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The productivity of temperate grassland is limited by the response of plants to low temperature, affecting winter persistence and seasonal growth rates. During the winter, the growth of perennial grasses is restricted by a combination of low temperature and the lack of available light, but during early spring low ground temperature is the main limiting factor. Once temperature increases, growth is stimulated, resulting in a peak in growth in spring before growth rates decline later in the season. Growth is not primarily limited by the ability to photosynthesize, but controlled by active regulatory processes that, e.g., enable plants to restrict growth and conserve resources for cold acclimation and winter survival. An insufficient ability to cold acclimate can affect winter persistence, thereby also reducing grassland productivity. While some mechanistic knowledge is available that explains how low temperature limits plant growth, the seasonal mechanisms that promote growth in response to increasing spring temperatures but restrict growth later in the season are only partially understood. Here, we assess the available knowledge of the physiological and signaling processes that determine growth, including hormonal effects, on cellular growth and on carbohydrate metabolism. Using data for grass growth in Ireland, we identify environmental factors that limit growth at different times of the year. Ideas are proposed how developmental factors, e.g., epigenetic changes, can lead to seasonality of the growth response to temperature. We also discuss perspectives for modeling grass growth and breeding to improve grassland productivity in a changing climate.

Background Life satisfaction among Korean students is declining substantially, and multifaceted improvement efforts are required. Methods We analyzed longitudinal change trajectories for exercise, grit, and life satisfaction levels among Korean adolescents using latent growth modeling with longitudinal data from the Korean Children and Youth Panel Surveys of 2,142 students (male: 1,070, female: 1,072) from sixth grade (2020) through eighth grade (2022). Results The model, which tracked linear changes in the students’ exercise, grit, and life satisfaction, showed consistent declines over three school years for all variables. We also identified a longitudinal causal relationship among exercise, grit, and life satisfaction. A higher grit intercept was associated with higher intercept for—and a partial mediating effect between—exercise and life satisfaction. The grit slope was positively related to the life satisfaction slope, and both the intercept and exercise slope had positive effects on life satisfaction. Moreover, grit had a longitudinal mediating effect between exercise and life satisfaction. Conclusions We discuss the longitudinal change trajectories of exercise, grit, and life satisfaction, the causal and mediating effects among them, and the implications of the findings. These findings bolster our understanding of Korean adolescents’ life satisfaction and have practical significance for designing programs to improve their quality of life.

Growth is a widely used term in plant science and ecology, but it can have different meanings depending on the context and the spatiotemporal scale of analysis. At the meristem level, growth is associated with the production of cells and initiation of new organs. At the organ or plant scale and over short time periods, growth is often used synonymously with tissue expansion, while over longer time periods the increase in biomass is a common metric. At even larger temporal and spatial scales, growth is mostly described as net primary production. Here, we first address the question 'what is growth?'. We propose a general framework to distinguish between the different facets of growth, and the corresponding physiological processes, environmental drivers and mathematical formalisms. Based on these different definitions, we then review how plant growth can be measured and analysed at different organisational, spatial and temporal scales. We conclude by discussing why gaining a better understanding of the different facets of plant growth is essential to disentangle genetic and environmental effects on the phenotype, and to uncover the causalities around source or sink limitations of plant growth.

We introduce the R package nlpsem (Liu, 2023 ), a comprehensive toolkit for analyzing longitudinal processes within the structural equation modeling (SEM) framework, incorporating individual measurement occasions. This package emphasizes nonlinear longitudinal models, especially intrinsic ones, across four key scenarios: (1) univariate longitudinal processes with latent variables, optionally including covariates such as time-invariant covariates (TICs) and time-varying covariates (TVCs); (2) multivariate longitudinal analyses to explore correlations or unidirectional relationships between longitudinal variables; (3) multiple-group frameworks for comparing manifest classes in scenarios (1) and (2); and (4) mixture models for scenarios (1) and (2), accommodating latent class heterogeneity. Built on the OpenMx R package, nlpsem supports flexible model designs and uses the full information maximum likelihood method for parameter estimation. A notable feature is its algorithm for determining initial values directly from raw data, improving computational efficiency and convergence. Furthermore, nlpsem provides tools for goodness-of-fit tests, cluster analyses, visualization, derivation of p  values and three types of confidence intervals, as well as model selection for nested models using likelihood-ratio tests and for non-nested models based on criteria such as Akaike information criterion and Bayesian information criterion. This article serves as a companion document to the nlpsem R package, providing a comprehensive guide to its modeling capabilities, estimation methods, implementation features, and application examples using synthetic intelligence growth data.

