Vaccination against COVID-19 was integral to controlling the pandemic that persisted with the continuous emergence of SARS-CoV-2 variants. Using a mathematical model describing SARS-CoV-2 within-host infection dynamics, we estimate differences in virus and immunity due to factors of infecting variant, age, and vaccination history (vaccination brand, number of doses and time since vaccination). We fit our model in a Bayesian framework to upper respiratory tract viral load measurements obtained from cases of Delta and Omicron infections in Singapore, of whom the majority only had one nasopharyngeal swab measurement. With this dataset, we are able to recreate similar trends in URT virus dynamics observed in past within-host modelling studies fitted to longitudinal patient data.We found that Omicron had higher R0,within values than Delta, indicating greater initial cell-to-cell spread of infection within the host. Moreover, heterogeneities in infection dynamics across patient subgroups could be recreated by fitting immunity-related parameters as vaccination history-specific, with or without age modification. Our model results are consistent with the notion of immunosenescence in SARS-CoV-2 infection in elderly individuals, and the issue of waning immunity with increased time since last vaccination. Lastly, vaccination was not found to subdue virus dynamics in Omicron infections as well as it had for Delta infections.This study provides insight into the influence of vaccine-elicited immunity on SARS-CoV-2 within-host dynamics, and the interplay between age and vaccination history. Furthermore, it demonstrates the need to disentangle host factors and changes in pathogen to discern factors influencing virus dynamics. Finally, this work demonstrates a way forward in the study of within-host virus dynamics, by use of viral load datasets including a large number of patients without repeated measurements.

BACKGROUND: Anaemia is a disease of public health importance with multi-causal pathways. Previous literature suggests the role of indoor air pollution (IAP) on haemoglobin levels, but this has been studied less due to logistic constraints. A high proportion of the population in developing countries, including India, still depends on unclean fuel, which exacerbates IAP. The objective was to study the association between anaemia and IAP among the older Indian adult population (≥ 45 years) as per gender.
METHODS: Our study analysed the nationally representative dataset of the Longitudinal Ageing Study in India (LASI 2017-18, Wave-1). We have documented the association of anaemia (outcome variable) with IAP (explanatory variable). To reduce the confounding effects of demographic and socioeconomic; health related and behavioural covariates; propensity score matching (PSM) was conducted. Nested multilevel regression modelling was conducted. States and union territories were categorised cross tabulated as low, middle and high as per anaemia and IAP exposure. P value < 0.05 was considered statistically significant. SATA version 17 was used for analysis.
RESULTS: More than half (52.52%) of the participants were exposed to IAP (male (53.55%) > female (51.63%)). The odds of having anaemia was significantly 1.19 times higher (AOR 1.19 (1.09-1.31)) among participants using unclean/ solid fuel. The adjusted odds were significantly higher among participants exposed to pollution-generating sources (AOR 1.30; 1.18-1.43), and household indoor smoking (AOR 1.17 (1.07-1.29). The odds of having anaemia were significantly higher (AOR 1.26; 1.15-1.38) among participants exposed to IAP, which was higher in males (AOR 1.36; 1.15-1.61) than females (AOR 1.21; 1.08-1.35). Empowered Action Group (EAG) states like Uttar Pradesh, Chhattisgarh, Madhya Pradesh, Bihar had both high anaemia and IAP exposure.
CONCLUSIONS: This study established the positive association of anaemia with indoor air pollution among older Indian adults through a nationally representative large dataset. The association was higher among men. Further research is recommended to understand detailed causation and to establish temporality. It is a high time to implement positive intervention nationally to decrease solid/ unclean fuel usage, vulnerable ventilation, indoor smoking, IAP and health hazards associated with these with more focused actions towards EAG states.

