Research on fertilization decision method for rice tillering stage based on the coupling of UAV hyperspectral remote sensing and WOFOST.

Introduction: The use of chemical fertilizers in rice field management directly affects rice yield. Traditional rice cultivation often relies on the experience of farmers to develop fertilization plans, which cannot be adjusted according to the fertilizer requirements of rice. At present, agricultural drones are widely used for early monitoring of rice, but due to their lack of rationality, they cannot directly guide fertilization. How to accurately apply nitrogen fertilizer during the tillering stage to stabilize rice yield is an urgent problem to be solved in the current large-scale rice production process. Methods: WOFOST is a highly mechanistic crop growth model that can effectively simulate the effects of fertilization on rice growth and development. However, due to its lack of spatial heterogeneity, its ability to simulate crop growth at the field level is weak. This study is based on UAV remote sensing to obtain hyperspectral data of rice canopy and assimilation with the WOFOST crop growth model, to study the decision-making method of nitrogen fertilizer application during the rice tillering stage. Extracting hyperspectral features of rice canopy using Continuous Projection Algorithm and constructing a hyperspectral inversion model for rice biomass based on Extreme Learning Machine. By using two data assimilation methods, Ensemble Kalman Filter and Four-Dimensional Variational, the inverted biomass of the rice biomass hyperspectral inversion model and the localized WOFOST crop growth model were assimilated, and the simulation results of the WOFOST model were corrected. With the average yield as the goal, use the WOFOST model to formulate fertilization decisions and create a fertilization prescription map to achieve precise fertilization during the tillering stage of rice. Results: The research results indicate that the training set R2 and RMSE of the rice biomass hyperspectral inversion model are 0.953 and 0.076, respectively, while the testing set R2 and RMSE are 0.914 and 0.110, respectively. When obtaining the same yield, the fertilization strategy based on the ENKF assimilation method applied less fertilizer, reducing 5.9% compared to the standard fertilization scheme. Discussion: This study enhances the rationality of unmanned aerial vehicle remote sensing machines through data assimilation, providing a new theoretical basis for the decision-making of rice fertilization.

Developing a simple and efficient modeling solution for predicting key phenological stages of table grapes in a non-traditional viticulture zone in south Asia.

Phenological shifts are one of the most visible signs of climatic variability and change in the biosphere. However, modeling plant phenological responses has always been a key challenge due to climatic variability and plant adaptation. Grapevine is a phenologically sensitive crop and, thus, its developmental stages are affected by the increase in temperature. The goal of this study was to develop a temperature-based grapevine phenology model (GPM) for predicting key developmental stages for different table grape cultivars for a non-traditional viticulture zone in south Asia. Experiments were conducted in two vineyards at two locations (Chakwal and Islamabad) in the Pothawar region of Pakistan during the 2019 and 2020 growing seasons for four cultivars including Perlette, King's Ruby, Sugraone and NARC Black. Detailed phenological observations were obtained starting in January until harvest of the grapes. The Mitscherlich monomolecular equation was used to develop the phenology model for table grapes. There was a strong non-linear correlation between the Eichhorn and Lorenz phenological (ELP) scale and growing degree days (GDD) for all cultivars with coefficient of determinations (R2) ranging from 0.90 to 0.94. The results for model development indicated that GPM was able to predict phenological stages with high skill scores, i.e., a root mean square (RMSE) of 2.14 to 2.78 and mean absolute error (MAE) of 1.86 to 2.26 days. The prediction variability of the model for the onset timings of phenological stages was up to 3 days. The results also reveal that the phenology model based on GDD approach provides an efficient planning tool for viticulture industry in different grape growing regions. The proposed methodology, being a simpler one, can be easily applied to other regions and cultivars as a predictor for grapevine phenology.

Metabolic performance and feed efficiency of black soldier fly larvae.

