Growth Model Research Articles

Differential growth regulates asymmetric size partitioning in Caulobacter crescentus.

Cell size regulation has been extensively studied in symmetrically dividing cells, but the mechanisms underlying the control of size asymmetry in asymmetrically dividing bacteria remain elusive. Here, we examine the control of asymmetric division in Caulobacter crescentus, a bacterium that produces daughter cells with distinct fates and morphologies upon division. Through comprehensive analysis of multi-generational growth and shape data, we uncover a tightly regulated cell size partitioning mechanism. We find that errors in division site positioning are promptly corrected early in the division cycle through differential growth. Our analysis reveals a negative feedback between the size of daughter cell compartments and their growth rates, wherein the larger compartment grows slower to achieve a homeostatic size partitioning ratio at division. To explain these observations, we propose a mechanistic model of differential growth, in which equal amounts of growth regulators are partitioned into daughter cell compartments of unequal sizes and maintained over time via size-independent synthesis.

Performance analysis of mathematical methods used to forecast the 2022 New York City Mpox outbreak.

In mid-2022, New York City (NYC) became the epicenter of the US mpox outbreak. We provided real-time mpox case forecasts to the NYC Department of Health and Mental Hygiene to aid in outbreak response. Forecasting methodologies evolved as the epidemic progressed. Initially, lacking knowledge of at-risk population size, we used exponential growth models to forecast cases. Once exponential growth slowed, we used a Susceptible-Exposed-Infectious-Recovered (SEIR) model. Retrospectively, we explored if forecasts could have been improved using an SEIR model in place of our early exponential growth model, with or without knowing the case detection rate. Early forecasts from exponential growth models performed poorly, as 2-week mean absolute error (MAE) grew from 53 cases/week (July 1-14)to 457 cases/week (July 15-28).However, when exponential growth slowed, providing insight into susceptible population size, an SEIR model was able to accurately predict the remainder of the outbreak (7-week MAE: 13.4 cases/week). Retrospectively, we found there was not enough known about the epidemiological characteristics of the outbreak to parameterize an SEIR model early on. However, if the at-risk population and case detection rate were known, an SEIR model could have improved accuracy over exponential growth models early in the outbreak.

Подбор параметров моделей роста радужной форели

The purpose of the presented work is to assess the adequacy and selection of optimal parameters of mathematical models of fish growth in application to forecasting the growth of aquaculture trout in closed water supply installations (CWSI). To perform the assessment and comparison, 8 nonlinear mathematical models of fish growth were considered. Tabular (parametric) models of feed for trout cultivation in the CWSI were taken as a standard, and on their basis the selection of optimal parameters was carried out, bringing each of the mathematical models closer to the standard one. The results were compared using an estimate of the standard error, the Akaike information criterion adjusted for a small sample size, and the Bayesian information criterion. As a result, the parameters providing the best approximation of the considered mathematical models of fish growth to the reference tabular function are obtained. As a result of the error estimation, it was found that the three-parameter von Bertalanfi model demonstrates the best accuracy. At the same time, other models (with the exception of exponential) have also demonstrated accuracy sufficient for practical use. The practical significance of the work is the conclusions on the adequacy of the use of nonlinear mathematical models for modeling trout growth in the CWSI, as well as the presented model parameters that provide the best approximation to the reference tabular function.

No association between markers of systemic inflammation and endothelial dysfunction with Alzheimer's disease progression: a longitudinal study.

Systemic inflammation and endothelial dysfunction are potentially modifiable factors implicated in Alzheimer's disease (AD), which offer potential therapeutic targets to slow disease progression. We investigated the relationship between baseline circulating levels of inflammatory (TNF-α, IL-1ß) and endothelial cell markers (VCAM-1, ICAM-1, E-selectin) and 18-month cognitive decline (ADAS-cog12) in 266 mild-to-moderate AD patients from the NILVAD study. We employed individual growth models to examine associations, potential mediation, and interaction effects while adjusting for confounders. The average increase in ADAS-cog12 scores over all patients was 8.1 points in 18months. No significant association was found between the markers and the rate of cognitive decline. Mediation analysis revealed no mediating role for endothelial cell markers, and interaction effects were not observed. Our results do not support the role of systemic inflammation or endothelial dysfunction in progression in persons with AD.

