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Background: Growth references play a crucial role in the screening, evaluation, and surveillance of children, aiding in the early identification of the requirement for diverse growth-promoting interventions. Variations in human growth across distinct ethnic cohorts arise from genetic disparities, lifestyle variances, nutritional diversity, and diverse social and environmental contexts. Consequently, the selection of growth references markedly influences the prevalence of developmental disorders and nutritional imbalances in children. The study aims to assess the growth percentile of children in the north-east of Iran and establish population-specific reference charts for length, weight, and head circumference spanning from birth to 24 months. Methods: This cross-sectional population-based research conducted in the north-east of Iran, from 2016 to 2023. The Data extracted from the electronic health records of Mashhad University of Medical Sciences. All apparently healthy children aged from birth to 24 months who were measured at least once by health staff at the ages of birth,1,2,4,6,7,9,12,15,18,24 months were included. The target population of the study were 479,089 children (96.21%), encompassing 233,565 girls (48.75%) and 245,524 boys (51.25%). Gender-specific percentile curves for length, weight, and head circumference concerning age, as well as weight concerning length, were derived using the GAMLSS approach. Results: From the anthropometric information of 479,089 children (245,524 boys and 233,565 girls), growth charts were constructed. In comparison to the standard WHO chart, Iranian neonates displayed lower weight across all percentiles during the first month after birth, exhibited decreased head circumference at the 3rd percentile, and boys showed reduced length across all percentiles. After this age, Iranian children demonstrated increased weight, length, and head circumference. Conclusions: This research introduces the inaugural large-scale endeavor for indigenous reference charts. Through the noted distinctions from the international reference, the utilization of this novel resource offers the potential to enhance the surveillance of children's growth within the area. Moreover, by accurately assessing growth anomalies such as underweight, stunting, and wasting, it expands the domain of impactful policies in this sphere. Simultaneously, it enables the exploration of the secular trend of children's growth in the forthcoming years. [ABSTRACT FROM AUTHOR]

Background: The study of growth traits is of interest to many animal scientists, regardless of specialization, due to the economic importance of growth rate, mature weight and other related traits. Objective: This study aimed to compare six non‐linear models for describing the growth of Lori‐Bakhtiari sheep. Methods: In order to collect weight data, 85 lambs (41 males and 44 females) were reared from birth to 140 days of age, and their growth patterns were recorded by measuring their body weight at 10‐day intervals. Various mathematical functions, including the negative exponential, Brody, Gompertz, Logistic, Morgan–Mercer–Flodin (MMF) and Weibull, were used to model the relationship between body weight records and age. Results: The results showed that the MMF and Gompertz models provided the best fit to the body weight data, whereas the negative exponential model exhibited the worst fit. In all models, the asymptotic weight of male lambs was higher than females. The research also revealed differences in growth patterns between male and female lambs. Overall, females had a lower absolute growth rate than males, but they reached their peak growth at an earlier period, and their growth rate declined faster. Conclusions: The differences in growth patterns between males and females indicate the importance of analysing male and female data separately when describing growth. As a result, Gompertz model can be recommended to Lori‐Bakhtiari female and male lamb breeders to determine more accurate growth traits. In addition, it should be considered that feeding male and female lambs separately according to absolute growth rate values may increase growth performance. [ABSTRACT FROM AUTHOR]

Wild edible mushrooms are an important food source globally and have a crucial role in forest ecosystems. However, there is limited research on the growth characteristics and the contribution of agronomic traits to biomass, and the environmental factors affecting mushroom growth are limited. This study was conducted in the Qilian Mountains, China, and focused on investigating the growth patterns and agronomic traits of Lyophyllum decastes and Coprinus comatus. The results revealed that the growth of these mushrooms followed a logical growth curve. By calculating the model parameters, we obtained the maximum daily growth of height (PH), pileus diameter (PD), and cluster perimeter (CP) of L. decastes on the 5th, 7th, and 7th days, respectively, with values of 0.55 cm d−1, 0.54 cm d−1, and 4.54 cm d−1, respectively. However, the maximum daily growth of PH, pileus length (PL), and PD of the C. comatus appeared on the 3rd day, 2nd day, and 2nd day of the observation, respectively. This study identified near-surface relative humidity, air relative humidity, and rainfall as the primary factors influencing mushroom growth, as indicated by Pearson's correlation analysis, redundancy analysis (RDA), and multiple linear and stepwise regression. Additionally, land surface temperature and air temperature were also identified as important factors affecting mushroom growth. By utilizing random forest and stepwise regression analysis, this study identified PH and stipe diameter (SD) as the most crucial agronomic traits affecting mushroom biomass. Overall, this study offers insights for industrial mushroom cultivation and basic fungal research. [ABSTRACT FROM AUTHOR]

Mathematical growth models are useful in describing the growth of livestock. The study was done to assess the predictive ability and accuracy of four three-parameter nonlinear mathematical models (namely: Gompertz, Gompertz-Laird, Logistic, and von Bertalanffy) and one four-parameter (namely: Richards) nonlinear mathematical model. Models were used to predict the body weight (BW) of commercial Ross broiler chickens adapted to tropical conditions (n = 1,286). Age-weight data were collected once every week for 6 weeks. The Gauss-Newton iterative process of the nonlinear procedure in SAS was employed to obtain the parameters for each model. In addition, each model's goodnessof-fit, residuals, and computational difficulty were estimated. Model parameters were evaluated using Akaike's information criterion (AIC), Bayesian information criterion (BIC), adjusted coefficient of determination (AdjR²) and root mean square error (RMSE). The AdjR² value for all five models was high; however, the highest value was observed in the Gompertz and Gompertz-Laird models. Furthermore, the lowest AIC, BIC and RMSE values were observed in the Gompertz models. Using a complimentary method (involving a subjective pairwise comparison of the observed and predicted BWs), the Logistic, Gompertz-Laird, von Bertalanffy, and Richards models fitted well for the data used. However, the best fitting was obtained in the Gompertz model. Some similarities were observed between the Logistic and Richards models. In conclusion, all five nonlinear mathematical models fitted the age weight data used in this study well, with the Gompertz model being the best. [ABSTRACT FROM AUTHOR]

