Dynamical analysis of a diffusive population-toxicant model with toxicant-taxis in polluted aquatic environments
Math Biosci. 2024 Apr 22:109193. doi: 10.1016/j.mbs.2024.109193. Online ahead of print.ABSTRACTThis paper deals with a diffusive population-toxicant model in polluted aquatic environments, with a toxicant-taxis term describing a toxicant-induced behavior change, that is, the population tends to move away from locations with high-level toxicants. The global existence of solutions is established by the techniques of the semigroup estimation and Moser iteration. Based on a detailed study on the properties of the principal eigenvalue for non-self-adjoint eigenvalue problems, we investigated the local and global stability of th...
Source: Mathematical Biosciences - April 24, 2024 Category: Statistics Authors: Jie Xing Qihua Huang Hua Nie Source Type: research
Modeling realistic synaptic inputs of CA1 hippocampal pyramidal neurons and interneurons via Adaptive Generalized Leaky Integrate-and-Fire models
This study introduces an Adaptive GLIF (A-GLIF) approach that addresses this limitation by incorporating a new set of update rules. The extended A-GLIF model successfully reproduces both constant and variable current inputs, and it was validated against the results obtained using a biophysical accurate model neuron. This enhancement provides researchers with a tool to optimize spiking neuron models using classic experimental traces under constant current injections, reliably predicting responses to synaptic inputs, which can be confidently used for large-scale network implementations.PMID:38640998 | DOI:10.1016/j.mbs.2024....
Source: Mathematical Biosciences - April 19, 2024 Category: Statistics Authors: A Marasco C Tribuzi C A Lupascu M Migliore Source Type: research
Modeling realistic synaptic inputs of CA1 hippocampal pyramidal neurons and interneurons via Adaptive Generalized Leaky Integrate-and-Fire models
This study introduces an Adaptive GLIF (A-GLIF) approach that addresses this limitation by incorporating a new set of update rules. The extended A-GLIF model successfully reproduces both constant and variable current inputs, and it was validated against the results obtained using a biophysical accurate model neuron. This enhancement provides researchers with a tool to optimize spiking neuron models using classic experimental traces under constant current injections, reliably predicting responses to synaptic inputs, which can be confidently used for large-scale network implementations.PMID:38640998 | DOI:10.1016/j.mbs.2024....
Source: Mathematical Biosciences - April 19, 2024 Category: Statistics Authors: A Marasco C Tribuzi C A Lupascu M Migliore Source Type: research
Does mutual interference stabilize prey-predator model with Bazykin-Crowley-Martin trophic function?
Math Biosci. 2024 Apr 16;372:109201. doi: 10.1016/j.mbs.2024.109201. Online ahead of print.ABSTRACTWe investigated a system of ordinary differential equations that describes the dynamics of prey and predator populations, taking into account the Allee effect affecting the reproduction of the predator population, and mutual interference amongst predators, which is modeled with the Bazykin-Crowley-Martin (BCM) trophic function. Bifurcation analysis revealed a rich spectrum of bifurcations occurring in the system. In particular, analytical conditions for the saddle-node, Hopf, cusp, and Bogdanov-Takens bifurcations were derive...
Source: Mathematical Biosciences - April 18, 2024 Category: Statistics Authors: Yuri Tyutyunov Deeptajyoti Sen Malay Banerjee Source Type: research
Comparing the long-term persistence of different Wolbachia strains after the release of bacteria-carrying mosquitoes
Math Biosci. 2024 Apr 15:109190. doi: 10.1016/j.mbs.2024.109190. Online ahead of print.ABSTRACTThis paper proposes a bidimensional modeling framework for Wolbachia invasion, assuming imperfect maternal transmission, incomplete cytoplasmic incompatibility, and direct infection loss due to thermal stress. Our model adapts to various Wolbachia strains and retains all properties of higher-dimensional models. The conditions for the durable coexistence of Wolbachia-carrying and wild mosquitoes are expressed using the model's parameters in a compact closed form. When the Wolbachia bacterium is locally established, the size of the...
