Growth Dynamics Research Articles

Energy allocation theory for bacterial growth control in and out of steady state

Efficient allocation of energy resources to key physiological functions allows living organisms to grow and thrive in diverse environments and adapt to a wide range of perturbations. To quantitatively understand how unicellular organisms utilize their energy resources in response to changes in growth environment, we introduce a theory of dynamic energy allocation that describes cellular growth dynamics by partitioning metabolizable energy into key physiological functions: growth, division, cell shape regulation, energy storage and loss through dissipation. By optimizing the energy flux for growth, we develop the equations governing the time evolution of cell morphology and growth rate in diverse environments. The resulting model accurately captures experimentally observed dependencies of bacterial cell size on growth rate, superlinear scaling of metabolic rate with cell size and predicts nutrient-dependent trade-offs between energy expended for growth, division and shape maintenance. By calibrating model parameters with experimental data for the model organism Escherichia coli , our model describes bacterial growth control in dynamic conditions, particularly during nutrient shifts and osmotic shocks. Integrating both the mechanical properties of the cell and underlying biochemical regulation, our model predicts the driving factors behind a wide range of observed morphological and growth phenomena with minimal added complexity.

Probabilistic analysis of tumor growth inhibition models to Support trial design

A large enough sample size of patients is required to statistically show that one treatment is better than another. However, too large a sample size is expensive and can also result in findings that are statistically significant, but not clinically relevant. How sample sizes should be chosen is a well-studied problem in classical statistics and analytical expressions can be derived from the appropriate test statistic. However, these expressions require information regarding the efficacy of the treatment, which may not be available, particularly for newly developed drugs. Tumor growth inhibition (TGI) models are frequently used to quantify the efficacy of newly developed anticancer drugs. In these models, the tumor growth dynamics are commonly described by a set of ordinary differential equations containing parameters that must be estimated using experimental data.One widely used endpoint in clinical trials is the proportion of patients in different response categories determined using the Response Evaluation Criteria In Solid Tumors (RECIST) framework. From the TGI model, we derive analytical expressions for the probability of patient response to combination therapy. The probabilistic expressions are used together with classical statistics to derive a parametric model for the sample size required to achieve a certain significance level and test power when comparing two treatments.Furthermore, the probabilistic expressions are used to generalize the Tumor Static Exposure concept to be more suitable for predicting clinical response. The derivatives of the probabilistic expressions are used to derive two additional expressions characterizing the exposure and its sensitivity. Finally, our results are illustrated using parameters obtained from calibrating the model to preclinical data.

Estimating pathogen spread using structured coalescent and birth–death models: A quantitative comparison

Elucidating disease spread between subpopulations is crucial in guiding effective disease control efforts. Genomic epidemiology and phylodynamics have emerged as key principles to estimate such spread from pathogen phylogenies derived from molecular data. Two well-established structured phylodynamic methodologies – based on the coalescent and the birth–death model – are frequently employed to estimate viral spread between populations. Nonetheless, these methodologies operate under distinct assumptions whose impact on the accuracy of migration rate inference is yet to be thoroughly investigated.In this manuscript, we present a simulation study, contrasting the inferential outcomes of the structured coalescent model with constant population size and the multitype birth–death model with a constant rate. We explore this comparison across a range of migration rates in endemic diseases and epidemic outbreaks. The results of the epidemic outbreak analysis revealed that the birth–death model exhibits a superior ability to retrieve accurate migration rates compared to the coalescent model, regardless of the actual migration rate. Thus, to estimate accurate migration rates, the population dynamics have to be accounted for. On the other hand, for the endemic disease scenario, our investigation demonstrates that both models produce comparable coverage and accuracy of the migration rates, with the coalescent model generating more precise estimates. Regardless of the specific scenario, both models similarly estimated the source location of the disease.This research offers tangible modelling advice for infectious disease analysts, suggesting the use of either model for endemic diseases. For epidemic outbreaks, or scenarios with varying population size, structured phylodynamic models relying on the Kingman coalescent with constant population size should be avoided as they can lead to inaccurate estimates of the migration rate. Instead, coalescent models accounting for varying population size or birth–death models should be favoured. Importantly, our study emphasises the value of directly capturing exponential growth dynamics which could be a useful enhancement for structured coalescent models.