UNASSIGNED: Medulloblastoma (MB) is the most common malignant pediatric brain tumor, with 5-year survival rates > 70%. Cranial radiotherapy (CRT) to the whole brain, with posterior fossa boost (PFB), underpins treatment for non-infants; however, radiotherapeutic insult to the normal brain has deleterious consequences to neurocognitive and physical functioning, and causes accelerated aging/frailty. Approaches to ameliorate radiotherapy-induced late-effects are lacking and a paucity of appropriate model systems hinders their development.
UNASSIGNED: We have developed a clinically relevant in vivo model system that recapitulates the radiotherapy dose, targeting, and developmental stage of childhood medulloblastoma. Consistent with human regimens, age-equivalent (postnatal days 35-37) male C57Bl/6J mice received computerized tomography image-guided CRT (human-equivalent 37.5 Gy EQD2, n = 12) ± PFB (human-equivalent 48.7 Gy EQD2, n = 12), via the small animal radiation research platform and were longitudinally assessed for > 12 months.
UNASSIGNED: CRT was well tolerated, independent of PFB receipt. Compared to a sham-irradiated group (n = 12), irradiated mice were significantly frailer following irradiation (frailty index; P = .0002) and had reduced physical functioning; time to fall from a rotating rod (rotarod; P = .026) and grip strength (P = .006) were significantly lower. Neurocognitive deficits were consistent with childhood MB survivors; irradiated mice displayed significantly worse working memory (Y-maze; P = .009) and exhibited spatial memory deficits (Barnes maze; P = .029). Receipt of PFB did not induce a more severe late-effect profile.
UNASSIGNED: Our in vivo model mirrored childhood MB radiotherapy and recapitulated features observed in the late-effect profile of MB survivors. Our clinically relevant model will facilitate both the elucidation of novel/target mechanisms underpinning MB late effects and the development of novel interventions for their amelioration.

This study aimed to predict the index of effectiveness (IE) and positive impulse proportion (PIP) to assess the cyclist\'s pedalling technique from lower limb kinematic variables. Several wrapped feature selection techniques were applied to select the best predictors. To predict IE and PIP two multiple linear regressions (MLR) composed of 11 predictors (R² = 0.81 ± 0.12, R² = 0.81 ± 0.05) and two artificial neural networks (ANN) composed of 21 and 28 predictors (R² = 0.95 ± 0.01, R² = 0.92 ± 0.02) were developed. The ANN predicts with accuracy, and the MLR shows the influence of each predictor.

COVID-19 surveillance in Ukraine ceased after the Russian invasion of the country in 2022, on a background of low vaccination rates of 34.5% for two doses at this time. We conducted a modelling study to estimate the epidemic trajectory of SARS-COV-2 in Ukraine after the start of the war. We use a COVID-19 deterministic Susceptible-Exposed-Infected-Recovered (SEIR) model for Ukraine to estimate the impact of increased vaccination coverage and masking as public health interventions. We fit the model output to case notification data between 6 January and 25 February 2022, then we forecast the COVID-19 epidemic trajectory in different scenarios of mask use and vaccine coverage. In the best-case scenario, 69% of the Ukrainian population would have been infected in the first half of 2022. Increasing mask use from 50 to 80% reduces cases and deaths by 17% and 30% respectively, while increasing vaccination rates to 60% and 9.6% for two and three doses respectively results in a 3% reduction in cases and 28% in deaths. However, if vaccination is increased to a higher coverage of 80% with two doses and 12.8% with three, or mask effectiveness is reduced to 40%, increasing vaccination coverage is more effective. The loss of health services, displacement, and destruction of infrastructure will amplify the risk of COVID-19 in Ukraine and make vaccine programs less feasible. Masks do not need the health infrastructure or cold-chain logistics required for vaccines and are more feasible for rapid epidemic control during war. However, increasing vaccine coverage will save more lives. Vaccination of refugees who have fled to other countries can be more feasibly achieved.

In 2020, anthropogenic methane (CH4) emissions decreased due to COVID-19 containment policies, but there was a substantial increase in the concentration of atmospheric CH4. Previous research suggested that this abnormal increase was linked to higher wetland CH4 emissions and a decrease in the atmospheric CH4 sink. However, the impact of changes in the soil CH4 sink remained unknown. To address this, we utilized a process-based model to quantify alterations in the soil CH4 sink of terrestrial ecosystems between 2019 and 2020. By implementing the model with various datasets, we consistently observed an increase in the global soil CH4 sink, reaching up to 0.35 ± 0.06 Tg in 2020 compared to 2019. This increase was primarily attributed to warmer soil temperatures in northern high latitudes. Our results emphasize the importance of considering the CH4 sink in terrestrial ecosystems, as neglecting this component can lead to an underestimation of both emission increases and reductions in atmospheric CH4 sink capacity. Furthermore, these findings highlight the potential role of increased soil warmth in terrestrial ecosystems in slowing the growth of CH4 concentrations in the atmosphere.

