Recently, mathematical and computational approaches have been incorporated into ICSI interventions as guiding tools. However, those tools carry no prognostic potential. Improving this capability may enhance ICSI attempts and assist clinicians working in infertility clinics. This study, thus aimed to investigate whether parental parameters could have predictive potential for the quality of resulting embryos with the ICSI approach using mathematical modelling techniques. Patient data including follicle number, MI and MII oocyte numbers, sperm number, sperm morphology and motility for 765 distinct couples attending British Cyprus IVF hospital was collected. Furthermore, morphological quality data as well as aneuploidy status for the 4123 resultant embryos were obtained. Regression analyses were conducted to observe the possible correlations between parental parameters and embryo quality and ploidy. Correlation analyses showed that follicle and oocyte numbers, as well as sperm parameters can be indicative of morphological quality of resulting embryos via ICSI (p values < 0.05). On the other hand, aneuploidy prediction remains too complicated to be predicted solely by these parameters (p values > 0.05). This study indicates a predictive potential of infertility measurements for male and female partners on ICSI success and is expected to act as a basis for the development of prognostic softwares to be used in IVF clinics.

Sustainability in Aotearoa New Zealand’s food system is essential for environmental health (taiao ora) and human well-being (tangata ora). However, achieving resilience in our food system faces significant cross-sector challenges, requiring a national food strategy that addresses environmental, economic, and social pressures(1). This work aims to develop the first national computational model of Aotearoa New Zealand’s food system, integrating key factors into a decision support tool. The model aims to support food system resilience by offering an accessible platform that could help inform decisions to strengthen preparedness for shocks, while also providing insights to enhance everyday food security. The Kai Anamata mō Aotearoa (KAMA) model leverages new data and indigenous crop trials to combine work across agriculture, environment, and human wellbeing, forming a comprehensive tool to examine food system resilience. This model will capture the resources required, outputs produced, and wellbeing outcomes of our food system. The KAMA model was built using a flow-state modelling approach, which allows for flexible configuration of land uses and ensures that the model can adapt to future technologies and climate change scenarios. The preliminary development the KAMA model was used to demonstrate the current production system and applied to a regional case study from Te Tauihu, integrating region-specific food production data, including apples, kiwifruit, mussels, wine, and hops production. Outputs included labour, carbon dioxide emissions and mass of production. Beyond food production, this model will enable users to explore the impacts of land use for commodity production, the effects of trade, nutrient supply, and the broader implications for well-being. model will be made publicly accessible online to allow any interested individual to explore the future of the national food system.

Although current estimates suggest that global food production is enough to meet nutritional needs, there are still significant challenges with equitable distribution(1). Tackling these disparities is essential for achieving global nutrition security now and in the future. This study uses the DELTA Model® to analyse global nutrient supply dynamics at national resolution and address nutritional shortfalls in specific countries(2). By examining the distribution of food commodities and nutrients in 2020, we project the future food and nutrient production needs for 2050 to ensure adequate global supply. Our findings indicate that while some nutrients are sufficiently supplied on a global scale, many countries face significant national deficiencies in essential nutrients such as vitamins A, B12, B2, potassium, and iron. Addressing these gaps will require substantial increases in nutrient supply or redistribution. For example, a 1% increase in global protein, targeted at countries with insufficient protein, could close the 2020 gaps. However, if current consumption patterns persist, the global food system will need a 26% increase in production by 2050 to accommodate population growth and changing consumption patterns. Our study developed a framework for exploring future production scenarios. This involves reducing surplus national nutrient supply linearly over decades while simultaneously increasing production of undersupplied nutrients. This framework provides a more practical assessment of future needs, transitioning from idealized production scenarios to realistic projections. Our study investigated a potential future for nutrient supply to meet minimum requirements by 2050. Calcium and vitamin E are crucial, and production must be increased to address significant gaps, given their severe deficiencies in 2020. Energy and fibre production will be required to peak between 2030 and 2040 before stabilizing back near 2020 levels. Predicted changes in nutrient supply from 2020 to 2050 vary: while calcium and vitamin E will need to increase, phosphorus, thiamine and the indispensable amino acids can decrease without compromising global nutrition with only minor redistribution. These results are essential for determining the food supply required to achieve adequate global nutrient supply in the future. Incorporating these insights into global food balance models will provide key stakeholders with evidence, refine future projections, and inform policy decisions aimed at promoting sustainable healthy diets worldwide.

We investigate the consequences of periodic, on–off glucose infusion on the glucose–insulin regulatory system based on a system-level mathematical model with two explicit time delays. Studying the effects of such infusion protocols is mathematically challenging yet a promising direction for probing the system response to infusion. We pay special attention to the interplay of periodic infusion with intermediate-time-scale, ultradian oscillations that arise as a result of the physiological response of glucose uptake and back-release into the bloodstream. By using numerical solvers and numerical continuation software, we investigate the response of the model to different infusion patterns, explore how these patterns affect the overall levels of glucose and insulin, and how this can lead to entrainment. By doing so, we provide a road-map of system responses that can potentially help identify new, less-invasive, test strategies for detecting abnormal responses to glucose uptake without falling into lockstep with the infusion pattern.