Abstract Background Growth references play a crucial role in the screening, evaluation, and surveillance of children, aiding in the early identification of the requirement for diverse growth-promoting interventions. Variations in human growth across distinct ethnic cohorts arise from genetic disparities, lifestyle variances, nutritional diversity, and diverse social and environmental contexts. Consequently, the selection of growth references markedly influences the prevalence of developmental disorders and nutritional imbalances in children. The study aims to assess the growth percentile of children in the north-east of Iran and establish population-specific reference charts for length, weight, and head circumference spanning from birth to 24 months. Methods This cross-sectional population-based research conducted in the north-east of Iran, from 2016 to 2023. The Data extracted from the electronic health records of Mashhad University of Medical Sciences. All apparently healthy children aged from birth to 24 months who were measured at least once by health staff at the ages of birth,1,2,4,6,7,9,12,15,18,24 months were included. The target population of the study were 479,089 children (96.21%), encompassing 233,565 girls (48.75%) and 245,524 boys (51.25%). Gender-specific percentile curves for length, weight, and head circumference concerning age, as well as weight concerning length, were derived using the GAMLSS approach. Results From the anthropometric information of 479,089 children (245,524 boys and 233,565 girls), growth charts were constructed. In comparison to the standard WHO chart, Iranian neonates displayed lower weight across all percentiles during the first month after birth, exhibited decreased head circumference at the 3rd percentile, and boys showed reduced length across all percentiles. After this age, Iranian children demonstrated increased weight, length, and head circumference. Conclusions This research introduces the inaugural large-scale endeavor for indigenous reference charts. Through the noted distinctions from the international reference, the utilization of this novel resource offers the potential to enhance the surveillance of children’s growth within the area. Moreover, by accurately assessing growth anomalies such as underweight, stunting, and wasting, it expands the domain of impactful policies in this sphere. Simultaneously, it enables the exploration of the secular trend of children’s growth in the forthcoming years.

Sheep were among the first animals domesticated by humans, and to this day, small ruminants are primarily raised for their meat, milk, and wool. This study evaluated the goodness of fit for growth curve models using observed age and weight data from crossbred lambs of various breeds based on the mean values between paired breeds. We employed a hybrid metaheuristic algorithm, combining a simulated annealing (SA) algorithm and a genetic algorithm (GA) called SAGAC, to determine the optimal parameter values for growth models, ensuring the best alignment between simulated and observed curves. The goodness of fit and model accuracy was assessed using the coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). Errors were measured by comparing the criteria differences between simulated and observed data. Thirty crossbreed combinations were simulated, considering the average weight. Analysis of the observed and simulated growth curves indicated that specific crossbreeding scenarios produced promising results. This simulation approach is believed to assist geneticists in predicting potential crossbreeding outcomes, thereby saving time and financial resources in field research. [ABSTRACT FROM AUTHOR]

Latent repeated measures ANOVA (L-RM-ANOVA) has recently been proposed as an alternative to traditional repeated measures ANOVA. L-RM-ANOVA builds upon structural equation modeling and enables researchers to investigate interindividual differences in main/interaction effects, examine custom contrasts, incorporate a measurement model, and account for missing data. However, L-RM-ANOVA uses maximum likelihood and thus cannot incorporate prior information and can have poor statistical properties in small samples. We show how L-RM-ANOVA can be used with Bayesian estimation to resolve the aforementioned issues. We demonstrate how to place informative priors on model parameters that constitute main and interaction effects. We further show how to place weakly informative priors on standardized parameters which can be used when no prior information is available. We conclude that Bayesian estimation can lower Type 1 error and bias, and increase power and efficiency when priors are chosen adequately. We demonstrate the approach using a real empirical example and guide the readers through specification of the model. We argue that ANOVA tables and incomplete descriptive statistics are not sufficient information to specify informative priors, and we identify which parameter estimates should be reported in future research; thereby promoting cumulative research. [ABSTRACT FROM AUTHOR]

Background & Objective: This study aimed to assess maternal and neonatal outcomes in cases of Intrauterine Growth Restriction (IUGR). Understanding these outcomes is vital for early identification of potential adverse effects and facilitating prompt intervention and management. Materials & Methods: This retrospective cohort study focused on mothers whose fetuses experienced IUGR at Imam Khomeini Hospital in Ahvaz, Iran, between March 2021 and 2022. Data on neurodevelopmental and clinical aspects were collected periodically up to 24 months’ post-birth, encompassing various parameters such as infant age, gender, developmental and motor coordination disorders, neurodevelopmental status, medical and speech disorders, and maternal health during pregnancy. Results: Over a one-year period, 80 breastfed infants were studied. At 6, 12, and 24 months after birth, 30% of children showed growth disorders. Boys had a slightly higher percentage of growth arrest (76.4% vs. 63.2%, P>0.05). Growth retardation was present in 13.16% of girls, compared to none of the boys. The most common medical outcomes were asthma and orthopedic disorders (5% each). Developmental disorders and motor disorders were the most common neurological and developmental outcomes, each affecting 11.25% of children. During pregnancy, 16.25% had gestational diabetes, 17.5% had hypertension, and 10% had hypothyroidism, which decreased to 6.25%, 11.25%, and 3.75%, respectively, after childbirth. Conclusion: The present study indicates a significant prevalence of growth and developmental disorders, as well as medical outcomes such as asthma and orthopedic issues at the age of two in infants with IUGR. Mothers of these infants also faced significant postpartum issues, such as diabetes and hypertension. [ABSTRACT FROM AUTHOR]

This work presents a low-cost, open-source turbidimeter, the 'Erlenmeter', designed to monitor the growth of microorganisms in batch cultures. It is easy to build, based exclusively on inexpensive off-the-shelf electronic components and 3D-printed parts. The Erlenmeter allows measuring the optical density of cultures on standard Erlenmeyer flasks without the need to open the flasks to collect aliquots, ensuring speed, minimal use of consumables, and elimination of the risk of contamination. These features make it particularly well-suited not just for routine research assays but also for experimental teaching. Here we illustrate the use of the Erlenmeter turbidimeter to record the growth of the microalga Phaeodactylum tricornutum, of the bacterium Escherichia coli, and of the yeast Saccharomyces cerevisiae, model organisms that are widely used in research and teaching. The Erlenmeter allows a detailed characterization of the growth curves of all organisms, confirming its usefulness for studying microbial populations dynamics both for research purposes and in classroom settings. [ABSTRACT FROM AUTHOR]

A constrained multivariate linear model is a multivariate linear model with the columns of its coefficient matrix constrained to lie in a known subspace. This class of models includes those typically used to study growth curves and longitudinal data. Envelope methods have been proposed to improve the estimation efficiency in unconstrained multivariate linear models, but have not yet been developed for constrained models. We pursue that development in this article. We first compare the standard envelope estimator with the standard estimator arising from a constrained multivariate model in terms of bias and efficiency. To further improve efficiency, we propose a novel envelope estimator based on a constrained multivariate model. We show the advantage of our proposals by simulations and by studying the probiotic capacity to reduced Salmonella infection. [ABSTRACT FROM AUTHOR]

