Age determinaton and age model of sediments from the Bay of Biscay

Continuous high-resolution mass accumulation rates (MAR) and X-ray fluorescence (XRF) measurements from marine sediment records in the Bay of Biscay (NE Atlantic) have allowed the determination of the timing and the amplitude of the 'Fleuve Manche' (Channel River) discharges during glacial stages MIS 10, MIS 8, MIS 6 and MIS 4-2. These results have yielded detailed insight into the Middle and Late Pleistocene glaciations in Europe and the drainage network of the western and central European rivers over the last 350 kyr. This study provides clear evidence that the 'Fleuve Manche' connected the southern North Sea basin with the Bay of Biscay during each glacial period and reveals that 'Fleuve Manche' activity during the glaciations MIS 10 and MIS 8 was significantly less than during MIS 6 and MIS 2. We correlate the significant 'Fleuve Manche' activity, detected during MIS 6 and MIS 2, with the extensive Saalian (Drenthe Substage) and the Weichselian glaciations, respectively, confirming that the major Elsterian glaciation precedes the glacial MIS 10. In detail, massive 'Fleuve Manche' discharges occurred at ca 155 ka (mid-MIS 6) and during Termination I, while no significant discharges are found during Termination II. It is assumed that a substantial retreat of the European ice sheet at ca 155 kyr, followed by the formation of ice-free conditions between the British Isles and Scandinavia until Termination II, allowed meltwater to flow northwards through the North Sea basin during the second part of the MIS 6. We assume that this glacial pattern corresponds to the Warthe Substage glacial maximum, therefore indicating that the data presented here equates to the Drenthe and the Warthe glacial advances at ca 175-160 ka and ca 150-140 ka, respectively. Finally, the correlation of our records with ODP site 980 reveals that massive 'Fleuve Manche' discharges, related to partial or complete melting of the European ice masses, were synchronous with strong decreases in both the rate of deep-water formation and the strength of the Atlantic thermohaline circulation. 'Fleuve Manche' discharges over the last 350 kyr probably participated, with other meltwater sources, in the collapse of the thermohaline circulation by freshening the northern Atlantic surface water.

Alkenone stable carbon isotopes, carbonate stable carbon and oxygen isotopes, planktic foraminifera boron isotopes and atmospheric CO2 calculations and box model results from Ras il-Pellegrin section, Malta

The development of a permanent, stable ice sheet in East Antarctica happened during the middle Miocene, about 14 million years (Myr) ago. The middle Miocene therefore represents one of the distinct phases of rapid change in the transition from the "greenhouse" of the early Eocene to the "icehouse" of the present day. Carbonate carbon isotope records of the period immediately following the main stage of ice sheet development reveal a major perturbation in the carbon system, represented by the positive d13C excursion known as carbon maximum 6 ("M6"), which has traditionally been interpreted as reflecting increased burial of organic matter and atmospheric pCO2 drawdown. More recently, it has been suggested that the d13C excursion records a negative feedback resulting from the reduction of silicate weathering and an increase in atmospheric pCO2. Here we present high-resolution multi-proxy (alkenone carbon and foraminiferal boron isotope) records of atmospheric carbon dioxide and sea surface temperature across CM6. Similar to previously published records spanning this interval, our records document a world of generally low (~300 ppm) atmospheric pCO2 at a time generally accepted to be much warmer than today. Crucially, they also reveal a pCO2 decrease with associated cooling, which demonstrates that the carbon burial hypothesis for CM6 is feasible and could have acted as a positive feedback on global cooling.

An R-package for the Estimation and Testing of Multiple Covariates and Biomarker Interactions for Survival Data Based on Local Partial Likelihood

When we study the variables that a ffect survival time, we usually estimate their eff ects by the Cox regression model. In biomedical research, e ffects of the covariates are often modi ed by a biomarker variable. This leads to covariates-biomarker interactions. Here biomarker is an objective measurement of the patient characteristics at baseline. Liu et al. (2015) has built up a local partial likelihood bootstrap model to estimate and test this interaction e ffect of covariates and biomarker, but the R code developed by Liu et al. (2015) can only handle one variable and one interaction term and can not t the model with adjustment to nuisance variables. In this project, we expand the model to allow adjustment to nuisance variables, expand the R code to take any chosen interaction terms, and we set up many parameters for users to customize their research. We also build up an R package called "lplb" to integrate the complex computations into a simple interface. We conduct numerical simulation to show that the new method has excellent fi nite sample properties under both the null and alternative hypothesis. We also applied the method to analyze data from a prostate cancer clinical trial with acid phosphatase (AP) biomarker.

