Spatial analysis of Dengue through the reproduction numbers relating to socioeconomic features: Case studies on two Brazilian urban centers

The study of the propagation of infectious diseases in urban centers finds a close connection with their population's social characteristics and behavior. This work performs a spatial analysis of dengue cases in urban centers based on the basic reproduction numbers, R0, and incidence by planning areas (PAs), as well as their correlations with the Human Development Index (HDI) and the number of trips. We analyzed dengue epidemics in 2002 at two Brazilian urban centers, Belo Horizonte (BH) and Rio de Janeiro (RJ), using PAs as spatial units. Our results reveal heterogeneous spatial scenarios for both cities, with very weak correlations between R0 and both the number of trips and the HDI; in BH, the values of R0 show a less spatial heterogeneous pattern than in RJ. For BH, there are moderate correlations between incidence and both the number of trips and the HDI; meanwhile, they weakly correlate for RJ. Finally, the absence of strong correlations between the considered measures indicates that the transmission process should be treated considering the city as a whole.

Biofilms communities in the soil: characteristic and interactions using mathematical model

There are many different kinds of microorganisms in the soil, and many of them are biofilms because they can make supracellular compounds. Surface-associated microorganisms in a biofilm are encased in a hydrated extracellular polymeric substance that aids in adherence and survival. Numerous different kinds of microorganisms call the soil home. Strong interactions with and among species are made possible by biofilms; this, in turn, might increase the effectiveness with which organic compounds and poisons in soil are degraded. This encouraged us to take a close look at soil biofilm ecosystems, which we do in this paper. In this research, we will look at how soil biofilms arise and how that affects the composition of microbial communities and their function in the soil. Recent years have seen an uptick in interest in questions about biofilm structure and the social interactions of various bacteria. Many concepts elucidating the underlying mathematics of biofilm growth are also presented. Since biofilms are so widespread, this breakthrough in soil biofilm inquiry might help scientists understand soil microbiomes better. Mathematical models further extrapolate the relationships between microbial communities and gives a more precise information as to what is happening in a biofilm. Biofilms can help plants cope with a variety of environmental challenges. Soil quality, plant nourishment, plant protection, bioremediation, and climate change are all influenced by the interplay of biofilm communities. Thus, biofilms play an important role in the development of environmentally friendly and sustainable agriculture.

Estimating the population effectiveness of interventions against COVID-19 in France: A modelling study

BACKGROUND

Non-pharmaceutical interventions (NPIs) and vaccines have been widely used to manage the COVID-19 pandemic. However, uncertainty persists regarding the effectiveness of these interventions due to data quality issues, methodological challenges, and differing contextual factors. Accurate estimation of their effects is crucial for future epidemic preparedness.

METHODS

To address this, we developed a population-based mechanistic model that includes the impact of NPIs and vaccines on SARS-CoV-2 transmission and hospitalization rates. Our statistical approach estimated all parameters in one step, accurately propagating uncertainty. We fitted the model to comprehensive epidemiological data in France from March 2020 to October 2021. With the same model, we simulated scenarios of vaccine rollout.

RESULTS

The first lockdown was the most effective, reducing transmission by 84 % (95 % confidence interval (CI) 83-85). Subsequent lockdowns had diminished effectiveness (reduction of 74 % (69-77) and 11 % (9-18), respectively). A 6 pm curfew was more effective than one at 8 pm (68 % (66-69) vs. 48 % (45-49) reduction), while school closures reduced transmission by 15 % (12-18). In a scenario without vaccines before November 2021, we predicted 159,000 or 168 % (95 % prediction interval (PI) 70-315) more deaths and 1,488,000 or 300 % (133-492) more hospitalizations. If a vaccine had been available after 100 days, over 71,000 deaths (16,507-204,249) and 384,000 (88,579-1,020,386) hospitalizations could have been averted.

CONCLUSION

Our results highlight the substantial impact of NPIs, including lockdowns and curfews, in controlling the COVID-19 pandemic. We also demonstrate the value of the 100 days objective of the Coalition for Epidemic Preparedness Innovations (CEPI) initiative for vaccine availability.

