Predicted Growth Rates Research Articles

What sets aeolian dune height?

Wherever a loose bed of sand is subject to sufficiently strong winds, aeolian dunes form at predictable wavelengths and growth rates. As dunes mature and coarsen, however, their growth trajectories become more idiosyncratic; nonlinear effects, sediment supply, wind variability and geologic constraints become increasingly relevant, resulting in complex and history-dependent dune amalgamations. Here we examine a fundamental question: do aeolian dunes stop growing and, if so, what determines their ultimate size? Earth’s major sand seas are populated by giant sand dunes, evolved over tens of thousands of years. We perform a global analysis of the topography of these giant dunes, and their associated atmospheric forcings and geologic constraints, and we perform numerical experiments to gain insight on temporal evolution of dune growth. We find no evidence of a previously proposed limit to dune size by atmospheric boundary layer height. Rather, our findings indicate that dunes may grow indefinitely in principle; but growth depends on morphology, slows with increasing size, and may ultimately be limited by sand supply.

Inter-individual variation in mitochondrial phosphorylation efficiency predicts growth rates in ectotherms at high temperatures.

There is increasing evidence that aquatic ectotherms are especially vulnerable to global warming since their metabolic demands increase with ambient temperature while water-oxygen content decreases. The possible role of shrinking aerobic scope in limiting performance has been much discussed; however, less attention has been given to whether tissue-level changes in the efficiency of oxygen usage occur at elevated temperatures. Here, we show that this varies widely among individuals, with consequences for performance. We examined the inter-individual variation in growth rate and mitochondrial function from white muscle and liver of brown trout (Salmo trutta) acclimated to either high (19.5°C) or near-optimal temperature (12°C). Liver (but not muscle) mitochondria showed a positive relationship between growth rate and maximal oxidative phosphorylation at both temperatures, and a negative relationship between growth rate and ROS release. There was a positive correlation in both tissues between individual mitochondrial phosphorylation efficiency and growth rate, but only at 19.5°C. In this representative of aquatic ectotherms, an individual's liver mitochondrial efficiency thus seems to dictate its capacity to grow at elevated temperatures. This suggests that individual heterogeneity in cellular function may cause variation in the thermal limits of aquatic ectotherms and could adversely affect wild populations in warming environments.

Model applicability to predict growth rate of insects throughout storage of corn grain (Zea mays L.)

Insect pest infestation in stored grains can cause several losses during storage, in addition to promoting the spread of fungi, changing the temperature of the grain mass, and reducing the value of the final product. Knowing the behavior of these insect pests and how they reproduce in the grain mass is essential to design more efficient control strategies and ensure a quality final product. Thus, this work aimed to accomplish modeling and simulation of the population growth of insects Rhyzopertha dominica, Sitophilus oryzae, Oryzaephilus surinamensis and Tribolium castaneum throughout the storage of corn grain, using data retrieved from digital sensors of temperature installed in three Brazilian storage facilities in different regions. Data were collected through managing system CERES (company Procer Automação e Sistemas) and retrieved from 1st of July to 29th September 2019. In each one of the facilities, a silo equipped with the aforementioned sensors was used. Mean weekly values of temperature of the grain mass and the intergranular relative humidity were used, calculated using the Modified Henderson equation. The silos evaluated in facilities 1, 2, and 3 have a static capacity of 2,100; 6,304, and 93 tones, respectively, considering soybean with a bulk density of 750 kg m-3. Higher growth rates of all assessed species were observed for the storage facility number 2; and lowest values for storage facility number 1. Storage facilities that presented a higher potential for the growth rate of insects are subjected to elevated levels of insect populations throughout time.

