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Existing insight suggests that maternal effects have a substantial impact on evolution, yet these predictions assume that maternal effects themselves are evolutionarily constant. Hence, it is poorly understood how natural selection shapes maternal effects in different ecological circumstances. To overcome this, the current study derives an evolutionary model of maternal effects in a quantitative genetics context. In constant environments, we show that maternal effects evolve to slight negative values that result in a reduction of the phenotypic variance (canalization). By contrast, in populations experiencing abrupt change, maternal effects transiently evolve to positive values for many generations, facilitating the transmission of beneficial maternal phenotypes to offspring. In periodically fluctuating environments, maternal effects evolve according to the autocorrelation between maternal and offspring environments, favoring positive maternal effects when change is slow, and negative maternal effects when change is rapid. Generally, the strongest maternal effects occur for traits that experience very strong selection and for which plasticity is severely constrained. By contrast, for traits experiencing weak selection, phenotypic plasticity enhances the evolutionary scope of maternal effects, although maternal effects attain much smaller values throughout. As weak selection is common, finding substantial maternal influences on offspring phenotypes may be more challenging than anticipated.

Accurate DNA replication is tightly regulated in eukaryotes to ensure genome stability during cell division and is performed by the multi-protein replisome. At the core an AAA+ hetero-hexameric complex, Mcm2-7, together with GINS and Cdc45 form the active replicative helicase Cdc45/Mcm2-7/GINS (CMG). It is not clear how this replicative ring helicase translocates on, and unwinds, DNA. We measure real-time dynamics of purified recombinant Drosophila melanogaster CMG unwinding DNA with single-molecule magnetic tweezers. Our data demonstrates that CMG exhibits a biased random walk, not the expected unidirectional motion. Through building a kinetic model we find CMG may enter up to three paused states rather than unwinding, and should these be prevented, in vivo fork rates would be recovered in vitro. We propose a mechanism in which CMG couples ATP hydrolysis to unwinding by acting as a lazy Brownian ratchet, thus providing quantitative understanding of the central process in eukaryotic DNA replication. How the eukaryotic helicase unzips DNA during replication is not well understood. By measuring the real-time motion of purified CMG unwinding DNA with magnetic tweezers, the authors reveal the dynamics where isolated CMG unwinds via a biased random walk with proclivity to pause.

While cooperation and risk aversion are considered to be evolutionarily advantageous in many circumstances, and selfish or risky behaviour can bring negative consequences for individuals and the community at large, selfish and risk-seeking behaviour is still often observed in human societies. In this paper we consider whether there are environmental and social conditions that favour selfish risk-seeking individuals within a community and whether tolerating such individuals may provide benefits to the community itself in some circumstances. We built an agent-based model including two types of agent—selfish risk-seeking and generous risk-averse—that harvest resources from the environment and share them (or not) with their community. We found that selfish risk-seekers can outperform generous risk-averse agents in conditions where their survival is moderately challenged, supporting the theory that selfish and risk-seeking traits combined are not dysfunctional but rather can be evolutionarily advantageous for agents. The benefit for communities is less clear, but when generous agents are unconditionally cooperative communities with a greater proportion of selfish risk-seeking agents grow to a larger population size suggesting some advantage to the community overall.

Many studies use a reductionist approach to isolate the influence of one factor in childhood on multimorbidity rather than consider the combined effect of wider determinants. We explored how potential multiple early life determinants of multimorbidity can be characterised across three UK cohort studies. We used the National Child Development Study (NCDS), the 1970 British Cohort Study (BCS70), and the Aberdeen Children of the 1950s Study (ACONF) to identified early life variables that fit into 12 conceptualised domains of early life determinants of multimorbidity. Variables were assigned into 12 domains; principal component analysis reduced the dimensionality of the data and structured variables into subgroups. The data audit identified 7 domains in ACONF, 10 domains in NCDS and 12 domains in BCS70. Dominant components included maternal fertility histories within the prenatal, antenatal and birth domain, long-term illnesses within the child health domain, educational ability within the child education and health literacy domain, ethnicity within the demography domain, parental health behaviours within the transgenerational domain, housing within the socioeconomic domain and parental-child interactions within the parental-family domain. We demonstrated that if multiple large scale longitudinal studies are used, there is enough data available for researchers to consider conceptualising early life risk factors of multimorbidity across groups or domains. Such conceptualisation can help challenge the existing understanding of disease aetiology and develop new ideas for prevention of multimorbidity.