Estimating body composition using CT scans of cross-bred lambs fed at two feeding levels and two stages of maturity to inform predictive growth models1.

Livestock producers would benefit from more precise predictions of the growth response from nutrients consumed. Previously published models are often limited by the realities of data collection and are unable to account for alterations to body composition, due in part to the response of visceral organs to an alternate diet. The computerized tomography (CT) scanning of lambs enables the analysis of changes in body composition of individual animals over time, potentially supporting better model development and testing. The aim of this experiment was to develop a repeatable method for the analysis of live lamb body composition using CT scans. A secondary aim was to compare the data collected from CT scanning during a feeding trial to two predictive lamb growth models. Cross-bred lambs were fed two feeding levels at two stages of maturity, with CT scans at the beginning and end of each eight-week feeding period. The CT scan derived values for body composition taken at the beginning of feeding periods were used as inputs for two existing lamb growth models. Predictions of body composition were compared with CT scan derived values at the end of feeding periods. The CT scan analysis method used a proportion of images from each lamb to reduce manual image editing. The method was developed by comparing the estimated mass and volume of empty body components using all available CT scans to estimated values using a reduced number of scans from 12 lambs. The CT scan derived lean tissue mass aligned with model predictions at the end of each feeding period, however, CT scan derived fat mass was greater than predictions by both models especially for the high feeding level at the later stage of maturity. These results highlight that the analysis of body composition using CT scans requires further validation, particularly for the viscera, and that models likely require refinement to better predict the efficiency of energy utilisation by different tissues. The use of live animal CT scans can provide more accurate predictions of the growth of saleable products than measuring liveweight alone and will enable ruminant growth models to better adapt to different genetics and changing diets than comparative slaughter. To replicate the current data using comparative slaughter would require four times the animals, as individual lambs were CT scanned four times in this study, demonstrating the potential value of CT scanning in live animal research.

Navigating the Startup Innovation Ecosystem: Strategies for Effective External Resource Management

This research examines how innovation ecosystems facilitate startup innovation across the creation, development, and market phases. We analyze the roles of diverse ecosystem actors, emphasizing their contributions to resource dynamics using the case method approach. Semi-structured interview-based data was analyzed using content analysis. The study revealed the significance of the strategy of using external resources for the success of startups. During the creation phase, non-market-oriented entities like universities and incubators support startups with crucial early-stage R&D funding and business model formulation. In the development phase, startups refine their products with backing from accelerators, investors, and customers, utilizing innovation resources for prototyping and validation, and transitioning into the market phase, startups scale operations with market-oriented actors, focusing on organizational readiness and leveraging established business models for growth. Throughout these phases, startups balance exploration and exploitation strategies, enabling innovation ambidexterity. Our findings highlight how innovation ecosystem actors provide essential resources—physical, social, financial, and human—that foster startup resilience and maturity. Grounded in the Resource-Based View framework, this study offers empirical insights into strategic adaptations within innovation ecosystems, illustrating their transformative impact on startup success.<

Linking genetic markers and crop model parameters using neural networks to enhance genomic prediction of integrative traits

IntroductionPredicting the performance (yield or other integrative traits) of cultivated plants is complex because it involves not only estimating the genetic value of the candidates to selection, the interactions between the genotype and the environment (GxE) but also the epistatic interactions between genomic regions for a given trait, and the interactions between the traits contributing to the integrative trait. Classical Genomic Prediction (GP) models mostly account for additive effects and are not suitable to estimate non-additive effects such as epistasis. Therefore, the use of machine learning and deep learning methods has been previously proposed to model those non-linear effects.MethodsIn this study, we propose a type of Artificial Neural Network (ANN) called Convolutional Neural Network (CNN) and compare it to two classical GP regression methods for their ability to predict an integrative trait of sorghum: aboveground fresh weight accumulation. We also suggest that the use of a crop growth model (CGM) can enhance predictions of integrative traits by decomposing them into more heritable intermediate traits.ResultsThe results show that CNN outperformed both LASSO and Bayes C methods in accuracy, suggesting that CNN are better suited to predict integrative traits. Furthermore, the predictive ability of the combined CGM-GP approach surpassed that of GP without the CGM integration, irrespective of the regression method used.DiscussionThese results are consistent with recent works aiming to develop Genome-to-Phenotype models and advocate for the use of non-linear prediction methods, and the use of combined CGM-GP to enhance the prediction of crop performances.