Quantile regression estimates of animal population trends

AbstractEcologists often estimate population trends of animals in time series of counts using linear regression to estimate parameters in a linear transformation of multiplicative growth models, where logarithms of rates of change in counts in time intervals are used as response variables. We present quantile regression estimates for the median (0.50) and interquartile (0.25, 0.75) relationships as an alternative to mean regression estimates for common density‐dependent and density‐independent population growth models. We demonstrate that the quantile regression estimates are more robust to outliers and require fewer distributional assumptions than conventional mean regression estimates and can provide information on heterogeneous rates of change ignored by mean regression. We provide quantile regression trend estimates for 2 populations of greater sage‐grouse (Centrocercus urophasianus) in Wyoming, USA, and for the Crawford population of Gunnison sage‐grouse (Centrocercus minimus) in southwestern Colorado, USA. Our selected Gompertz models of density dependence for both populations of greater sage‐grouse had smaller negative estimates of density‐dependence terms and less variation in corresponding predicted growth rates (λ) for quantile than mean regression models. In contrast, our selected Gompertz models of density dependence with piecewise linear effects of years for the Crawford population of Gunnison sage‐grouse had predicted changes in λ across years from quantile regressions that varied more than those from mean regression because of heterogeneity in estimated λs that were both less and greater than mean estimates. Our results add to literature establishing that quantile regression provides better behaved estimates than mean regression when there are outlying growth rates, including those induced by adjustments for zeros in the time series of counts. The 0.25 and 0.75 quantiles bracketing the median provide robust estimates of population changes (λ) for the central 50% of time series data and provide a 50% prediction interval for a single new prediction without making parametric distributional assumptions or assuming homogeneous λs. Compared to mean estimates, our quantile regression trend estimates for greater sage‐grouse indicated less variation in density‐dependent λs by minimizing sensitivity to outlying values, and for Gunnison sage‐grouse indicated greater variation in density‐dependent λs associated with heterogeneity among quantiles.

Diameters of phloem sieve elements can predict stem growth rates of woody plants.

Understanding forest dynamics is crucial to addressing climate change and reforestation challenges. Plant anatomy can help predict growth rates of woody plants, contributing key information on forest dynamics. Although features of the water-transport system (xylem) have long been used to predict plant growth, the potential contribution of carbon-transporting tissue (phloem) remains virtually unexplored. Here, we use data from 347 woody plant species to investigate whether species-specific stem diameter growth rates can be predicted by the diameter of both the xylem and phloem conducting cells when corrected for phylogenetic relatedness. We found positive correlations between growth rate, phloem sieve element diameter and xylem vessel diameter in liana species sampled in the field. Moreover, we obtained similar results for data extracted from the Xylem Database, an online repository of functional, anatomical and image data for woody plant species. Information from this database confirmed the correlation of sieve element diameter and growth rate across woody plants of various growth forms. Furthermore, we used data subsets to explore potential influences of biomes, growth forms and botanical family association. Subsequently, we combined anatomical and geoclimatic data to train an artificial neural network to predict growth rates. Our results demonstrate that sugar transport architecture is associated with growth rate to a similar degree as water-transport architecture. Furthermore, our results illustrate the potential value of artificial neural networks for modeling plant growth under future climatic scenarios.

Research on fatigue crack growth behavior: Experimental and prediction method of titanium alloy

A new type of titanium alloy for the pressure shell of a manned deep-sea submersible is studied. The fatigue performance and the fracture toughness of the titanium alloy are obtained under R = 0.03, 0.3, and 0.5. The curves of fatigue crack growth rate under three load ratios are predicted based on the existing fatigue crack growth rate prediction model. The results show that the fatigue crack growth threshold decreases with the increase of the load ratio, and the fatigue crack growth rate increases with the increase of the load ratio. The fatigue crack growth prediction model can accurately predict the fatigue crack growth rate of titanium alloy under different load ratios.

