Search Results (143)

Accurate age determination is fundamental for investigating fish population dynamics and growth patterns. This study used the lapillus to determine age in Triplophysa strauchii populations from an oxbow lake and a stream. Growth patterns were evaluated using three models (the Von Bertalanffy, Gompertz, and Logistic models). The oxbow lake population showed faster growth and longer lifespan (6 years in Dacao Lake; 5 years in Liutiao Stream). Conversely, the stream population displayed a trend toward smaller size and younger age. Both populations exhibited higher Fulton’s condition factor in juveniles than in adults. The species exhibited a fast-growth type, with similar fitting results across models. These findings reflect subtle differentiation in life history strategies across habitats, likely related to environmental conditions and competitive pressure from invasive species. These insights into T. strauchii life history underscore the importance of further research to support conservation and sustainable management of this endemic species. Full article

The impact of donor age on Adipose-derived mesenchymal stem cell (ASC) functionality and safety remains insufficiently characterized, particularly in equine models. This study investigates the influence of age on ASCs proliferation dynamics and the expression of tumorigenic and apoptosis-related markers. Equine ASCs were isolated from juvenile (<5 years), middle-aged (5–15 years), and geriatric (>15 years) horses and assayed across multiple passages. The relative mRNA expressions of pluripotency (Oct4), tumorigenic (CA9), and apoptosis-related (Bax and Bcl 2) markers were evaluated. The Gompertz growth model, population doubling time (PDT), and tissue non-specific ALP activity also followed. The expression of pluripotency and tumorigenic markers showed passage-dependent up-regulation, raising concerns about prolonged culture expansion. Apoptotic regulation displayed a shift with aging, as evidenced by alterations in the Bax/Bcl2 ratio, suggesting compromised cell survival in older ASCs. An age-associated decline in proliferation rates was established, as evidenced by declining alkaline phosphatase (ALP) activity. These findings underscore the necessity for stringent age-based selection criteria in equine stem cell therapies and the challenges associated with using autologous stem cells for regenerative therapies in aged horses. Future research should focus on molecular interventions to mitigate age-related functional decline, ensuring the safety and efficacy of ASCs-based regenerative medicine in equine practice. Full article

Microcystis aeruginosa is a cyanobacterium frequently associated with toxic blooms in eutrophic freshwater systems. Certain strains produce microcystins (MCs), a group of hepatotoxins with significant ecological and public health implications. In this study, we examined the quantitative response of a temperate native M. aeruginosa strain to combinations of temperature (26, 30, and 36 °C), light intensity (30, 50, and 70 µmol photons·m⁻²·s⁻¹), and N:P ratio (10, 100, 150), using a full-factorial experimental design. Growth parameters (µ, lag phase duration, and maximum cell density), chlorophyll-a production, and MC-LR synthesis were modeled using Gompertz, linear, and dynamic approaches. High temperature and irradiance increased the specific growth rate but decreased final biomass, while elevated N:P ratios shortened the lag phase. MC-LR production peaked under low temperature, low irradiance, and low N:P ratio. Although MC-LR synthesis did not correlate positively with growth rate, and the environmental conditions maximizing growth differed from those enhancing toxin production, a population-level coupling between both processes was observed using the Long model. These findings suggest that MC-LR synthesis in M. aeruginosa is not merely a metabolic by-product of growth, but a context-dependent trait with potential adaptive significance. Full article

The Gompertz distribution has long been a cornerstone for analyzing growth processes and mortality patterns across various scientific disciplines. However, as the intricacies of real-world phenomena evolve, there is a pressing need for more versatile probability distributions that can accurately capture a wide array of data characteristics. In response to this demand, we introduce the Marshall–Olkin Power Gompertz (MOPG) distribution, an innovative and powerful extension of the traditional Gompertz model. The MOPG distribution is crafted by enhancing the Power Gompertz cumulative distribution function through the Marshall–Olkin transformation. This distribution yields two pivotal contributions: a power parameter (c) that significantly increases the model’s adaptability to diverse data patterns and the Marshall–Olkin transformation, which modifies tail behavior to enhance predictive accuracy. Furthermore, we derived the distribution’s essential statistical properties and evaluate its performance through extensive Monte Carlo simulations, along with a maximum likelihood estimation of model parameters. Our empirical validation, utilizing three real-world data sets, compellingly demonstrated that the MOPG distribution not only surpasses several well-established lifetime distributions but is also superior in terms of flexibility and tail behavior characterization. The results highlight that the proposed MOPG stands out as a superior choice, delivering the most precise fit to the data when compared to various competing models, and its performance makes it a compelling option worth considering. Full article