Model for integrates community tourism in Ecuador, case study Pastaza

The tourism industry globally has grown steadily in recent decades, showing a progressive interest oriented toward rural areas due to characteristics of tranquility, nature, biodiversity, traditions and culture. Therefore, such aspects should be preserved and can be leveraged through adequate strategic orientation. Within the framework of global tourism trends community tourism is among the options that arise in the tourism market, and is one that is more likely to grow in the future. In the case of Ecuador, community tourism has become more dynamic over the years since many of the natural reserves in the country are in the hands of indigenous communities. Sustainable tourism in this sense is concerned with the maintenance of ethnic, cultural and biological diversity of the country, and current projects and regulatory laws support its development. Therefore, the aim of this paper is to present a strategy for the integrated management of community tourism following the principles of cooperation and collaboration between stakeholders, this study focuses on the Amazon region of Ecuador, specifically the province of Pastaza, in respect to how community tourism contributes to local development

A Generalized Fuzzy Linguistic Model for Predicting Component Concentrations in an Optical Gas Sensing System

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NOx, SO2 etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.

A generalized fuzzy linguistic model for predicting component concentrations in an optical gas sensing system

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NO_x, SO₂ etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.

Characterization of the glial scar tissue in a murine model of spinal cord compression, with focus on hyaluronan

Spinal cord injury (SCI) is a devastating neurological disorder that affects thousands of people each year. Although in recent decades significant progress has been made in relation to understanding the molecular and cellular events underlying the nervous damage, spinal cord injury is still a highly disabling condition for which there is no curative therapy. People affected by spinal cord injuries manifested dysfunction or loss, temporary or permanent, of motor, sensory and / or autonomic functions depending on the spinal lesion damaged. Currently, the incidence rate of this type of injury is approximately 15-40 cases per million people worldwide. At the origin of these lesions are: road accidents, falls, interpersonal violence and the practice of sports. In this work we placed the hypothesis that HA is one of the component of the scar tissue formed after a compressive SCI, that it is likely synthetised by the perilesional glial cells and that it might support the permeation of the glial scar during the late phase of SCI. Nowadays, much focus is drawn on the recovery of CNS function, made impossible after SCI due to the high content of sulfated proteoglycans in the extracellular matrix. Counterbalancing the ratio between these proteoglycans and hyaluronic acid could be one of the experimental therapy to re-permeate the glial scar tissue formed after SCI, making possible axonal regrowth and functional recovery. Therefore, we established a model of spinal cord compression in mice and studied the glial scar tissue, particularly through the characterization of the expression of enzymes related to the metabolism of HA and the subsequent concentration thereof at different distances of the lesion epicenter. Our results show that the lesion induced in mice shows results similar to those produced in human lesions, in terms of histologic similarities and behavioral results. but these animals demonstrate an impressive spontaneous reorganization mechanism of the spinal cord tissue that occurs after injury and allows for partial recovery of the functions of the CNS. As regards the study of the glial scar, changes were recorded at the level of mRNA expression of enzymes metabolizing HA i.e., after injury there was a decreased expression of HA synthases 1-2 (HAS 1-2) and an increase of the expression HAS3 synthase mRNA, as well as the enzymes responsible for the HA catabolism, HYAL 1-2. But the amount of HA measured through the ELISA test was found unchanged after injury, it is not possible to explain this fact only with the change of expression of enzymes. At two weeks and in response to SCI, we found synthesized HA by reactive astrocytes and probably by others like microglial cells as it was advanced by the HA/GFAP+ and HA/IBA1+ cells co-location.

The stochastic geometric machine model

This paper introduces the stochastic version of the Geometric Machine Model for the modelling of sequential, alternative, parallel (synchronous) and nondeterministic computations with stochastic numbers stored in a (possibly infinite) shared memory. The programming language L(D! 1), induced by the Coherence Space of Processes D! 1, can be applied to sequential and parallel products in order to provide recursive definitions for such processes, together with a domain-theoretic semantics of the Stochastic Arithmetic. We analyze both the spacial (ordinal) recursion, related to spacial modelling of the stochastic memory, and the temporal (structural) recursion, given by the inclusion relation modelling partial objects in the ordered structure of process construction.

Building monitoring during the partial implosion stage

The partial collapse of a building in Colombia caused severe damage to its structural components -- An implosion was realized to induce the collapse of 50% of the deteriorated building -- To evaluate the influence of the implosion on the remaining structure, a monitoring survey was realized using triaxial accelerometers -- Time signals associated with ambient, seismic and forced vibration were obtained -- A study of the records in the time and the frequency domain was made -- The analysis of the information allowed determining some structural properties that were useful to calibrate the analytical model of the structure

Future Bloom and Blossom Frost Risk for Malus domestica Considering Climate Model and Impact Model Uncertainties

The future bloom and risk of blossom frosts for Malus domestica were projected using regional climate realizations and phenological (= impact) models. As climate impact projections are susceptible to uncertainties of climate and impact models and model concatenation, the significant horizon of the climate impact signal was analyzed by applying 7 impact models, including two new developments, on 13 climate realizations of the IPCC emission scenario A1B. Advancement of phenophases and a decrease in blossom frost risk for Lower Saxony (Germany) for early and late ripeners was determined by six out of seven phenological models. Single model/single grid point time series of bloom showed significant trends by 2021-2050 compared to 1971-2000, whereas the joint signal of all climate and impact models did not stabilize until 2043. Regarding blossom frost risk, joint projection variability exceeded the projected signal. Thus, blossom frost risk cannot be stated to be lower by the end of the 21st century despite a negative trend. As a consequence it is however unlikely to increase. Uncertainty of temperature, blooming date and blossom frost risk projection reached a minimum at 2078-2087. The projected phenophases advanced by 5.5 d K-1, showing partial compensation of delayed fulfillment of the winter chill requirement and faster completion of the following forcing phase in spring. Finally, phenological model performance was improved by considering the length of day.

