Brett Allen McKinney

This study develops and analyzes a nonlinear tumor–immune–chemotherapy model to investigate how chemotherapy influences tumor suppression in the presence of heterogeneous tumor subpopulations and immune responses. The model incorporates two tumor cell populations with different immunogenic characteristics, innate and adaptive immune responses, and a dynamic chemotherapy effect. We analytically identify the equilibrium points and assess the conditions for their stability in the presence of chemotherapy. A bifurcation analysis with respect to the chemotherapy tumor killing rate of the less immunogenic tumor reveals Hopf bifurcations, indicating transitions to oscillatory behavior and complex tumor–immune dynamics under varying treatment intensities. The effects of chemotherapy dosing and drug potency are investigated through six-month numerical treatment simulations to evaluate tumor suppression and long-term system behavior under different treatment scenarios. An optimal control problem is then formulated to determine chemotherapy schedules that reduce tumor burden while limiting drug exposure. Numerical results indicate that the optimal chemotherapy protocol produces faster and more efficient tumor reduction than a constant-dose treatment strategy. Overall, this study provides a treatment-focused dynamical framework for understanding how chemotherapy interacts with tumor heterogeneity and immune response, providing theoretical insight into treatment-dependent tumor–immune dynamics and chemotherapy scheduling.

Bacterial growth requires strategic allocation of limited intracellular resources, especially under cold stress, where stabilized messenger ribonucleic acid (mRNA) secondary structures slow translation by impairing ribosome binding. Escherichia coli (E. coli) counters this bottleneck by inducing the cold-shock protein A (CspA), an RNA chaperone that remodels inhibitory structures. However, synthesizing CspA diverts biosynthetic capacity from ribosome production and metabolism, creating a fundamental resource-allocation trade-off. In this work, we develop a dynamical model capturing the interplay between metabolic precursors, ribosomes, and CspA, and use it to examine how growth and allocation patterns shift with temperature. Steady-state analysis shows that each temperature produces a distinct, locally stable equilibrium, illustrating how cold environments reshape cellular priorities. We then formulate growth maximization as an optimal control problem, solved using Pontryagin’s Maximum Principle, to identify allocation strategies that balance translation maintenance and biomass production. The resulting optimal strategies exhibit bang-bang and singular structures, highlighting periods of extreme and intermediate allocation that reflect how bacteria might dynamically prioritize competing cellular functions. These control patterns converge to their corresponding steady state allocations and provide quantitative insight into optimal resource management under cold stress. These results provide a quantitative optimal-control framework linking RNA-level cold-shock adaptation to proteome allocation and growth, yielding testable predictions for how bacteria balance translational maintenance and biomass production at suboptimal temperatures.

Explainable machine learning (ML) is important for biosignature prediction on future astrobiology missions to minimize the risk of false positives due to geochemical biotic mimicry and false negatives due to environmental factors that mask biosignatures. ML models often use feature importance scores to provide insights into model prediction mechanisms by quantifying each variable’s contribution to the prediction. Global variable importance methods aggregate information across all training samples and therefore do not provide interpretation for the classification of a single sample. In contrast, local variable importance scores quantify the contribution of variables to the classification of a single sample and can therefore help explain why the sample was predicted to be in a certain class and diagnose whether it is a false prediction. We present a new local variable importance method that handles nonlinearity, statistical interactions, and includes penalized feature selection. Our approach represents a local version of Nearest-neighbor Projected Distance Regression (NPDR) feature selection. We evaluate local-NPDR on complex simulated data and real data from a study of carbon and oxygen isotopic biosignatures using laboratory-generated ocean world analogue brines. The ability of local-NPDR to differentiate between true and false predictions is compared with other common local importance methods. Local-NPDR is able to diagnose individual false predictions using the concordance between global and local scores, and it can explain mechanisms of true and false predictions. These features allow local-NPDR to integrate scientific explanations of single-sample ML predictions to support a more comprehensive framework for biosignature detection.

The rising incidence of skin cancer, coupled with limited public awareness and a shortfall in clinical expertise, underscores an urgent need for advanced diagnostic aids. Artificial Intelligence (AI) has emerged as a promising tool in this domain, particularly for distinguishing malignant from benign skin lesions. Leveraging publicly available datasets of skin lesions, researchers have been developing AI-based diagnostic solutions. However, the integration of such computer systems in clinical settings is still nascent. This study aims to bridge this gap by employing a fusion of image processing techniques and machine learning algorithms, specifically neuro-fuzzy and colonial competition approaches. Applied to dermoscopic images from the ISIC database, our method achieved a notable accuracy of 94% on a dataset of 560 images. These results underscore the potential of our approach in aiding clinicians in the early detection of melanoma, thereby contributing significantly to skin cancer diagnostics.