The impact of triple doses vaccination and other interventions for controlling the outbreak of COVID-19 cases and mortality in Australia: A modelling study

COVID-19 is a significant public health problem around the globe, including in Australia. Despite this, Australia's Ministry of Health has expanded COVID-19 control measures widely, logistical trials exist, and the disease burden still needs more clarity. One of the best methods to comprehend the dynamics of disease transmission is by mathematical modeling of COVID-19, which also makes it possible to quantify factors in many places, including Australia. In order to understand the dynamics of COVID-19 in Australia, we examine a mathematical modeling framework for the virus in this study. Australian COVID-19 actual incidence data from January to December 2021 was used to calibrate the model. We also performed a sensitivity analysis of the model parameters and found that the COVID-19 transmission rate was the primary factor in determining the basic reproduction number (R0). Gradually influential intervention policies were established, with accurate effect and coverage regulated with the help of COVID-19 experts in Australia. We simulated data for the period from April 2022 to August 2023. To ascertain which of these outcomes is most effective in lowering the COVID-19 burden, we here assessed the COVID-19 burden (as shown by the number of incident cases and mortality) under a range of intervention scenarios. Regarding the policy of single intervention, the fastest and most efficient way to lower the incidence of COVID-19 is via increasing the first-dose immunization rate, while an improved treatment rate for the afflicted population is also helps to lower mortality in Australia. Furthermore, our results imply that integrating more therapies at the same time increases their efficacy, particularly for mortality, which significantly reduced with a moderate effort, while lowering the number of COVID-19 instances necessitates a major and ongoing commitment.

The impact of Covid-19 vaccination in Aotearoa New Zealand: A modelling study

Aotearoa New Zealand implemented a Covid-19 elimination strategy in 2020 and 2021, which enabled a large majority of the population to be vaccinated before being exposed to the virus. This strategy delivered one of the lowest pandemic mortality rates in the world. However, quantitative estimates of the population-level health benefits of vaccination are lacking. Here, we use a validated mathematical model of Covid-19 in New Zealand to investigate counterfactual scenarios with differing levels of vaccine coverage in different age and ethnicity groups. The model builds on earlier research by adding age- and time-dependent case ascertainment, the effect of antiviral medications, improved hospitalisation rate estimates, and the impact of relaxing control measures. The model was used for scenario analysis and policy advice for the New Zealand Government in 2022 and 2023. We compare the number of Covid-19 hospitalisations, deaths, and years of life lost in each counterfactual scenario to a baseline scenario that is fitted to epidemiological data between January 2022 and June 2023. Our results estimate that vaccines saved 6650 (95% credible interval [4424, 10180]) lives, and prevented 74500 [51000, 115400] years of life lost and 45100 [34400, 55600] hospitalisations during this 18-month period. Making the same comparison before the benefit of antiviral medications is accounted for, the estimated number of lives saved by vaccines increases to 7604 [5080, 11942]. Due to inequities in the vaccine rollout, vaccination rates among Māori were lower than in people of European ethnicity. Our results show that, if vaccination rates had been equitable, an estimated 11%-26% of the 292 Māori Covid-19 deaths that were recorded in this time period could have been prevented. We conclude that Covid-19 vaccination greatly reduced health burden in New Zealand and that equity needs to be a key focus of future vaccination programmes.

Algorithm design of a combinatorial mathematical model for computer random signals

To improve the processing effect of computer random signals, the manuscript employs the intelligent signal recognition algorithm to design a combinatorial mathematical model for computer random signals, and studies the parameter estimation of conventional frequency hopping signal (FHS) based on optimizing kernel function (KF). First, the mathematical form and graphical representation of the ambiguity function of the conventional FHS are explored. Furthermore, a new KF is presented according to its fuzzy function (FF) and the parameters of conventional FHSs are estimated according to the time-frequency distribution corresponding to the KF. Then, simulation experiments are carried out in different types of interference noise environments. The proposed combinatorial mathematical model for computer random signals shows a practical impact, and can effectively improve the effect of random signal combination.