The black soldier fly (BSF), Hermetia illucens, is used in entomoremediation processes because its larvae can use a variety of organic residues with high efficiency. However, feed efficiencies are variable and characterized by uncertainties. Recently developed growth and metabolic performance models have predicted across different studies that BSF larvae have used 53%-58% of the feed components they have assimilated, in terms of carbon equivalents, for growth throughout their lifetime when reared on chicken feed. This is termed their average net growth efficiency. The remainder of the carbon has been lost as CO2. However, mass balances made under similar conditions show that the weight gained by BSF larvae corresponds to only 14%-48% of the feed substrates removed, indicating substrate conversion efficiency. Both performance indicators show even greater variability if more feed substrates are considered. Feed assimilation and growth rates, costs of growth, maintenance, and larval lifespan have been shown to affect how efficiently BSF larvae convert feed into growth. The differences between average net growth efficiencies and substrate conversion efficiencies further indicate that feed is often not used optimally in entomoremediation processes and that the overall yield of such processes is not determined by larval performance alone but is the result of processes and interactions between larvae, substrates, microbes, and their physical environment. The purpose of this study is to illustrate how quantification of the metabolic performance of BSF larvae can help improve our understanding of the role of the larvae in entomoremediation processes.

Executive Functions and Social-Emotional Problems in Toddlerhood: Nature, Persistence, and Direction of Associations.

Social-emotional problems can emerge as early as the first years of life and are associated with a broad range of negative outcomes throughout the lifespan. There is convincing evidence that poorer executive functions (EF) are associated with more social-emotional problems during childhood and adolescence. However, the nature, persistence, and direction of the associations between different components of EF and social-emotional problems in toddlerhood remain unclear. Using two complementary statistical approaches, the present study aimed to (a) identify the role of EF components (inhibitory control, cognitive flexibility, and working memory) in the emergence and maintenance of social-emotional problems during toddlerhood, and (b) explore potential bidirectional associations between toddlers' EF and social-emotional problems. EF and social-emotional problems were assessed around 13, 19, and 28 months of age in a sample of 133 typically developing toddlers (51% boys) from mostly White middle-class families. At each time point, EF were measured with three behavioral tasks and social-emotional problems with a well-validated questionnaire completed by mothers. Multilevel growth models revealed a significant increase in social-emotional problems across toddlerhood and a negative association between inhibitory control and social-emotional problems that persisted across time. Controlling for stability across time, cross-lagged panel models indicated that child inhibitory control at 19 months negatively predicted child social-emotional problems at 28 months, but not the reverse. This study highlights that toddlerhood is a period of significant increase in social-emotional problems and provides evidence for the protective role of early inhibitory control skills against the development of social-emotional problems during toddlerhood.

Co-developmental trajectories of suicidal ideation and non-suicidal self-injury among Chinese adolescents: Transdiagnostic predictors and association with suicide attempts.

INTRODUCTION: Adolescent suicidal ideation (SI) and non-suicidal self-injury (NSSI) are crucial public health issues, yet their co-developmental trajectories during early adolescence and their associations with predictors and outcomes are unclear. This study aimed to (a) identify heterogeneous co-developmental trajectories of SI and NSSI, (b) explore associations between transdiagnostic predictors and trajectories, and (c) assess suicide attempt risk across trajectories. METHODS: Four hundred fifty-three adolescents (Mage = 12.35 years, 48.3% boys) completed surveys at 6-month intervals across 2 years. At Time 1 (Nov 2020), participants completed surveys encompassing SI, and NSSI, along with family, peer, and individual predictors. Subsequent surveys (Times 2-4) measured SI and NSSI, with suicide attempts queried at Time 4. RESULTS: Parallel process latent class growth models revealed three co-developmental groups (i.e., Stable low NSSI and SI; Moderate-NSSI and high-SI, parallel decreasing; High-NSSI and moderate-SI, parallel increasing). Multivariate logistic regression indicated that group membership was predicted by parental rejection, parental warmth, bullying victimization, depressive and anxiety symptoms, thwarted belongingness, and perceived burdensomeness. Adolescents in the "High-NSSI and moderate-SI, parallel increasing" group reported the highest suicide attempt frequency. CONCLUSION: These findings underscore subgroup distinctions and transdiagnostic predictors in comprehending SI and NSSI progression, emphasizing the necessity of dynamic monitoring and tailored interventions for distinct subgroup characteristics.