The efficacy of COVID-19 vaccines against the Delta variant has been observed to be high, both against severe disease and infection. The full population level vaccine effectiveness, however, also contains the indirect effects of vaccination, which require analysis of transmission dynamics to uncover. Finland was close to naïve to SARS-CoV-2 infections before the Delta dominant era, and non-pharmaceutical interventions (NPIs) were at an internationally low level. We utilize Finnish register data and a mathematical model for transmission and COVID-19 disease burden to construct a completely unvaccinated control population and estimate the different components of the vaccine effectiveness. The estimated direct effectiveness was 72% against COVID-19 cases and 87–96% against severe disease outcomes, but the estimated indirect effectiveness was even better, 93% against cases and 94–97% against severe disease. The total and overall effectiveness, including both direct and indirect effects of vaccination, were thus excellent. Our results show how well the population was protected by vaccination during the Delta era, especially by the indirect effectiveness, providing protection also to the unvaccinated part of the population. The estimated averted numbers of hospitalizations, ICU admissions, and deaths in Finland during the Delta era under the implemented NPIs were about 100 times the observed numbers.

Weight loss results in obligatory reductions in energy expenditure (EE) due to loss of metabolically active fat-free mass (FFM). This is accompanied by adaptive reductions (i.e. adaptive thermogenesis) designed to restore energy balance while in an energy crisis. While the ‘3500-kcal rule’ is used to advise weight loss in clinical practice, the assumption that EE remains constant during energy restriction results in a large overestimation of weight loss. Thus, this work proposes a novel method of weight-loss prediction to more accurately account for the dynamic trajectory of EE. A mathematical model of weight loss was developed using ordinary differential equations relying on simple self-reported inputs of weight and energy intake to predict weight loss over a specified time. The model subdivides total daily EE into resting EE, physical activity EE, and diet-induced thermogenesis, modelling obligatory and adaptive changes in each compartment independently. The proposed model was tested and refined using commercial weight-loss data from participants enrolled on a very low-energy total-diet replacement programme (LighterLife UK, Essex). Mathematical modelling predicted post-intervention weight loss within 0.75% (1.07 kg) of that observed in females with overweight or obesity. Short-term weight loss was consistently underestimated, likely due to considerable FFM reductions reported on the onset of weight loss. The best model agreement was observed from 6 to 9 weeks where the predicted end-weight was within 0.35 kg of that observed. The proposed mathematical model simulated rapid weight loss with reasonable accuracy. Incorporated terms for energy partitioning and adaptive thermogenesis allow us to easily account for dynamic changes in EE, supporting the potential use of such a model in clinical practice.

Until the early twentieth century, populations on many Pacific Islands had never experienced measles. As travel to the Pacific Islands by Europeans became more common, the arrival of measles and other pathogens had devastating consequences. In 1911, Rotuma in Fiji was hit by a measles epidemic, which killed 13% of the island population. Detailed records show two mortality peaks, with individuals reported as dying solely from measles in the first and from measles and diarrhoea in the second. Measles is known to disrupt immune system function. Here, we investigate whether the pattern of mortality on Rotuma in 1911 was a consequence of the immunosuppressive effects of measles. We use a compartmental model to simulate measles infection and immunosuppression. Whilst immunosuppressed, we assume that individuals are vulnerable to dysfunctional reactions triggered by either (i) a newly introduced infectious agent arriving at the same time as measles or (ii) microbes already present in the population in a pre-existing equilibrium state. We show that both forms of the immunosuppression model provide a plausible fit to the data and that the inclusion of immunosuppression in the model leads to more realistic estimates of measles epidemiological parameters than when immunosuppression is not included.