What Influence Could the Acceptance of Visitors Cause on the Epidemic Dynamics of a Reinfectious Disease?: A Mathematical Model

The globalization in business and tourism becomes crucial more and more for the economical sustainability of local communities. In the presence of an epidemic outbreak, there must be such a decision on the policy by the host community as whether to accept visitors or not, the number of acceptable visitors, or the condition for acceptable visitors. Making use of an SIRI type of mathematical model, we consider the influence of visitors on the spread of a reinfectious disease in a community, especially assuming that a certain proportion of accepted visitors are immune. The reinfectivity of disease here means that the immunity gained by either vaccination or recovery is imperfect. With the mathematical results obtained by our analysis on the model for such an epidemic dynamics of resident and visitor populations, we find that the acceptance of visitors could have a significant influence on the disease's endemicity in the community, either suppressive or supportive.

Spatially-dependent model for rods and cones in the retina

We develop a mathematical model for photoreceptors in the retina. We focus on rod and cone outer segment dynamics and interactions with a nutrient source associated with the retinal pigment epithelium cells. Rod and cone densities (number per unit area of retinal surface) are known to have significant spatial dependence in the retina with cones located primarily near the fovea and the rods located primarily away from the fovea. Our model accounts for this spatial dependence of the rod and cone photoreceptor density as well as for the possibility of nutrient diffusion. We present equilibrium and dynamic solutions, discuss their relation to existing models, and estimate model parameters through comparisons with available experimental measurements of both spatial and temporal photoreceptor characteristics. Our model compares well with existing data on spatially-dependent regrowth of photoreceptor outer segments in the macular region of Rhesus Monkeys. Our predictions are also consistent with existing data on the spatial dependence of photoreceptor outer segment length near the fovea in healthy human subjects. We focus primarily on the healthy eye but our model could be the basis for future efforts designed to explore various retinal pathologies, eye-related injuries, and treatments of these conditions.

Prediction of effectiveness of universal rotavirus vaccination in Southwestern Vietnam based on a dynamic mathematical model

Vaccinating young children against rotavirus (RV) is a promising preventive strategy against rotavirus gastroenteritis (RVGE). We evaluated the relative risk reduction of RVGE induced by universal vaccination in Vietnam through dynamic model analysis. We developed an age-stratified dynamic Vaccinated-Susceptible-Infectious-Recovered-Susceptible model to analyze RV transmission and assess vaccine effectiveness (VE). We assumed 3 different vaccine efficacies: 55%, 70%, and 85%. For model calibration, we used a database of patients under 5 years of age admitted to Ho Chi Minh No.1 Hospital with RVGE between January 2013 and December 2018. Assuming a vaccination rate of 95%, the number of RVGE hospitalizations after 5 years from universal RV vaccination decreased from 92,502 cases to 45,626 with 85% efficacy, to 54,576 cases with 70% efficacy, and to 63,209 cases with 55% efficacy. Additionally, RVGE hospitalizations after 10 years decreased from 177,950 to 89,517 with 85% efficacy and to 121,832 cases with 55% efficacy. The relative risk reductions of RVGE after 10 years were 49.7% with 85% efficacy, 40.6% with 70% efficacy, and 31.5% with 55% efficacy. The VE was 1.10 times (95% CI, 1.01-1.22) higher in the 4-months to 1-year-old age group than in the other age groups (P = 0.038), when applying 85% efficacy with 95% coverage. In conclusion, despite its relatively lower efficacy compared to high-income countries, RV vaccination remains an effective intervention in Southwestern Vietnam. In particular, implementing universal RV vaccination with higher coverage would result in a decrease in RVGE hospitalizations among Vietnamese children under 5 years of age.