Vertical multilayer structure of MoS2 flakes prepared by thermal evaporative deposition

In this investigation, we explore the synthesis of MoS2 through thermal evaporative deposition, seeking to uncover the growth dynamics and structural evolution of MoS2 under varied experimental conditions. By adjusting the temperature and the carrier gas, this study targets the fabrication of vertical multilayer MoS2 and its growth mode. Our analysis demonstrates that thermal conditions markedly influence flake morphology and dimensions, leading to a notable shift from AA to AB stacking configurations as temperatures rise in the vacuum. Additionally, the application of carrier distinctly modifies growth behaviors, enhancing the uniformity of nucleation sites and the propensity for lateral flake expansion. The insights gained from this research highlight the adaptability and effectiveness of thermal evaporative deposition in producing MoS2, thereby enriching our understanding of the fundamental mechanisms guiding MoS2 synthesis.

Unveiling high-temperature stability and growth dynamics of CVD-SiOC coatings across different deposition conditions and environments

Abstract In this study, we systematically investigated the high-temperature protection performance and evolution behavior of three different SiOC coatings (1050SiOC, 1100SiOC, 1150SiOC) under different atmospheres. The coatings were prepared by the organometallic chemical vapor deposition (CVD) method and characterized using scanning electron microscopy (SEM), grazing incidence x-ray diffraction (GIXRD), x-ray photoelectron spectroscopy (XPS) techniques. It was found that the composition and microstructure of SiOC coatings, environmental atmosphere, and heat treatment temperature can affect the thermal stability and high-temperature reaction mechanism of SiOC coatings. Further, it was revealed that the three SiOC coatings only exhibit the same high-temperature evolution behavior and reaction mechanism in an air environment while exhibiting different high-temperature evolution behavior and reaction mechanisms in both an inert atmosphere and a reduced air atmosphere. Among the coatings prepared, the 1050SiOC coating demonstrated the highest on-set oxidation temperature under identical oxygen content conditions. This characteristic may contribute to the coating’s excellent resistance to high-temperature oxidation.

Data-driven corporate growth: A dynamic financial modelling framework for strategic agility

This research aimed to develop a Dynamic Financial Growth Model (DFGM) to enhance corporate growth by promoting strategic agility through data-driven decision-making. The main objective was to optimize corporate value by integrating real-time data, dynamic decision-making, risk management, and scenario analysis. The research employed a mathematical modelling framework that combined predictive analytics, real options theory, and scenario-based optimization to represent dynamic corporate financial decisions. The numerical example demonstrated how the model adjusts strategic decisions in response to changes in market data and evaluates corporate value under optimistic, pessimistic, and baseline scenarios. The main results indicated that the DFGM is effective in optimizing corporate value by allowing for continuous adjustments and strategic flexibility, distinguishing itself from traditional static financial models that lack real-time adaptability. The findings highlighted the value of incorporating risk constraints and scenario analysis, resulting in a balanced approach that manages both growth and uncertainty. However, the study identified limitations, including the need for empirical validation, more complex predictive analytics, and accounting for behavioral factors affecting decision-making. The conclusion emphasizes that the DFGM provides an adaptable and data-driven framework that enhances corporate strategic agility, making it a valuable tool for managing growth in rapidly changing environments, while also suggesting future research to refine the model's practical application

Innovations in teaching: analysis of scientific publications in the Scopus database

The article analyzes scientific works related to innovative technologies in education. The increase in the number of publications in the Scopus database from 2000 to 2023 confirmed the relevance of the proposed topic. The purpose of the study is to identify the main trends and directions in this field based on a systematic review of 6653 scientific articles identified by the keywords “innovation” and “learning” from the Scopus database. The research methodology includes data collection and analysis using SPSS to determine the growth dynamics of scientific publications, as well as geographic and industry distribution. The theoretical significance lies in expanding knowledge about innovative technologies in education and forming the multidisciplinary nature of this field. The increase in the number of publications identified as a result of the study from 136 works in 2000 to 3255 in 2023 indicates an increased scientific interest in innovative technologies in the field of education in the global space.

Monitoring and Optimization of Potato Growth Dynamics under Different Nitrogen Forms and Rates Using UAV RGB Imagery