Morphological heterogeneity description enabled early and parallel non-invasive prediction of T-cell proliferation inhibitory potency and growth rate for facilitating donor selection of human mesenchymal stem cells

BackgroundWithin the extensively developed therapeutic application of mesenchymal stem cells (MSCs), allogenic immunomodulatory therapy is among the promising categories. Although donor selection is a critical early process that can maximize the production yield, determining the promising candidate is challenging owing to the lack of effective biomarkers and variations of cell sources. In this study, we developed the morphology-based non-invasive prediction models for two quality attributes, the T-cell proliferation inhibitory potency and growth rate.MethodsEleven lots of mixing bone marrow-derived and adipose-derived MSCs were analyzed. Their morphological profiles and growth rates were quantified by image processing by acquiring 6 h interval time-course phase-contrast microscopic image acquisition. T-cell proliferation inhibitory potency was measured by employing flow cytometry for counting the proliferation rate of peripheral blood mononuclear cells (PBMCs) co-cultured with MSCs. Subsequently, the morphological profile comprising 32 parameters describing the time-course transition of cell population distribution was used for explanatory parameters to construct T-cell proliferation inhibitory potency classification and growth rate prediction models. For constructing prediction models, the effect of machine learning methods, parameter types, and time-course window size of morphological profiles were examined to identify those providing the best performance.ResultsUnsupervised morphology-based visualization enabled the identification of anomaly lots lacking T-cell proliferation inhibitory potencies. The best performing machine learning models exhibited high performances of predictions (accuracy > 0.95 for classifying risky lots, and RMSE < 1.50 for predicting growth rate) using only the first 4 days of morphological profiles. A comparison of morphological parameter types showed that the accumulated time-course information of morphological heterogeneity in cell populations is important for predicting the potencies.ConclusionsTo enable more consistent cell manufacturing of allogenic MSC-based therapeutic products, this study indicated that early non-invasive morphology-based prediction can facilitate the lot selection process for effective cell bank establishment. It was also found that morphological heterogeneity description is important for such potency prediction. Furthermore, performances of the morphology-based prediction models trained with data consisting of origin-different MSCs demonstrated the effectiveness of sharing morphological data between different types of MSCs, thereby complementing the data limitation issue in the morphology-based quality prediction concept.

Basin-scale biogeography of marine phytoplankton reflects cellular-scale optimization of metabolism and physiology.

Extensive microdiversity within Prochlorococcus, the most abundant marine cyanobacterium, occurs at scales from a single droplet of seawater to ocean basins. To interpret the structuring role of variations in genetic potential, as well as metabolic and physiological acclimation, we developed a mechanistic constraint-based modeling framework that incorporates the full suite of genes, proteins, metabolic reactions, pigments, and biochemical compositions of 69 sequenced isolates spanning the Prochlorococcus pangenome. Optimizing each strain to the local, observed physical and chemical environment along an Atlantic Ocean transect, we predicted variations in strain-specific patterns of growth rate, metabolic configuration, and physiological state, defining subtle niche subspaces directly attributable to differences in their encoded metabolic potential. Predicted growth rates covaried with observed ecotype abundances, affirming their significance as a measure of fitness and inferring a nonlinear density dependence of mortality. Our study demonstrates the potential to interpret global-scale ecosystem organization in terms of cellular-scale processes.

Application of finite volume particle method for axisymmetric modeling of droplet formation in dripping and Rayleigh regimes

An axisymmetric model has been developed in the finite volume particle method (FVPM). FVPM is a conservative, consistent, meshless particle method that incorporates properties of both smoothed particle hydrodynamics (SPH) and the mesh-based finite volume method (FVM). The surface tension effect amplifies capillary instability which is the main mechanism in dripping and jet disintegration. The simulations are performed in 2D cylindrical coordinates by only considering the liquid flow (one phase). The model is validated for free liquid droplet evolved from cylindrical state, dripping from a capillary tube, Rayleigh instability of capillary liquid cylinder columns, and Rayleigh breakup of viscous jets. The FVPM-predicted pressure and oscillating period are in good agreement with theoretical solution for a free droplet. The FVPM-computed dripping length shows good consistency with provided experimental data in literature. The predicted growth rate of capillary instability is in good agreement with analytical solutions for flows with different orders of Ohnesorge numbers. The numerical breakup length of jet with Weber numbers between 2.5 and 40 has been determined with good accuracy based on analytical experimental solution.