Impacts of Vaccination and Severe Acute Respiratory Syndrome Coronavirus 2 Variants Alpha and Delta on Coronavirus Disease 2019 Transmission Dynamics in Four Metropolitan Areas of the United States

To characterize Coronavirus Disease 2019 (COVID-19) transmission dynamics in each of the metropolitan statistical areas (MSAs) surrounding Dallas, Houston, New York City, and Phoenix in 2020 and 2021, we extended a previously reported compartmental model accounting for effects of multiple distinct periods of non-pharmaceutical interventions by adding consideration of vaccination and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants Alpha (lineage B.1.1.7) and Delta (lineage B.1.617.2). For each MSA, we found region-specific parameterizations of the model using daily reports of new COVID-19 cases available from January 21, 2020 to October 31, 2021. In the process, we obtained estimates of the relative infectiousness of Alpha and Delta as well as their takeoff times in each MSA (the times at which sustained transmission began). The estimated infectiousness of Alpha ranged from 1.1x to 1.4x that of viral strains circulating in 2020 and early 2021. The estimated relative infectiousness of Delta was higher in all cases, ranging from 1.6x  to 2.1x. The estimated Alpha takeoff times ranged from February 1 to February 28, 2021. The estimated Delta takeoff times ranged from June 2 to June 26, 2021. Estimated takeoff times are consistent with genomic surveillance data.

The role of vaccine status homophily in the COVID-19 pandemic: a cross-sectional survey with modelling

BACKGROUND

Vaccine homophily describes non-heterogeneous vaccine uptake within contact networks. This study was performed to determine observable patterns of vaccine homophily, as well as the impact of vaccine homophily on disease transmission within and between vaccination groups under conditions of high and low vaccine efficacy.

METHODS

Residents of British Columbia, Canada, aged ≥ 16 years, were recruited via online advertisements between February and March 2022, and provided information about vaccination status, perceived vaccination status of household and non-household contacts, compliance with COVID-19 prevention guidelines, and history of COVID-19. A deterministic mathematical model was used to assess transmission dynamics between vaccine status groups under conditions of high and low vaccine efficacy.

RESULTS

Vaccine homophily was observed among those with 0, 2, or 3 doses of the vaccine. Greater homophily was observed among those who had more doses of the vaccine (p < 0.0001). Those with fewer vaccine doses had larger contact networks (p < 0.0001), were more likely to report prior COVID-19 (p < 0.0001), and reported lower compliance with COVID-19 prevention guidelines (p < 0.0001). Mathematical modelling showed that vaccine homophily plays a considerable role in epidemic growth under conditions of high and low vaccine efficacy. Furthermore, vaccine homophily contributes to a high force of infection among unvaccinated individuals under conditions of high vaccine efficacy, as well as to an elevated force of infection from unvaccinated to suboptimally vaccinated individuals under conditions of low vaccine efficacy.

INTERPRETATION

The uneven uptake of COVID-19 vaccines and the nature of the contact network in the population play important roles in shaping COVID-19 transmission dynamics.

Mutualism at the leading edge: insights into the eco-evolutionary dynamics of host-symbiont communities during range expansion

The evolution of mutualism between host and symbiont communities plays an essential role in maintaining ecosystem function and should therefore have a profound effect on their range expansion dynamics. In particular, the presence of mutualistic symbionts at the leading edge of a host-symbiont community should enhance its propagation in space. We develop a theoretical framework that captures the eco-evolutionary dynamics of host-symbiont communities, to investigate how the evolution of resource exchange may shape community structure during range expansion. We consider a community with symbionts that are mutualistic or parasitic to various degrees, where parasitic symbionts receive the same amount of resource from the host as mutualistic symbionts, but at a lower cost. The selective advantage of parasitic symbionts over mutualistic ones is increased with resource availability (i.e. with host density), promoting mutualism at the range edges, where host density is low, and parasitism at the population core, where host density is higher. This spatial selection also influences the speed of spread. We find that the host growth rate (which depends on the average benefit provided by the symbionts) is maximal at the range edges, where symbionts are more mutualistic, and that host-symbiont communities with high symbiont density at their core (e.g. resulting from more mutualistic hosts) spread faster into new territories. These results indicate that the expansion of host-symbiont communities is pulled by the hosts but pushed by the symbionts, in a unique push-pull dynamic where both the host and symbionts are active and tightly-linked players.

