Observed and modeled Greenland ice sheet snow accumulation, 1958-2003, and links with regional climate forcing

Author Posting. © American Meteorological Society 2006. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 19 (2006): 344–358, doi:10.1175/JCLI3615.1.Annual and monthly snow accumulation for the Greenland Ice Sheet was derived from ECMWF forecasts [mainly 40-yr ECMWR Re-Analysis (ERA-40)] and further meteorological modeling. Modeled accumulation was validated using 58 ice core accumulation datasets across the ice sheet and was found to be 95% of the observed accumulation on average, with a mean correlation of 0.53 between modeled and observed. Many of the ice core datasets are new and are presented here for the first time. Central and northern interior parts of the ice sheet were found to be 10%–30% too dry in ERA-40, in line with earlier ECMWF analysis, although too much (>50% locally) snow accumulation was modeled for interior southern parts of Greenland. Nevertheless, 47 of 58 sites show significant correlation in temporal variability of modeled with observed accumulation. The model also captures the absolute amount of snow accumulation at several sites, most notably Das1 and Das2 in southeast Greenland. Mean modeled accumulation over the ice sheet was 0.279 (standard deviation 0.034) m yr−1 for 1958–2003 with no significant trend for either the ice sheet or any of the core sites. Unusually high accumulation in southeast Greenland in 2002/03 leads the authors to study meteorological synoptic forcing patterns and comment on the prospect of enhanced climate variability leading to more such events as a result of global warming. There is good agreement between precipitation measured at coastal meteorological stations in southern Greenland and accumulation modeled for adjacent regions of the ice sheet. There is no significant persistent relation between the North Atlantic Oscillation index and whole or southern Greenland accumulation.JM acknowledges support from NASA’s Cryospheric Sciences Program and the Arctic Section of NSF’s Office of Polar Programs

Modelling informative time points: an evolutionary process approach

Real time series sometimes exhibit various types of "irregularities": missing observations, observations collected not regularly over time for practical reasons, observation times driven by the series itself, or outlying observations. However, the vast majority of methods of time series analysis are designed for regular time series only. A particular case of irregularly spaced time series is that in which the sampling procedure over time depends also on the observed values. In such situations, there is stochastic dependence between the process being modelled and the times of the observations. In this work, we propose a model in which the sampling design depends on all past history of the observed processes. Taking into account the natural temporal order underlying available data represented by a time series, then a modelling approach based on evolutionary processes seems a natural choice. We consider maximum likelihood estimation of the model parameters. Numerical studies with simulated and real data sets are performed to illustrate the benefits of this model-based approach.- The authors acknowledge Foundation FCT (FundacAo para a Ciencia e Tecnologia) as members of the research project PTDC/MAT-STA/28243/2017 and Center for Research & Development in Mathematics and Applications of Aveiro University within project UID/MAT/04106/2019

Ultrastructure of the Interlamellar Membranes of the Nacre of the Bivalve Pteria hirundo, Determined by Immunolabelling

The current model for the ultrastructure of the interlamellar membranes of molluscan nacre imply that they consist of a core of aligned chitin fibers surrounded on both sides by acidic proteins. This model was based on observations taken on previously demineralized shells, where the original structure had disappeared. Despite other earlier claims, no direct observations exist in which the different components can be unequivocally discriminated. We have applied different labeling protocols on non-demineralized nacreous shells of the bivalve Pteria. With this method, we have revealed the disposition and nature of the different fibers of the interlamellar membranes that can be observed on the surface of the nacreous shell of the bivalve Pteria hirundo by high resolution scanning electron microscopy (SEM). The minor chitin component consists of very thin fibers with a high aspect ratio and which are seemingly disoriented. Each fiber has a protein coat, which probably forms a complex with the chitin. The chitin-protein-complex fibers are embedded in an additional proteinaceous matrix. This is the first time in which the sizes, positions and distribution of the chitin fibers have been observed in situ.AJOM was financed by a PhD Grant of the FPI program from the Spanish Ministerio de Ciencia e Innovación; TCB's PhD Grant belonged to the FPU Program of the same Ministry. AJOM and AGC were supported by Projects CGL2010-20748-C02-01 and CGL2013-48247-P of the mentioned Ministry, and RNM6433 of the Consejería de Economía, Innovación y Ciencia of the Junta de Andalucía. The European COST Action TD0903 contributed via two Short Term Scientific Missions to AJOM in FM's lab in Dijon

