Integrating remote sensing datasets into ecological modelling: a Bayesian approach

Process-based models have been used to simulate 3-dimensional complexities of forest ecosystems and their temporal changes, but their extensive data requirement and complex parameterisation have often limited their use for practical management applications. Increasingly, information retrieved using remote sensing techniques can help in model parameterisation and data collection by providing spatially and temporally resolved forest information. In this paper, we illustrate the potential of Bayesian calibration for integrating such data sources to simulate forest production. As an example, we use the 3-PG model combined with hyperspectral, LiDAR, SAR and field-based data to simulate the growth of UK Corsican pine stands. Hyperspectral, LiDAR and SAR data are used to estimate LAI dynamics, tree height and above ground biomass, respectively, while the Bayesian calibration provides estimates of uncertainties to model parameters and outputs. The Bayesian calibration contrasts with goodness-of-fit approaches, which do not provide uncertainties to parameters and model outputs. Parameters and the data used in the calibration process are presented in the form of probability distributions, reflecting our degree of certainty about them. After the calibration, the distributions are updated. To approximate posterior distributions (of outputs and parameters), a Markov Chain Monte Carlo sampling approach is used (25 000 steps). A sensitivity analysis is also conducted between parameters and outputs. Overall, the results illustrate the potential of a Bayesian framework for truly integrative work, both in the consideration of field-based and remotely sensed datasets available and in estimating parameter and model output uncertainties

Single-molecule Studies Of p53 Sliding Along DNA

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Visualizing Single-molecule DNA Replication with Fluorescence Microscopy

We describe a simple fluorescence microscopy-based real-time method for observing DNA replication at the single-molecule level. A circular, forked DNA template is attached to a functionalized glass coverslip and replicated extensively after introduction of replication proteins and nucleotides (Figure 1). The growing product double-strand DNA (dsDNA) is extended with laminar flow and visualized by using an intercalating dye. Measuring the position of the growing DNA end in real time allows precise determination of replication rate (Figure 2). Furthermore, the length of completed DNA products reports on the processivity of replication. This experiment can be performed very easily and rapidly and requires only a fluorescence microscope with a reasonably sensitive camera

Droplet microfluidics: a tool for biology, chemistry and nanotechnology

The ability to perform laboratory operations on small scales using miniaturized devices provides numerous benefits, including reduced quantities of reagents and waste as well as increased portability and controllability of assays. These operations can involve reaction components in the solution phase and as a result, their miniaturization can be accomplished through microfluidic approaches. One such approach, droplet microfluidics, provides a high-throughput platform for a wide range of assays and approaches in chemistry, biology and nanotechnology. We highlight recent advances in the application of droplet microfluidics in chip-based technologies, such as single-cell analysis tools, small-scale cell cultures, in-droplet chemical synthesis, high-throughput drug screening, and nanodevice fabrication

A probabilistic risk assessment for the vulnerability of the European carbon cycle to weather extremes: The ecosystem perspective

Extreme weather events are likely to occur more often under climate change and the resulting effects on ecosystems could lead to a further acceleration of climate change. But not all extreme weather events lead to extreme ecosystem response. Here, we focus on hazardous ecosystem behaviour and identify coinciding weather conditions. We use a simple probabilistic risk assessment based on time series of ecosystem behaviour and climate conditions. Given the risk assessment terminology, vulnerability and risk for the previously defined hazard are estimated on the basis of observed hazardous ecosystem behaviour. We apply this approach to extreme responses of terrestrial ecosystems to drought, defining the hazard as a negative net biome productivity over a 12-month period. We show an application for two selected sites using data for 1981–2010 and then apply the method to the pan-European scale for the same period, based on numerical modelling results (LPJmL for ecosystem behaviour; ERA-Interim data for climate). Our site-specific results demonstrate the applicability of the proposed method, using the SPEI to describe the climate condition. The site in Spain provides an example of vulnerability to drought because the expected value of the SPEI is 0.4 lower for hazardous than for non-hazardous ecosystem behaviour. In northern Germany, on the contrary, the site is not vulnerable to drought because the SPEI expectation values imply wetter conditions in the hazard case than in the non-hazard case. At the pan-European scale, ecosystem vulnerability to drought is calculated in the Mediterranean and temperate region, whereas Scandinavian ecosystems are vulnerable under conditions without water shortages. These first model-based applications indicate the conceptual advantages of the proposed method by focusing on the identification of critical weather conditions for which we observe hazardous ecosystem behaviour in the analysed data set. Application of the method to empirical time series and to future climate would be important next steps to test the approach

Inferring DNA sequences from mechanical unzipping data: the large-bandwidth case

The complementary strands of DNA molecules can be separated when stretched apart by a force; the unzipping signal is correlated to the base content of the sequence but is affected by thermal and instrumental noise. We consider here the ideal case where opening events are known to a very good time resolution (very large bandwidth), and study how the sequence can be reconstructed from the unzipping data. Our approach relies on the use of statistical Bayesian inference and of Viterbi decoding algorithm. Performances are studied numerically on Monte Carlo generated data, and analytically. We show how multiple unzippings of the same molecule may be exploited to improve the quality of the prediction, and calculate analytically the number of required unzippings as a function of the bandwidth, the sequence content, the elasticity parameters of the unzipped strands

Impact of droughts on the carbon cycle in European vegetation : a probabilistic risk analysis using six vegetation models

Peer reviewedPublisher PD

Direct Observation of Enzymes Replicating DNA Using a Single-molecule DNA Stretching Assay

We describe a method for observing real time replication of individual DNA molecules mediated by proteins of the bacteriophage replication system. Linearized λ DNA is modified to have a biotin on the end of one strand, and a digoxigenin moiety on the other end of the same strand. The biotinylated end is attached to a functionalized glass coverslip and the digoxigeninated end to a small bead. The assembly of these DNA-bead tethers on the surface of a flow cell allows a laminar flow to be applied to exert a drag force on the bead. As a result, the DNA is stretched close to and parallel to the surface of the coverslip at a force that is determined by the flow rate (Figure 1). The length of the DNA is measured by monitoring the position of the bead. Length differences between single- and double-stranded DNA are utilized to obtain real-time information on the activity of the replication proteins at the fork. Measuring the position of the bead allows precise determination of the rates and processivities of DNA unwinding and polymerization (Figure 2)