Introduction: Angelman syndrome (AS) is a rare, genetic, neurodevelopmental disorder characterized by severe impairments in speech, cognition, and motor skills accompanied by unique behaviors, distinct facial features, and high prevalence of epilepsy and sleep problems. Despite some reports of short stature among AS patients, this feature is not included in the clinical criteria defined in 2005. We investigated growth patterns among AS patients with respect to mutation type, growth periods, family history, and endocrine abnormalities. Methods: Data were collected from patients' medical files in AS National Clinic. Mutation subtypes were divided to deletion and non-deletion. Four growth periods were defined: preschool, childhood, peak height velocity, and final height. Results: The cohort included 88 individuals (46 males), with 54 (61.4%) carrying deletion subtype. A median of 3 observations per individual produced 280 data points. Final height SDS was significantly lower compared to general population (−1.23 ± 1.26, p < 0.001), and in deletion group versus non-deletion (−1.67 ± 1.3 vs. −0.65 ± 0.96, p = 0.03). Final height SDS was significantly lower compared to height SDS in preschool period (−1.32 vs. −0.47, p = 0.007). Patient's final height SDS was significantly lower than the parents' (∆final-height SDS = 0.94 ± 0.99, p = 0.002). IGF1-SDS was significantly decreased compared to general population (−0.55 ± 1.61, p = 0.04), with lower values among deletion group (−0.70 ± 1.44, p = 0.01). Conclusions: AS patients demonstrate specific growth pattern with deceleration during childhood and adolescence, resulting in significantly decreased final height compared to normal population, and even lower among deletion subgroup, which could be attributed to reduced IGF1 levels. We propose adding short stature to the clinical criteria and developing adjusted growth curves for AS population. [ABSTRACT FROM AUTHOR]

Background: The aim of the present study was to provide ocular biometry percentile values for Indian children between the ages of 6 and 12 and to validate the usefulness of centiles in predicting myopia development. Methods: The study was part of a longitudinal study—the Sankara Nethralaya Tamil Nadu Essilor Myopia Study (STEM), where objective refraction and ocular biometry were measured for children studying in grades 1, 4, and 6 at baseline (2019–2020). These data were used to generate ocular biometry percentile curves (both for axial length (AL) and AL/corneal curvature (AL/CR) ratios). The usefulness of percentile values in predicting myopia development was estimated from follow-up data (2022). Results: The total number of children in the three grades at baseline was 4514 (age range 6 to 12). Boys represented 54% (n = 2442) of the overall sample. The prevalence of myopia at baseline was 11.7% (95% CI from 10.8 to 12.7%) in these three grades. Both the AL and AL/CR ratio centiles showed a linear trend with an increase in AL and AL/CR with increasing grades (p < 0.001) for all percentiles (2, 5, 10, 25, 50, 75, 90, 95, 98, and 99) when stratified by sex. In the follow-up data (n = 377), the 75th and 50th percentiles of the AL/CR ratio had an area under the curve (AUC) of 0.79 and 0.72 to predict myopia onset for grade 4 and 6 children at baseline. Combining baseline AL with the centile shift in follow-up as a predictor increased the AUC to 0.83. Conclusions: The present study has provided centile values specific for Indian children between the ages of 6 and 12 to monitor and intervene where children are at a higher risk of myopia development. [ABSTRACT FROM AUTHOR]

Despite some progress in reducing income inequality between countries over the last two decades, there is evidence that income inequality is on the rise within nations. This article uses panel data compiled from multiple sources and a growth curve modeling approach to examine the relationship between foreign direct investment (FDI) and within-nation income inequality across two samples: one comprising 2,095 country-year observations nested in 141 countries from 1991 to 2018 to explore the broader FDI-income inequality relationship, and a second, more focused sample of 405 country-year observations nested in 61 countries from 2000 to 2018, designed to analyze effects of the sectoral distribution of FDI. The analyses find that the relationship between FDI and income inequality is positive and linear for non-OECD economies but follows an inverted U-shaped path for developed countries. Moreover, in this article, I have developed and introduced two new indices, the FDI Pluralism Index and FDI Sectoral Gini Coefficient, to measure the distribution of FDI among the sectors of the host economy. Results show that a concentration of FDI in one sector aggravates income inequality since foreign capital tends to flow into the most profitable industries, creating uneven growth between sectors and increasing between-sector income inequality. [ABSTRACT FROM AUTHOR]

Abstract Background The study of growth traits is of interest to many animal scientists, regardless of specialization, due to the economic importance of growth rate, mature weight and other related traits. Objective This study aimed to compare six non‐linear models for describing the growth of Lori‐Bakhtiari sheep. Methods In order to collect weight data, 85 lambs (41 males and 44 females) were reared from birth to 140 days of age, and their growth patterns were recorded by measuring their body weight at 10‐day intervals. Various mathematical functions, including the negative exponential, Brody, Gompertz, Logistic, Morgan–Mercer–Flodin (MMF) and Weibull, were used to model the relationship between body weight records and age. Results The results showed that the MMF and Gompertz models provided the best fit to the body weight data, whereas the negative exponential model exhibited the worst fit. In all models, the asymptotic weight of male lambs was higher than females. The research also revealed differences in growth patterns between male and female lambs. Overall, females had a lower absolute growth rate than males, but they reached their peak growth at an earlier period, and their growth rate declined faster. Conclusions The differences in growth patterns between males and females indicate the importance of analysing male and female data separately when describing growth. As a result, Gompertz model can be recommended to Lori‐Bakhtiari female and male lamb breeders to determine more accurate growth traits. In addition, it should be considered that feeding male and female lambs separately according to absolute growth rate values may increase growth performance.

Background Objectives Materials and Methods Results Discussion and Conclusion Testicular volume is a marker of male pubertal development. Various clinical conditions and their treatments may influence testicular growth.To create ruler‐based age‐dependent pubertal testicular volume references that enable calculation of standard deviation (SD) scores.Study cohort comprised 65 boys who attended clinical examination twice a year from the age of 8.5 years until the attainment of final testicular size. Forty‐nine (75.4%) boys completed the follow‐up and 16 (24.6%) boys dropped out before the attainment of final post‐pubertal testicular size. At each follow‐up visit testicular size was measured with a ruler, orchidometer, and ultrasonography. LMS or LMSP method served as the technique for creating reference growth curves for testicular volumes. Using the novel references for ruler measurements, development of SD scores was assessed in a cohort of boys with unilateral cryptorchidism.Reference growth curves were constructed separately for ruler, orchidometer, and ultrasonography measurements. Median orchidometer volume of 4 mL, marker of male pubertal onset, occurred at the age of 11.7 years, whereas +2SD curve surpassed 4 mL at 10.2 years and −2SD curve at 13.7 years. Modeled ages at the attainment of 4 mL testicular volume based on ruler measurements were 9.7 years for +2SD curve, 11.5 years for median curve, and 13.6 years for −2SD curve. Ultrasonography‐based volume of 1.3 mL corresponded with the median modeled orchidometer‐based volume of 4 mL. In boys with unilateral cryptorchidism, ruler‐based SD scores decreased during puberty in undescended testes, but not in descended testes.The present study provides reference values for pubertal testicular volume measured with a ruler enabling an age‐dependent assessment of testicular size. Comparison with measurements by an orchidometer and ultrasonography is also presented. [ABSTRACT FROM AUTHOR]

