemical properties of sediment records from the southernmost continental margin of South America. Combined with published sediment records from the Scotia Sea, we argue for a considerable total reduction of DP transport and reveal an up to ~40% decrease in flow speed along the northernmost ACC pathway entering the DP during glacial times. Superimposed on this long-term decrease are high-amplitude, millennial-scale variations, which parallel Southern Ocean and Antarctic temperature patterns. The glacial intervals of strong weakening of the ACC entering the DP imply an enhanced export of northern ACC surface and intermediate waters into the South Pacific Gyre and reduced Pacific-Atlantic exchange through the DP ("cold water route"). We conclude that changes in DP throughflow play a critical role for the global meridional overturning circulation and interbasin exchange in the Southern Ocean, most likely regulated by variations in the westerly wind field and changes in Antarctic sea ice extent.

Gridded climate data from 5 Global Climate Models (GCM) of the Last Glacial Maximum (LGM) downscaled to 30 arc seconds for Europe, with links to NetCDF files

Studies on the impact of historical, current and future global change require very high-resolution climate data (less or equal 1km) as a basis for modelled responses, meaning that data from digital climate models generally require substantial rescaling. Another shortcoming of available datasets on past climate is that the effects of sea level rise and fall are not considered. Without such information, the study of glacial refugia or early Holocene plant and animal migration are incomplete if not impossible. Sea level at the last glacial maximum (LGM) was approximately 125m lower, creating substantial additional terrestrial area for which no current baseline data exist. Here, we introduce the development of a novel, gridded climate dataset for LGM that is both very high resolution (1km) and extends to the LGM sea and land mask. We developed two methods to extend current terrestrial precipitation and temperature data to areas between the current and LGM coastlines. The absolute interpolation error is less than 1°C and 0.5 °C for 98.9% and 87.8% of all pixels for the first two 1 arc degree distance zones. We use the change factor method with these newly assembled baseline data to downscale five global circulation models of LGM climate to a resolution of 1km for Europe. As additional variables we calculate 19 'bioclimatic' variables, which are often used in climate change impact studies on biological diversity. The new LGM climate maps are well suited for analysing refugia and migration during Holocene warming following the LGM.

Transcriptional response to cardiac injury in the zebrafish: systematic identification of genes with highly concordant activity across in vivo models

BACKGROUND Zebrafish is a clinically-relevant model of heart regeneration. Unlike mammals, it has a remarkable heart repair capacity after injury, and promises novel translational applications. Amputation and cryoinjury models are key research tools for understanding injury response and regeneration in vivo. An understanding of the transcriptional responses following injury is needed to identify key players of heart tissue repair, as well as potential targets for boosting this property in humans. RESULTS We investigated amputation and cryoinjury in vivo models of heart damage in the zebrafish through unbiased, integrative analyses of independent molecular datasets. To detect genes with potential biological roles, we derived computational prediction models with microarray data from heart amputation experiments. We focused on a top-ranked set of genes highly activated in the early post-injury stage, whose activity was further verified in independent microarray datasets. Next, we performed independent validations of expression responses with qPCR in a cryoinjury model. Across in vivo models, the top candidates showed highly concordant responses at 1 and 3 days post-injury, which highlights the predictive power of our analysis strategies and the possible biological relevance of these genes. Top candidates are significantly involved in cell fate specification and differentiation, and include heart failure markers such as periostin, as well as potential new targets for heart regeneration. For example, ptgis and ca2 were overexpressed, while usp2a, a regulator of the p53 pathway, was down-regulated in our in vivo models. Interestingly, a high activity of ptgis and ca2 has been previously observed in failing hearts from rats and humans. CONCLUSIONS We identified genes with potential critical roles in the response to cardiac damage in the zebrafish. Their transcriptional activities are reproducible in different in vivo models of cardiac injury.

On nonlinearity in neural encoding models applied to the primary visual cortex

Within the regression framework, we show how different levels of nonlinearity influence the instantaneous firing rate prediction of single neurons. Nonlinearity can be achieved in several ways. In particular, we can enrich the predictor set with basis expansions of the input variables (enlarging the number of inputs) or train a simple but different model for each area of the data domain. Spline-based models are popular within the first category. Kernel smoothing methods fall into the second category. Whereas the first choice is useful for globally characterizing complex functions, the second is very handy for temporal data and is able to include inner-state subject variations. Also, interactions among stimuli are considered. We compare state-of-the-art firing rate prediction methods with some more sophisticated spline-based nonlinear methods: multivariate adaptive regression splines and sparse additive models. We also study the impact of kernel smoothing. Finally, we explore the combination of various local models in an incremental learning procedure. Our goal is to demonstrate that appropriate nonlinearity treatment can greatly improve the results. We test our hypothesis on both synthetic data and real neuronal recordings in cat primary visual cortex, giving a plausible explanation of the results from a biological perspective.