Long-term trajectories of densely reported depressive symptoms during an extended period of the COVID-19 pandemic in Switzerland: Social worries matter.

Previous mental health trajectory studies were mostly limited to the months before access to vaccination. They are not informing on whether public mental health has adapted to the pandemic. The aim of this analysis was to 1) investigate trajectories of monthly reported depressive symptoms from July 2020 to December 2021 in Switzerland, 2) compare average growth trajectories across regions with different stringency phases, and 3) explore the relative impact of self-reported worries related to health, economic and social domains as well as socio-economic indicators on growth trajectories. As part of the population-based Corona Immunitas program of regional, but harmonized, adult cohorts studying the pandemic course and impact, participants repeatedly reported online to the DASS-21 instrument on depressive symptomatology. Trajectories of depressive symptoms were estimated using a latent growth model, specified as a generalised linear mixed model. The time effect was modelled parametrically through a polynomial allowing to estimate trajectories for participants' missing time points. In all regions level and shape of the trajectories mirrored those of the KOF Stringency-Plus Index, which quantifies regional Covid-19 policy stringency. The higher level of average depression in trajectories of those expressing specific worries was most noticeable for the social domain. Younger age, female gender, and low household income went along with higher mean depression score trajectories throughout follow-up. Interventions to promote long-term resilience are an important part of pandemic preparedness, given the observed lack of an adaptation in mental health response to the pandemic even after the availability of vaccines in this high-income context.

Numerical and biomass growth study of Bulimulus bonariensis (Rafinesque, 1833) (Gastropoda: Bulimulidae) under laboratory conditions.

Bulimulus bonariensis is considered a species of relevance to agribusiness, having been declared a pest with indirect damage because of its negative effects on several crops such as soybeans, chickpeas, and corn in central and northern Argentina. The objective of this work was to analyze the growth pattern of a population born under laboratory conditions, to explore population aspects such as survival and mortality, to estimate the age and size at gonadal maturity and first reproduction, and to contribute to the knowledge of the reproductive biology of this gastropod. From the clutches obtained, the basic biologic parameters were calculated and the individuals hatched under laboratory conditions counted and measured every two weeks. The clutches contained an average of 44 eggs, which took about 13.7 days to hatch at a birth rate of 41.82%. The growth pattern in the five clutches was analyzed individually, and the logistic model used was the one with the highest degree of fit to that observed growth pattern, followed by the Gompertz model, and finally the von Bertalanffy model. In addition, the models were applied to the 102 specimens analyzed together as a cohort, where the best fitting model was also proved to be the logistic growth model. A concave type III survival curve was obtained from the horizontal life table. The cohort was reduced by 48% during the first 50 days after birth. Beyond one month of hatching, life expectancy gradually increased and remained high between 65-302 days of life. After day 330, life expectancy decreased and only 13.72% exceeded one year of birth, with an average length of 16.68 mm. The last specimen died after 23 months at a total length of 20.24 mm, and the life expectancy was estimated at almost three years. In addition, it was inferred that gonadal maturity, when these gastropods reach 12 mm of total shell length, is reached after 200 days of life. Therefore, the individuals that are born are able to reproduce for the first time a year after birth, when they have the approximate size of 16.68 mm.

A Parallel Process Growth Curve Analysis of Teacher-Student Relationships and Academic Achievement.