The present study analyzed the growth pattern of the stingray Urotrygon chilensis caught as bycatch by the shrimp fishery in the southeastern Mexican Pacific. From January to December 2012, the thoracic vertebrae of 491 females and 205 males were collected. Female ages ranged from 0 to 14 years, whereas male ages ranged from 0 to 12 years. The marginal increment and edge analyses suggested the annual formation of growth bands in the vertebrae. The size-at-age data were analyzed using the multimodel inference approach; six candidate growth models were compared, including models with a theoretical age-at-zero total length, mean size-at-birth, and generalized models. Based on Akaike’s information criterion, the best statistical fit to the size-at-age data was the two-phase Gompertz growth model (k = −0.13, G = 1.59, $L_0$ = 10.40) for males and the two-parameter Gompertz growth model (k = 1.42, α = 0.15, $L_0$ = 10.90) for females. In this study, we compare the growth parameters among batoid species, finding that U. chilensis has a relatively short lifespan, slower growth, and that females are larger than males. Full article

The aim of this study was to characterize the vegetative growth development of hop plants grown in the subtropics under a two-crop-a-year system with artificial supplementation lighting. The development of ‘Mapuche’ and ‘Spalter’ hops was compared during the summer 2022–2023, fall 2023, summer 2023–2024 and fall 2024 harvest seasons, considering the effects of the air temperature on the vegetative growth of plants from thermal sums in a subtropical climate region. The experiment was conducted in Palotina, Paraná, Brazil (24°17′40.05″ S, 55°50′23.16″ W, at 332 m elevation). The hops were trained on a 5.5 m high vertical trellis, using a ‘V’-shaped training system. Vegetative growth was evaluated based on the plant height development (m), hop growth rate (HGR), and classification of four growth stages based on the HGR. The phenology of the hop cultivars was determined visually according to the duration in days of the phenological stages. The development of the plant height and HGR was analyzed by nonlinear regressions of the Gompertz model and Gaussian function, respectively. ‘Mapuche’ and ‘Spalter’ hops had complete vegetative growth and phenological phases in the summer and fall seasons, with greater precocity in plant development in the summer season. The growth model based on the air temperature demonstrated that under subtropical conditions, the growth was maximized in seasons with higher temperatures. The duration of the phenological phases and the complete cycle of the plants was influenced by the vegetative growth of each cultivar in each harvest season. Therefore, double annual crop production of the hop cultivars ‘Mapuche’ and ‘Spalter’ is possible in a subtropical climate with artificial light supplementation. Full article

Cyperus esculentus, a drought-resistant Cyperaceae with ecological and economic value (stems/leaves as feed, tubers as oil source), stabilizes arid soils through its extensive root system. Understanding its biomass allocation strategies is crucial for comprehending carbon storage in arid environments. The results showed that allometric models best described leaf biomass, while Gompertz and logistic models provided superior accuracy (evaluated using R², p-value, AIC, RMSE, and RSS) for estimating root, tuber, and whole plant biomass. In our study, the equilibrium biomass showed that underground (74.29 g and 64.22 g) was superior to aboveground (63.63 g and 58.72 g); and the growth rate showed the same result, underground (0.112 and 0.055) surpassed aboveground (0.083 and 0.046). The initial inflection point (POI1 = 11) suggests that leaves are prioritized in acquiring limited resources to support growth. In conclusion, the tiller number is a reliable predictor for developing robust biomass models for C. esculentus. The Gompertz model is best for leaves, roots, and total biomass, while the logistic model is optimal for predicting tuber biomass in arid areas. The tiller number is a reliable predictor for developing robust biomass models for C. esculentus. The research findings have supplied useful insights into the growth modifications, production potential, and management experience gained from Cyperus esculentus plant agriculture. Full article

Burkholderia gladioli pathovar cocovenenans (BGC) is a highly lethal foodborne pathogen responsible for outbreaks of food poisoning with the highest recorded mortality rates among bacterial foodborne illnesses in China. In this study, the ecological behavior of BGC and its Bongkrekic Acid (BA) production dynamics in fresh wet rice noodles (FWRN) were investigated under isothermal conditions ranging from 4 °C to 37 °C. Growth kinetics were modeled using the Huang, Baranyi, and modified Gompertz primary models, with secondary models (Huang square root model and Ratkowsky square root model) describing the influence of temperature on growth parameters. Among these, the Huang–Huang model combination exhibited the best performance, with a root mean square error (RMSE) of 0.009 and bias factor (B_f) and accuracy factor (A_f) values close to 1. Additionally, we examined the impact of BGC contamination on the quality attributes of FWRN, including pH, color (L*, a*, b*), hardness, and moisture content. The results indicated that BGC growth significantly increased pH and yellowing (b*) values, while changes in texture and moisture were less pronounced. A probabilistic model was further developed to predict BA production under various temperature scenarios, revealing that BA formation was most likely to occur between 24 °C and 30 °C. While this study provides valuable predictive tools for microbial risk assessment and quality control of FWRN, limitations include the exclusion of additional environmental factors such as oxygen and relative humidity, as well as the lack of direct investigation into the degradation behavior of BA. Future research will expand model parameters and include sensory evaluations and advanced microbiological analyses to enhance applicability under real-world storage and transportation conditions. Full article