The aim of this study was to apply a comprehensive mathematical model designed to assess the current and future availability of food, as well as the resulting nutrient availability and intake, in the context of the Norwegian population. The model explores various scenarios, including self-sufficiency levels for food supply and impacts on nutrition recommendations. A food mass balance charting production, import, export, feed, seed, and consumer allocation in the Norwegian food system provided insights into the nutrients available to the population. The analysis included a comparison with two versions of the Nordic Nutrient Recommendations (NNR)1, with the latest version recommending substantial changes in food consumption compared to current dietary patterns in Norway. The nutrient analysis compared the food mass to Matvaretabellen2 and was supplemented with data from the United Nations World Population Prospects3 and the Food and Agriculture Organisation/World Health Organisation/United Nations4 report to ensure comprehensive nutrient analysis. Micronutrient gaps were observed in Iodine (94% of the target intake) and Vitamin D (46% of the target intake), while saturated-fatty acids slightly exceeded the recommended requirements (107% of the upper limit) based on the current baseline scenario. The updated NNR4 recommends changes to specific food categories, namely fruits, vegetables, nuts, and seeds. A secondary scenario testing compared against the updated NNR found that increasing the availability of the supply of these groups does not result in any new nutrient gaps, demonstrating the feasibility of addressing the issue on a national supply basis. These approach offers a mathematical-modelling based tool that can be used to provide information for a national food system. Leveraging the model’s capacity to simulate various scenarios, informed decisions to optimise self-sufficiency levels and align food supply with recommended nutritional guidelines can be made. To improve the model, higher data resolution and clearer categorisation of food groups are required which can then be linked into a more complete national food system. The mathematical model presented in this study provides a framework for understanding of food and nutrient availability in Norway. By identifying critical nutrient gaps and potential solutions, this research contributes knowledge for a healthier and sustainable food future for the nation.

In this work, a three degrees-of-freedom (3-DoF) static quadcopter unmanned aerial vehicle (UAV) test-rig of a pendulum-type configuration is custom-designed, developed, instrumented, and interfaced with a PC. The rig serves as a test bed to develop high-fidelity mathematical models as well as to investigate autopilot designs and real-time closed-loop controllers’ performances. The Simulink Desktop Real-Time software is employed for the quadcopter’s attitude signals acquisition and real-time implementation of closed-loop controllers on a target microcontroller hardware. The mathematical models for pitch, roll, and yaw axes are derived via the first principle and validated with the experimental linear system identification (SI) techniques. Subsequently, employing the multi-parameter root contour technique, the classical proportional integral derivative (PID) controllers are designed and implemented in real-time on the quadcopter UAV test rig. This served as a benchmark controller for comparing it with an integral-based linear quadratic regulator (LQR) controller. Further, to improve the transient response of the LQR controller, a novel robust integral-based LQR controller with a feedforward term (LQR-FF) is implemented, which shows much superior performance than the benchmark and basic LQR controller. This work thus will act as a precursor for a more complex 3-DoF autopilot design of an untethered quadcopter.

Introduction of African swine fever (ASF) to China in mid-2018 and the subsequent transboundary spread across Asia devastated regional swine production, affecting live pig and pork product-related markets worldwide. To explore the spatiotemporal spread of ASF in China, we reconstructed possible ASF transmission networks using nearest neighbour, exponential function, equal probability, and spatiotemporal case-distribution algorithms. From these networks, we estimated the reproduction numbers, serial intervals, and transmission distances of the outbreak. The mean serial interval between paired units was around 29 days for all algorithms, while the mean transmission distance ranged 332 –456 km. The reproduction numbers for each algorithm peaked during the first two weeks and steadily declined through the end of 2018 before hovering around the epidemic threshold value of 1 with sporadic increases during 2019. These results suggest that 1) swine husbandry practices and production systems that lend themselves to long-range transmission drove ASF spread; 2) outbreaks went undetected by the surveillance system. Efforts by China and other affected countries to control ASF within their jurisdictions may be aided by the reconstructed spatiotemporal model. Continued support for strict implementation of biosecurity standards and improvements to ASF surveillance is essential for halting transmission in China and spread across Asia.

Mosquito-borne disease is a significant public health issue and within Australia Ross River virus (RRV) is the most reported. This study combines a mechanistic model of mosquito development for two mosquito vectors; Aedes vigilax and Aedes camptorhynchus, with climate projections from three climate models for two Representative Concentration Pathways (RCPs), to examine the possible effects of climate change and sea-level rise on a temperate tidal saltmarsh habitat in Perth, Western Australia. The projections were run under no accretion and accretion scenarios using a known mosquito habitat as a case study. This improves our understanding of the possible implications of sea-level rise, accretion and climate change for mosquito control programmes for similar habitats across temperate tidal areas found in Southwest Western Australia. The output of the model indicate that the proportion of the year mosquitoes are active increases. Population abundances of the two Aedes species increase markedly. The main drivers of changes in mosquito population abundances are increases in the frequency of inundation of the tidal wetland and size of the area inundated, increased minimum water temperature, and decreased daily temperature fluctuations as water depth increases due to sea level changes, particularly under the model with no accretion. The effects on mosquito populations are more marked for RCP 8.5 when compared to RCP 4.5 but were consistent among the three climate change models. The results indicate that Ae. vigilax is likely to be the most abundant species in 2030 and 2050, but that by 2070 Aedes camptorhynchus may become the more abundant species. This increase would put considerable pressure on existing mosquito control programmes and increase the risk of mosquito-borne disease and nuisance biting to the local community, and planning to mitigate these potential impacts should commence now.

