Effects of Scots pine paternal genotypes of two contiguous seed orchards on the budset and frost hardening of first-year progeny

In Scots pine (Pinus sylvestris L.), it has been shown that the parental conditions have a role in the phenological variation among first-year seedlings. For this reason, it is argued that they should be comprehensively controlled before estimating the parental genotype effects. This controlled-cross study examined the effects of a set of fathers of Scots pines on the timing of budset and autumn frost hardening of first-year seedlings. The paternal genotypes had either a northern or southern provenance, but had spent a period of over 25 years as grafts in a shared climatic environment in two closely located southern orchards. Pollen applied in the crosses was collected from these orchards in one year and all the maternal genotypes were pollinated in only one seed orchard. The results of freeze tests and budset observations of the consequent progeny were analysed and additionally compared with results obtained using seedlings from seed lots of natural forests in order to estimate the ability of northern paternal genotypes to maintain a northern effect under southern conditions. This environmentally controlled study demonstrated a significant effect of the paternal genotype on the budset and autumn frost hardening of first-year seedling of Scots pine. With the applied study design, no significant indication of an environmental influence on the effect of the paternal genotype was obtained. The accuracy of the observations is discussed. It is concluded that the results suggest a minor role of mutability in the effects of Scots pine paternal genotypes.Peer reviewe

Modelling of errors due to speed of sound variations in photoacoustic tomography using a Bayesian framework

Inverse problem of estimating initial pressure in photoacoustic tomography is ill-posed and thus sensitive to errors in modelling and measurements. In practical experiments, accurate knowledge of the speed of sound of the imaged target is commonly not available, and therefore an approximate speed of sound is used in the computational model. This can result in errors in the solution of the inverse problem that can appear as artefacts in the reconstructed images. In this paper, the inverse problem of photoacoustic tomography is approached in a Bayesian framework. Errors due to uncertainties in the speed of sound are modelled using Bayesian approximation error modelling. Estimation of the initial pressure distribution together with information on the reliability of these estimates are considered. The approach was studied using numerical simulations. The results show that uncertainties in the speed of sound can cause significant errors in the solution of the inverse problem. However, modelling of these uncertainties improves the accuracy of the solution

Bayesian approach to image reconstruction in photoacoustic tomography

Photoacoustic tomography is a hybrid imaging method that has a variety of biomedical applications. In photoacoustic tomography, the image reconstruction problem (inverse problem) is to resolve the initial pressure distribution from detected ultrasound waves generated within an object due to an illumination of a short light pulse. In this work, this problem is approached in Bayesian framework. Image reconstruction is investigated with numerical simulations in different detector geometries, including limited view setup, and utilizing different prior information. Furthermore, assessing the reliability of the estimates is investigated. The simulations show that the Bayesian approach can produce accurate estimates of the initial pressure distribution and uncertainty information even in a limited view setup if proper prior information is utilized

ValoMC: a Monte Carlo software and MATLAB toolbox for simulating light transport in biological tissue

A Monte Carlo method for photon transport has gained wide popularity in biomedical optics for studying light behaviour in tissue. Nowadays, typical computation times range from a few minutes to hours. Although various implementations of the Monte Carlo algorithm exist, there is only a limited number of free software available. In addition, these packages may require substantial learning efforts. To address these issues, we present a new Monte Carlo software with a user-friendly interface. The simulation geometry is defined using an unstructured (triangular or tetrahedral) mesh. The program solves the photon fluence in the computation domain and the exitance at the domain boundary. It is capable of simulating complex measurement geometries with spatially varying optical parameter distributions and supports several types of light sources as well as intensity modulated light. Furthermore, attention is given to ease of use and fast problem set up with a MATLAB (The MathWorks Inc., Natick, MA) interface. The simulation code is written in C++ and parallelized using OpenMP. The simulation code has been validated against analytical and numerical solutions of radiative transfer equation and other Monte Carlo software in good agreement. The software is available for download from the homepage https://inverselight.github.io/ValoMC/ and the source code from GitHub https://github.com/InverseLight/ValoMC

Modelling of errors and uncertainties in photoacoustic tomography using a Bayesian framework

Photoacoustic tomography is studied in the framework of Bayesian inverse problems. Modelling of errors and uncertainties using Bayesian approximation error modelling is investigated. The approach is tested with simulation

Computationally Efficient Forward Operator for Photoacoustic Tomography Based on Coordinate Transformations

