An Investigation of Stochastic Variance Reduction Algorithms for Relative Difference Penalised 3D PET Image Reconstruction

Penalised PET image reconstruction algorithms are often accelerated during early iterations with the use of subsets. However, these methods may exhibit limit cycle behaviour at later iterations due to variations between subsets. Desirable converged images can be achieved for a subclass of these algorithms via the implementation of a relaxed step size sequence, but the heuristic selection of parameters will impact the quality of the image sequence and algorithm convergence rates. In this work, we demonstrate the adaption and application of a class of stochastic variance reduction gradient algorithms for PET image reconstruction using the relative difference penalty and numerically compare convergence performance to BSREM. The two investigated algorithms are: SAGA and SVRG. These algorithms require the retention in memory of recently computed subset gradients, which are utilised in subsequent updates. We present several numerical studies based on Monte Carlo simulated data and a patient data set for fully 3D PET acquisitions. The impact of the number of subsets, different preconditioners and step size methods on the convergence of regions of interest values within the reconstructed images is explored. We observe that when using constant preconditioning, SAGA and SVRG demonstrate reduced variations in voxel values between subsequent updates and are less reliant on step size hyper-parameter selection than BSREM reconstructions. Furthermore, SAGA and SVRG can converge significantly faster to the penalised maximum likelihood solution than BSREM, particularly in low count data

An Investigation of Stochastic Variance Reduction Algorithms for Relative Difference Penalized 3D PET Image Reconstruction

Penalised PET image reconstruction algorithms are often accelerated during early iterations with the use of subsets. However, these methods may exhibit limit cycle behaviour at later iterations due to variations between subsets. Desirable converged images can be achieved for a subclass of these algorithms via the implementation of a relaxed step size sequence, but the heuristic selection of parameters will impact the quality of the image sequence and algorithm convergence rates. In this work, we demonstrate the adaption and application of a class of stochastic variance reduction gradient algorithms for PET image reconstruction using the relative difference penalty and numerically compare convergence performance to BSREM. The two investigated algorithms are: SAGA and SVRG. These algorithms require the retention in memory of recently computed subset gradients, which are utilised in subsequent updates. We present several numerical studies based on Monte Carlo simulated data and a patient data set for fully 3D PET acquisitions. The impact of the number of subsets, different preconditioners and step size methods on the convergence of regions of interest values within the reconstructed images is explored. We observe that when using constant preconditioning, SAGA and SVRG demonstrate reduced variations in voxel values between subsequent updates and are less reliant on step size hyper-parameter selection than BSREM reconstructions. Furthermore, SAGA and SVRG can converge significantly faster to the penalised maximum likelihood solution than BSREM, particularly in low count data

In situ analysis of mTORC1/2 and cellular metabolism-related proteins in human Lymphangioleiomyomatosis

Lymphangioleiomyomatosis (LAM) is a rare progressive cystic lung disease with features of a low-grade neoplasm. It is primarily caused by mutations in TSC1 or TSC2 genes. Sirolimus, an inhibitor of mTOR complex 1 (mTORC1), slows down disease progression in some, but not all patients. Hitherto, other potential therapeutic targets such as mTOR complex 2 (mTORC2) and various metabolic pathways have not been investigated in human LAM tissues. The aim of this study was to assess activities of mTORC1, mTORC2 and various metabolic pathways in human LAM tissues through analysis of protein expression. Immunohistochemical analysis of p-S6 (mTORC1 downstream protein), Rictor (mTORC2 scaffold protein) as well as GLUT1, GAPDH, ATPB, GLS, MCT1, ACSS2 and CPT1A (metabolic pathway markers) were performed on lung tissue from 11 patients with sporadic LAM. Immunoreactivity was assessed in LAM cells with bronchial smooth muscle cells as controls. Expression of p-S6, Rictor, GAPDH, GLS, MCT1, ACSS2 and CPT1A was significantly higher in LAM cells than in bronchial smooth muscle cells (P<.01). No significant differences were found between LAM cells and normal bronchial smooth muscle cells in GLUT1 and ATPB expression. The results are uniquely derived from human tissue and indicate that, in addition to mTORC1, mTORC2 may also play an important role in the pathobiology of LAM. Furthermore, glutaminolysis, acetate utilization and fatty acid β-oxidation appear to be the preferred bioenergetic pathways in LAM cells. mTORC2 and these preferred bioenergetic pathways appear worthy of further study as they may represent possible therapeutic targets in the treatment of LAM