Teacher-student relationships (TSR) have been a key focus of study for developmental and educational psychology researchers interested in improving proximal and distal academic outcomes for children and youth. Although prior empirical work suggests some degree of association between TSR and achievement, the co-development of TSR and achievement during elementary grades remains unclear with most findings limited to reading and mathematics achievement. The current study used parallel process growth curve models (PPGCMs) to examine the longitudinal growth trajectories of teacher-student closeness and conflict, and science, reading, and mathematics achievement simultaneously for children followed from kindergarten to third grade in the Early Childhood Longitudinal Study, Kindergarten Class of 2010-2011 (N = 13,490). Findings from the final PPGCM showed teacher-student closeness in kindergarten was positively associated with science, reading and mathematics achievement in kindergarten (r = 0.234 to 0.277) and the linear growth of achievement through third grade (r = 0.068 to 0.156). Teacher-student conflict in kindergarten was negatively associated with science, reading, and mathematics achievement in kindergarten (r = -0.099 to -0.203) and the linear growth of achievement through third grade (r = -0.081 to -0.135). Child biological sex, family socioeconomic status, and child racial and ethnic identity predicted TSR and achievement developmental trends. Implications of the findings and future directions for research are discussed.

Modeling tumor growth using fractal calculus: Insights into tumor dynamics.

Important concepts like fractal calculus and fractal analysis, the sum of squared residuals, and Aikaike's information criterion must be thoroughly understood in order to correctly fit cancer-related data using the proposed models. The fractal growth models employed in this work are classified in three main categories: Sigmoidal growth models (Logistic, Gompertz, and Richards models), Power Law growth model, and Exponential growth models (Exponential and Exponential-Lineal models)". We fitted the data, computed the sum of squared residuals, and determined Aikaike's information criteria using Matlab and the web tool WebPlotDigitizer. In addition, the research investigates "double-size cancer" in the fractal temporal dimension with respect to various mathematical models.

Alcohol Use Disorder Polygenic Risk Scores and Trajectories of Early Adolescent Externalizing Behaviors: Examining the Role of Parenting and Family Conflict in the Racially/Ethnically Diverse ABCD Sample.

This study examined the independent and interactive effects of alcohol use disorder genome-wide polygenic scores (AUD-PGS) and parenting and family conflict on early adolescent externalizing behaviors. Data were drawn from White (N = 6181, 46.9% female), Black/African American (N = 1784, 50.1% female), and Hispanic/Latinx (N = 2410, 48.0% female) youth from the adolescent brain cognitive development Study (ABCD). Parents reported on youth externalizing behaviors at baseline (T1, age 9/10), 1-year (T2, age 10/11) and 2-year (T3, age 11/12) assessments. Youth reported on parenting and family environment at T1 and provided saliva or blood samples for genotyping. Results from latent growth models indicated that in general externalizing behaviors decreased from T1 to T3. Across all groups, higher family conflict was associated with more externalizing behaviors at T1, and we did not find significant associations between parental monitoring and early adolescent externalizing behaviors. Parental acceptance was associated with lower externalizing behaviors among White and Hispanic youth, but not among Black youth. Results indicated no significant main effect of AUD-PGS nor interaction effect between AUD-PGS and family variables on early adolescent externalizing behaviors. Post hoc exploratory analysis uncovered an interaction between AUD-PGS and parental acceptance such that AUD-PGS was positively associated with externalizing rule-breaking behaviors among Hispanic youth, but only when parental acceptance was very low. Findings highlight the important role of family conflict and parental acceptance in externalizing behaviors among early adolescents, and emphasize the need to examine other developmental pathways underlying genetic risk for AUD across diverse populations.

Making Connections: Neurodevelopmental Changes in Brain Connectivity After Adverse Experiences in Early Adolescence.