Seismic air gun exposure during early-stage embryonic development does not negatively affect spiny lobster Jasus edwardsii larvae (Decapoda:Palinuridae)

Marine seismic surveys are used to explore for sub-seafloor oil and gas deposits. These surveys are conducted using air guns, which release compressed air to create intense sound impulses, which are repeated around every 8-12 seconds and can travel large distances in the water column. Considering the ubiquitous worldwide distribution of seismic surveys, the potential impact of exposure on marine invertebrates is poorly understood. In this study, egg-bearing female spiny lobsters (Jasus edwardsii) were exposed to signals from three air gun configurations, all of which exceeded sound exposure levels (SEL) of 185 dB re 1 µPa2·s. Lobsters were maintained until their eggs hatched and the larvae were then counted for fecundity, assessed for abnormal morphology using measurements of larval length and width, tested for larval competency using an established activity test and measured for energy content. Overall there were no differences in the quantity or quality of hatched larvae, indicating that the condition and development of spiny lobster embryos were not adversely affected by air gun exposure. These results suggest that embryonic spiny lobster are resilient to air gun signals and highlight the caution necessary in extrapolating results from the laboratory to real world scenarios or across life history stages

Non-linear Autoregressive Neural Networks to Forecast Short-Term Solar Radiation for Photovoltaic Energy Predictions

Nowadays, green energy is considered as a viable solution to hinder CO2 emissions and greenhouse effects. Indeed, it is expected that Renewable Energy Sources (RES) will cover 40% of the total energy request by 2040. This will move forward decentralized and cooperative power distribution systems also called smart grids. Among RES, solar energy will play a crucial role. However, reliable models and tools are needed to forecast and estimate with a good accuracy the renewable energy production in short-term time periods. These tools will unlock new services for smart grid management. In this paper, we propose an innovative methodology for implementing two different non-linear autoregressive neural networks to forecast Global Horizontal Solar Irradiance (GHI) in short-term time periods (i.e. from future 15 to 120min). Both neural networks have been implemented, trained and validated exploiting a dataset consisting of four years of solar radiation values collected by a real weather station. We also present the experimental results discussing and comparing the accuracy of both neural networks. Then, the resulting GHI forecast is given as input to a Photovoltaic simulator to predict energy production in short-term time periods. Finally, we present the results of this Photovoltaic energy estimation discussing also their accuracy

An iterative strategy combining biophysical criteria and duration hidden Markov models for structural predictions of Chlamydia trachomatis σ66 promoters

<p>Abstract</p> <p>Background</p> <p>Promoter identification is a first step in the quest to explain gene regulation in bacteria. It has been demonstrated that the initiation of bacterial transcription depends upon the stability and topology of DNA in the promoter region as well as the binding affinity between the RNA polymerase σ-factor and promoter. However, promoter prediction algorithms to date have not explicitly used an ensemble of these factors as predictors. In addition, most promoter models have been trained on data from <it>Escherichia coli</it>. Although it has been shown that transcriptional mechanisms are similar among various bacteria, it is quite possible that the differences between <it>Escherichia coli </it>and <it>Chlamydia trachomatis </it>are large enough to recommend an organism-specific modeling effort.</p> <p>Results</p> <p>Here we present an iterative stochastic model building procedure that combines such biophysical metrics as DNA stability, curvature, twist and stress-induced DNA duplex destabilization along with duration hidden Markov model parameters to model <it>Chlamydia trachomatis </it>σ⁶⁶promoters from 29 experimentally verified sequences. Initially, iterative duration hidden Markov modeling of the training set sequences provides a scoring algorithm for <it>Chlamydia trachomatis </it>RNA polymerase σ⁶⁶/DNA binding. Subsequently, an iterative application of Stepwise Binary Logistic Regression selects multiple promoter predictors and deletes/replaces training set sequences to determine an optimal training set. The resulting model predicts the final training set with a high degree of accuracy and provides insights into the structure of the promoter region. Model based genome-wide predictions are provided so that optimal promoter candidates can be experimentally evaluated, and refined models developed. Co-predictions with three other algorithms are also supplied to enhance reliability.</p> <p>Conclusion</p> <p>This strategy and resulting model support the conjecture that DNA biophysical properties, along with RNA polymerase σ-factor/DNA binding collaboratively, contribute to a sequence's ability to promote transcription. This work provides a baseline model that can evolve as new <it>Chlamydia trachomatis </it>σ⁶⁶promoters are identified with assistance from the provided genome-wide predictions. The proposed methodology is ideal for organisms with few identified promoters and relatively small genomes.</p