Macroeconomic uncertainty and the excess returns of stock

This paper proposes a theoretical model based on traditional ICAPM and dividend growth models but with variance-belief formation features such as overextrapolation and sticky expectation to link the macroeconomic uncertainty (MU hereafter) and stock excess returns. We predict a nuanced, possibly negative, intertemporal MU-return trade-off and a negative contemporaneous relationship between the change in MU and excess returns. The empirical analysis utilizing panel data from 46 stock markets validates our model but also reveals the heterogeneity across markets with different economic levels, financial development, and national culture. The impact of MU is amplified during global and country-specific financial crises. In addition, we also suggest that MU indeed is a significant source of realized variance of excess returns in stock markets.

PhysioFit: a software to quantify cell growth parameters and extracellular fluxes.

Quantification of growth parameters and extracellular uptake and production fluxes is central in systems and synthetic biology. Fluxes can be estimated using various mathematical models by fitting time-course measurements of the concentration of cells and extracellular substrates and products. A single tool is available to non-computational biologists to calculate extracellular fluxes, but it is hardly interoperable and is limited to a single hard-coded growth model. We present our open-source flux calculation software, PhysioFit, which can be used with any growth model and is interoperable by design. PhysioFit includes some of the most common growth models, and advanced users can implement additional models to calculate extracellular fluxes and other growth parameters for metabolic systems or experimental setups that follow alternative kinetics. PhysioFit can be used as a Python library and offers a graphical user interface for intuitive use by end-users and a command-line interface to streamline integration into existing pipelines. PhysioFit v3 is implemented in Python 3 and was tested on Windows, Unix and MacOS platforms. The source code and the documentation are freely distributed under GPL3 license at https://github.com/MetaSys-LISBP/PhysioFit/ and https://physiofit.readthedocs.io/. Supplementary data are available at Bioinformatics online.

Creep damage evolution by cavity nucleation and growth considering the cavity closure under cyclic loading conditions

AbstractCreep damage assessment is crucial for ensuring the long‐term reliability of key components operating at high temperatures. However, the existing studies on creep damage are mainly focused on constant loading conditions, while the effect of cyclic loading, which is a common loading mode in practice, remains unclear. In this paper, a series of stress‐controlled cyclic creep tests on the Inconel 718 superalloy were performed to investigate the influence of cyclic loading on creep damage evolution. The ex‐situ microstructural analyses, including fracture surface observations and EBSD measurements, were conducted to reveal the damage mechanisms under cyclic creep conditions. Furthermore, a cavity nucleation and growth model that accounts for the additional cavity closure was developed for the healing effect of cyclic creep damage evolution. The prediction results were consistent with the experimental data of cavity nucleation life and experimental life within a factor of 2.

The association of dispositional anxiety with the NoGo N2 under relaxation instruction versus speed/accuracy instruction

Prior research suggests that cognitive control, indicated by NoGo N2 amplitudes in Go/NoGo tasks, is associated with dispositional anxiety. This negative association tends to be reduced in anxiety-enhancing experimental conditions. However, anxiety-reducing conditions have not yet been investigated systematically. Thus, the present study compares the effect of a relaxation instruction with the conventional speed/accuracy instruction in a Go/NoGo task on the correlation of the NoGo N2 with two subconstructs of dispositional anxiety, namely anxious apprehension and anxious arousal. As the test of differences between correlations needs considerable statistical power, the present study was included into the multi-lab CoScience Project. The hypotheses, manipulation checks, and the main path of pre-processing and statistical analysis were preregistered. Complete data sets of 777 participants were available for data analysis. Preregistered general linear models revealed that the different instructions of the task (speed/accuracy vs. relaxation) had no effect on the association between dispositional anxiety and the NoGo N2 amplitude in general. This result was supported by Cooperative-Forking-Path analysis. In contrast, a preregistered latent growth model with categorical variables revealed that anxious arousal was a negative predictor of the NoGo N2 intercept and a positive predictor of the NoGo N2 slope. Non-preregistered growth models, allowing for correlations of anxious apprehension with anxious arousal, revealed that higher anxious apprehension scores were associated with more negative NoGo N2 amplitudes with increased relaxation. Results are discussed in the context of the compensatory error monitoring hypothesis and the revised Reinforcement Sensitivity Theory.