The temporal dynamics of canopy growth are closely related to the accumulation and distribution of plant dry matter. Recently, unmanned aerial vehicles (UAVs) equipped with various sensors have been increasingly adopted in crop growth monitoring. In this study, two potato varieties were used as materials, and treated with different combinations of nitrogen forms (nitrate and ammonium) and application rates (0, 150, and 300 kg ha−1). A canopy development model was then constructed using low-cost time-series RGB imagery acquired by UAV. The objectives of this study were to quantify the variation in canopy development parameters under different nitrogen treatments and to explore the model parameters that represent the dynamics of plant dry matter accumulation, as well as those that contribute significantly to yield. The results showed that, except for the thermal time to canopy senescence (t2), other parameters of the potato canopy development model exhibited varying degrees of variation under different nitrogen treatments. The model parameters were more sensitive to nitrogen forms, such as ammonium and nitrate, than to application rates. The integral area (At) under the canopy development curve had a direct effect on plant dry matter accumulation (path coefficient of 0.78), and the two were significantly positively correlated (Pearson correlation coefficient of 0.93). Integral area at peak flowering (AtII) was significantly correlated with yield for both single and mixed potato varieties, having the greatest effect on yield (total effect of 1.717). In conclusion, UAV-acquired time-series RGB imagery could effectively quantify the variation of potato canopy development parameters under different nitrogen treatments and monitor the dynamic changes in plant dry matter accumulation. The regulation of canopy development parameters is of great importance and practical value for optimizing nitrogen management strategies and improving yield.

Environmental Factors Drive the Biogeographic Pattern of Hippophae rhamnoides Root Endophytic Fungal Diversity in the Arid Regions of Northwest China.

Hippophae rhamnoides subsp. sinensis Rousi (Abbrev. H. rhamnoides) stands as a vital botanical asset in ameliorating the ecological landscape of the arid regions in Northwest China, where its rhizospheric microorganisms serve as linchpins in its growth and developmental dynamics. This study aimed to explore the community structure characteristics and origin differences of root endophytic fungi in H. rhamnoides. Samples were collected from 25 areas where H. rhamnoides is naturally distributed along an altitude gradient in the northwest region. Then, endophytic fungi from different regions were analyzed by using high-throughput sequencing technology to compare the structural characteristics of endophytic fungi and examine their association with environmental factors. FUNGuild was employed to analyze the community structure and functions of endophytic fungi, and the results showed that each region had its own dominant endophytic fungal flora, demonstrating the differences in origin of endophytic fungi, and the specific endophytic flora acquired from the original soil in the growing season of H. rhamnoides will help us construct the microecological community structure. Furthermore, the study identified and assessed the diversity of fungi, elucidating the species structure and highlighting dominant species. The RDA analysis revealed that available phosphorus (AP), available potassium (AK), and total nitrogen (TN) exhibit significant correlations with the composition and diversity of root-associated fungi. In conclusion, the fungal community structure is similar within the same region, while significant differences exist in the taxonomic structure and biodiversity among different regions. These findings shed light on the intricate interplay and mechanisms governing the ecological restoration of H. rhamnoides, offering a valuable framework for advancing green ecology initiatives and harnessing the potential of root-associated microorganisms in this species.

Microbial Growth Inhibition Effect, Polyphenolic Profile, and Antioxidative Capacity of Plant Powders in Minced Pork and Beef

Consumer interest in healthier meat products has grown in recent years. Therefore, the use of plant powders as natural preservatives in the composition of pork and beef products could be an alternative to traditional meat products. This study aimed to assess the effect of different powders, such as blackcurrant, chokeberry, rowan berries, apple, tomato, garlic, and rhubarb, on the microbial growth dynamics in minced pork and beef during refrigerated storage. The total counts of aerobic microorganisms, Pseudomonas spp., yeasts, and molds were examined according to ISO methods. The polyphenolic profiles of plant powders and supplemented minced pork and beef samples were determined by HPLC-MS. The antioxidative capacity of the plant powders was analyzed using a spectrophotometric method. The findings of the study revealed that supplemented minced pork and beef samples had similar polyphenolic profiles and microbial growth dynamics. The highest antioxidative capacity was observed for anthocyanin-rich berry powders. In both minced pork and beef, rhubarb powder was the most effective plant material for inhibiting microbial growth, followed by blackcurrant pomace powder. In conclusion, all of the plant powders used in the present study can be used for the valorization of minced meat products, providing both antimicrobial and antioxidant effects.

Arm Length and Height Relationship in Young Indian Adults: A Correlation and Regression Analysis

Abstract Introduction: Anthropometric studies on body proportions such as arm length (AL) and height (Ht) are essential for understanding physical growth patterns and health indicators, particularly amongst young adults. However, limited research exists on this relationship in the Indian population. Materials and Methods: This study examined the correlation and regression between AL and Ht in 500 healthy Indian adults aged 18–23 years. Standard anthropometric tools were used to measure AL and Ht, and statistical analyses were conducted to determine the relationship between these variables. Results: The results showed varying degrees of correlation between AL and Ht across different age groups and genders. Males generally exhibited higher AL to Ht ratios compared to females, with significant gender differences observed in younger age groups. Conclusion: These findings contribute to our understanding of human growth patterns amongst young Indian adults and have important implications for clinical assessments and anthropological research. Further, longitudinal studies are recommended to better understand the dynamics of growth and body proportions over time in this population.