The GesCoN model was evaluated for its capability to simulate growth, nitrogen uptake, and productivity of open field tomato grown under different environmental and cultural conditions. Five datasets collected from experimental trials carried out in Foggia (IT) were used for calibration and 13 datasets collected from trials conducted in Foggia, Perugia (IT), and Florida (USA) were used for validation. The goodness of fitting was performed by comparing the observed and simulated shoot dry weight (SDW) and N crop uptake during crop seasons, total dry weight (TDW), N uptake and fresh yield (TFY). In SDW model calibration, the relative RMSE values fell within the good 10-15% range, percent BIAS (PBIAS) ranged between -11.5 and 7.4%. The Nash-Sutcliffe efficiency (NSE) was very close to the optimal value 1. In the N uptake calibration RRMSE and PBIAS were very low (7%, and -1.78, respectively) and NSE close to 1. The validation of SDW (RRMSE = 16.7%; NSE = 0.96) and N uptake (RRMSE = 16.8%; NSE = 0.96) showed the good accuracy of GesCoN. A model under- or overestimation of the SDW and N uptake occurred when higher or a lower N rates and/or a more or less efficient system were used compared to the calibration trial. The in-season adjustment, using the \"SDWcheck\" procedure, greatly improved model simulations both in the calibration and in the validation phases. The TFY prediction was quite good except in Florida, where a large overestimation (+16%) was linked to a different harvest index (0.53) compared to the cultivars used for model calibration and validation in Italian areas. The soil water content at the 10-30 cm depth appears to be well-simulated by the software, and the GesCoN proved to be able to adaptively control potential yield and DW accumulation under limited N soil availability scenarios and consequently to modify fertilizer application. The DSSwell simulate SDW accumulation and N uptake of different tomato genotypes grown under Mediterranean and subtropical conditions.

Background Diabetes is a chronic disease, and hyperglycemia can increase the risk of diabetic complications and the need for more inpatient services. Therefore, the prevention and control of diabetes are important. This study aimed to identify the trajectories of metabolic control and its correlates in people with type 1 diabetes. Method This is a longitudinal study with 2020 type 1 diabetic individuals aged 18 to 59 years. The participants’ glycosylated hemoglobin (HbA 1c ) was measured three times with a six-month interval between each measurement. The data were analyzed using group-based trajectory modeling. Multinomial logistic regression was used to determine the factors related to these groups. Results The results showed four trajectories of safe controlled (46.2%), moderate stable risk (28.7%), moderate increasing risk (12.5%), and high decreasing risk trajectory (12.6%) (entropy = 0.70). The results of multinomial logistic regression showed dyslipidemia could increase the odds of being in the three risk trajectories. Education, physical inactivity, and poor psychological status could also increase the odds of being in the moderate stable and high decreasing trajectories. Moreover, sex, job, and BMI could increase the odds of being in the high decreasing risk group ( p  < 0.05). Conclusion Since there are different trajectories of metabolic control of diabetes, it is necessary for healthcare providers and health experts to plan behavioral interventions based on the location of individuals in different trajectories and the related significant risk factors. In this way, appropriate prevention, care, and treatment programs can be provided for the people in each group.