A hierarchical Bayesian model for understanding the spatiotemporal dynamics of the intestinal epithelium

Our work addresses two key challenges, one biological and one methodological. First, we aim to understand how proliferation and cell migration rates in the intestinal epithelium are related under healthy, damaged (Ara-C treated) and recovering conditions, and how these relations can be used to identify mechanisms of repair and regeneration. We analyse new data, presented in more detail in a companion paper, in which BrdU/IdU cell-labelling experiments were performed under these respective conditions. Second, in considering how to more rigorously process these data and interpret them using mathematical models, we use a probabilistic, hierarchical approach. This provides a best-practice approach for systematically modelling and understanding the uncertainties that can otherwise undermine the generation of reliable conclusions-uncertainties in experimental measurement and treatment, difficult-to-compare mathematical models of underlying mechanisms, and unknown or unobserved parameters. Both spatially discrete and continuous mechanistic models are considered and related via hierarchical conditional probability assumptions. We perform model checks on both in-sample and out-of-sample datasets and use them to show how to test possible model improvements and assess the robustness of our conclusions. We conclude, for the present set of experiments, that a primarily proliferation-driven model suffices to predict labelled cell dynamics over most time-scales

MRI of Arterial Flow Reserve in Patients with Intermittent Claudication: Feasibility and Initial Experience

Objectives: The aim of this work was to develop a MRI method to determine arterial flow reserve in patients with intermittent claudication and to investigate whether this method can discriminate between patients and healthy control subjects. Methods: Ten consecutive patients with intermittent claudication and 10 healthy control subjects were included. All subjects underwent vector cardiography triggered quantitative 2D cine MR phase-contrast imaging to obtain flow waveforms of the popliteal artery at rest and during reactive hyperemia. Resting flow, maximum hyperemic flow and absolute flow reserve were determined and compared between the two groups by two independent MRI readers. Also, interreader reproducibility of flow measures was reported. Results: Resting flow was lower in patients compared to controls (4.961.6 and 11.163.2 mL/s in patients and controls, respectively (p,0.01)). Maximum hyperemic flow was 7.362.9 and 16.463.2 mL/s (p,0.01) and the absolute flow reserve was 2.461.6 and 5.361.3 mL/s (p,0.01), respectively in patients and controls. The interreader coefficient of variation was below 10 % for all measures in both patients and controls. Conclusions: Quantitative 2D MR cine phase-contrast imaging is a promising method to determine flow reserve measures in patients with peripheral arterial disease and can be helpful to discriminate patients with intermittent claudication fro

New Insights into the Mechanisms of Embryonic Stem Cell Self-Renewal under Hypoxia: A Multifactorial Analysis Approach

Previous reports have shown that culturing mouse embryonic stem (mES) cells at different oxygen tensions originated different cell proliferation patterns and commitment stages depending on which signaling pathways are activated or inhibited to support the pluripotency state. Herein we provide new insights into the mechanisms by which oxygen is influencing mES cell self-renewal and pluripotency. A multifactorial approach was developed to rationally evaluate the singular and interactive control of MEK/ERK pathway, GSK-3 inhibition, and LIF/STAT3 signaling at physiological and non-physiological oxygen tensions. Collectively, our methodology revealed a significant role of GSK-3-mediated signaling towards maintenance of mES cell pluripotency at lower O2 tensions. Given the central role of this signaling pathway, future studies will need to focus on the downstream mechanisms involved in ES cell self-renewal under such conditions, and ultimately how these findings impact human models of pluripotency