Adaptive Strategies and Underlying Response Mechanisms of Ciliates to Salinity Change with Note on Fluctuation Properties.

The adaptability of marine organisms to changes in salinity has been a significant research area under global climate change. However, the underlying mechanisms of this adaptability remain a debated subject. We hypothesize that neglecting salinity fluctuation properties is a key contributing factor to the controversy. The ciliate Euplotes vannus was used as the model organism, with two salinity fluctuation period sets: acute (24 h) and chronic (336 h). We examined its population growth dynamics and energy metabolism parameters following exposure to salinity levels from 15‱ to 50‱. The carrying capacity (K) decreased with increasing salinity under both acute and chronic stresses. The intrinsic growth rate (r) decreased with increasing salinity under acute stress. Under chronic stress, the r initially increased with stress intensity before decreasing when salinity exceeded 40‱. Overall, glycogen and lipid content decreased with stress increasing and were significantly higher in the acute stress set compared to the chronic one. Both hypotonic and hypertonic stresses enhanced the activities of metabolic enzymes. A trade-off between survival and reproduction was observed, prioritizing survival under acute stress. Under chronic stress, the weight on reproduction increased in significance. In conclusion, the tested ciliates adopted an r-strategy in response to salinity stress. The trade-off between reproduction and survival is a significant biological response mechanism varying with salinity fluctuation properties.

Influence of Primary Light Exposure on the Morphophysiological Characteristics and Phenolic Compounds Accumulation of a Tea Callus Culture (Camellia sinensis L.).

Tea plant calli (Camellia sinensis L.) are characterized by the accumulation of various phenolic compounds (PC)-substances with high antioxidant activity. However, there is still no clarity on the response of tea cells to light exposure of varying intensity. The purpose of the research was to study tea callus cultures grown under the influence of primary exposure to different light intensities (50, 75, and 100 µmol·m-2·s-1). The cultures' growth, morphology, content of malondialdehyde and photosynthetic pigments (chlorophyll a and b), accumulation of various PC, including phenylpropanoids and flavanols, and the composition of catechins were analyzed. Primary exposure to different light intensities led to the formation of chloroplasts in tea calli, which was more pronounced at 100 µmol·m-2·s-1. Significant similarity in the growth dynamics of cultures, accumulation of pigments, and content of malondialdehyde and various phenolics in tea calli grown at light intensities of 50 and 75 µmol·m-2·s-1 has been established, which is not typical for calli grown at 100 µmol·m-2·s-1. According to data collected using high-performance liquid chromatography, (+)-catechin, (-)-epicatechin, epigallocatechin, gallocatechin gallate, epicatechin gallate, and epigallocatechin gallate were the main components of the tea callus culture's phenolic complex. Its content changed under the influence of primary exposure to light, reaching the greatest accumulation in the final stages of growth, and depended on the light intensity. The data obtained indicate changes in the morphophysiological and biochemical characteristics of tea callus cultures, including the accumulation of PC and their individual representatives under primary exposure to light exposure of varying intensity, which is most pronounced at its highest values (100 µmol·m-2·s-1).

Maximizing diclofenac bioremoval efficiency using Chlorella vulgaris strain H1 and Chlorella sorokiniana strain H2: Unveiling the impact of acetic acid on microalgae