Projected Future Scenario of Electric Vehicles and Their Latent Impact on Electricity Demand in Kenya

Abstract-This study projects the future scenario of electric vehicle (EV) adoption and its impact on electricity demand in Kenya. Using data from the Kenya National Bureau of Statistics on EVs and internal combustion engine vehicles (ICEVs) registered from 2010 to 2022. Logistic growth model (LGM) used for projections was executed using MATLAB. The projected number of EVs by 2050 for conservative (25%) moderate (50%), optimistic (75%), and original (100%) adoption rates were respectively 3,927,131; 7,475,274; 9,880,293 and 10,807,843 with corresponding electricity demand of 14.8 TWh, 28.2 TWh, 37.3 TWh and 40.8 TWh, which increases to 18.7 TWh, 35.7 TWh, 47.2 TWh, and 51.6 TWh when generation and transmission losses are added. Adding electricity demand by other sectors, net electricity generation increases in the range from 37.4 TWh to 70.3 TWh for conservative and original scenarios respectively by 2050, which represent 3-times and 5.5 times the 2022-generation of 13 TWh. In conclusion, EVs penetration into the Kenyan market is currently low. However, their eventual replacement of ICEVs will have significant implications for electricity demand and supply, even under the conservative scenario. Key Words: transportation, electric vehicles, energy demand, logistics growth model, climate change

The impact of ESG performance on corporate sustainable growth from the perspective of carbon sentiment

With the increasing importance of environmental and economic sustainability concerns, the concept of Environmental, Social, and Governance (ESG) has gained significant attention. In the era of digitalization, a research approach called carbon sentiment analysis has emerged as an innovative method. This study aims to explore the connections between carbon sentiment, ESG, and corporate sustainable growth within the context of the green economy. By using Ordinary Least Squares (OLS) regression analysis and establishing a panel data model of ESG performance and sustainable growth for Chinese listed companies, a notable positive correlation between the two variables was observed. Endogeneity was addressed using the two-stage instrumental variable method (2SLS) and the dynamic panel Generalized Method of Moments (GMM) model, with the results remaining robust both before and after the COVID-19 pandemic. Carbon-related news and textual information were collected and analyzed using advanced deep learning methods in Natural Language Processing (NLP), specifically Bidirectional Encoder Representations from Transformers (BERT) and Long Short-Term Memory (LSTM) models. This analysis enabled sentiment analysis and identification of the sentiment orientation of carbon news. The obtained sentiment scores were then integrated with company data to establish a moderation effect model. The findings of the study reveal that carbon sentiment significantly and positively moderates ESG performance in relation to corporate sustainable growth. Furthermore, the construction of a mediation effect model showed that carbon sentiment can moderate ESG performance by reducing environmental uncertainty, enhancing social trust, and alleviating financing constraints, thereby influencing corporate sustainable growth. The results of the heterogeneity group regression analysis demonstrate that the impact of ESG performance driven by carbon sentiment on sustainable growth is more pronounced in carbon market pilot regions, non-heavily polluting industries, and labor-intensive industries. This research provides a fresh perspective for understanding the dynamics of ESG, online carbon sentiment, and their implications for corporate sustainable growth. Additionally, it contributes to the development of the green economy and the formulation of environmental management policies.

Power-law growth models explain incidences and sizes of pancreatic cancer precursor lesions and confirm spatial genomic findings.