The coming perfect storm: Diminishing sustainability of coastal human–natural systems in the Anthropocene

We review impacts of climate change, energy scarcity, and economic frameworks on sustainability of natural and human systems in coastal zones, areas of high biodiversity, productivity, population density, and economic activity. More than 50% of the global population lives within 200 km of a coast, mostly in tropical developing countries. These systems developed during stable Holocene conditions. Changes in global forcings are threatening sustainability of coastal ecosystems and populations. During the Holocene, the earth warmed and became wetter and more productive. Climate changes are impacting coastal systems via sea level rise, stronger tropical cyclones, changes in basin inputs, and extreme weather events. These impacts are passing tipping points as the fossil fuel-powered industrial-technological-agricultural revolution has overwhelmed the source–sink functions of the biosphere and degraded natural systems. The current status of industrialized society is primarily the result of fossil fuel (FF) use. FFs provided more than 80% of global primary energy and are projected to decline to 50% by mid-century. This has profound implications for societal energy requirements, including the transition to a renewable economy. The development of the industrial economy allowed coastal social systems to become spatially separated from their dominant energy and food sources. This will become more difficult to maintain with the fading of cheap energy. It seems inevitable that past growth in energy use, resource consumption, and economic growth cannot be sustained, and coastal areas are in the forefront of these challenges. Rapid planning and cooperation are necessary to minimize impacts of the changes associated with the coming transition. There is an urgent need for a new economic framework to guide society through the transition as mainstream neoclassical economics is not based on natural sciences and does not adequately consider either the importance of energy or the work of nature

A tool to balance benefit and harm when deciding about adjuvant therapy

Adjuvant therapy aims to prevent outgrowth of residual disease but can induce serious side effects. Weighing conflicting treatment effects and communicating this information with patients is not elementary. This study presents a scheme balancing benefit and harm of adjuvant therapy vs no adjuvant therapy. It is illustrated by the available evidence on adjuvant pelvic external beam radiotherapy (RT) for intermediate-risk stage I endometrial carcinoma patients. The scheme comprises five outcome possibilities of adjuvant therapy: patients who benefit from adjuvant therapy (some at the cost of complications) vs those who neither benefit nor contract complications, those who do not benefit but contract severe complications, or those who die. Using absolute risk differences, a fictive cohort of 1000 patients receiving adjuvant RT is categorised. Three large randomised clinical trials were included. Recurrences will be prevented by adjuvant RT in 60 patients, a majority of 908 patients will neither benefit nor suffer severe radiation-induced harm but 28 patients will suffer severe complications due to adjuvant RT and an expected four patients will die. This scheme readily summarises the different possible treatment outcomes and can be of practical value for clinicians and patients in decision making about adjuvant therapies

Parameter identification problems in the modelling of cell motility

We present a novel parameter identification algorithm for the estimation of parameters in models of cell motility using imaging data of migrating cells. Two alternative formulations of the objective functional that measures the difference between the computed and observed data are proposed and the parameter identification problem is formulated as a minimisation problem of nonlinear least squares type. A Levenberg–Marquardt based optimisation method is applied to the solution of the minimisation problem and the details of the implementation are discussed. A number of numerical experiments are presented which illustrate the robustness of the algorithm to parameter identification in the presence of large deformations and noisy data and parameter identification in three dimensional models of cell motility. An application to experimental data is also presented in which we seek to identify parameters in a model for the monopolar growth of fission yeast cells using experimental imaging data. Our numerical tests allow us to compare the method with the two different formulations of the objective functional and we conclude that the results with both objective functionals seem to agree