Source: Mathematical Biosciences - April 17, 2024 Category: Statistics Authors: Jose L Orozco-Gonzales Antone Dos Santos Benedito Daiver Cardona-Salgado Claudia Pio Ferreira Helenice de Oliveira Florentino Lilian S Sepulveda-Salcedo Olga Vasilieva Source Type: research
Comparing the long-term persistence of different Wolbachia strains after the release of bacteria-carrying mosquitoes
Math Biosci. 2024 Apr 15:109190. doi: 10.1016/j.mbs.2024.109190. Online ahead of print.ABSTRACTThis paper proposes a bidimensional modeling framework for Wolbachia invasion, assuming imperfect maternal transmission, incomplete cytoplasmic incompatibility, and direct infection loss due to thermal stress. Our model adapts to various Wolbachia strains and retains all properties of higher-dimensional models. The conditions for the durable coexistence of Wolbachia-carrying and wild mosquitoes are expressed using the model's parameters in a compact closed form. When the Wolbachia bacterium is locally established, the size of the...
Source: Mathematical Biosciences - April 17, 2024 Category: Statistics Authors: Jose L Orozco-Gonzales Antone Dos Santos Benedito Daiver Cardona-Salgado Claudia Pio Ferreira Helenice de Oliveira Florentino Lilian S Sepulveda-Salcedo Olga Vasilieva Source Type: research
A stochastic programming approach to the antibiotics time machine problem
Math Biosci. 2024 Apr 10;372:109191. doi: 10.1016/j.mbs.2024.109191. Online ahead of print.ABSTRACTAntibiotics Time Machine is an important problem to understand antibiotic resistance and how it can be reversed. Mathematically, it can be modeled as follows: Consider a set of genotypes, each of which contain a set of mutated and unmutated genes. Suppose that a set of growth rate measurements of each genotype under a set of antibiotics is given. The transition probabilities of a 'realization' of a Markov chain associated with each arc under each antibiotic are computable via a predefined function given the growth rate realiz...
Source: Mathematical Biosciences - April 11, 2024 Category: Statistics Authors: O ğuz Mesüm Ali Rana Atilgan Burak Kocuk Source Type: research
A stochastic programming approach to the antibiotics time machine problem
Math Biosci. 2024 Apr 10;372:109191. doi: 10.1016/j.mbs.2024.109191. Online ahead of print.ABSTRACTAntibiotics Time Machine is an important problem to understand antibiotic resistance and how it can be reversed. Mathematically, it can be modeled as follows: Consider a set of genotypes, each of which contain a set of mutated and unmutated genes. Suppose that a set of growth rate measurements of each genotype under a set of antibiotics is given. The transition probabilities of a 'realization' of a Markov chain associated with each arc under each antibiotic are computable via a predefined function given the growth rate realiz...
Source: Mathematical Biosciences - April 11, 2024 Category: Statistics Authors: O ğuz Mesüm Ali Rana Atilgan Burak Kocuk Source Type: research
A stochastic programming approach to the antibiotics time machine problem
Math Biosci. 2024 Apr 10;372:109191. doi: 10.1016/j.mbs.2024.109191. Online ahead of print.ABSTRACTAntibiotics Time Machine is an important problem to understand antibiotic resistance and how it can be reversed. Mathematically, it can be modeled as follows: Consider a set of genotypes, each of which contain a set of mutated and unmutated genes. Suppose that a set of growth rate measurements of each genotype under a set of antibiotics is given. The transition probabilities of a 'realization' of a Markov chain associated with each arc under each antibiotic are computable via a predefined function given the growth rate realiz...