IEEE Photoacoustic tomography (PAT) is an imaging modality that utilizes the photoacoustic effect. In PAT, a photoacoustic image is computed from measured data by modeling ultrasound propagation in the imaged domain and solving an inverse problem utilizing a discrete forward operator. However, in realistic measurement geometries with several ultrasound transducers and relatively large imaging volume, an explicit formation and use of the forward operator can be computationally prohibitively expensive. In this work, we propose a transformation based approach for efficient modeling of photoacoustic signals and reconstruction of photoacoustic images. In the approach, the forward operator is constructed for a reference ultrasound transducer and expanded into a general measurement geometry using transformations that map the formulated forward operator in local coordinates to the global coordinates of the measurement geometry. The inverse problem is solved using a Bayesian framework. The approach is evaluated with numerical simulations and experimental data. The results show that the proposed approach produces accurate three-dimensional photoacoustic images with a significantly reduced computational cost both in memory requirements and in time. In the studied cases, depending on the computational factors such as discretization, over 30-fold reduction in memory consumption and was achieved without a reduction in image quality compared to a conventional approach

Utilising the radiative transfer equation in quantitative photoacoustic tomography

Quantitative photoacoustic tomography is an emerging imaging technique aimed at estimating optical parameters inside tissue from photoacoustic images. This optical parameter estimation problem is an ill-posed inverse problem, and thus it is sensitive to measurement and modelling errors. Therefore, light propagation in quantitative photoacoustic tomography needs to be accurately modelled. A widely accepted model for light propagation in biological tissue is the radiative transfer equation. In this work, the radiative transfer equation is utilised in quantitative photoacoustic tomography. Estimating absorption and scattering distributions in quantitative photoacoustic tomography using various illuminations is investigated

Fluorescence measurements show stronger cold inhibition of photosynthetic light reactions in Scots pine compared to Norway spruce as well as during spring compared to autumn

We studied the photosynthetic activity of Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies [L.] Karst) in relation to air temperature changes from March 2013 to February 2014. We measured the chlorophyll fluorescence of approximately 50 trees of each species growing in southern Finland. Fluorescence was measured 13 times per week. We began by measuring shoots present in late winter (i.e., March 2013) before including new shoots once they started to elongate in spring. By July, when the spring shoots had achieved similar fluorescence levels to the older ones, we proceeded to measure the new shoots only.We analysed the data by fitting a sigmoidal model containing four parameters to link sliding averages of temperature and fluorescence. A parameter defining the temperature range over which predicted fluorescence increased most rapidly was the most informative with in describing temperature dependence of fluorescence.The model generated similar fluorescence patterns for both species, but differences were observed for critical temperature and needle age. Down regulation of the light reaction was stronger in spring than in autumn. Pine showed more conservative control of the photosynthetic light reactions, which were activated later in spring and more readily attenuated in autumn. Under the assumption of a close correlation of fluorescence and photosynthesis, spruce should therefore benefit more than pine from the increased photosynthetic potential during warmer springs, but be more likely to suffer frost damage with a sudden cooling following a warm period. The winter of 20132014 was unusually mild and similar to future conditions predicted by global warming models. During the mild winter, the activity of photosynthetic light reactions of both conifers, especially spruce, remained high. Because light levels during winter are too low for photosynthesis, this activity may translate to a net carbon loss due to respiration

Perturbation Monte Carlo Method for Quantitative Photoacoustic Tomography

Quantitative photoacoustic tomography aims at estimating optical parameters from photoacoustic images that are formed utilizing the photoacoustic effect caused by the absorption of an externally introduced light pulse. This optical parameter estimation is an ill-posed inverse problem, and thus it is sensitive to measurement and modeling errors. In this work, we propose a novel way to solve the inverse problem of quantitative photoacoustic tomography based on the perturbation Monte Carlo method. Monte Carlo method for light propagation is a stochastic approach for simulating photon trajectories in a medium with scattering particles. It is widely accepted as an accurate method to simulate light propagation in tissues. Furthermore, it is numerically robust and easy to implement. Perturbation Monte Carlo maintains this robustness and enables forming gradients for the solution of the inverse problem. We validate the method and apply it in the framework of Bayesian inverse problems. The simulations show that the perturbation Monte Carlo method can be used to estimate spatial distributions of both absorption and scattering parameters simultaneously. These estimates are qualitatively good and quantitatively accurate also in parameter scales that are realistic for biological tissues

Substorm occurrence during quiet solar wind driving

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