Bromeliaceae, a neo-tropical plant family encompassing over 3,000 species, exhibit two modes of reproduction: sexual reproduction via flowers and seeds, and asexual reproduction via genetically identical clonal rosettes. The vegetative bodies of bromeliads form rosettes with new leaves emerging from the center and clonal rosettes emerging above a single leaf in the rosette, resulting in a genetic individual consisting of a seed-grown rosette and multiple iterations of clonal rosettes. This research develops a combinatorial model of probability that a single genetic individual will include at least n clonal rosettes when a single rosette can produce at most 1 or 2 clonal rosettes. The rosette composition of a single genetic individual is conceptualized as an unordered binary tree which can be mapped onto Łukasiewicz paths. The derived models are used to generate the expected value of the minimum number of rosettes comprising a single genetic individual over the model's parameter space.
In response to a significant tuberculosis outbreak in Wyandotte and Johnson Counties, Kansas, this study presents a compartmental model of ordinary differential equations to evaluate the impact of standard antibiotic treatment. The model incorporates latent, active, and treated disease states. Parameter values were informed by epidemiological data and uncertain parameter value ranges were explored systematically through uncertainty analysis using constrained Latin hypercube sampling. Cumulative infections and deaths, and the basic reproduction number, were computed over a five-year simulation period. Sensitivity analyses using partial rank correlation coefficients identified symptomatic treatment rate and transmission rate as primary drivers of cumulative infections and deaths. Model results indicate that treatment significantly reduces both infections and mortality, but that relying solely on treatment has high associated costs underscoring the importance of integrating transmission mitigation measures alongside treatment. This modeling framework offers valuable insight for public health policy, especially in managing resource allocation during tuberculosis outbreaks.
Ischemic hepatitis (IH) results from shock-related conditions that impair oxygenated blood flow to the liver, causing hepatocyte death. Diagnosis relies largely on clinical history due to the absence of specific diagnostic tests and limited ability to predict outcomes. This study applies machine learning methods to real-world IH patient data to improve outcome prediction. Biomedical indicators analyzed include creatinine, international normalized ratio (INR), aspartate aminotransferase (AST), alanine transaminase (ALT), and bilirubin. Data were collected from multiple U.S. centers through the Acute Liver Failure Study Group (ALFSG), a multicenter network focused on this rare condition. We implemented logistic regression, regression tree methods (including random forests and Bayesian Additive Regression Trees), and neural networks. To enhance model performance, we applied Synthetic Minority Oversampling Technique (SMOTE), along with optimization and feature selection techniques. Results show that INR and bilirubin are consistently selected as key predictors across all models, which demonstrated comparable predictive performance.
Phenotypically plastic organisms are capable of adjusting their phenotypes in a variable environment. Padilla and Adolph (1996) modeled this process and included a time lag between detection of an environmental change and the responding change in phenotype. We extended the Padilla and Adolph model from two to three discrete environments and phenotypes and analyzed the model computationally. Our results were consistent with their findings: the fitness advantage of phenotypic plasticity is higher when (1) the lag time is shorter, (2) the probability of environmental change is lower, and (3) the fitness disadvantage of mismatched phenotypes and environments is more severe. Additionally, we observed that there is a critical lag time beyond which it is no longer advantageous to be phenotypically plastic, compared to being phenotypically fixed (unchanging), with the exception of special symmetric cases. Thus, lag time can be a critical determinant of whether phenotypic plasticity is favored.
Deforestation, driven by industrial agriculture and cattle farming, has significantly impacted communities and ecosystems across the globe. In this paper, we develop a discrete-time Markov model to evaluate the effectiveness of market-based interventions, such as payments for ecosystem services (PES) and taxes, in addressing forest loss within the context of large-scale agriculture. Specifically, we focus on how commodity price variability influences deforestation decisions. To model these price fluctuations, we represent agricultural prices as geometric Brownian motion (GBM). By linking commodity price dynamics to land use transitions, our model aims to provide insight into how financial mechanisms can slow or stop deforestation. We find that the structure of the implemented policies have a large effect even if the total dollar amount across different structures were to remain constant. Therefore, decision makers should carefully choose the structure of their policies depending on their objectives with regards to poverty alleviation, deforestation prevention, and reforestation incentives.