The Zero Emissions Commitment Model Intercomparison Project (ZECMIP) contribution to C4MIP: Quantifying committed climate changes following zero carbon emissions

The amount of additional future temperature change following a complete cessation of CO2 emissions is a measure of the unrealized warming to which we are committed due to CO2 already emitted to the atmosphere. This “zero emissions commitment” (ZEC) is also an important quantity when estimating the remaining carbon budget – a limit on the total amount of CO2 emissions consistent with limiting global mean temperature at a particular level. In the recent IPCC Special Report on Global Warming of 1.5 ∘C, the carbon budget framework used to calculate the remaining carbon budget for 1.5 ∘C included the assumption that the ZEC due to CO2 emissions is negligible and close to zero. Previous research has shown significant uncertainty even in the sign of the ZEC. To close this knowledge gap, we propose the Zero Emissions Commitment Model Intercomparison Project (ZECMIP), which will quantify the amount of unrealized temperature change that occurs after CO2 emissions cease and investigate the geophysical drivers behind this climate response. Quantitative information on ZEC is a key gap in our knowledge, and one that will not be addressed by currently planned CMIP6 simulations, yet it is crucial for verifying whether carbon budgets need to be adjusted to account for any unrealized temperature change resulting from past CO2 emissions. We request only one top-priority simulation from comprehensive general circulation Earth system models (ESMs) and Earth system models of intermediate complexity (EMICs) – a branch from the 1 % CO2 run with CO2 emissions set to zero at the point of 1000 PgC of total CO2 emissions in the simulation – with the possibility for additional simulations, if resources allow. ZECMIP is part of CMIP6, under joint sponsorship by C4MIP and CDRMIP, with associated experiment names to enable data submissions to the Earth System Grid Federation. All data will be published and made freely available

Search for Charginos with a Small Mass Difference with the Lightest Supersymmetric Particle at \sqrt{s} = 189 GeV

A search for charginos nearly mass-degenerate with the lightest supersymmetric particle is performed using the 176 pb^-1 of data collected at 189 GeV in 1998 with the L3 detector. Mass differences between the chargino and the lightest supersymmetric particle below 4 GeV are considered. The presence of a high transverse momentum photon is required to single out the signal from the photon-photon interaction background. No evidence for charginos is found and upper limits on the cross section for chargino pair production are set. For the first time, in the case of heavy scalar leptons, chargino mass limits are obtained for any \tilde{\chi}^{+-}_1 - \tilde{\chi}^0_1 mass difference

Search for Branons at LEP

We search, in the context of extra-dimension scenarios, for the possible existence of brane fluctuations, called branons. Events with a single photon or a single Z-boson and missing energy and momentum collected with the L3 detector in e^+ e^- collisions at centre-of-mass energies sqrt{s}=189-209$ GeV are analysed. No excess over the Standard Model expectations is found and a lower limit at 95% confidence level of 103 GeV is derived for the mass of branons, for a scenario with small brane tensions. Alternatively, under the assumption of a light branon, brane tensions below 180 GeV are excluded

Search for Low Scale Gravity Effects in e+e- Collisions at LEP

Recent theories propose that quantum gravity effects may be observable at LEP energies via gravitons that couple to Standard Model particles and propagate into extra spatial dimensions. The associated production of a graviton and a photon is searched for as well as the effects of virtual graviton exchange in the processes: e+e- -> gamma gamma, ZZ, WW, mu mu, tau tau, qq and ee No evidence for this new interaction is found in the data sample collected by the L3 detector at LEP at centre-of-mass energies up to 183 GeV. Limits close to 1 TeV on the scale of this new scenario of quantum gravity are set