Understanding the ideal wound healing mechanistic behavior using in silico modelling perspectives: A review

Complexity of the entire body precludes an accurate assessment of the specific contributions of tissues or cells during the healing process, which might be expensive and time consuming. Because of this, controlling the wound's size, depth, and dimensions may be challenging, and there is not yet an efficient and reliable chronic wound model representation. Furthermore, given the inherent challenges associated with conducting non-invasive in vivo investigations, it becomes peremptory to explore alternative methodologies for studying wound healing. In this context, biologically-realistic mathematical and computational models emerge as a valuable framework that can effectively address this need. Therefore, it might improve our approach to understanding the process at its core. This article will examines all facets of wound healing, including the kinds, pathways, and most current developments in wound treatment worldwide, particularly in silico modelling utilizing both mathematical and structure-based modelling techniques. It may be helpful to identify the crucial traits through the feedback loop of computer models and experimental investigations in order to build innovative therapies to cure wounds. Hence the effectiveness of personalised medicine and more targeted therapy in the healing of wounds may be enhanced by this interdisciplinary expertise.

Design of a central tool for sustainability evaluation and weighting to decision-making support of contaminated soils remediation

Sustainable remediation remains unstandardized and ambiguously regulated, thereby limiting its adoption in the management of contaminated areas. Although a considerable number of tools are available for this purpose, numerous shortcomings continue to be detected, especially with regard to the integration of a complete assessment of impact and sustainability into a single framework. In view of these problems, the objective in this study was to develop and validate an integrated tool for assessing the sustainability of remediation techniques for contaminated soils. To support the sustainability analysis, were prepared impact assessment matrices, which list components and criteria for obtaining integrated impact scores for each sustainability dimension/pillar (i.e., social, economic, and environmental), factor, and component. These impact scores were incorporated and fitted into a mathematical model used to ascertain the sustainability of the techniques. The tool was subsequently validated by comparing and analyzing the sustainability with which five techniques: phytoremediation, electrokinetics and excavation/disposal (Case Study I), nanoremediation and soil washing (Case Study II). The techniques' probabilities of sustainability followed the order presented in the preceding statement. The determination of sustainability was supported by the direct interaction between the effects derived under each dimension and technique. These findings led to the conclusion that the proposed tool prioritizes the basic principles of sustainability, which call for harmony among the three pillars, and that it is a favorable instrument for the evaluation and selection of a sustainable remediation technique.

Accurate correction model of blood potassium concentration in hemolytic specimens

BACKGROUND AND AIMS

The results of blood potassium can be seriously affected by specimen hemolysis which may interfere with clinicians' interpretation of test results. Redrawing blood and retesting may delay treatment time and it is not feasible for critically ill patients with difficulty in specimen collection. Therefore, it is significant to establish a mathematical model that can quickly correct the blood potassium concentration of hemolytic specimens.

MATERIALS AND METHODS

The residual blood samples from 107 patients at Peking University Third Hospital were collected to establish potassium correction model. Samples with different hemolysis indexes were obtained by ultrasonic crushing method. Blood potassium correction models of hemolysis specimens were established by linear regression and curve fitting using SPSS and MATLAB, respectively. In addition, blood samples from another 85 patients were used to verify the accuracy of the models and determine the optimal model.

RESULTS

Variation of potassium (ΔK) was 0.003HI-0.03 (R2 = 0.9749) in linear regression model which had high correlation in ΔK and HI, and the correction formula was Kcorrection = Khemolysis-0.003 × HI + 0.03. Average rate of potassium change (αaverage) was 0.003 ± 0.0002 mmol/L in curve fitting model, and correction formula was Kcorrection = Khemolysis-0.003 × HI, and both men and women can use the same correction model. The accuracy of linear regression model was 96.5 %, and there was statistical difference between the verification results and the measured values (p < 0.05), while the accuracy of curve fitting model was 100 %, and there was no statistical difference between the verification results and the measured values (p = 0.552). The model was validated in an independent set of samples and all were within the TEa of 6 % and the accuracy of 100 %.