Multiphysics modeling of environment assisted cracking of buried pipelines in contact with solutions of near-neutral pH

This paper presents a multiphysics model to predict environment assisted cracking in buried pipelines exposed to near-neutral pH solutions. In the calculation of crack growth rates, we involve two main cracking modes: hydrogen-facilitated fatigue and hydrogen-facilitated anodic dissolution. The former is fully coupled under the hydrogen-enhanced localized plasticity theory with a focus on hydrogen embrittlement produced by hydrogen diffusion and trapping. This provides formalism to account for contributions of electrochemical corrosion and variation of mechanically-diffusive hydrogen on the crack growth rates. The latter is explicitly quantified using the high-resolution crack tip fields. A competitive-based model is employed to evaluate crack growth rates. The model is verified by comparing experimentally measured and numerically simulated crack growth rates of a full-scale test reported in the literature. The results of this investigation show that the effect of hydrogen on the anodic-dissolution results in conservative predictions of the crack growth rates.

ECMpy, a Simplified Workflow for Constructing Enzymatic Constrained Metabolic Network Model.

Genome-scale metabolic models (GEMs) have been widely used for the phenotypic prediction of microorganisms. However, the lack of other constraints in the stoichiometric model often leads to a large metabolic solution space being inaccessible. Inspired by previous studies that take an allocation of macromolecule resources into account, we developed a simplified Python-based workflow for constructing enzymatic constrained metabolic network model (ECMpy) and constructed an enzyme-constrained model for Escherichia coli (eciML1515) by directly adding a total enzyme amount constraint in the latest version of GEM for E. coli (iML1515), considering the protein subunit composition in the reaction, and automated calibration of enzyme kinetic parameters. Using eciML1515, we predicted the overflow metabolism of E. coli and revealed that redox balance was the key reason for the difference between E. coli and Saccharomyces cerevisiae in overflow metabolism. The growth rate predictions on 24 single-carbon sources were improved significantly when compared with other enzyme-constrained models of E. coli. Finally, we revealed the tradeoff between enzyme usage efficiency and biomass yield by exploring the metabolic behaviours under different substrate consumption rates. Enzyme-constrained models can improve simulation accuracy and thus can predict cellular phenotypes under various genetic perturbations more precisely, providing reliable guidance for metabolic engineering.

On the Theoretical Prediction of Microalgae Growth for Parallel Flow

The established microalgae growth models are semi-empirical or considerable fitting coefficients exist currently. Therefore, the ability of the model prediction is reduced by the numerous fitting coefficients. Furthermore, the predicted results of the established models are dependent on the size of the photobioreactor (PBR), light intensity, flow and concentration field. The growth mechanism of microalgae has not clearly understood in PBR cultivation. It is difficult to predict the microalgae growth by theoretical methods, owing to the aforementioned factors. We developed an exploratory bridging microalgae growth model to predict the microalgae growth rate in PBRs by using the nondimensional method which is effectively in fluid dynamics and heat transfer. The analytical solution of the growth rate was obtained for the parallel flow. The nondimensional growth rate expressed as function of Reynolds number and Schmidt number, which can be used for arbitrary parallel flow due to the solution was expressed as nondimensional quantities. The theoretically predicted growth rate is compared with the experimentally measured microalgae growth rate on the order of magnitude. The nondimensional method successfully applied to the microalgae growth problem for the first time. The general nondimensional solution can unify the numerous experimental data for different laboratory conditions, and give a direction for the disorder of the microalgae growth problem. The nondimensional solution may be useful to explain the growth mechanism of microalgae and design large-scale PBRs for microalgae biofuel production. The significance of the work is to give a theoretical foundation and methodology of biological theory of microalgae growth.