Peanut is a critical food crop worldwide, and the development of high-throughput phenotyping techniques is essential for enhancing the crop's genetic gain rate. Given the obvious challenges of directly estimating peanut yields through remote sensing, an approach that utilizes above-ground phenotypes to estimate underground yield is necessary. To that end, this study leveraged unmanned aerial vehicles (UAVs) for high-throughput phenotyping of surface traits in peanut. Using a diverse set of peanut germplasm planted in 2021 and 2022, UAV flight missions were repeatedly conducted to capture image data thatwere used to construct high-resolution multitemporal sigmoidal growth curves based on apparent characteristics, such as canopy cover and canopy height. Latent phenotypes extracted from these growth curves and their first derivatives informed the development of advanced machine learning models, specifically random forest and eXtreme Gradient Boosting (XGBoost), to estimate yield in the peanut plots. The random forest model exhibited exceptional predictive accuracy (R2 = 0.93), while XGBoost was also reasonably effective (R2 = 0.88). When using confusion matrices to evaluate the classification abilities of each model, the two models proved valuable in a breeding pipeline, particularly for filtering out underperforming genotypes. In addition, the random forest model excelled in identifying top-performing material while minimizing Type I and Type II errors. Overall, these findings underscore the potential of machine learning models, especially random forests and XGBoost, in predicting peanut yield and improving the efficiency of peanut breeding programs. [ABSTRACT FROM AUTHOR]

Growth in individual body size amongst different species can to a greater or lesser extent depend on environmental factors such as resource availability. Individual growth curves can therefore be largely fixed or more plastic. Classic theory about phenotypic plasticity assumes that such plasticity has associated costs. In contrast, according to dynamic energy budget theory, maintaining a fixed growth rate in the face of variable resource availability would incur additional energetic costs. In this article, we explore the simultaneous evolution of the degree of plasticity in individual growth curves and the rate of non-plastic, environment-independent individual growth. We explore different relations between possible additional energetic costs and the degree of growth curve plasticity. To do so, we use adaptive dynamics to analyze a size-structured population model that is based on dynamic energy budget theory to account for the energetic trade-offs within an individual. We show that simultaneous evolution of the degree of growth curve plasticity and the rate of non-plastic individual growth will drive these traits to intermediate values at first. Afterwards, the degree of growth curve plasticity might evolve slowly towards extreme values depending on whether energetic costs increase or decrease with the degree of plasticity. In addition, the analysis shows that it is unlikely to encounter species in which individual growth is entirely fixed or entirely plastic, opposing general assumptions in dynamic energy budget theory. [ABSTRACT FROM AUTHOR]

The aim of this study was to analyze the effects of non-genetic factors on the estimation of genetic parameters of early growth traits in hybrid mutton sheep using ASReml software, in order to provide theoretical basis for screening the optimal hybriding combinations and accelerating the breeding process of new breeds of specialized housed-feeding mutton sheep. We selected the wellgrown hybrid Southhu (Southdown × Hu sheep) and Dorhu (Dorset × Hu sheep) sheep as the research objects, constructed weight correction formulae for SH and DH sheep at 60 and 180 days; and used ASReml software to investigate the effects of non-genetic factors on the estimation of genetic parameters of early growth traits in hybrid sheep. The results showed that the birth month and birth type were found significant for all traits (p

This work presents a low-cost, open-source turbidimeter, the ‘Erlenmeter’, designed to monitor the growth of microorganisms in batch cultures. It is easy to build, based exclusively on inexpensive off-the-shelf electronic components and 3D-printed parts. The Erlenmeter allows measuring the optical density of cultures on standard Erlenmeyer flasks without the need to open the flasks to collect aliquots, ensuring speed, minimal use of consumables, and elimination of the risk of contamination. These features make it particularly well-suited not just for routine research assays but also for experimental teaching. Here we illustrate the use of the Erlenmeter turbidimeter to record the growth of the microalga Phaeodactylum tricornutum, of the bacterium Escherichia coli, and of the yeast Saccharomyces cerevisiae, model organisms that are widely used in research and teaching. The Erlenmeter allows a detailed characterization of the growth curves of all organisms, confirming its usefulness for studying microbial populations dynamics both for research purposes and in classroom settings.

Alpacas represent an exotic species for Italy, introduced no more than 20–30 years ago. Nevertheless, they are currently the most widespread camelid species reared in this country, highlighting the growing interest in breeding this species. So, it is useful to assess parameters on their growth in relation to the new environment. Thus, body weight and body measures were recorded on 49 healthy alpacas of Huacaya type (27 females, 22 males), along a period of 27 months. Overall, 111 individual observations were made. Data were analysed by ANOVA, as growth curves were estimated by applying the Gompertz’s model. Multiple regression was finally used to estimate body weight from body measures. Body weight and linear measures, except for the distance between iliac crest and the length of the rump, were influenced by the category (calves, yearlings, adults) and by the age class within category (p 0.05). Asymptotic weigh of males was 8.25% higher than females (p

Wild edible mushrooms are an important food source globally and have a crucial role in forest ecosystems. However, there is limited research on the growth characteristics and the contribution of agronomic traits to biomass, and the environmental factors affecting mushroom growth are limited. This study was conducted in the Qilian Mountains, China, and focused on investigating the growth patterns and agronomic traits of Lyophyllum decastes and Coprinus comatus. The results revealed that the growth of these mushrooms followed a logical growth curve. By calculating the model parameters, we obtained the maximum daily growth of height (PH), pileus diameter (PD), and cluster perimeter (CP) of L. decastes on the 5th, 7th, and 7th days, respectively, with values of 0.55 cm d−1, 0.54 cm d−1, and 4.54 cm d−1, respectively. However, the maximum daily growth of PH, pileus length (PL), and PD of the C. comatus appeared on the 3rd day, 2nd day, and 2nd day of the observation, respectively. This study identified near-surface relative humidity, air relative humidity, and rainfall as the primary factors influencing mushroom growth, as indicated by Pearson’s correlation analysis, redundancy analysis (RDA), and multiple linear and stepwise regression. Additionally, land surface temperature and air temperature were also identified as important factors affecting mushroom growth. By utilizing random forest and stepwise regression analysis, this study identified PH and stipe diameter (SD) as the most crucial agronomic traits affecting mushroom biomass. Overall, this study offers insights for industrial mushroom cultivation and basic fungal research.