CONCLUSIONS

Both linear regression and curve fitting models of potassium correction had high accuracy, and can effectively correct the potassium concentration of hemolytic specimens, while the curve fitting model have superior accuracy.

Pre-optimization and one-step preparation of cascade enzymes system with broad substrates by model guidance: Application of chiral L-norvaline and L-phenylglycine biosynthesis

Cascade biocatalyst systems with catalytic promiscuity can be used for synthesis of a class of chiral chemicals but the optimization of these systems by model guidance is poorly explored. In this study, a cascade system with broad substrate spectrum was characterized and simulated by kinetic model with substrates of DL-Norvaline (DL-Nor) and DL-Phenylglycine (DL-Phg) as examples. To evaluate the optimal cascade system, maximum accumulation of intermediate products and conversion rate in the process were investigated by simultaneous solution of the rate equations for varying enzyme quantities. According to the simulation results, the cascade system was optimized by regulating the expression of D-amino acid oxidase and formate dehydrogenase and was prepared by one-step. The conversion efficiency of DL-Nor and DL-Phg have been significantly improved compared with that of before optimization. Moreover, the total of L-Nor and L-Phg were reached 498.2 mM and 79.5 mM through a gradient fed-batch conversion strategy, respectively.

Impacts of aging on circadian rhythm and related sleep disorders

Circadian rhythm is an essential component of biology that organizes the internal synchrony of the organism in response to the environment. Aging significantly impacts circadian rhythm and is also associated with specific sleep complaints in mammals, including earlier awakening and decreased sleep consolidation at the end of the night. However, the regulation mechanism of aging on the circadian rhythm is far from clear. To further understand the impact of aging, we use an existing mathematical model of circadian rhythm combined with the aging system to explore the effects of aging on circadian rhythm and two kinds of sleep disorders, familial late sleep syndrome (FASPS) and delayed sleep syndrome (DSPS). We get a few intriguing findings from numerical simulations. Aging weakens rhythmicity by reducing the amplitude of circadian rhythm. Aging exacerbates the sleep pattern of being early to bed and early to rise by shortening the period of circadian rhythm and advancing the entrainment phase. Aging reduces the ability of the circadian rhythm to respond to light. The elderly need stronger light to get entrainment with the environmental light cycle. It is more difficult for the elderly to recover from disturbed light. Especially elderly people take a longer time to overcome jet lag. Aging worsens the "morningness" of FASPS disorder patients and improves the symptoms of DSPS disorder patients. This study helps to better understand the impacts of aging on circadian rhythm and sleep disorders and provides theoretical support for the treatment of circadian disorders in the elderly.

SARS-CoV-2 Delta and Omicron community transmission networks as added value to contact tracing

OBJECTIVES

Calculations of SARS-CoV-2 transmission networks at a population level have been limited. Networks that estimate infections between individuals and whether this results in a mutation, can be a way to evaluate fitness of a mutational clone by how much it expands in number as well as determining the likelihood a transmission results in a new variant.

METHODS

Australian Delta and Omicron SARS-CoV-2 sequences were downloaded from GISAID. Transmission networks of infection between individuals were estimated using a novel mathematical method.

RESULTS

Many of the sequences were identical, with clone sizes following power law distributions driven by negative binomial probability distributions for both the number of infections per individual and the number of mutations per transmission (median 0.74 nucleotide changes for Delta and 0.71 for Omicron). Using these distributions, an agent-based model was able to replicate the observed clonal network structure, providing a basis for more detailed COVID-19 modelling. Possible recombination events, tracked by insertion/deletion (indel) patterns, were identified for each variant in these outbreaks.

CONCLUSIONS

This modelling approach reveals key transmission characteristics of SARS-CoV-2 and may complement traditional contact tracing. This methodology can also be applied to other diseases as genetic sequencing of viruses becomes more commonplace.