Phenomenological models are highly effective tools for forecasting disease dynamics using real-world data, particularly in scenarios where detailed knowledge of disease mechanisms is limited. However, their reliability depends on the model parameters’ structural and practical identifiability. In this study, we systematically analyze the identifiability of six commonly used growth models in epidemiology: the generalized growth model (GGM), the generalized logistic model (GLM), the Richards model, the generalized Richards model (GRM), the Gompertz model, and a modified SEIR model with inhomogeneous mixing. To address challenges posed by non-integer power exponents in these models, we reformulate them by introducing additional state variables. This enables rigorous structural identifiability analysis using the StructuralIdentifiability.jl package in JULIA. We validated the structural identifiability results by performing parameter estimation and forecasting using the GrowthPredict MATLAB Toolbox. This toolbox is designed to fit and forecast time series trajectories based on phenomenological growth models. We applied it to three epidemiological datasets: weekly incidence data for monkeypox, COVID-19, and Ebola. Additionally, we assessed practical identifiability through Monte Carlo simulations to evaluate parameter estimation robustness under varying levels of observational noise. Our results confirm that all six models are structurally identifiable under the proposed reformulation. Furthermore, practical identifiability analyses demonstrate that parameter estimates remain robust across different noise levels, though sensitivity varies by model and dataset. These findings provide critical insights into the strengths and limitations of phenomenological models to characterize epidemic trajectories, emphasizing their adaptability to real-world challenges and their role in informing public health interventions. Full article

The large-scale use of air-conditioning equipment, while providing a comfortable living environment, has also brought about a series of problems. This study focuses on the growth and reproduction of Staphylococcus aureus on the surface of activated carbon in air-conditioning filtration systems. Experimental data were obtained under temperature conditions of 20 °C and 30 °C and relative humidity conditions of 10%, 50%, and 75% RH. Based on the experimental data, a mathematical model was established to predict the growth and reproduction of Staphylococcus aureus. The Logistic and Gompertz equations were used to fit the growth and reproduction curves under different temperature and humidity conditions, and the two models, commonly used for simulating microbial growth curves, were compared. The model with the best fit was selected to predict the amount of Staphylococcus aureus, providing some guidance for the actual lifespan of the adsorbent in filters. Full article

Reporter systems are widely used to study biomolecular interactions and processes in vivo, representing one of the basic tools used to characterize synthetic regulatory circuits. Here, we developed a method that enables the monitoring of RNA–protein interactions through a reporter system in bacteria with high temporal resolution. For this, we used a Real-Time Protein Expression Assay (RT-PEA) technology for real-time monitoring of a fluorescent reporter protein, while having bacteria growing on solid media. Experimental results were analyzed by fitting a three-variable Gompertz growth model. To validate the method, the interactions between a set of RNA sequences and the RNA-binding protein (RBP) Musashi-1 (MSI1) were evaluated, as well as the allosteric modulation of the interaction by a small molecule (oleic acid). This new approach proved to be suitable to quantitatively characterize RNA–RBP interactions, thereby expanding the toolbox to study molecular interactions in living bacteria, including allosteric modulation, with special relevance for systems that are not suitable to be studied in liquid media. Full article

The weight information of broilers is important for understanding the growth progress of broilers and adjusting the breeding schedule, and predicting the broiler live weight at the time of shipment is an important task for producing high-quality broilers that meet consumer demand. To this end, we plan to analyze the broiler weight data automatically measured in a smart broiler house with an intelligent system and conduct a study to predict the weight until the time of shipment. To estimate the accurate daily body weight representative value of broiler body weight data, the K-means clustering method and the kernel density estimation method were applied, and the growth trends generated by each method were used as training data for the Prophet predictor, double exponential smoothing predictor, ARIMA predictor, and Gompertz growth model. The experimental results showed that the K-means + Prophet predictor model recorded the best prediction performance among the algorithm combinations proposed in this paper. The prediction results of the algorithm presented in this paper can analyze the growth progress of broilers in actual broiler houses and can be used as meaningful judgment data for adjusting the breeding schedule considering the time of shipment. Full article