Effect of underloads in small fatigue crack growth

There is considerable cost incurred within the aircraft industry due to the inability to accurately predict fatigue crack growth rates for complex loading scenarios. This is particularly the case for small cracks. This paper aims to facilitate the improvement of fatigue crack growth by investigating the effects of underloads on small cracks grown in the common airframe structural aluminium alloy; AA7050-T7451. Six novel loading spectra were used to quantify how underload spacing, magnitude, and consecutive application impact crack growth. Quantitative fractography of post-test fractures enabled crack growth measurements to be made at crack lengths as small as 100 µm. It was found that underloads accelerate growth rates most significantly for small cracks, though this effect appears to progressively diminish as the crack grows. The results also suggest that underloads may have both short and long-term effects on crack growth. This research paves the way for accounting for the acceleration of crack growth rates caused for small cracks in predictions, which if not considered, could lead to non-conservative fatigue life predictions. Ultimately, this should allow for more accurate growth rate prediction of high strength aluminium structures subject to underloads, as well as provide quantitative basis for future mechanistic studies.

Machine Learning Based Predictions of Fatigue Crack Growth Rate of Additively Manufactured Ti6Al4V

The present work focusses on machine learning assisted predictions of the fatigue crack growth rate (FCGR) of Ti6Al4V (Ti64) processed through laser powder bed fusion (L-PBF) and post processing. Various machine learning techniques have provided a flexible approach for explaining the complex mathematical interrelationship among processing-structure-property of the materials. In the present work, four machine learning (ML) algorithms, such as K- Nearest Neighbor (KNN), Decision Trees (DT), Random Forests (RF), and Extreme Gradient Boosting (XGB) algorithms are implemented to analyze the Fatigue Crack growth rate (FCGR) of Ti64 alloy. After tuning the hyper parameters for these algorithms, the trained models were found to estimate the unseen data as equally well as the trained data. The four tested ML models are compared with each other over the training as well as testing phase, based on their mean squared error and R2 scores. Extreme Gradient Boosting has performed better for the FCGR predictions providing least mean squared errors and higher R2 scores compared to other models.

Prediction of fatigue crack growth rate in aircraft aluminum alloys using optimized neural networks

In aerospace industry, Fatigue Crack Propagation pose a serious threat to the professionals involved in designing mechanical assembly of the aircraft structures. In these structures crack growth is a problem that needs to be handled seriously, as human life risk is concerned in addition to economic loss. Fatigue Crack Growth (FCG) Rate is the rate at which crack grows with number of cycles subjected to constant amplitude loading. FCG curve is drawn between crack growth rate on y-axis and Stress Intensity Factor (SIF) range on x-axis. It needs to be predicted accurately to avoid losses. Upon analyzing the curve, it becomes obvious that the correlation between Stress Intensity Factor (SIF) ranges “ΔK” with FCG rate “da/dN” is deviating linear relationship considering region II of the curve that is also called Paris Region. Empirical formulation methods cannot deal with linearity factor satisfactorily. Other hybrid techniques are also found incapable of dealing with non-linearity suitably. In contrast to the prior methods, machine learning algorithms are capable to deal with the non-linearity issue in a much better way owing to their admirable learning ability and flexible nature. In this research work three distinct MLA based Optimized Neural Networks are utilized for prediction of FCG rate. The used algorithms include Genetic Algorithm based Optimized Neural Network, Hill Climbing based Optimized Neural Network and Simulated Annealing based Optimized Neural Network. The algorithms presented in the proposed technique are validated by testing on different aluminum alloys used for aerospace industry that includes 2324-T39, 7055-T7511 and 6013-T651 aluminum alloys. The least predicted MSE for 2324-T39 aluminum alloy is achieved by Hill Climbing based Optimized Neural Network that is 3.1069×10-8. For 7055-T7511 alloy, minimum predicted MSE is 1.4284×10-9 which is achieved by Hill Climbing based Optimized Neural Network. Finally, the least predicted MSE for 6013-T651 is 1.0559×10-9 that is achieved by Simulated Annealing based optimized Neural Network. Taking all alloys on which experiments were held with used algorithms, the least predicted MSE that is attained is 1.0559×10-9 for 6013-T651 Aluminum Alloy with Simulated Annealing based Optimized Neural Network. Moreover, the results demonstrate an exceptional conformity to the data conceived during experimentation process.