Source: Mathematical Biosciences - April 11, 2024 Category: Statistics Authors: O ğuz Mesüm Ali Rana Atilgan Burak Kocuk Source Type: research
A stochastic programming approach to the antibiotics time machine problem
Math Biosci. 2024 Apr 9:109191. doi: 10.1016/j.mbs.2024.109191. Online ahead of print.ABSTRACTAntibiotics Time Machine is an important problem to understand antibiotic resistance and how it can be reversed. Mathematically, it can be modelled as follows: Consider a set of genotypes, each of which contain a set of mutated and unmutated genes. Suppose that a set of growth rate measurements of each genotype under a set of antibiotics are given. The transition probabilities of a 'realization' of a Markov chain associated with each arc under each antibiotic are computable via a predefined function given the growth rate realizati...
Source: Mathematical Biosciences - April 11, 2024 Category: Statistics Authors: O ğuz Mesüm Ali Rana Atilgan Burak Kocuk Source Type: research
A probabilistic model of relapse in drug addiction
We present a mathematical model of relapse in drug addiction that draws on known psychiatric concepts such as the "positive activation; negative activation" paradigm and the "peak-end" rule to construct a relapse rate that depends on external factors (intensity and timing of life events) and individual traits (mental responses to these events). We analyze which combinations and ordering of stressors, cues, and positive events lead to the largest relapse probability and propose interventions to minimize the likelihood of relapse. We find that the best protective factor is exposure to a mild, yet continuous, source of conten...
Source: Mathematical Biosciences - April 6, 2024 Category: Statistics Authors: Sayun Mao Tom Chou Maria R D'Orsogna Source Type: research
A probabilistic model of relapse in drug addiction
We present a mathematical model of relapse in drug addiction that draws on known psychiatric concepts such as the "positive activation; negative activation" paradigm and the "peak-end" rule to construct a relapse rate that depends on external factors (intensity and timing of life events) and individual traits (mental responses to these events). We analyze which combinations and ordering of stressors, cues and positive events lead to the largest relapse probability and propose interventions to minimize the likelihood of relapse. We find that the best protective factor is exposure to a mild, yet continuous, source of content...
Source: Mathematical Biosciences - April 6, 2024 Category: Statistics Authors: Sayun Mao Tom Chou Maria R D'Orsogna Source Type: research
A probabilistic model of relapse in drug addiction
We present a mathematical model of relapse in drug addiction that draws on known psychiatric concepts such as the "positive activation; negative activation" paradigm and the "peak-end" rule to construct a relapse rate that depends on external factors (intensity and timing of life events) and individual traits (mental responses to these events). We analyze which combinations and ordering of stressors, cues and positive events lead to the largest relapse probability and propose interventions to minimize the likelihood of relapse. We find that the best protective factor is exposure to a mild, yet continuous, source of content...
Source: Mathematical Biosciences - April 6, 2024 Category: Statistics Authors: Sayun Mao Tom Chou Maria R D'Orsogna Source Type: research
A probabilistic model of relapse in drug addiction
We present a mathematical model of relapse in drug addiction that draws on known psychiatric concepts such as the "positive activation; negative activation" paradigm and the "peak-end" rule to construct a relapse rate that depends on external factors (intensity and timing of life events) and individual traits (mental responses to these events). We analyze which combinations and ordering of stressors, cues and positive events lead to the largest relapse probability and propose interventions to minimize the likelihood of relapse. We find that the best protective factor is exposure to a mild, yet continuous, source of content...
Source: Mathematical Biosciences - April 6, 2024 Category: Statistics Authors: Sayun Mao Tom Chou Maria R D'Orsogna Source Type: research
A probabilistic model of relapse in drug addiction
We present a mathematical model of relapse in drug addiction that draws on known psychiatric concepts such as the "positive activation; negative activation" paradigm and the "peak-end" rule to construct a relapse rate that depends on external factors (intensity and timing of life events) and individual traits (mental responses to these events). We analyze which combinations and ordering of stressors, cues and positive events lead to the largest relapse probability and propose interventions to minimize the likelihood of relapse. We find that the best protective factor is exposure to a mild, yet continuous, source of content...
Source: Mathematical Biosciences - April 6, 2024 Category: Statistics Authors: Sayun Mao Tom Chou Maria R D'Orsogna Source Type: research