Adverse childhood experiences have been linked to detrimental mental health outcomes in adulthood. This study investigates a potential neurodevelopmental pathway between adversity and mental health outcomes: brain connectivity. We used data from the prospective, longitudinal Adolescent Brain Cognitive Development (ABCD) study (N ≍ 12.000, participants aged 9-13 years, male and female) and assessed structural brain connectivity using fractional anisotropy (FA) of white matter tracts. The adverse experiences modeled included family conflict and traumatic experiences. K-means clustering and latent basis growth models were used to determine subgroups based on total levels and trajectories of brain connectivity. Multinomial regression was used to determine associations between cluster membership and adverse experiences. The results showed that higher family conflict was associated with higher FA levels across brain tracts (e.g., t (3) = -3.81, ß = -0.09, p bonf = 0.003) and within the corpus callosum (CC), fornix, and anterior thalamic radiations (ATR). A decreasing FA trajectory across two brain imaging timepoints was linked to lower socioeconomic status and neighborhood safety. Socioeconomic status was related to FA across brain tracts (e.g., t (3) = 3.44, ß = 0.10, p bonf = 0.01), the CC and the ATR. Neighborhood safety was associated with FA in the Fornix and ATR (e.g., t (1) = 3.48, ß = 0.09, p bonf = 0.01). There is a complex and multifaceted relationship between adverse experiences and brain development, where adverse experiences during early adolescence are related to brain connectivity. These findings underscore the importance of studying adverse experiences beyond early childhood to understand lifespan developmental outcomes.

Bayesian analysis of the effect of exosomes in a mouse xenograft model of chronic myeloid leukemia.

The aim of this work is to estimate the effect of Imatinib, exosomes, and Imatinib-exosomes mixture in chronic myeloid leukemia (CML). For this purpose, mathematical models based on Gompertzian and logistic growth differential equations were proposed. The models contained parameters representing the effects of the three components on CML proliferation. Parameters estimation was performed under the Bayesian statistical approach. Experimental data reported in the literature were used, corresponding to four trials of a human leukemia xenograft in BALB/c female rats over a period of forty days. The models were fitted to the following growth dynamics: normal tumor growth, growth with exosomes, growth with Imatinib, and growth with exosomes-Imatinib mixture. For the proposed logistic growth model, it was determined that when using Imatinib treatment the growth rate is 0.93 (95% CrI: 84.33-99.64) slower and reduces the tumor volume to approximately 10% (95% CrI : 8.67-10.81). In the presence of exosome treatment, the growth rate is 0.83 (95% CrI: 1.52-16.59) faster and the tumor volume is expanded by 40% (95% CrI: 25.36-57.28). Finally, in the presence of Imatinib-exosomes mixture treatment, the growth rate is 0.82 (95% CrI: 76.87-88.51) slower and the tumor volume is reduced by 95% (95% CrI: 86.76-99.85). It is concluded that the presence of exosomes partially inactivates the effect of the Imatinib drug on tumor growth in a mouse xenograft model.

Fitness and transcriptomic analysis of pathogenic Vibrio parahaemolyticus in seawater at different shellfish harvesting temperatures.

IMPORTANCE: Given the involvement of Vibrio parahaemolyticus (Vp) in a wide range of seafood outbreaks, a systematical characterization of Vp fitness and transcriptomic changes at temperatures of critical importance for seafood production and storage is needed. In this study, one of each virulent Vp strain (tdh+ and trh+) was tested. While no difference in survival behavior of the two virulent strains was observed at 10°C, the tdh+ strain had a faster growth rate than the trh+ strain at 30°C. Transcriptomic analysis showed that a significantly higher number of genes were upregulated at 30°C than at 10°C. The majority of differentially expressed genes of Vp at 30°C were annotated to functional categories supporting cellular growth. At 10°C, the downregulation of the biofilm formation and histidine metabolism indicates that the current practice of storing seafood at low temperatures not only protects seafood quality but also ensures seafood safety.

Exploring the potential of Diplosphaera mucosa VSPA for the treatment of petroleum effluent with simultaneous lipid production.