Less variable than life history parameters (LHPs), it is life history ratios (LHRs) that define how taxa allocate energy between growth, maintenance and reproduction, and respond to fishing pressure. Limited by small samples, variable data quality, and a focus on LHP estimation, previous meta-analyses have failed to settle debate about the extent to which LHRs are relatively invariant across all taxa or characteristic of specific taxa. We collected de novo 1335 published studies and applying rigorous standardization and quality control procedures developed, and make available, a database of high-quality M / K and L m/ L ∞ estimates. We describe two parallel but independent meta-analyses: a cross-validation study of the predictability of M / K by taxonomic category and an evaluation of alternative relationships between the LHRs using Akaike information criteria. These analyses demonstrate that the LHRs are correlated and vary predictably by taxa, with aggregation to the level of family and genera having the most predictive power in our database. We postulate that the LHRs of taxa may relate to their stoichiometric niches, which could open up interesting lines for ecological research and provide new tools for predicting the LHRs of poorly studied taxa. [ABSTRACT FROM AUTHOR]

Alpacas represent an exotic species for Italy, introduced no more than 20–30 years ago. Nevertheless, they are currently the most widespread camelid species reared in this country, highlighting the growing interest in breeding this species. So, it is useful to assess parameters on their growth in relation to the new environment. Thus, body weight and body measures were recorded on 49 healthy alpacas of Huacaya type (27 females, 22 males), along a period of 27 months. Overall, 111 individual observations were made. Data were analysed by ANOVA, as growth curves were estimated by applying the Gompertz's model. Multiple regression was finally used to estimate body weight from body measures. Body weight and linear measures, except for the distance between iliac crest and the length of the rump, were influenced by the category (calves, yearlings, adults) and by the age class within category (p < 0.05), while gender and interaction between gender and category were never significant (p > 0.05). Asymptotic weigh of males was 8.25% higher than females (p < 0.05). Moreover, males at maturity had higher withers (+0.96%), longer body (+2.70%) and chest measurements (+3.18%) than females. Asymptotic rump width (both at the ilium and at the ischium) was higher in females (respectively + 6.19%; +12.90%). According to our work, the best equation for the estimate of body weight from body measures was the following: 5.691 + 0.00005624 * chest circumference3 + 0.00002298 * trunk length3 − 1.155 * rump width between ischial tuberosities + 0.00001545 * height at rump3 (R2=0.957; SE of estimate = 4.36 kg), that is important for a better management of the species. Alpacas are the most widespread camelid species in Italy. Rump dimensions develop earlier than other body measures. It is possible to improve the management and welfare of animals by estimating the live weight starting from somatic measures. [ABSTRACT FROM AUTHOR]

Dealing with social rejection is challenging, especially during childhood when behavioral and neural responses to social rejection are still developing. In the current longitudinal study, we used a Bayesian multilevel growth curve model to describe individual differences in the development of behavioral and neural responses to social rejection in a large sample (n > 500). We found a peak in aggression following negative feedback (compared to neutral feedback) during late childhood, as well as individual differences during this developmental phase, possibly suggesting a sensitive window for dealing with social rejection across late childhood. Moreover, we found evidence for individual differences in the linear development of neural responses to social rejection in our three brain regions of interest: The anterior insula, the medial prefrontal cortex, and the dorsolateral prefrontal cortex. In addition to providing insights in the individual trajectories of dealing with social rejection during childhood, this study also makes a meaningful methodological contribution: Our statistical analysis strategy (and can be found in this study’s online supplementary materials at https://jeroendmulder.github.io/social-emotion-regulation/) can be used as an example on how to take into account the many complexities of developmental neuroimaging datasets, while still enabling researchers to answer interesting questions about individual-level relationships.

Peanut is a critical food crop worldwide, and the development of high-throughput phenotyping techniques is essential for enhancing the crop’s genetic gain rate. Given the obvious challenges of directly estimating peanut yields through remote sensing, an approach that utilizes above-ground phenotypes to estimate underground yield is necessary. To that end, this study leveraged unmanned aerial vehicles (UAVs) for high-throughput phenotyping of surface traits in peanut. Using a diverse set of peanut germplasm planted in 2021 and 2022, UAV flight missions were repeatedly conducted to capture image data that were used to construct high-resolution multitemporal sigmoidal growth curves based on apparent characteristics, such as canopy cover and canopy height. Latent phenotypes extracted from these growth curves and their first derivatives informed the development of advanced machine learning models, specifically random forest and eXtreme Gradient Boosting (XGBoost), to estimate yield in the peanut plots. The random forest model exhibited exceptional predictive accuracy (R2 = 0.93), while XGBoost was also reasonably effective (R2 = 0.88). When using confusion matrices to evaluate the classification abilities of each model, the two models proved valuable in a breeding pipeline, particularly for filtering out underperforming genotypes. In addition, the random forest model excelled in identifying top-performing material while minimizing Type I and Type II errors. Overall, these findings underscore the potential of machine learning models, especially random forests and XGBoost, in predicting peanut yield and improving the efficiency of peanut breeding programs.

This study was developed with longitudinal data measurements of Norfolk rabbits from birth to 119 days of age to estimate the average growth curve, with the primary objective of proposing a non-linear model. It also selected the most appropriate sigmoidal model to describe the growth of Norfolk rabbits. The adjustments provided by the logistic, von Bertalanffy, Gompertz, Brody, Richards, and proposed models were compared. The parameters were estimated using the “nls” function of the “stats” package in R software, the least-squares method, and the Gauss-Newton convergence algorithm. The goodness-of-fit comparison was based on the following criteria: adjusted coefficient of determination (), mean square error (MSE), mean absolute deviation (MAD), Akaike information criterion (AIC), and Bayesian information criterion (BIC). Cluster analysis helped select and classify the non-linear growth models, considering the other goodness-of-fit criteria results. The proposed non-linear, von Bertalanffy, Gompertz, and Richards models described the growth curve of Norfolk rabbits satisfactorily, providing parameters with practical interpretations. The goodness-of-fit criteria showed that the proposed and von Bertalanffy models best represented the growth of rabbits.

Evaluating the behavior of fruit width and length characters throughout the pepper crop cycle is essential for researchers in decision-making aimed at developing appropriate management techniques and harvesting fruits at proper growth stages. The Nonlinear Mixed-Effect Models (NLME) method is recommended to jointly model the residuals and the entire database, including group effects, to describe growth curves. This work compared four nonlinear equations (Gompertz, Logistic, Richards, and von Bertalanffy) by including groups (pepper and bell pepper) to describe the pepper genotypes' length and width growth. Of the eight genotypes used, three were bell pepper, and five were pepper. For each, fruit length and width were measured in 10 periods. According to the fit-quality measures studied, the best model for adjusting the length of the fruit was the Richards ( R a d j. 2 = 0.9960 ), while for the width, it was the Logistic ( R a d j. 2 = 0.9957 ). The estimated random effects showed that for asymptotic length and time to the inflection point presented a correlation of 0.75, indicating a positive association between these traits. For width, however, this result was different: −0.02. NLME adjustment allowed efficient prediction of values and efficient characterization of the studied genotypes, proving to be an efficient method for longitudinal data. [ABSTRACT FROM AUTHOR]