Reduced water availability and environmental pollution caused by nitrogen (N) losses have increased the need for rational management of irrigation and N fertilization in horticultural systems. Decision support systems (DSS) could be powerful tools to assist farmers to improve irrigation and N fertilization efficiency. Currently, fertilization by drip irrigation system (fertigation) is used for many vegetable crops around the world. The paper illustrates the theoretical basis, the methodological approach and the structure of a DSS called GesCoN for fertigation management in open field vegetable crops. The DSS is based on daily water and N balance, considering the water lost by evapotranspiration (ET) and the N content in the aerial part of the crop (N uptake) as subtraction and the availability of water and N in the wet soil volume most effected by roots as the positive part. For the water balance, reference ET can be estimated using the Penman-Monteith (PM) or the Priestley-Taylor and Hargreaves models, specifically calibrated under local conditions. Both single or dual Kc approach can be used to calculate crop ET. Rain runoff and deep percolation are considered to calculate the effective rainfall. The soil volume most affected by the roots, the wet soil under emitters and their interactions are modeled. Crop growth is modeled by a non-linear logistic function on the basis of thermal time, but the model takes into account thermal and water stresses and allows an in-season calibration through a dynamic adaptation of the growth rate to the specific genetic and environmental conditions. N crop demand is related to DM accumulation by the N critical curve. N mineralization from soil organic matter is daily estimated. The DSS helps users to evaluate the daily amount of water and N fertilizer that has to be applied in order to fulfill the water and N-crop requirements to achieve the maximum potential yield, while reducing the risk of nitrate outflows.

Purpose Little is known about the course of fathers’ psychological distress and associated risk factors beyond the postnatal period. Therefore, the current study aimed to: (a) assess the course of distress over 7 years postnatally; (b) identify classes of fathers defined by their symptom trajectories; and (c) identify early postnatal factors associated with persistent symptoms. Method Data from 2,470 fathers in the Longitudinal Study of Australian Children were analysed using latent growth modelling. Fathers’ psychological distress was assessed using the Kessler-6 (Kessler et al. in Arch Psychiatry 60:184–189, 2003 ) when their children were aged 0–1, 2–3, 4–5 and 6–7 years. Results Overall, distress was highest in the first postnatal year and then decreased over time. Two distinct trajectories were identified. The majority of fathers (92 %) were identified as having minimal distress in the first postnatal year which decreased over time, whilst 8 % had moderate distress which increased over time. Low parental self-efficacy, poor relationship and job quality were associated with ‘persistent and increasing distress’. Conclusions Early postnatal factors associated with fathers’ persistent distress were identified, providing opportunities for early identification and targeted early intervention.

Empathy is a relevant attribute in the context of patient care. However, a decline in empathy throughout medical education has been reported in North-American medical schools, particularly, in the transition to clinical training. The present study aims to longitudinally model empathy during medical school at three time points: at the entrance, final of pre-clinical phase and at the beginning of clinical training. Data collected with the adaptation to Portuguese of the Jefferson Scale of Physician Empathy (student version) were analysed with latent growth modelling, conditioned by gender, openness and agreeableness. Empathy scores at all times were higher for females than for males, but only significantly at the end of the preclinical phase. The model showed a satisfactory fit level and the primary finding was that undergraduate medical student's empathy did not decline over time. Empathy scores were significantly and positively related with Openness to Experience and Agreeableness at admission, but the empathy rate of change across time was not significant. The stability of empathy revealed by a longitudinal methodology applied for the first time to empathy studying, contradicts previous results of decline and contributes to the understanding of the empathy development of medical students.

This cross-country four-year longitudinal study investigated the development of processing speed throughout primary school education. The analyses were conducted on data accumulated from 441 pupils in grades from 1 to 4 (aged 6.42 to 11.85 years) in Kyrgyzstan and Russia. Mixed effects growth modeling was applied to estimate average and individual growth trajectories for processing speed in two cross-country samples. Latent class growth modeling was conducted to describe various types of growth trajectories for processing speed and to compare the distribution of the types within the analyzed samples. According to the results, processing speed significantly increases across primary school years. The trajectory is described by nonlinear changes with most dynamic growth between grades 1 and 2, which slows down until grade 4. No significant cross-country differences were found in the initial score of processing speed or developmental changes in processing speed across primary school years. The development of processing speed is described by a model including three quadratic growth types but this minimally differs. It is concluded that in both samples, the development of processing speed may be characterized by homogeneity, with the most intensive growth from grade 1 to grade 2 and subsequent linear improvement until grade 4.