We investigate the magnetization dynamics in nanomagnet vertices often found in artificial spin ices. Our analysis involves creating a simplified model that depicts edge magnetization using magnetic charges. We utilize the model to explore the energy landscape, its associated curvatures, and the fundamental modes. Our study uncovers specific magnonic regimes and transitions between magnetization states, marked by zero-modes, which can be understood within the framework of Landau theory. To verify our model, we compare it with micromagnetic simulations, demonstrating a noteworthy agreement.

Collective cell migration, where cells move as a cohesive unit, is a vital process underlying morphogenesis and cancer metastasis. Thanks to recent advances in imaging and modelling, we are beginning to understand the intricate relationship between a cell and its microenvironment and how this shapes cell polarity, metabolism and modes of migration. The use of biophysical and mathematical models offers a fresh perspective on how cells migrate collectively, either flowing in a fluid-like state or transitioning to more static states. Continuing to unite researchers in biology, physics and mathematics will enable us to decode more complex biological behaviours that underly collective cell migration; only then can we understand how this coordinated movement of cells influences the formation and organisation of tissues and directs the spread of metastatic cancer. In this Perspective, we highlight exciting discoveries, emerging themes and common challenges that have arisen in recent years, and possible ways forward to bridge the gaps in our current understanding of collective cell migration.

The occurrence of wildfire threats has increased in the last few decades creating serious challenges for thousands of communities around the world. Understanding the physical and social dynamics imposed by wildfires is fundamental to assessing and reducing the ensuing risk to different communities. Although, several studies investigating household wildfire risk perception and decision-making are available in the literature, modelling solutions to predict household behaviour in wildfire scenarios are still in their early stages. Hence the lack of a behavioural model suitable for embedding within a simulation tool. In this paper, we propose a mathematical framework aimed at simulating how householders perceive the risk associated to wildfires and how they take protective actions in response to such threats. A conceptual Wildfire Decision Model, based on nine assumptions derived from existing literature on human behaviour in wildfires, is introduced. A mathematical framework is then proposed to implement such a model within a simulation tool. The proposed modelling solution can help identify the information required to generate new dynamic and behavioural travel demand models for wildfire evacuation.

Floating photovoltaics (FPV) are an emerging renewable energy technology. Although they have received extensive attention in recent years, understanding of their environmental impacts is limited. To address this knowledge gap, we measured water temperature and meteorological parameters for six months under FPV arrays and in the control open water site and constructed a numerical model reflecting the water energy balance. Our results showed that FPV arrays caused diurnal variation in water temperature and microclimate. Specifically, we found that FPV had a cooling effect on their host waterbody during the daytime and a heat preservation effect at night, reducing diurnal variation. The diel oscillation of water temperature below FPV panels lagged behind that of open waters by approximately two hours. The microclimate conditions below FPV panels also changed, with wind speed decreasing by 70%, air temperature increasing during the daytime (averaging +2.01°C) and decreasing at night (averaging -1.27°C). Notably, the trend in relative humidity was the opposite (-3.72%, +14.43%). Correlation analysis showed that the degree of water temperature affected by FPV was related to local climate conditions. The numerical model could capture the energy balance characteristics with a correlation coefficient of 0.80 between the simulated and actual data. The shortwave radiation and latent heat flux below FPV panels was significantly reduced, and the longwave radiation emitted by FPV panels became one of the heat sources during the daytime. The combined variations of these factors dominated the water energy balance below FPV panels. The measured data and simulation results serve as a foundation for evaluating the impact of FPV systems on water temperature, energy budget, and aquatic environment, which would also provide a more comprehensive understanding of FPV systems.