Acta Biotheoretica最新文献

Understanding the relationship between adult fish populations (the "stock") and the number of new fish entering the population (the "recruits") is essential for effective fisheries management. Traditionally, this relationship is represented by a stock-recruitment (SR) function, which is a simplified mathematical model that directly links stock size to recruitment. However, fish populations pass through several life stages, each stage influenced by unique population dynamic factors. Current SR functions often overlook these complexities, assuming that recruitment depends solely on the adult population size. In this study, we use a multi-stage, age-structured discrete-time population dynamic model that accounts for all life stages and the transitions between them. We demonstrate that, in general, a closed-form, univariate SR function may not accurately represent the recruitment process when these life stages are considered. Instead, we identify specific mathematical conditions under which a SR function is equivalent to our multi-stage model. Our findings suggest a re-evaluation of conventional SR models, advocating for multi-stage approaches to support fisheries management decisions.

This study presents a wastewater-based mathematical model for assessing the transmission dynamics of the SARS-CoV-2 pandemic in Miami-Dade County, Florida. The model, which takes the form of a deterministic system of nonlinear differential equations, monitors the temporal dynamics of the disease, as well as changes in viral RNA concentration in the county’s wastewater system (which consists of three sewage treatment plants). The model was calibrated using the wastewater data during the third wave of the SARS-CoV-2 pandemic in Miami-Dade (specifically, the time period from July 3, 2021 to October 9, 2021). The calibrated model was used to predict SARS-CoV-2 case and hospitalization trends in the county during the aforementioned time period, showing a strong correlation between the observed (detected) weekly case data and the corresponding weekly data predicted by the calibrated model. The model’s prediction of the week when maximum number of SARS-CoV-2 cases will be recorded in the county during the simulation period precisely matches the time when the maximum observed/reported cases were recorded (which was August 14, 2021). Furthermore, the model’s projection of the maximum number of cases for the week of August 14, 2021 is about 15 times higher than the maximum observed weekly case count for the county on that day (i.e., the maximum case count estimated by the model was 15 times higher than the actual/observed count for confirmed cases). This result is consistent with the result of numerous SARS-CoV-2 modeling studies (including other wastewater-based modeling, as well as statistical models) in the literature. Furthermore, the model accurately predicts a one-week lag between the peak in weekly COVID-19 case and hospitalization data during the time period of the study in Miami-Dade, with the model-predicted hospitalizations peaking on August 21, 2021. Detailed time-varying global sensitivity analysis was carried out to determine the parameters (wastewater-based, epidemiological and biological) that have the most influence on the chosen response function—the cumulative viral load in the wastewater. This analysis revealed that the transmission rate of infectious individuals, shedding rate of infectious individuals, recovery rate of infectious individuals, average fecal load per person per unit time and the proportion of shed viral RNA that is not lost in sewage before measurement at the wastewater treatment plant were most influential to the response function during the entire time period of the study. This study shows, conclusively, that wastewater surveillance data can be a very powerful indicator for measuring (i.e., providing early-warning signal and current burden) and predicting the future trajectory and burden (e.g., number of cases and hospitalizations) of emerging and re-emerging infectious diseases, such as SARS-CoV-2, in a community.

Conflicts within the tsetse fly belt revealed a strong correlation between the dynamics of bovine trypanosomosis and the insurgency involving farmers and herders in Nigeria and parts of West Africa. This study examined the history, causes and influence of farmers-herdsmen conflicts on banditry, terrorism and food security as it relates to the epidemiology of African animal trypanosomosis (AAT). A combination of literature database searches, semi-structured questionnaires, and mathematical modeling was employed. The study found that transhumance contributes significantly to conflicts between farmers and herdsmen. An average of 6.46 persons per attack were reported between 2005 and 2021. Only 8.4(%) (95(%) CI: 5.0(-)12.9) of farmers and 18.2(%) (95(%) CI: 12.4(-)25.4) of herdsmen have engaged in conflict resolution efforts. The study shows that both conflict and the spread of trypanosomosis can be effectively controlled when (R_0 < 1), ensuring that the sub-population remains in the basin of attraction of the trypanosomosis-conflict-free equilibrium ((T_{0c})). The partial derivative of the basic reproduction number, (R_0), with respect to improved conflict resolution, suggests that halting transhumance can prevent a portion of the cattle recruitment rate ((Lambda_c)) from becoming infected with AAT. Climate change exacerbates these issues, leading to settlement and resettlement strategies within the fly belt regions. The model indicates that the basic reproduction number can only be reduced to less than one ((R_0 < 1)) to become globally asymptotically stable if there is effective conflict resolution involving both farmers and herders. The study advocates for the establishment of ranching in tsetse-free zones with adequate social amenities, improved marketing strategies for animals and animal products led by government agencies through public-private partnerships, the banning of open grazing, and strict enforcement of policies against violators.