Congenital Zika is a devastating consequence of maternal Zika virus infections. Estimates of age-dependent seroprevalence profiles are central to our understanding of the force of Zika virus infections. We set out to calculate the age-dependent seroprevalence of Zika virus infections in Brazil. We analyzed serum samples stratified by age and geographic location, collected from 2016 to 2019, from about 16,000 volunteers enrolled in a Phase 3 dengue vaccine trial led by the Institute Butantan in Brazil. Our results show that Zika seroprevalence has a remarkable age-dependent and geographical distribution, with an average age of the first infection varying from region to region, ranging from 4.97 (3.03–5.41) to 7.24 (6.98–7.90) years. The calculated basic reproduction number, $ {R}_0 $, varied from region to region, ranging from 1.18 (1.04–1.41) to 2.33 (1.54–3.85). Such data are paramount to determine the optimal age to vaccinate against Zika, if and when such a vaccine becomes available.

In Beijing, the capital of China, routine measles mass vaccination has been in place for decades with high coverage; and since the 2000s, catch-up vaccination programmes have been implemented for migrant workers coming to the city. However, measles epidemics in Beijing persisted. Here, we explored the contributing factors of persistent measles transmission in Beijing using an epidemic model in conjunction with a particle filter. Model inputs included data on birth, death, migration, and vaccination. We formulated a series of hypotheses covering the impact of migrant influx, early waning of maternal immunity, and increased mixing among infants; we compared the plausibility of the hypotheses based on model fit to age-grouped, weekly measles incidence data from January 2005 to December 2014, and out-of-fit prediction during 2015–2019. Our best models showed close agreement with the data, and the out-of-fit prediction generally captured the trend of measles incidence from 2015 to 2019. We found that large influx of migrants with considerably higher susceptibility likely contributed to the persistent measles transmission in Beijing. Our findings suggest that stronger catch-up vaccination programmes for migrants may help eliminate measles transmission in Beijing.

Human monkeypox (mpox) virus is a viral zoonosis that belongs to the Orthopoxvirus genus of the Poxviridae family, which presents with similar symptoms as those seen in human smallpox patients. Mpox is an increasing concern globally, with over 80,000 cases in non-endemic countries as of December 2022. In this review, we provide a brief history and ecology of mpox, its basic virology, and the key differences in mpox viral fitness traits before and after 2022. We summarize and critique current knowledge from epidemiological mathematical models, within-host models, and between-host transmission models using the One Health approach, where we distinguish between models that focus on immunity from vaccination, geography, climatic variables, as well as animal models. We report various epidemiological parameters, such as the reproduction number, R0, in a condensed format to facilitate comparison between studies. We focus on how mathematical modelling studies have led to novel mechanistic insight into mpox transmission and pathogenesis. As mpox is predicted to lead to further infection peaks in many historically non-endemic countries, mathematical modelling studies of mpox can provide rapid actionable insights into viral dynamics to guide public health measures and mitigation strategies.

Humans harbour diverse microbial communities, and this interaction has fitness consequences for hosts and symbionts. Understanding the mechanisms that preserve host–symbiont association is an important step in studying co-evolution between humans and their mutualist microbial partners. This association is promoted by vertical transmission, which is known to be imperfect. It is unclear whether host–microbial associations can generally be maintained despite ‘leaky’ vertical transmission. Cultural practices of the host are expected to be important in bacterial transmission as they influence the host's interaction with other individuals and with the environment. There is a need to understand whether and how cultural practices affect host–microbial associations. Here, we develop a mathematical model to identify the conditions under which the mutualist can persist in a population where vertical transmission is imperfect. We show with this model that several factors compensate for imperfect vertical transmission, namely, a selective advantage to the host conferred by the mutualist, horizontal transmission of the mutualist through an environmental reservoir and transmission of a cultural practice that promotes microbial transmission. By making the host–microbe association more likely to persist in the face of leaky vertical transmission, these factors strengthen the association which in turn enables host–mutualist co-evolution.

Quantitative analysis of experimental metabolic data is frequently challenged by non-intuitive, complex patterns which emerge from regulatory networks. The complex output of metabolic regulation can be summarised by metabolic functions which comprise information about dynamics of metabolite concentrations. In a system of ordinary differential equations, metabolic functions reflect the sum of biochemical reactions which affect a metabolite concentration, and their integration over time reveals metabolite concentrations. Further, derivatives of metabolic functions provide essential information about system dynamics and elasticities. Here, invertase-driven sucrose hydrolysis was simulated in kinetic models on a cellular and subcellular level. Both Jacobian and Hessian matrices of metabolic functions were derived for quantitative analysis of kinetic regulation of sucrose metabolism. Model simulations suggest that transport of sucrose into the vacuole represents a central regulatory element in plant metabolism during cold acclimation which preserves control of metabolic functions and limits feedback-inhibition of cytosolic invertases by elevated hexose concentrations.