Timber harvesting on fragile ground and impacts of uncertainties in the operational costs

ABSTRACT Forested wetlands with high water tables are sensitive to disruption from harvesting yet support commercially desired tree species like northern white-cedar. Winter harvest was conducted in Maine, USA, to compare operational costs and productivity of cut-to-length harvesting in cedar (fragile soil) and non-cedar stands (mixedwood, sturdy soil), evaluate uncertainties in harvesting costs and influential factors, and forecast time for post-harvest recovery to pre-harvest volumes. Operational costs were calculated using detailed time and motion studies. Operational costs for the cedar stands were higher than non-cedar. Regression models were developed for harvesters, forwarders, and self-loading trucks; number of logs per cycle was a common factor. Sensitivity analysis showed the dependence of operational costs on labor and fuel costs. Forest Vegetation Simulator projections were used to assess harvest sustainability and suggested the time required to regrow harvested merchantable volume is comparable to cutting cycles recommended for similar treatments in the region. Predicted growth rates exceed those reported regionally on similar sites, suggesting additional study of post-harvest response is warranted. Results highlight site constraints on both operational and stand productivity in lowlands and will be useful for timber harvesting decision-making and forest management planning if combined with assessment of residual stand growth response.

Abstract 12495: Differentiating Slow- And Rapid-growing Abdominal Aortic Aneurysms Using Computational Modeling

Introduction: Abdominal aortic aneurysms (AAA) are common among individuals over age 65 with a prevalence of 9%. Most AAAs are small and require surveillance imaging to monitor size and growth rates. Tools capable of predicting growth rates would be clinically useful. Hypothesis: We investigated how computational hemodynamics derived from CT angiography (CTA) differentiated slow and rapid-growing AAAs. Methods: Using readily available computing power in the clinical workflow, 3D models were generated for 25 subjects with AAA from available 3D CTA data. AAA growth rates were categorized as slow ( less than 5 mm/year) or rapid (greater than 5 mm/year) based on serial imaging. A geometrical analysis was performed using 7 indices (Volume, Surface Area, Length, Max/Min radius, Expansion Ratio [Mean radius/vessel radius], Proximal vessel area, Voronoi diagram-based metrics) for each AAA. Computational hemodynamics were performed using commercial software (ANSYS-Fluent). Computational hemodynamics were completed for all cases (see Fig. 1). In-house software assessed wall shear stress-(WSS) and vortex-based hemodynamics. Support vector machine (SVM) was run with cross-validation (50 iterations) to assess predictive strength using these 16 parameters. Results: Among 25 AAAs studied, 7 were rapid and 18 were slow-growing. The combination of proximal vessel area, spatiotemporally averaged WSS, and spatially averaged OSI provided the best accuracy for growth differentiation. The area under receiving operating curve (AUROC) and total accuracy are 0.770 and 0.765, respectively, with 48% and 86% of rapid and slow-growing AAAs correctly identified, respectively. Conclusions: Computational hemodynamics appears feasible with reasonable growth rate prediction. With optimization, this tool may enable resource utilization refinement for imaging surveillance strategies by distinguishing rapid from slow-growing AAAs.

High-Quality Genome-Scale Reconstruction of Corynebacterium glutamicum ATCC 13032.