High-Throughput Prediction of Acacia and Eucalypt Lignin Syringyl/Guaiacyl Content Using FT-Raman Spectroscopy and Partial Least Squares Modeling

High-throughput techniques are necessary to efficiently screen potential lignocellulosic feedstocks for the production of renewable fuels, chemicals, and bio-based materials, thereby reducing experimental time and expense while supplanting tedious, destructive methods. The ratio of lignin syringyl (S) to guaiacyl (G) monomers has been routinely quantified as a way to probe biomass recalcitrance. Mid-infrared and Raman spectroscopy have been demonstrated to produce robust partial least squares models for the prediction of lignin S/G ratios in a diverse group of Acacia and eucalypt trees. The most accurate Raman model has now been used to predict the S/G ratio from 269 unknown Acacia and eucalypt feedstocks. This study demonstrates the application of a partial least squares model composed of Raman spectral data and lignin S/G ratios measured using pyrolysis/molecular beam mass spectrometry (pyMBMS) for the prediction of S/G ratios in an unknown data set. The predicted S/G ratios calculated by the model were averaged according to plant species, and the means were not found to differ from the pyMBMS ratios when evaluating the mean values of each method within the 95 % confidence interval. Pairwise comparisons within each data set were employed to assess statistical differences between each biomass species. While some pairwise appraisals failed to differentiate between species, Acacias, in both data sets, clearly display significant differences in their S/G composition which distinguish them from eucalypts. This research shows the power of using Raman spectroscopy to supplant tedious, destructive methods for the evaluation of the lignin S/G ratio of diverse plant biomass materials. © 2015, The Author(s).

Rare sex or out of reach equilibrium? The dynamics of F-IS in partially clonal organisms

Background: Partially clonal organisms are very common in nature, yet the influence of partial asexuality on the temporal dynamics of genetic diversity remains poorly understood. Mathematical models accounting for clonality predict deviations only for extremely rare sex and only towards mean inbreeding coefficient (F-IS) over bar < 0. Yet in partially clonal species, both F-IS < 0 and F-IS > 0 are frequently observed also in populations where there is evidence for a significant amount of sexual reproduction. Here, we studied the joint effects of partial clonality, mutation and genetic drift with a state-and-time discrete Markov chain model to describe the dynamics of F-IS over time under increasing rates of clonality. Results: Results of the mathematical model and simulations show that partial clonality slows down the asymptotic convergence to F-IS = 0. Thus, although clonality alone does not lead to departures from Hardy-Weinberg expectations once reached the final equilibrium state, both negative and positive F-IS values can arise transiently even at intermediate rates of clonality. More importantly, such "transient" departures from Hardy Weinberg proportions may last long as clonality tunes up the temporal variation of F-IS and reduces its rate of change over time, leading to a hyperbolic increase of the maximal time needed to reach the final mean (F-IS,F-infinity) over bar value expected at equilibrium. Conclusion: Our results argue for a dynamical interpretation of F-IS in clonal populations. Negative values cannot be interpreted as unequivocal evidence for extremely scarce sex but also as intermediate rates of clonality in finite populations. Complementary observations (e.g. frequency distribution of multiloci genotypes, population history) or time series data may help to discriminate between different possible conclusions on the extent of clonality when mean (F-IS) over bar values deviating from zero and/or a large variation of F-IS over loci are observed.

Genes found in partial sequencing od Phytophthora cinnamomi

Members of the oomycete cause extensive losses in agriculture and widespread degradation in natural plant communities, being responsible for the death of thousands of trees every year. Two of the representative species are Phytophthora infestans, which causes late blight of potato, and Phytophthora cinnamomi, which causes chestnut ink disease, responsible for losses on sweet chestnut production in Europe. Genome sequencing efforts have been focused on the study of three species: P. infestans, P. sojae and P. ramorum. Phytophthora infestans has been developed as the model specie for the genus, possessing excellent genetic and genomics resources including genetic maps, BAC libraries, and EST sequences. Our research team is trying to sequence the genome of P. cinnamomi in order to gain a better understanding of this oomycete, to study changes in plant-pathogen relationships including those resulting from climate change and trying to decrease the pathogen’s impact on crops and plants in natural ecosystems worldwide. We present here a preliminary report of partially sequenced genomic DNA from P. cinnamomi encoding putative protein-coding sequences and tRNAs. Database analysis reveals the presence of genes conserved in oomycetes.

Sustainable deployment of QTLs conferring quantitative resistance to crops: first lessons from a stochastic model

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