In this article, we compare two ways of modelling measures of fetal growth. The goal is to impute the missing information for certain ultrasound measurements that are observed at different times and with different numbers of observations. To analyze the effect that other variables have, such as environmental exposure to certain substances or diet, on fetal growth based on these data, we need to handle the information measured at the same instant of time for all the individuals under study, preferably in three time windows of pregnancy (first trimester, week 12; second trimester, week 20; third trimester, week 34). For this, data at these chosen times, in case they are not available, must be imputed from the available information using an appropriate statistical model. One option is to use a linear model, specifically a generalized least squares model that is fitted to the features shown in the data. The other option is to use diffusion processes, estimating their parameters based on the available information. In both options, missing data can be estimated with the unconditional fitted model, conditional on the previous available measurement, or conditional to the closest measurement. [ABSTRACT FROM AUTHOR]

Simple Summary: Following the growth of a fattening animal is an important aspect since farmers aim to maximize their profits by improving their slaughter weight. Growth models can fit the age–weight data into biologically interpretable parameters that provide a growth curve for monitoring animal growth, thus identifying the growth declining point during the fattening period. In this study, we aimed to define the best fitting growth model for describing Kivircik lamb growth. While the Gompertz model was identified as the best fitting growth model, it was determined that male lambs, twin lambs, and winter-born lambs had a higher mature weight. Even though they may be lighter at birth, the proportion of their prospective weight gain was found to be higher compared to female, single-born, and spring-born lambs. Our results showed that the estimation error of the Gompertz model ranged between −0.43 kg to 0.60 kg. In conclusion, according to the Gompertz model's estimation, Kivircik lambs reached 40.7% of their slaughter weight at weaning and had a 66.3% degree of maturity at the slaughter age, which indicated inappropriate management. The Kivircik is an indigenous sheep breed from Turkey, and it has superior meat quality compared to other indigenous breeds. Therefore, farmers prioritize Kivircik lamb fattening instead of milk production. Here, we aimed to determine the best nonlinear growth model, i.e., Gompertz, Logistic, Von Bertalanffy, and Brody, to describe the growth curve of Kivircik lambs. The body weight data from birth until 150 days of age belonging to 612 lambs were used as the material of this study. The best fitting model was selected by considering the adjusted coefficient of determination (R2adj), residual mean square, and Akaike's (AIC) and Bayesian information criteria (BIC). Even though the Brody model had a better statistical fit, considering its biological interpretation, the Gompertz model was identified as an appropriate model for describing Kivircik lamb growth. Male lambs, twin lambs, and lambs born in winter had higher mature live weights (44.2 kg, 71.2 kg, and 38.5 kg, respectively) and rate of weight gain (2.1, 2.6, and 2.0, respectively). However, our subgroups revealed a similar rate of maturity (0.01). Growth models are important tools for deciding the optimal slaughter age and they provide valuable information on the management practices of both sexes, birth types, and birth seasons. These results can be applied to breeding programs for early selection, enabling intervention strategies when needed. [ABSTRACT FROM AUTHOR]

Following the completion of an adaptive evolution experiment, fitness evaluations are routinely conducted to assess the magnitude of adaptation. In doing so, proper consideration should be given when determining the appropriate methods as trade-offs may exist between accuracy and throughput. Here, we present three instances in which small changes in the framework or execution of fitness evaluations significantly impacted the outcomes. The first case illustrates that discrepancies in fitness conclusions can arise depending on the approach to evaluating fitness, the culture vessel used, and the sampling method. The second case reveals that variations in environmental conditions can occur associated with culture vessel material. Specifically, these subtle changes can greatly affect microbial physiology leading to changes in the culture pH and distorting fitness measurements. Finally, the last case reports that heterogeneity in CFU formation time can result in inaccurate fitness conclusions. Based on each case, considerations and recommendations are presented for future adaptive evolution experiments. [ABSTRACT FROM AUTHOR]

Objectives: The literature regarding the effectiveness of long‐term psychological interventions delivered in tertiary care is scarce. This study sought to quantify and evaluate outcomes delivered in a UK tertiary care psychotherapy service against equivalent service benchmarks. Design: A retrospective analysis of outcomes on the Outcome Questionnaire‐45 (OQ‐45) over a 10‐year period in a tertiary care psychotherapy service. The modalities evaluated were cognitive‐behavioural, cognitive‐analytic, and psychoanalytic psychotherapies. Methods: Effectiveness was calculated at the service level and for each modality using pre‐post‐effect sizes and recovery rates. Benchmarking included a random‐effects meta‐analysis. Trajectories of change for each modality were examined using growth curve models. Results: Baseline distress on the OQ‐45 was higher than comparative norms (M = 102.57, SD = 22.79, N = 364). The average number of sessions was 48.68 (SD = 42.14, range = 5–335). There was a moderate pre‐post‐treatment effect (d =.46, 95% CI =.37–.55) which was lower than available benchmarks. The modalities differed in duration but were largely equivalent in terms of outcome. The reliable improvement rate was 29.95%, and the recovery rate was 10.16%, and change over time was best explained using a nonlinear (cubic) time trend. Conclusions: The elevated distress at baseline appears to create the conditions for relatively lengthy interventions and attenuated clinical outcomes. Suggestions are made regarding the clinical role, function, and evaluation of tertiary care psychotherapy services. [ABSTRACT FROM AUTHOR]

Breed-specific growth curves (GCs) are needed for neonatal puppies, but breed-specific data may be insufficient. We investigated an unsupervised clustering methodology for modeling GCs by augmenting breed-specific data with data from breeds having similar growth profiles. Puppy breeds were grouped by median growth profiles (bodyweights between birth and Day 20) using hierarchical clustering on principal components. Median bodyweights for breeds in a cluster were centered to that cluster's median and used to model cluster GCs by Generalized Additive Models for Location, Shape and Scale. These were centered back to breed growth profiles to produce cluster-scale breed GCs. The accuracy of breed-scale GCs modeled with breed-specific data only and cluster-scale breed GCs were compared when modeled from diminishing sample sizes. A complete dataset of Labrador Retriever bodyweights (birth to Day 20) was split into training (410 puppies) and test (460 puppies) datasets. Cluster-scale breed and breed-scale GCs were modelled from defined sample sizes from the training dataset. Quality criteria were the percentages of observed data in the test dataset outside the target growth centiles of simulations. Accuracy of cluster-scale breed GCs remained consistently high down to sampling sizes of three. They slightly overestimated breed variability, but centile curves were smooth and consistent with breed-scale GCs modeled from the complete Labrador Retriever dataset. At sampling sizes ≤ 20, the quality of breed-scale GCs reduced notably. In conclusion, GCs for neonatal puppies generated using a breed-cluster hybrid methodology can be more satisfactory than GCs at purely the breed level when sample sizes are small. [ABSTRACT FROM AUTHOR]