BackgroundDiclofenac (DFC) is a commonly detected pharmaceutical pollutant in wastewater, posing environmental risks. Microalgae have emerged as potential candidates for bioremediation due to their ability to degrade pollutants. This study focuses on investigating the biodegradation potential of two newly isolated microalgae strains, Chlorella vulgaris strain H1 and Chlorella sorokiniana strain H2, towards DFC removal. The optimization of pH is crucial for enhancing the efficiency of bioremediation processes. Therefore, in addition to assessing the biodegradation potential of microalgae, this study also investigates the impact of adjusting the pH of the culture medium using acetic acid as an additional carbon source on the biodegradation process. MethodsThrough genetic analysis using 18S rDNA sequencing, the microalgae strains were identified. Various parameters including growth dynamics, chlorophyll content, cell proliferation, photosynthetic activity, and DFC biodegradation efficiency were comprehensively assessed. Additionally, the impact of incorporating acetic acid as an additional carbon source in the culture medium on the biodegradation process was examined. Significant FindingsC. sorokiniana strain H2 demonstrated superior biodegradation rates compared to C. vulgaris strain H1 across varying DFC concentrations. Specifically, C. sorokiniana strain H2 exhibited remarkable biodegradation rates of 84 %, 83.72 %, and 29.57 % for DFC concentrations of 12.5 mg L−1, 25 mg L−1, and 100 mg L−1, respectively. In contrast, C. vulgaris strain H1 showed lower biodegradation rates of 66.64 %, 29.24 %, and 1.83 % for the corresponding DFC concentrations. The study highlights the potential of C. sorokiniana strain H2 as a promising candidate for the removal of pharmaceutical pollutants like DFC from wastewater. Furthermore, the use of acetic acid as a supplementary carbon source enhanced the biodegradation efficiency, suggesting a potential strategy for optimizing bioremediation processes.

Private benefit of β-lactamase dictates selection dynamics of combination antibiotic treatment

β-lactam antibiotics have been prescribed for most bacterial infections since their discovery. However, resistance to β-lactams, mediated by β-lactamase (Bla) enzymes such as extended spectrum β-lactamases (ESBLs), has become widespread. Bla inhibitors can restore the efficacy of β-lactams against resistant bacteria, an approach which preserves existing antibiotics despite declining industry investment. However, the effects of combination treatment on selection for β-lactam resistance are not well understood. Bla production confers both private benefits for resistant cells and public benefits which faster-growing sensitive cells can also exploit. These benefits may be differentially impacted by Bla inhibitors, leading to non-intuitive selection dynamics. In this study, we demonstrate strain-to-strain variation in effective combination doses, with complex growth dynamics in mixed populations. Using modeling, we derive a criterion for the selection outcome of combination treatment, dependent on the burden and effective private benefit of Bla production. We then use engineered strains and natural isolates to show that strong private benefits of Bla are associated with increased selection for resistance. Finally, we demonstrate that this parameter can be coarsely estimated using high-throughput phenotyping of clonal populations. Our analysis shows that quantifying the phenotypic responses of bacteria to combination treatment can facilitate resistance-minimizing optimization of treatment.

Comparative Governance Quality's Impact on Monetary Policy, Saving, and Economic Growth Dynamics: Empirical Evidence From Nigeria, Egypt and South Africa

Comparative Governance Quality's Impact on Monetary Policy, Saving, and Economic Growth Dynamics: Empirical Evidence From Nigeria, Egypt and South Africa

Investigations of microbial adaptation to singular, binary, and fully formulated quaternary ammonium compounds.

The study was conducted to inform risk assessments concerning microbial exposure to quaternary ammonium biocides (QUATs) by investigating their effects on 10 microbial strains of hygiene relevance. Biocides were divided into three categories: simple aqueous solutions, biocide mixtures, and formulated biocides. Organisms were grown in the presence of biocides for 10 generations and then subsequently for another 10 generations in biocide-free media. Control organisms were passaged 20 times in biocide-free media. Strains were then assessed for biocide and antibiotic susceptibility, changes in growth dynamics, and single nucleotide polymorphisms (SNPs). Biocide mixtures demonstrated greater antimicrobial potency than singular and formulated biocides. Susceptibility changes of under twofold were observed for all biocides tested. Susceptibility decreased significantly for organisms passaged with singular biocides (1.29- to 4.35-fold) and biocide mixtures (1.4- to 1.5-fold), but not for formulated biocides (1.3- to 1.84-fold) compared to controls. Antibiotic susceptibility both increased and decreased in passaged organisms, with heightened susceptibility occurring more frequently in the singular biocide group. Changes in susceptibility and growth dynamics were similar in the passaged and unexposed controls for fitness measures of adapted bacteria; there were no significant differences between biocide groups, but significantly lower generation and doubling times in organisms exposed to singular biocides. Similar frequencies in SNPs occurred for the three biocide groups and unexposed controls. While some adaptations occurred, particularly with singular biocides, the impact on antibiotic resistance and genomic mutations was limited. These findings suggest that the use of formulated QUATs may pose a comparatively lower risk for antimicrobial resistance.IMPORTANCEBiocides are used globally to control microbial growth and effective assessment of the risks and benefits of their use is therefore a high priority. Much of the data used to assess risk has been based on sub-lethal exposure of bacteria to singular biocides in simple aqueous solutions. This work builds on limited prior realism-based studies to demonstrate enhanced potency in biocidal mixtures; the mitigation of resistance selection by formulations and inconsistent cross-resistance effects with both increases and decreases in susceptibility for a wide range of antibiotics. These data can be used to better inform risk assessments of biocide deployment.