Assembling models of embryo development: Image analysis and the construction of digital atlases

Digital atlases of animal development provide a quantitative description of morphogenesis, opening the path toward processes modeling. Prototypic atlases offer a data integration framework where to gather information from cohorts of individuals with phenotypic variability. Relevant information for further theoretical reconstruction includes measurements in time and space for cell behaviors and gene expression. The latter as well as data integration in a prototypic model, rely on image processing strategies. Developing the tools to integrate and analyze biological multidimensional data are highly relevant for assessing chemical toxicity or performing drugs preclinical testing. This article surveys some of the most prominent efforts to assemble these prototypes, categorizes them according to salient criteria and discusses the key questions in the field and the future challenges toward the reconstruction of multiscale dynamics in model organisms.

Brain injury MRI simulator based on theoretical models of neuroanatomic damage

In order to improve the body of knowledge about brain injury impairment is essential to develop image database with different types of injuries. This paper proposes a new methodology to model three types of brain injury: stroke, tumor and traumatic brain injury; and implements a system to navigate among simulated MRI studies. These studies can be used on research studies, to validate new processing methods and as an educational tool, to show different types of brain injury and how they affect to neuroanatomic structures.

An Architecture forRepresenting Biological Processes based on Networks of Bio-inspired Processors

n this paper we propose the use of Networks of Bio-inspired Processors (NBP) to model some biological phenomena within a computational framework. In particular, we propose the use of an extension of NBP named Network Evolutionary Processors Transducers to simulate chemical transformations of substances. Within a biological process, chemical transformations of substances are basic operations in the change of the state of the cell. Previously, it has been proved that NBP are computationally complete, that is, they are able to solve NP complete problems in linear time, using massively parallel computations. In addition, we propose a multilayer architecture that will allow us to design models of biological processes related to cellular communication as well as their implications in the metabolic pathways. Subsequently, these models can be applied not only to biological-cellular instances but, possibly, also to configure instances of interactive processes in many other fields like population interactions, ecological trophic networks, in dustrial ecosystems, etc.

NDT to identify biological damage in wood

Nondestructive techniques are widely used to assess existing timber structures. The models proposed for these methods are usually performed in the laboratory using small clear wood specimens. But in real situations many anomalies, defects and biological damage are found in wood. In these cases the existing models only indicate that the values are outside normality without providing any other information. To solve this problem, a study of non-destructive probing methods for wood was performed, testing the behaviour of four different techniques (penetration resistance, pullout resistance, drill resistance and chip drill extraction) on wood samples with different biological damage, simulating an in-situ test. The wood samples were obtained from existing Spanish timber structures with biotic damage caused by borer insects, termites, brown rot and white rot. The study concludes that all of the methods offer more or less detailed information about the degree of deterioration of wood, but that the first two methods (penetration and pullout resistance) cannot distinguish between pathologies. On the other hand, drill resistance and chip drill extraction make it possible to differentiate pathologies and even to identify species or damage location. Finally, the techniques used were compared to characterize their advantages and disadvantages.

Prospectives of monitoring biological activity in a red-legged partridge incubator with a carbon dioxide probe

This study focuses on the relationship between CO2 production and the ultimate hatchability of the incubation. A total amount of 43316 eggs of red-legged partridge (Alectoris rufa) were supervised during five actual incubations: three in 2012 and two in 2013. The CO2 concentration inside the incubator was monitored over a 20-day period, showing sigmoidal growth from ambient level (428 ppm) up to 1700 ppm in the incubation with the highest hatchability. Two sigmoid growth models (logistic and Gompertz) were used to describe the CO2 production by the eggs, with the result that the logistic model was a slightly better fit (r2=0.976 compared to r2=0.9746 for Gompertz). A coefficient of determination of 0.997 between the final CO2 estimation (ppm) using the logistic model and hatchability (%) was found.

Instability of signaling resolution models of parent–offspring conflict

Recent signaling resolution models of parent–offspring conflict have provided an important framework for theoretical and empirical studies of communication and parental care. According to these models, signaling of need is stabilized by its cost. However, our computer simulations of the evolutionary dynamics of chick begging and parental investment show that in Godfray’s model the signaling equilibrium is evolutionarily unstable: populations that start at the signaling equilibrium quickly depart from it. Furthermore, the signaling and nonsignaling equilibria are linked by a continuum of equilibria where chicks above a certain condition do not signal and we show that, contrary to intuition, fitness increases monotonically as the proportion of young that signal decreases. This result forces us to reconsider much of the current literature on signaling of need and highlights the need to investigate the evolutionary stability of signaling equilibria based on the handicap principle.