Corynebacterium glutamicum belongs to the microbes of enormous biotechnological relevance. In particular, its strain ATCC 13032 is a widely used producer of L-amino acids at an industrial scale. Its apparent robustness also turns it into a favorable platform host for a wide range of further compounds, mainly because of emerging bio-based economies. A deep understanding of the biochemical processes in C. glutamicum is essential for a sustainable enhancement of the microbe's productivity. Computational systems biology has the potential to provide a valuable basis for driving metabolic engineering and biotechnological advances, such as increased yields of healthy producer strains based on genome-scale metabolic models (GEMs). Advanced reconstruction pipelines are now available that facilitate the reconstruction of GEMs and support their manual curation. This article presents iCGB21FR, an updated and unified GEM of C. glutamicum ATCC 13032 with high quality regarding comprehensiveness and data standards, built with the latest modeling techniques and advanced reconstruction pipelines. It comprises 1042 metabolites, 1539 reactions, and 805 genes with detailed annotations and database cross-references. The model validation took place using different media and resulted in realistic growth rate predictions under aerobic and anaerobic conditions. The new GEM produces all canonical amino acids, and its phenotypic predictions are consistent with laboratory data. The in silico model proved fruitful in adding knowledge to the metabolism of C. glutamicum: iCGB21FR still produces L-glutamate with the knock-out of the enzyme pyruvate carboxylase, despite the common belief to be relevant for the amino acid's production. We conclude that integrating high standards into the reconstruction of GEMs facilitates replicating validated knowledge, closing knowledge gaps, and making it a useful basis for metabolic engineering. The model is freely available from BioModels Database under identifier MODEL2102050001.

A conserved expression signature predicts growth rate and reveals cell & lineage-specific differences.

Isogenic cells cultured together show heterogeneity in their proliferation rate. To determine the differences between fast and slow-proliferating cells, we developed a method to sort cells by proliferation rate, and performed RNA-seq on slow and fast proliferating subpopulations of pluripotent mouse embryonic stem cells (mESCs) and mouse fibroblasts. We found that slowly proliferating mESCs have a more naïve pluripotent character. We identified an evolutionarily conserved proliferation-correlated transcriptomic signature that is common to all eukaryotes: fast cells have higher expression of genes for protein synthesis and protein degradation. This signature accurately predicted growth rate in yeast and cancer cells, and identified lineage-specific proliferation dynamics during development, using C. elegans scRNA-seq data. In contrast, sorting by mitochondria membrane potential revealed a highly cell-type specific mitochondria-state related transcriptome. mESCs with hyperpolarized mitochondria are fast proliferating, while the opposite is true for fibroblasts. The mitochondrial electron transport chain inhibitor antimycin affected slow and fast subpopulations differently. While a major transcriptional-signature associated with cell-to-cell heterogeneity in proliferation is conserved, the metabolic and energetic dependency of cell proliferation is cell-type specific.

Effect of Material Heterogeneity on Environmentally Assisted Cracking Growth Rate of Alloy 600 for Safe-End Welded Joints.

Environmentally assisted cracking (EAC) is essential in predicting light water reactors’ structural integrity and service life. Alloy 600 (equivalent to Inconel 600) has excellent corrosion resistance and is often used as a welding material in welded joints, but material properties of the alloy are heterogeneous in the welded zone due to the complex welding process. To investigate the EAC crack growth behavior of Alloy 600 for safe-end welded joints, the method taken in this paper concerns the probability prediction of the EAC crack growth rate. It considers the material heterogeneity, combining the film slip-dissolution/oxidation model, and the elastic-plastic finite element method. The strain rate at the crack tip is a unique factor to describe the mechanical state. Still, it is challenging to accurately predict it because of the complicated and heterogeneous material microstructure. In this study, the effects of material heterogeneity on the EAC crack growth behavior are statistically analyzed. The results show that the material heterogeneity of Alloy 600 can not be ignored because it affects the prediction accuracy of the crack growth rate. The randomness of yield strength has the most influence on the EAC growth rate, while Poisson’s ratio has the smallest.