Maternal exposure to childhood maltreatment (CM) is associated with offspring behavioral problems; however, little work has examined these associations longitudinally across child development. This study examined the effects of maternal history of CM on trajectories of child internalizing and externalizing behavior measured from toddlerhood to preschool, and the role of maternal depressive symptoms and parenting behavior as potential mediators. Participants included 115 mother–child dyads recruited from a hospital maternity ward. Maternal CM was measured at 3-months postpartum. At 18, 36, and 60 months, maternal depressive symptoms and child behavior were assessed via maternal report and parenting behavior was assessed through direct observation. Findings indicated that children of mothers exposed to CM demonstrated poorer trajectories of problem behavior across early childhood. Maternal depressive symptoms mediated the relation between CM and children's internalizing problems. Findings highlight the importance of screening for maternal depressive symptoms and early intervention for maternal and child mental health. [ABSTRACT FROM AUTHOR]

Research shows that the quality of mother‐child interactions is a robust antecedent of child socioemotional functioning. Yet, relatively little is known about the evolution of this relational quality over time, and even less about how changes in relational quality may bear on child adjustment. This study aimed to describe the trajectory of quality of mother‐child interactions between ages 2 and 7 and to investigate associations between individual differences in this trajectory and child socioemotional functioning at age 8. In a sample of 233 mother‐child dyads primarily comprised of White French‐Canadian mothers, the quality of interactions was assessed during 10‐min play sequences when children were aged 2, 4, and 7 years. Child internalizing, externalizing, and prosocial behaviors were reported by a subsample of 171 teachers at age 8. The results showed that on average, quality of mother‐child interactions decreased over time. In terms of individual differences, children who experienced a slower decrease in the quality of interactions with their mother from 2 to 7 years showed less internalizing behavior at age 8, over and above initial quality at 2 years. Children involved in higher quality interactions with their mother at 2 years showed more prosocial and less externalizing behavior at age 8, independent of the subsequent decrease in the quality of those interactions. The findings suggest that initial levels and subsequent changes in the quality of mother‐child interactions are two distinct indicators of their relationship with potentially different implications for child adjustment. [ABSTRACT FROM AUTHOR]

Cell culturing methods are increasingly used to reduce and replace the use of live animals in biomedical research and chemical toxicity testing. Although live animals are avoided when using cell culturing methods, they often contain animal-derived components of which one of the most commonly used is foetal bovine serum (FBS). FBS is added to cell culture media among other supplements to support cell attachment/spreading and cell proliferation. The safety, batch-to-batch variation, and ethical problems with FBS are acknowledged and therefore world-wide efforts are ongoing to produce FBS free media. Here, we present the composition of a new defined medium with only human proteins either recombinant or derived from human tissues. This defined medium supports long-term culturing/routine culturing of normal cells and of cancer cells, and can be used for freezing and thawing of cells, i.e. for cell banking. Here, we show for our defined medium, growth curves and dose response curves of cells grown in two and three dimensions, and applications such as cell migration. Cell morphology was studied in real time by phase contrast and phase holographic microscopy time-lapse imaging. The cell lines used are human cancer-associated fibroblasts, keratinocytes, breast cancer JIMT-1 and MDA-MB-231 cells, colon cancer CaCo-2 cells, and pancreatic cancer MiaPaCa-2 cells as well as the mouse L929 cell line. In conclusion, we present the composition of a defined medium without animal-derived products which can be used for routine culturing and in experimental settings for normal cells and for cancer cells, i.e. our defined medium provides a leap towards a universal animal product free cell culture medium.

Objective: Measuring head circumference is a quick, simple, cheap, noninvasive and reliable procedure for determining underlying brain size and provides information about risk for medical problems. Growth percentages of head vary from one community to another. Methods: We aim to create head circumference growth curves for children aged 0-18 years in Turkish community. Measurements were performed by same person using a rigid and calibrated tape meter. In addition, the parents were asked to fill out a questionnaire with variables that could affect children's growth. Results: Data analysis has shown that there is no relationship between the children's head circumference and whether there exists someone with a large/small head circumference in their families, or the average monthly income of their families and the parent’s education level. Only the independent variables child’s school performance, birth weight, nutrition, smoking are significantly effective on the HC measures of boys at alpha=0.05 level. For girls, only the independent variables birth weight and nutrition form are significantly important in explaining the variability of HC at the level of alpha=0.05. One gram increase in birth weight causes a 0.001 cm increase in the HC of both genders. In addition, head circumference growth curves were created for both genders to include all children aged 0-18. Conclusions: Our study offers recent and gender-specific head circumference norms. These new values can be used more effectively in child follow-up.

Algal biotechnology has received significant attention over the past two decades in fields ranging from biofuels to cosmeceuticals. However, the development of domesticated or genetically engineered microalgal strains for commercial applications depends on accurate and reliable growth data. To this end, several companies have developed lab-scale photobioreactors (PBRs) that enable precision control of conditions and automated growth recording. Whilst the transition from manual control of conditions and measurements to automated systems has allowed researchers to greatly improve the accuracy and scope of cultivation experiments, it has also presented novel challenges. The most pertinent of these being the analysis of the copious quantities of data produced. A standard PBR experiment can contain tens or even hundreds of thousands of data points, and often features outliers, noise, and a requirement for datasets to be calibrated with a standard curve or merged with replicates. Furthermore, complex analysis of multiple curves may be required in order to extract information such as the gradient or fit to a growth model. This can be laborious, time consuming and is not standardized between research groups. Proprietary software provided with most PBRs tends to lack these more advanced features and is typically unable to process data from other PBR manufacturers. To address these issues, we have developed the Algal Data Analyser (ADA), an open-source software platform providing the tools to rapidly plot and analyse microalgal data. ADA can simultaneously interpret datasets from three major PBR suppliers (Algenuity, Industrial Plankton, Photon Systems Instruments), and can also incorporate data from manual readings. Users can rapidly produce standardized, publication ready plots, and analyse multiple growth curves in parallel. Future iterations of ADA will include compatibility with datasets from other PBR suppliers as they become available, with the aim of making it a universal platform for all PBR data.

Background Growth centiles and growth curves are two ways to present child anthropometry; however, they differ in the type of data used, the method of analysis, the biological parameters fitted and the form of interpretation. Aim To fit and compare height growth centiles and curves in Indian children. Subjects and methods 1468 children (796 boys) from Pune India aged 6–18 years with longitudinal data on age and height (n = 7781) were analysed using GAMLSS (Generalised Additive Models for Location Scale and Shape) for growth centiles, and SITAR (SuperImposition by Rotation and Translation) for growth curves. Results SITAR explained 98.7% and 98.8% of the height variance in boys and girls, with mean age at peak height velocity 13.1 and 11.0 years, and mean peak velocity 9.0 and 8.0 cm/year, respectively. GAMLSS (Box-Cox Cole Green model) also captured the pubertal growth spurt but the centiles were shallower than the SITAR mean curve. Boys showed a mid-growth spurt at age 8 years. Conclusion GAMLSS displays the distribution of height in the population by age and sex, while SITAR effectively and parsimoniously summarises the pattern of height growth in individual children. The two approaches provide distinct, useful information about child growth.