Removal of nitric oxide in bioreactors: a review on the pathways, governing factors and mathematical modelling

The constant surge in nitric oxide in the atmosphere results in severe environmental degradation, negatively impacting human health and ecosystems, and is presently a global concern. Widely used physicochemical technologies for nitric oxide (NO) removal comes with high installation and operational costs and the production of secondary pollutants. Thus, biological treatment has been emphasized over the last two decades, but the poor solubility of NO in water makes it a challenging issue. The present article reviews the various technical aspects of biological treatment of nitric oxide, including the removal pathways and reactor configurations involved in the process. The most widely used technologies in this regard are chemical adsorption processes followed by biological reactors like biofilters, biotrickling filters and membrane bioreactors that enhance NO solubility and offer the flexibility and scope of further improvement in process design. The effect of various experimental and operational parameters on NO removal, including pH, carbon source, gas flow rate, gas residence time and presence of inhibitory components in the flue gas, is also discussed along with the developed mathematical models for predicting NO removal in a biological treatment system. There is an extensive scope of investigation regarding the development of an economical system to remove NO, and an exhaustive model that would optimize the process considering maximum practical parameters encountered during such operation. A detailed discussion made in this article gives a proper insight into all these areas.

Describing and understanding the time course of the property listing task

To study linguistically coded concepts, researchers often resort to the Property Listing Task (PLT). In a PLT, participants are asked to list properties that describe a concept (e.g., for DOG, subjects may list "is a pet", "has four legs", etc.). When PLT data is collected for many concepts, researchers obtain Conceptual Properties Norms (CPNs), which are used to study semantic content and as a source of control variables. Though the PLT and CPNs are widely used across psychology, only recently a model that describes the listing course of a PLT has been developed and validated. That original model describes the listing course using order of production of properties. Here we go a step beyond and validate the model using response times (RT), i.e., the time from cue onset to property listing. Our results show that RT data exhibits the same regularities observed in the previous model, but now we can also analyze the time course, i.e., dynamics of the PLT. As such, the RT validated model may be applied to study several similar memory retrieval tasks, such as the Free Listing Task, Verbal Fluidity Task, and to research related cognitive processes. To illustrate those kinds of analyses, we present a brief example of the difference in PLT's dynamics between listing properties for abstract versus concrete concepts, which shows that the model may be fruitfully applied to study concepts.

Mathematical modeling of vehicle carbon dioxide emissions

The demand for transportation, driven by an increasing global population, is continuously rising. This has led to a higher number of vehicles on the road and an increased reliance on fossil fuels. Consequently, the rise in atmospheric carbon dioxide (C O 2 ) levels has contributed to global warming. Therefore, it is important to consider sustainable transportation practices to meet climate change mitigation targets. In this research paper, a non-linear mathematical model is developed to study the dynamics of atmospheric C O 2 concentration in relation to human population, economic activities, forest biomass, and vehicle population. The developed model is analyzed qualitatively to understand the long-term behavior of the system's dynamics. Model parameters are fitted to actual data of world population, human economic activities, atmospheric C O 2 , forest biomass, and vehicle population. It is shown that increased vehicular C O 2 emissions have a potential contribution to the increase in atmospheric C O 2 and the decline of human population. Numerical simulations are carried out to verify the analytical findings and we performed global sensitivity analysis to explore the impacts of different sensitive parameters on the C O 2 dynamics.

A mathematical model for radiation-induced life-shortening attributed to cancer

PURPOSE

In this paper, we described our mathematical model for radiation-induced life shortening in detail and applied the model to the experimental data on mice to investigate the effect of radiation on cancer-related life-shortening.

MATERIALS AND METHODS

Our mathematical model incorporates the following components: (i) occurrence of cancer, (ii) progression of cancer over time, and (iii) death from cancer. We evaluated the progression of cancer over time by analyzing the cancer incidence data and cumulative mortalities data obtained from mice experiments conducted at the Institute for Environmental Sciences (IES).

RESULTS

We analyzed non-irradiated control and 20 mGy/day × 400 days irradiated groups. In the analysis, all malignant neoplasms were lumped together and referred to as 'cancer'. Our analysis showed that the reduction in lifespan (104 days in median) was the result of the early onset of cancer (68 days in median) and the shortening of the cancer progression period (48 days in median).