Cocaine reward models: Conditioned place preference can be established in dopamine- and in serotonin-transporter knockout mice

Cocaine and methylphenidate block uptake by neuronal plasma membrane transporters for dopamine, serotonin, and norepinephrine. Cocaine also blocks voltage-gated sodium channels, a property not shared by methylphenidate. Several lines of evidence have suggested that cocaine blockade of the dopamine transporter (DAT), perhaps with additional contributions from serotonin transporter (5-HTT) recognition, was key to its rewarding actions. We now report that knockout mice without DAT and mice without 5-HTT establish cocaine-conditioned place preferences. Each strain displays cocaine-conditioned place preference in this major mouse model for assessing drug reward, while methylphenidate-conditioned place preference is also maintained in DAT knockout mice. These results have substantial implications for understanding cocaine actions and for strategies to produce anticocaine medications.

Folding protein models with a simple hydrophobic energy function: The fundamental importance of monomer inside/outside segregation

The present study explores a “hydrophobic” energy function for folding simulations of the protein lattice model. The contribution of each monomer to conformational energy is the product of its “hydrophobicity” and the number of contacts it makes, i.e., E(h⃗, c⃗) = −Σi=1N cihi = −(h⃗.c⃗) is the negative scalar product between two vectors in N-dimensional cartesian space: h⃗ = (h1, … , hN), which represents monomer hydrophobicities and is sequence-dependent; and c⃗ = (c1, … , cN), which represents the number of contacts made by each monomer and is conformation-dependent. A simple theoretical analysis shows that restrictions are imposed concomitantly on both sequences and native structures if the stability criterion for protein-like behavior is to be satisfied. Given a conformation with vector c⃗, the best sequence is a vector h⃗ on the direction upon which the projection of c⃗ − c̄⃗ is maximal, where c̄⃗ is the diagonal vector with components equal to c̄, the average number of contacts per monomer in the unfolded state. Best native conformations are suggested to be not maximally compact, as assumed in many studies, but the ones with largest variance of contacts among its monomers, i.e., with monomers tending to occupy completely buried or completely exposed positions. This inside/outside segregation is reflected on an apolar/polar distribution on the corresponding sequence. Monte Carlo simulations in two dimensions corroborate this general scheme. Sequences targeted to conformations with large contact variances folded cooperatively with thermodynamics of a two-state transition. Sequences targeted to maximally compact conformations, which have lower contact variance, were either found to have degenerate ground state or to fold with much lower cooperativity.

Crowdsourcing without a crowd : Reliable online species identification using Bayesian models to minimize crowd size

Acknowledgment This research is supported by an award made by the RCUK Digital Economy program to the University of Aberdeen’s dot.rural Digital Economy Hub (ref. EP/G066051/1).

Up-regulation of specific tyrosinase mRNAs in mouse melanomas with the c2j gene substituted for the wild-type tyrosinase allele: Utilization in design of syngeneic immunotherapy models

The expression of cell-specialization genes is likely to be changing in tumor cells as their differentiation declines. Functional changes in these genes might yield unusual peptide epitopes with anti-tumor potential and could occur without modification in the DNA sequence of the gene. Melanomas undergo a characteristic decline in melanization that may reflect altered contributions of key melanocytic genes such as tyrosinase. Quantitative reverse transcriptase–PCR of the wild-type (C) tyrosinase gene in transgenic (C57BL/6 strain) mouse melanomas has revealed a shift toward alternative splicing of the pre-mRNA that generated increased levels of the Δ1b and Δ1d mRNA splice variants. The spontaneous c2j albino mutation of tyrosinase (in the C57BL/6 strain) changes the pre-mRNA splicing pattern. In c2j/c2j melanomas, alternative splicing was again increased. However, while some mRNAs (notably Δ1b) present in C/C were obligatorily absent, others (Δ3 and Δ1d) were elevated. In c2j/c2j melanomas, the percentage of total tyrosinase transcripts attributable to Δ3 reached approximately 2-fold the incidence in c2j/c2j or C/C skin melanocytes. The percentage attributable to Δ1d rose to approximately 2-fold the incidence in c2j/c2j skin, and to 10-fold that in C/C skin. These differences provide a basis for unique mouse models in which the melanoma arises in skin grafted from a C/C or c2j/c2j transgenic donor to a transgenic host of the same or opposite tyrosinase genotype. Immunotherapy designs then could be based on augmenting those antigenic peptides that are novel or overrepresented in a tumor relative to the syngeneic host.