The discovery of unexplored, robust microalgal strains will assist in treating highly polluted industrial effluent, including petroleum effluent. In the current analysis, a newly isolated microalgal strain, Diplosphaera mucosa VSPA, was used to treat petroleum effluent in a lab-scale raceway bioreactor. Its treatment efficiency was compared with a well-known species, Chlorella pyrenoidosa. The D. mucosa VSPA strain proliferated in petroleum effluent at a high growth rate, with final biomass, and lipid concentrations reaching 6.93 g/L and 2.72 g/L, respectively. Treatment efficiency was calculated based on the final removal efficiency of ammonium nitrogen, phosphate phosphorus, and chemical oxygen demand, which was more than 90%. Control experiments suggested that the maximum removal of pollutants from petroleum effluent was due to microalgae growth. Some growth models, including the Gompertz, Logistic, Stannard, Richard, and Schnute, were used to simulate the experimental data, verifying the results. Good fitting of all models was obtained, with the R2 value reaching more than 0.90. The development of a suitable model can help in decreasing the efforts required for the scale-up of the process.

Regression-Equivalent Effect Sizes for Latent Growth Modeling and Associated Null Hypothesis Significance Tests.

The effect of an independent variable on random slopes in growth modeling with latent variables is conventionally used to examine predictors of change over the course of a study. This tutorial demonstrates that the same effect of a covariate on growth can be obtained by using final status centering for parameterization and regressing the random intercepts (or the intercept factor scores) on both the independent variable and a baseline covariate--the framework used to study change with classical regression analysis. Examples are provided that illustrate the application of an intercept-focused approach to obtain effect sizes--the unstandardized regression coefficient, the standardized regression coefficient, squared semi-partial correlation, and Cohen's f2 --that estimate the same parameters as respective effect sizes from a classical regression analysis. Moreover, statistical power to detect the effect of the predictor on growth was greater when using random intercepts than the conventionally used random slopes.

Data Analytics of Broiler Growth Dynamics and Feed Conversion Ratio of Broilers Raised to 35 d under Commercial Tropical Conditions.

Data collection is standard in commercial broiler production; however, growth modeling is still a challenge since this data often lacks an inflection point. This study evaluated body weight (BW) dynamics, feed intake, BW gain, feed conversion ratio (FCR), and mortality of broiler flocks reared under commercial tropical conditions with controlled feeding to optimize FCR. The data analyzed included performance records of 1347 male and 1353 female Ross 308 AP broiler flocks with a total of 95.4 million chickens housed from 2018 to 2020. Decision trees determined high- and low-feed-efficiency groups using FCR at 35 d. Logistic, Gompertz-Laird, and von Bertalanffy growth models were fitted with weekly BW data for each flock within performance groups. The logistic model indicated more accurate estimates with biological meaning. The high-efficiency males and females (p < 0.001) were offered less feed than the low-efficiency group and were consistently more efficient. In conclusion, greater feeding control between the second and the fourth week of age, followed by higher feed allowance during the last week, was associated with better feed efficiency at 35 d in males and females. Additionally, models demonstrated that a reduced growth rate resulted in heavier chickens at 35 d with better feed efficiency and greater BW gain.

A Bootstrap Approach for Evaluating Uncertainty in the Number of Groups Identified by Latent Class Growth Models.

The use of longitudinal finite mixture models such as group-based trajectory modeling has seen a sharp increase during the last few decades in the medical literature. However, these methods have been criticized, especially because of the data-driven modeling process, which involves statistical decision-making. In this paper, we propose an approach that uses the bootstrap to sample observations with replacement from the original data to validate the number of groups identified and to quantify the uncertainty in the number of groups. The method allows investigation of the statistical validity and uncertainty of the groups identified in the original data by checking to see whether the same solution is also found across the bootstrap samples. In a simulation study, we examined whether the bootstrap-estimated variability in the number of groups reflected the replicationwise variability. We evaluated the ability of 3 commonly used adequacy criteria (average posterior probability, odds of correct classification, and relative entropy) to identify uncertainty in the number of groups. Finally, we illustrate the proposed approach using data from the Quebec Integrated Chronic Disease Surveillance System to identify longitudinal medication patterns between 2015 and 2018 in older adults with diabetes.