ABSTRACT Rabbit farming is an activity with high growth potential due to its easy handling, high prolificacy, low polluting impact, and easy adaptability to family farming systems, producing meat of high biological value. Therefore, the aim of this work was to evaluate, using von Bertalanffy's nonlinear model, growth curves of weight as a function of age in ‘Flemish Giant Rabbits’ and ‘New Zealand White’ crossbred rabbits. Two different data collections were used: the longitudinal method and the cross-sectional method. The experiment was carried out at the Federal University of Lavras, located in the municipality of Lavras, Minas Gerais, Brazil, where 10 crossbred rabbits were evaluated, and animals were weighed from 0 to 150 days of age. Both methods proved to be adequate to describe the development of rabbits and the cross-sectional method proved to be an adequate alternative to obtention of growth curves, saving time in data collection and showing consistent estimates.

Models considered in the current special issue represent valuable additions to the statistical toolbox of prevention researchers for many types of research questions and designs. Their appropriate use, however, depends on critical evaluation relative to previously existing techniques. This evaluation includes (a.) model choice involving "right-sizing" of the model relative to the amount and quality of data at hand, (b.) examination of the external validity of identified associations relative to observed or latent subgroups, (c.) confirmation of the reasonableness of the functional form assumed by the model, and (d.) identification of influential or outlying observations which unduly affect model fit or parameter estimates. Models in this issue allow for testing of new types of hypotheses in prevention research, and can constitute counterarguments to existing statistical practice. These models may, however, in turn be the object of critical examination of counterarguments a reasonable skeptic may offer. [ABSTRACT FROM AUTHOR]

Child growth measurements are critical vital signs to track, with every individual child growth curve potentially revealing a story about a child's health and well‐being. Simply put, every baby born requires basic building blocks to grow and thrive: proper nutrition, love and care, and medical health. To ensure that every child who is missing one of these vital aspects is identified, growth is traditionally measured at birth and each well‐child visit. While the blue and pink growth curves appear omnipresent in pediatric clinics, it is surprising to realize that their use only became standard of care in 1977 when the National Center for Health Statistics (NCHS) adopted the growth curve as a clinical tool for health. Behind this practice lies a socioeconomically, culturally, and politically complex interplay of individuals and institutions around the world. In this review, we highlight the often forgotten past, current state of practice, and future potential of this powerful clinical tool: the growth reference chart, with a particular focus on clinical genetics practice. The goal of this article is to understand ongoing work in the field of anthropometry (the scientific study of human measurements) and its direct impact on modern pediatric and genetic patient care. [ABSTRACT FROM AUTHOR]

BACKGROUND: When the human population increases, so does the need to explore a wider range of feedstocks and biomasses, such as cyanobacteria. However, a deeper understanding of the growth patterns and pigment production is required to support the selection of the most beneficial species and conditions for industrial production. The growth and pigment production (i.e., chlorophyll a and C‐phycocyanin) of three cyanobacterium species were evaluated following a three‐fold aim. The first goal was to compare among a species commonly selected for exploitation (Arthrospira platensis) and two alternative species (Anabaena cylindrica and Nostoc muscorum). The second goal was analyzing pigment production in the long‐term. The last goal involved comparing different methods (spectrophotometry and fluorimetry) to understand whether there is an appropriate proxy of biomass increase and pigment production that can be used for monitoring purposes. RESULTS: All species showed high longevity and proved capable of growing for more than 100 days without any additional supplementation. However, the maximum quantum yield of PS II (Fv /Fm) revealed that their photosynthetic efficiency varied over time with a clear decrease after 2 months. Pigment analysis showed a heterogeneous pattern during the growth periods of all three species that could only be captured by the parameter Fv /Fm, but the pattern was only present for A. cylindrica and N. muscorum in some stages of the culture period. CONCLUSION: N. muscorum was found to be the best chlorophyll a and C‐phycocyanin producer, with the production peaking for all species at defined time periods within the growth profile. © 2022 The Authors. Journal of Chemical Technology and Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI). [ABSTRACT FROM AUTHOR]

Opuntia joconostle is a semi-wild cactus cultivated for its fruit. However, the cladodes are often discarded, wasting the potentially useful mucilage in them. The mucilage is composed primarily of heteropolysaccharides, characterized by their molar mass distribution, monosaccharide composition, structural features (by vibrational spectroscopy, FT IR, and atomic force microscopy, AFM), and fermentability by known saccharolytic commensal members of the gut microbiota. After fractionation with ion exchange chromatography, four polysaccharides were found: one neutral (composed mainly of galactose, arabinose, and xylose) and three acidic, with a galacturonic acid content from 10 to 35%mol. Their average molar masses ranged from 1.8 × 105 to 2.8 × 105 g·mol−1. Distinct structural features such as galactan, arabinan, xylan, and galacturonan motifs were present in the FT IR spectra. The intra- and intermolecular interactions of the polysaccharides, and their effect on the aggregation behavior, were shown by AFM. The composition and structural features of these polysaccharides were reflected in their prebiotic potential. Lactobacilli and Bifidobacteria were not able to utilize them, whereas members of Bacteroidetes showed utilization capacity. The obtained data suggest a high economic potential for this Opuntia species, with potential uses such as animal feed in arid areas, precise prebiotic, and symbiotic formulations, or as the carbon skeleton source in a green refinery. Our methodology can be used to evaluate the saccharides as the phenotype of interest, helping to guide the breeding strategy. [ABSTRACT FROM AUTHOR]

AIMS: Data on the clinical course of patients with cystic fibrosis (CF) from childhood to CF-related diabetes (CFRD) diagnosis in adulthood are limited. We evaluate whether childhood trajectories of parameters of interest in CF are associated with the risk of abnormal glucose tolerance (AGT) in early adulthood. Methods: Pediatric and adult data from 108 subjects with CF followed annually were paired. Participants were grouped according to predominant childhood trajectories for weight, height, body mass index, lung function, glycated hemoglobin levels, fasting glycemia, and 2h post-oral glucose tolerance test glucose levels. Multivariable logistic regression was performed to identify parameters that predict glucose tolerance status in adulthood. Results: Univariate analyses reveal that the risk of developing an AGT in adulthood is greater in subjects who are homozygous vs. heterozygous for the ΔF508 mutation, have pancreatic insufficiency vs. sufficiency, or have higher fasting glycemia values at 10 years old rising rapidly vs. lower values that are gradually rising until 17 years old. Multivariable logistic regression retains only fasting glycemia as a significant predictor for the occurrence of AGT in adulthood. Conclusions: Fasting glycemia may be a clinical marker of interest to better target children with CF at risk of developing an AGT in early adulthood.