The vehicle fleets in Latin America are increasingly incorporating hybrid electric vehicles due to the economic and non-economic incentives provided by governments aiming to reduce energy consumption and emissions in the transportation sector. However, the impacts of implementing hybrid vehicles remain uncertain, especially in Latin American, which poses a risk to the achievement of environmental objectives in developing countries. The aim of this study is to evaluate the benefits of incorporating hybrid vehicles to replace internal combustion vehicles, considering the improvement in the level of emission standards. This study uses data reported by Colombian vehicle importers during the homologation process in Colombia and the number of vehicles registered in the country between 2010 and 2022. The Gompertz model and logistic growth curves are used to project the total number of vehicles, taking into account the level of hybridization and including conventional natural gas and electric vehicles. In this way, tailpipe emissions and energy efficiency up to 2040 are also projected for different hybrid vehicle penetration scenarios. Results show that the scenario in which the share of hybrid vehicles remains stable (Scenario 1) shows a slight increase in energy consumption compared to the baseline scenario, about 1.72% in 2035 and 2.87% in 2040. The scenario where the share of MHEVs, HEVs, and PHEVs reaches approximately 50% of the vehicle fleet in 2040 (Scenario 2) shows a reduction in energy consumption of 24.64% in 2035 and 33.81% in 2040. Finally, the scenario that accelerates the growth of HEVs and PHEVs while keeping MHEVs at the same level of participation from 2025 (Scenario 3) does not differ from Scenario 2. Results show that the introduction of full hybrids and plug-in hybrid vehicles improve fleet fuel consumption and emissions. Additionally, when the adoption rates of these technologies are relatively low, the benefits may be questionable, but when the market share of hybrid vehicles is high, energy consumption and emissions are significantly reduced. Nevertheless, this study also shows that Mild Hybrid Electric Vehicles (MHEVs) do not provide a significant improvement in terms of fuel consumption and emissions. Full article

Biopreservation technology has emerged as a promising approach to enhance food safety and extend shelf life by leveraging the antimicrobial properties of beneficial microorganisms. This study aims to develop precise predictive models to characterize the growth and interaction dynamics of lactic acid bacteria (LAB) and Listeria monocytogenes, which serve as bioprotective agents in food systems. Using both traditional and machine learning modelling approaches, we analyzed data from previously published growth curves in broth (BHI) and milk under isothermal conditions (4, 10, and 30 °C). The models evaluated mono-culture conditions for L. monocytogenes and LAB, as well as their competitive interactions in co-culture scenarios. The modified Gompertz model demonstrated the best performance for mono-culture simulations, while a combination of the modified Gompertz and Lotka–Volterra models effectively described co-culture interactions, achieving high adjusted R-squared values (adjusted R² = 0.978 and 0.962) and low root mean square errors (RMSE = 0.324 and 0.507) for BHI and milk, respectively. Machine learning approaches further validated these findings, with improved statistical indices (adjusted R² = 0.988 and 0.966, RMSE = 0.242 and 0.475 for BHI and milk, respectively), suggesting their potential as robust alternatives to traditional methods. The integration of machine learning-assisted software developed in this work into predictive microbiology demonstrates significant advancements by bypassing the conventional primary and secondary modelling steps, enabling a streamlined, precise characterization of microbial interactions in food products. Full article

Natural mixed forests’ carbon sequestration capacity is crucial for mitigating climate change and maintaining ecological balance. However, most of the current studies only consider the role of forest age, ignoring the influence of carbon growth grade and stand structural diversity, which leads to an increase in uncertainty in large-scale forest carbon sink assessment. The aim of this study was to quantify the effects of carbon growth grade and stand structure diversity on the carbon sink of natural mixed forests and to establish a more accurate stand carbon growth model. Based on sample data from the National Forest Inventory (NFI) of China, the stand carbon growth model was established based on Gompertz and Logistic theoretical growth models, and the forest carbon sink at the regional scale was predicted. It was found that the stand carbon growth model considering only the stand age as a single variable often had poor results, with R² less than 0.36, while R² values of the optimal model introducing carbon growth grade and stand structural diversity were 0.87 and 0.48, respectively, which significantly improved the prediction accuracy of the model, and both had significant effects on stand carbon stocks. By predicting the future forest carbon sink, it was found that the forest carbon sink of the natural coniferous–broadleaved mixed forests in Jilin Province would reach 791 (781–801) t c/a and 843 (833–852) t c/a in 2030 and 2060, respectively, which were 17% lower and 51% higher than that of the forest carbon sink estimated by considering only the age. Moreover, the model considering structural diversity predicted a more positive carbon sink trend, indicating that forest carbon stocks could be more effectively maintained and carbon sinks increased by increasing the complexity of stand diameter at breast height structure, which has important guiding significance for future forest carbon sink management. This study provides scientific support for achieving the goal of “carbon neutrality” proposed by China. Full article