Two decades of harnessing standing genetic variation for physiological traits to improve drought tolerance in maize.

We review approaches to maize breeding for improved drought tolerance during flowering and grain filling in the central and western US corn belt and place our findings in the context of results from public breeding. Here we show that after two decades of dedicated breeding efforts, the rate of crop improvement under drought increased from 6.2 g m-2 year-1 to 7.5 g m-2 year-1, closing the genetic gain gap with respect to the 8.6 g m-2 year-1 observed under water-sufficient conditions. The improvement relative to the long-term genetic gain was possible by harnessing favourable alleles for physiological traits available in the reference population of genotypes. Experimentation in managed stress environments that maximized the genetic correlation with target environments was key for breeders to identify and select for these alleles. We also show that the embedding of physiological understanding within genomic selection methods via crop growth models can hasten genetic gain under drought. We estimate a prediction accuracy differential (Δr) above current prediction approaches of ~30% (Δr=0.11, r=0.38), which increases with increasing complexity of the trait environment system as estimated by Shannon information theory. We propose this framework to inform breeding strategies for drought stress across geographies and crops.

A MATLAB toolbox to fit and forecast growth trajectories using phenomenological growth models: Application to epidemic outbreaks.

Background: Simple dynamic modeling tools can be useful for generating real-time short-term forecasts with quantified uncertainty of the trajectory of diverse growth processes unfolding in nature and society, including disease outbreaks. An easy-to-use and flexible toolbox for this purpose is lacking. Results: In this tutorial-based primer, we introduce and illustrate a user-friendly MATLAB toolbox for fitting and forecasting time-series trajectories using phenomenological dynamic growth models based on ordinary differential equations. This toolbox is accessible to various audiences, including students training in time-series forecasting, dynamic growth modeling, parameter estimation, parameter uncertainty and identifiability, model comparison, performance metrics, and forecast evaluation, as well as researchers and policymakers who need to conduct short-term forecasts in real-time. The models included in the toolbox capture exponential and sub-exponential growth patterns that typically follow a rising pattern followed by a decline phase, a common feature of contagion processes. Models include the 2-parameter generalized-growth model, which has proved useful to characterize and forecast the ascending phase of epidemic outbreaks, and the Gompertz model as well as the 3-parameter generalized logistic-growth model and the Richards model, which have demonstrated competitive performance in forecasting single peak outbreaks.The toolbox provides a tutorial for forecasting time-series trajectories that include the full uncertainty distribution, derived through parametric bootstrapping, which is needed to construct prediction intervals and evaluate their accuracy. Functions are available to assess forecasting performance across different models, estimation methods, error structures in the data, and forecasting horizons. The toolbox also includes functions to quantify forecasting performance using metrics that evaluate point and distributional forecasts, including the weighted interval score. Conclusions: We have developed the first comprehensive toolbox to characterize and forecast time-series data using simple phenomenological growth models. As a contagion process takes off, the tools presented in this tutorial can facilitate policymaking to guide the implementation of control strategies and assess the impact of interventions. The toolbox functionality is demonstrated through various examples, including a tutorial video, and is illustrated using weekly data on the monkeypox epidemic in the USA.

Tumor Evolution Models of Phase-Field Type with Nonlocal Effects and Angiogenesis.

In this survey article, a variety of systems modeling tumor growth are discussed. In accordance with the hallmarks of cancer, the described models incorporate the primary characteristics of cancer evolution. Specifically, we focus on diffusive interface models and follow the phase-field approach that describes the tumor as a collection of cells. Such systems are based on a multiphase approach that employs constitutive laws and balance laws for individual constituents. In mathematical oncology, numerous biological phenomena are involved, including temporal and spatial nonlocal effects, complex nonlinearities, stochasticity, and mixed-dimensional couplings. Using the models, for instance, we can express angiogenesis and cell-to-matrix adhesion effects. Finally, we offer some methods for numerically approximating the models and show simulations of the tumor's evolution in response to various biological effects.