Pancreatic ductal adenocarcinoma is a rare but lethal cancer. Recent evidence suggests that pancreatic intraepithelial neoplasia (PanIN), a microscopic precursor lesion that gives rise to pancreatic cancer, is larger and more prevalent than previously believed. Better understanding of the growth-law dynamics of PanINs may improve our ability to understand how a miniscule fraction makes the transition to invasive cancer. Here, using three-dimensional tissue mapping, we analyzed >1000 PanINs and found that lesion size is distributed according to a power law. Our data suggest that in bulk, PanIN size can be predicted by general growth behavior without consideration for the heterogeneity of the pancreatic microenvironment or an individual's age, history, or lifestyle. Our models suggest that intraductal spread and fusing of lesions drive our observed size distribution. This analysis lays the groundwork for future mathematical modeling efforts integrating PanIN incidence, morphology, and molecular features to understand tumorigenesis and demonstrates the utility of combining experimental measurement with dynamic modeling in understanding tumorigenesis.

The relationships between teachers’ evaluation of children’s academic readiness and children’s later outcomes

Kindergarten is emphasized as a critical first entry into the education system, as politicians and pundits believe kindergartners’ success can lead to later academic achievement. Therefore, a comprehensive understanding of kindergarten school readiness data should consider how it is measured and how that affects learning. Using the Early Childhood Longitudinal Studies—Kindergarten (ECLS-K) cohort 2010–2011 data, the author examined the relationship between teacher reports about children’s academic readiness at kindergarten entrance and their later outcomes in reading, math, and science through fifth grade. This study used a latent basis growth model with time-invariant predictors to analyze the relationship's trajectory. Overall, findings determined that teachers’ perceptions of children’s Approaches to Learning, mathematical thinking, and science significantly impacted later achievements in math and science direct assessments throughout elementary education. This research discussed the practice and policy implications on teachers’ perceptions of school readiness and its impact on later academic outcomes.

Sibling relational aggression: Developmental change and associations with family relationships across adolescence.

Sibling relational aggression is an understudied social and family process that is of developmental significance in adolescence, a period of interpersonal relationship development. This study examined developmental change in sibling relational aggression across adolescence and used multilevel actor-partner independence models to test its longitudinal associations with mother-adolescent, father-adolescent, and sibling relationship qualities. Participants were 196 adolescent (firstborn-secondborn) sibling pairs from predominantly White families who averaged 16.47 years (SD = .80) and 13.88 years (SD = 1.15) of age, respectively, at baseline. Data were collected separately from each sibling during home interviews at three timepoints, each spaced a year apart. Longitudinal growth modeling revealed declines in sibling relational aggression from early to late adolescence. Findings from longitudinal multilevel actor-partner independence models revealed a negative within-person (WP) effect of father-adolescent intimacy and a positive WP effect of father-adolescent conflict on sibling relational aggression: On occasions when adolescents reported lower intimacy or more conflict with their fathers than usual (compared to individuals' own cross-time averages), adolescents reported experiencing more sibling relational aggression than usual. A between-person (BP) effect of mother-adolescent conflict also indicated that the cross-time average of mother-adolescent conflict was linked positively to the cross-time average of sibling relationship aggression. Primarily at the WP level, associations also emerged between lower sibling intimacy and higher sibling conflict and sibling relational aggression. For relationships with fathers and siblings, some effects were moderated by sibling structural characteristics. Discussion highlights the interplay of family relationship dynamics, including gender dynamics, and sibling relational aggression in adolescence. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

ANALYSIS OF EXPORT-LED GROWTH MODEL FOR TÜRKİYE ECONOMY WITH MARKOV REGIME SWITCHING MODEL

Research on the export-led growth model is significant due to the contribution of exports to the development of domestic sectors and its positive impact on national values, especially employment and growth. Therefore, countries prefer the export-led growth model by increasing their export volumes with an outward-oriented policy. When Turkiye's growth figures are analyzed, it is feasible to say that there is a strong connection between exports and economic growth. In this study, within the framework of export-led growth hypotheses, the additive of exports to Turkiye's growth dynamics between 1980 and 2022 is investigated. The aim of the study is to econometrically test the intercourse between exports and GDP in the Turkiye economy. In the analysis, first linear unit root analyses were implemented. Then, the dynamic correlation relationship between exports and GDP was analyzed with DCC-GARCH, and the dynamic regression relationship was performed with the Markov regime switching model. As a consequence of the examines, it was seen that the dynamic correlation indicators and Markov model results were consistent with each other. While it was found that the correlation decreased during the high volatility period, a decrease in the number of regressions was observed according to the Markov model results.