Coarse-grain models are essential to understand the biological function of DNA molecules because the length and time scales of the sequence-dependent physical properties of DNA are often beyond the reach of experimental and all-atom computational methods. Simulating coarse-grain models of DNA, e.g. using Langevin dynamics, requires the parametrization of both potential and kinetic energy functions. Many studies have shown that the flexibility (i.e., potential energy) of a DNA molecule depends on its sequence. In contrast, little is known about the sequence-dependence of DNA mass parameters required to model its kinetic energy. In this paper, an algebraic expression is derived for the kinetic energy as a function of linear and angular velocities of each DNA base parameterized by its mass, center of mass, and rotational inertia tensor. The parameters of this function are then approximated from a set of fine-grain molecular dynamics simulations representing all combinations of the four DNA base pairs AT, TA, GC, and CG, in different sequence contexts. Compatibility conditions associated with the assumption of each base being modeled as a rigid body were verified to be good approximations. The kinetic parameters were found to be significantly different between the four G, C, A, and T bases, and to not be dependent on the sequence context. This suggests that the effective kinetic parameters of a DNA base may depend only on the base itself, not on its neighbors.

Accurate prediction of epidemic evolution faces challenges such as understanding disease dynamics and inadequate epidemiological data. A recent approach faced these issues by modeling susceptible-infectious-susceptible (SIS) dynamics based on the first two statistical moments. Here, we improve this approach by including finite-size populations and analyzing the stability of the resulting model. Results underscore the influence of uncertainties and population size in the natural history of the epidemic.

Using delay differential equations to study mathematical models of Parkinson's disease and Huntington's disease is important to show how important it is for synchronization between basal ganglia loops to work together. We used the delay circuit RLC (resistor, inductor, capacitor) model to show how the direct pathway and the indirect pathway in the basal ganglia excite and inhibit the motor cortex, respectively. A term has been added to the mathematical model without time delay in the case of the hyperdirect pathway. It is proposed to add a non-linear term to adjust the synchronization. We studied Hopf bifurcation conditions for the proposed models. The desynchronization of response times between the direct pathway and the indirect pathway leads to different symptoms of Parkinson's disease. Tremor appears when the response time in the indirect pathway increases at rest. The simulation confirmed that tremor occurs and the motor cortex is in an inhibited state. The direct pathway can increase the time delay in the dopaminergic pathway, which significantly increases the activity of the motor cortex. The hyperdirect pathway regulates the activity of the motor cortex. The simulation showed bradykinesia occurs when we switch from one movement to another that is less exciting for the motor cortex. A decrease of GABA in the striatum or delayed excitation of the substantia nigra from the subthalamus may be a major cause of Parkinson's disease. An increase in the response time delay in one of the pathways results in the chaotic movement characteristic of Huntington's disease.

In clinical endocrinology, it is often assumed that the results of thyroid hormone function tests (TFTs) before total thyroidectomy are considered euthyroid when the circulating concentrations of thyrotropin [TSH] and free thyroxine [FT4] are within the normal reference ranges. Postoperative thyroid replacement therapy with levothyroxine (L-T4) is aimed to reproduce the preoperative euthyroid condition. Currently, intra-individual changes in the euthyroid set point before and after total thyroidectomy are only partly understood. After total thyroidectomy, a greater postoperative [FT4] than preoperative [FT4] for equivalent euthyroid [TSH] was found, with differences ranging from 3 to 8 pmol/L. This unexplained difference can be explained by the use of a mathematical model of the hypothalamus-pituitary-thyroid (HPT) axis set point theory. In this article, the postoperative HPT euthyroid set point was calculated using a dataset of total thyroidectomized patients with at least three distinguishable postoperative TFTs. The postoperative [TSH] set point was used as a homeostatic reference for the comparison of preoperative TFTs. The preoperative [FT4] value was equal to the postoperative [FT4] value in 50% of the patients, divided by a factor of ~ 1.25 (within +/- 10%). The factor of 1.25 stems from the lack of postoperative use of thyroidal triiodothyronine (T3). Furthermore, approximately 25% of the patients presented a greater preoperative [FT4] difference than postoperative [FT4]/1.25 combined with a normal [TSH] difference. Based on these observations, the effect of T3 on the value of the [FT4] set point was analyzed and explained from a control theory perspective.

A recent paper shows that in gene expression space the manifold spanned by normal tissues and the manifold spanned by the corresponding tumors are disjoint. The statement is based on a two-dimensional projection of gene expression data. In the present paper, we show that, for the multi-dimensional vectors defining the centers of cloud samples: 1. The closest tumor to a given normal tissue is the tumor developed in that tissue, 2. Two normal tissues define quasi-orthogonal directions, 3. A tumor may have a projection onto its corresponding normal tissue, but it is quasi-orthogonal to all other normal tissues, and 4. The cancer manifold is roughly obtained by translating the normal tissue manifold along an orthogonal direction defined by a global cancer progression axis. These geometrical properties add a new characterization of normal tissues and tumors and may have biological significance. Indeed, normal tissues at the vertices of a high-dimensional simplex could indicate genotype optimization for given tissue functions, and a way of avoiding errors in embryonary development. On the other hand, the cancer progression axis could define relevant pan-cancer genes and seems to be consistent with the atavistic theory of tumors.