A computational probabilistic procedure to quantify the time of breast cancer recurrence after chemotherapy administration

We propose a random linear differential equation with jumps to model the dynamics of breast tumor growth using real patients’ data. The model considers the effect of chemotherapy administration and tumor resection. Also, the inherent randomness of the data and the model are taken into account.The model is probabilistically solved by combining two main methods belonging to Uncertainty Quantification: the principle of maximum entropy (PME) and the random variable transformation technique (RVT). The PME is applied to assign proper probability distributions to model parameters. The RVT technique is used to determine the probability density function (PDF) of the solution of the random differential equation, which is a stochastic process.We apply computational optimization techniques to determine the PDF of model parameters so that the PDF of the solution matches the ones assigned to real patients’ data.Once random tumor dynamics has been modeled, we simulate, after surgery, different adjuvant chemotherapy strategies with the aim of delaying tumor recurrence as long as possible. Results are in concordance with medical literature, and tumor relapse is delayed as more cycles of chemotherapy are administered.Although the proposed model has a common general structure, it is shown how it can be customized to patients in order to construct projections about the tumor’s growth. To better illustrate the applicability of the proposed approach, we carefully show how our model can be tailored to two real patients treated at the Hospital Clínico de Valencia (Spain). The obtained results have been overseen by doctors from this hospital.

Growth Rate in Cultured Crustose Coralline Algae of the Genus Phymatolithon and Sporolithon From Taiwan

ABSTRACTCoastal algal reef ecosystems, which are formed by the skeletal carbonate of crustose coralline algae (CCA), provide vital habitats for a diverse range of marine organisms and serve as valuable archives of long‐term environmental data. Despite the importance of these reef ecosystems, there is currently a lack of available information regarding the accretion rates of CCA in subtropical intertidal zones in the Taoyuan algal reef. We measured the vertical accretion and horizontal growth of CCA cultured in two aquaculture tanks over a 9‐month period. The vertical accretion and horizontal growth rate of CCA was approximately 0.29–0.43 and 5.5 μm·day−1 (the extrapolated annual value is equivalent to 0.11–0.16 and 2.0 mm·year−1, respectively). Newly colonized CCA had faster horizontal growth of approximately 110 μm·day−1 (equivalent to 40.2 mm·year−1). These results highlight the slow and gradual process of algal reef ecosystem formation in the subtropical intertidal zones. The CCA had a faster lateral growth rate as seen in the newly settled CCA. These findings contribute to our understanding of the overall growth dynamics of CCA.

Spatially Explicit Analysis of Landscape Structures, Urban Growth, and Economic Dynamics in Metropolitan Regions

Assuming that settlement morphologies and landscape structures are the result of economic transformations, the present study illustrates a statistical framework investigating metropolitan growth due to the inherent changes in landscape configurations vis à vis socio-demographic functions. Focusing on the evolution of their spatial drivers over time, metropolitan development was studied by adopting land parcels (or ‘patches’, as they are referred to in the ecological literature) as the elementary analysis unit—with the individual surface area and a specific shape indicator as the dependent variables and background socioeconomic attributes as predictors of landscape change over time. We specifically ran a Multiscale Geographically Weighted Regression (MGWR) testing the spatial dependence of the size and shape of landscape parcels on a vast ensemble of socioeconomic factors in a dense region (metropolitan Athens, Greece) with natural landscapes exposed to increasing human pressure. To investigate the spatial direction and intensity of the settlement expansion and landscape change, local regressions using the parcel area and fractal index (perimeter-to-area ratio) as the dependent variables and the elevation, distance from selected economic nodes, transport infrastructures, and natural amenities as the predictors were run separately for 1990 and 2018, representative of, respectively, a mono-centric configuration and a moderately polycentric organization of economic spaces. In a strictly mono-centric setting (1990), the parcel size showed a linear dependence on the distance from business districts, elevation, and wealth. Changes in the relationship between the parcel size and spatial (economic and non-economic) drivers may suggest a latent process of settlement de-concentration, and a possible shift toward polycentric development (2018), as documented in earlier studies. By integrating socioeconomic and ecological dimensions of landscape analysis and land evaluation, the empirical results of this study outline the increased complexity of dispersed landscape structures within dense metropolitan regions and along urban–rural gradients in Europe.