Precipitate Dissolution and Grain Growth during Solution Treatment of Ce‐Containing 15Cr–22Ni–1Nb Austenitic Heat‐Resistant Steel

The precipitate dissolution and grain growth during the solution treatment of a newly developed Ce‐containing 15Cr–22Ni–1Nb steel are investigated. The dissolution of eutectic NbC is the result of the elements diffusion along the concentration gradients, and honeycomb (Fe,Cr,Ni,Si)2(Nb,Mo) Laves phase transforms into a more stable Mo2C‐type M2C carbide. As the cerium content is increased from 0 to 0.0630 mass%, the area fraction of eutectic precipitates in forged and solution‐treated 15Cr–22Ni steel is decreased first and then increased. For the steel with 0.0055 mass% cerium content, the substantial reduction in the area fraction of precipitates during hot forging and solution treatment is ascribed to the severe abnormal grain growth. In the case of 15Cr–22Ni steel with 0.0019 and 0.0630 mass% cerium content, the average size of austenite grains after solution treatment is reduced, the multipeak development of grain size distribution is restrained, the range of grain size distribution is narrowed, and the variation coefficient of grain growth, the critical size of abnormally grown grain, and the proportion of abnormally grown grain are all decreased. The variation coefficient of grain growth and the critical size of abnormally grown grain in the steel are increased as the solution time is extended, whereas the proportion of abnormally grown grain presents a general trend of reduction. The established mathematical model of grain growth in the paper can be well applied to predict and control the average size of austenite grains in 15Cr–22Ni steel after solution treatment.

Social Capital, Government Expenditures and Growth

Abstract This paper shows that social capital increases economic growth by raising government investment in human capital through better political incentives and selection. We provide empirical evidence that a greater share of output is spent on public education where social capital is higher, both across countries and across U.S. states. We develop a theoretical model of stochastic endogenous growth with imperfect political agency. Only some people correctly anticipate the future returns to current spending on public education. Greater social diffusion of information makes this knowledge more widespread among voters. As a result, social capital alleviates myopic political incentives to underinvest in human capital. It also helps voters select politicians who ensure high productivity in public education. Through this mechanism, we show that social capital raises the equilibrium growth rate of output and reduces its volatility.

Research on Multi-Step Fruit Color Prediction Model of Tomato in Solar Greenhouse Based on Time Series Data

Color change is the most obvious characteristic of the tomato ripening stage and an important indicator of the tomato ripening condition, which directly affects the commodity value of tomato. To visualize the color change of tomato fruit during the mature stage, this paper proposes a gated recurrent unit network with an encoder–decoder structure. This structure dynamically simulates the growth and development of tomatoes using time-dependent lines, incorporating real-time information such as tomato color and shape. Firstly, the .json file was converted into a mask.png file, the tomato mask was extracted, and the tomato was separated from the complex background environment, thus successfully constructing the tomato growth and development dataset. The experimental results showed that for the gated recurrent unit network with the encoder–decoder structure proposed, when the hidden layer number was 1 and hidden layer number was 512, a high consistency and similarity between the model predicted image sequence and the actual growth and development image sequence was realized, and the structural similarity index measure was 0.746. It was proved that when the average temperature was 24.93 °C, the average soil temperature was 24.06 °C, and the average light intensity was 11.26 Klux, the environment was the most suitable for tomato growth. The environmental data-driven tomato growth model was constructed to explore the growth status of tomato under different environmental conditions, and thus, to understand the growth status of tomato in time. This study provides a theoretical foundation for determining the optimal greenhouse environmental conditions to achieve tomato maturity and it offers recommendations for investigating the growth cycle of tomatoes, as well as technical assistance for standardized cultivation in solar greenhouses.