CONCLUSIONS

We described in detail our mathematical model for radiation-induced life-shortening attributed to cancer. We analyzed the mice data obtained from the experiment conducted at the IES using our model. We decomposed radiation-induced life-shortening into the early onset of cancer and the shortening of the cancer progression period.

A nowcast model to predict outdoor flea activity in real time for the contiguous United States

BACKGROUND

The cat flea (Ctenocephalides felis), a parasite commonly found on both dogs and cats, is a competent vector for several zoonotic pathogens, including Dipylidium caninum (tapeworms), Bartonella henselae (responsible for cat scratch disease) and Rickettsia felis (responsible for flea-borne spotted fever). Veterinarians recommend that both cats and dogs be routinely treated with medications to prevent flea infestation. Nevertheless, surveys suggest that nearly one third of pet owners do not routinely administer appropriate preventatives.

METHODS

A mathematical model based on weighted averaging over time is developed to predict outdoor flea activity from weather conditions for the contiguous United States. This 'nowcast' model can be updated in real time as weather conditions change and serves as an important tool for educating pet owners about the risks of flea-borne disease. We validate our model using Google Trends data for searches for the term 'fleas.' This Google Trends data serve as a proxy for true flea activity, as validating the model by collecting fleas over the entire USA is prohibitively costly and time-consuming.

RESULTS

The average correlation (r) between the nowcast outdoor flea activity predictions and the Google Trends data was moderate: 0.65, 0.70, 0.66, 0.71 and 0.63 for 2016, 2017, 2018, 2019 and 2020, respectively. However, there was substantial regional variation in performance, with the average correlation in the East South Atlantic states being 0.81 while the average correlation in the Mountain states was only 0.45. The nowcast predictions displayed strong seasonal and geographic patterns, with predicted activity generally being highest in the summer months.

CONCLUSIONS

The nowcast model is a valuable tool by which to educate pet owners regarding the risk of fleas and flea-borne disease and the need to routinely administer flea preventatives. While it is ideal for domestic cats and dogs to on flea preventatives year-round, many pets remain vulnerable to flea infestation. Alerting pet owners to the local increased risk of flea activity during certain times of the year may motivate them to administer appropriate routine preventives.

Research on the evacuation of people from a road tunnel fire based on a mathematical model

A mathematical model for the evacuation of people from a road tunnel is created, taking into account various factors such as the speed at which people move, the density of the flow of people, and the outcome of the fire. This model allows for the precise calculation of the evacuation time and the optimization of the evacuation route in a fire scenario. The constructed mathematical model was used to determine how long it would take for people to evacuate this road tunnel, and the findings of the Pathfinder simulation were compared. The findings demonstrate a relationship between the model's evacuation time and the human flow density, movement velocity, and fire product characteristics. The evacuation time is closer to the outcome of the actual fire scene when the impact of the fire environment on the speed of evacuation is quantified. The mathematical model of human evacuation's calculation of the evacuation time is essentially accurate when compared to the Pathfinder simulation's calculation, with an error of only 0.77 %. The model provides recommendations for optimizing the evacuation of people from a road tunnel in the case of a fire by not only predicting where the crowding would occur but also by calculating the duration of the crowding.

Methods for Functional Physiological Phenotyping and High-Order Data Quantification

This chapter presents the application of Plantarray, a high-throughput platform commercially available for noninvasive monitoring of plant functional physiology phenotyping (FPP). The platform continuously measures water flux in the soil-plant-atmosphere for each plant in dynamic environments. To better interpret the massive phenotypic data acquired with FPP, several quantitative analysis methods were demonstrated for various types of data. Simple mathematical models were utilized to fit characteristic parameters of plant transpiration response to drought stress. Additionally, ecophysiological models were employed to quantify the sensitivity of transpiration to radiation and vapor pressure deficit (VPD) as component traits and predict more complex higher-order traits. The established protocols provide a tangible tool for integrating FPP and model analysis to address complex traits.