Vaccine misinformation fuels vaccine hesitancy, spreading through social networks and can thus lead to the formation of unprotected communities, increasing the risk of larger-scale disease outbreaks. In this study, through an agent-based model that integrates coupled diffusion processes–vaccine opinions and disease diffusion–we design counter-campaigns that counteract vaccine misinformation to promote vaccine uptake aiming to curb the spread of an epidemic. We frame this as an optimization problem, developing adaptive targeting strategies that respond to evolving vaccine attitudes subject to budget constraints. We find that the efficiency of campaigns depends on both the network structure and the timing of the intervention. For early intervention, we demonstrate that targeting neutral individuals connected to anti-vaccine opinion adopters within their social networks can effectively limit the spread of negative influence. Moreover, we find that targeting agents that have the potential to propagate the positive influence in their neighbourhoods is significantly more effective than solely protecting the most vulnerable agents from negative influence. For late intervention, as large anti-vaccine communities begin to emerge, shielding bridging regions in small-world and regular lattice networks becomes a more effective containment strategy. However, this approach is less effective in scale-free and random networks due to the distinct clustering patterns observed there. We also find that controlling negative opinion diffusion becomes more challenging the longer the intervention is delayed. However, it can be controlled more efficiently with fewer resources in small-world and regular lattice networks than in others.

Background Living with multiple long-term conditions (MLTCs) involves ‘work’. A recent qualitative synthesis identified eight patient-centred work themes: ‘learning and adapting’, ‘accumulation and complexity’, ‘investigation and monitoring’, ‘health service and administration’ and ‘symptom’, ‘emotional’, ‘medication’ and ‘financial’ work. These themes may be underrepresented in electronic health records (EHRs). This study aimed to evaluate the representation of these themes and their constituent concepts in EHR data in a general population and among individuals with history of a mental health condition. Methods Using the OpenCodelists builder from OpenSAFELY, clinical code lists corresponding to work concepts were developed using Systematised Nomenclature of Medicine Clinical Terms (SNOMED CT) and validated by two clinicians. Additional concepts were engineered within the Clinical Practice Research Datalink (CPRD) and the Secure Anonymised Information Linkage (SAIL) Databank. We analysed trends in recording rates over 20 years across a SAIL general population cohort (n=5,180,602) and a CPRD cohort comprising individuals with a mental health diagnosis (n=3,616,776) and matched controls (n=4,457,225). Results 55 code lists and seven engineered concepts were developed across the themes. The proportion of patients with codes related to ‘investigation and monitoring’ exceeded 40%, while ‘accumulation and complexity’ and ‘financial work’ were poorly represented (<2% and <1% of the study population respectively). Recording was generally higher among individuals with a mental health diagnosis history. Conclusion While EHR data captures some aspects of MLTC work, patient-centred concepts are under-represented. Future research should explore reasons behind variability in coding practices, and innovative methods for enriching structured records with patient-centred data.

Background Most people living with multiple long-term condition multimorbidity (MLTC-M) are under 65 (defined as ‘early onset’). Earlier and greater accrual of long-term conditions (LTCs) may be influenced by the timing and nature of exposure to key risk factors, wider determinants or other LTCs at different life stages. We have established a research collaboration titled ‘MELD-B’ to understand how wider determinants, sentinel conditions (the first LTC in the lifecourse) and LTC accrual sequence affect risk of early-onset, burdensome MLTC-M, and to inform prevention interventions. Aim Our aim is to identify critical periods in the lifecourse for prevention of early-onset, burdensome MLTC-M, identified through the analysis of birth cohorts and electronic health records, including artificial intelligence (AI)-enhanced analyses. Design We will develop deeper understanding of ‘burdensomeness’ and ‘complexity’ through a qualitative evidence synthesis and a consensus study. Using safe data environments for analyses across large, representative routine healthcare datasets and birth cohorts, we will apply AI methods to identify early-onset, burdensome MLTC-M clusters and sentinel conditions, develop semi-supervised learning to match individuals across datasets, identify determinants of burdensome clusters, and model trajectories of LTC and burden accrual. We will characterise early-life (under 18 years) risk factors for early-onset, burdensome MLTC-M and sentinel conditions. Finally, using AI and causal inference modelling, we will model potential ‘preventable moments’, defined as time periods in the life course where there is an opportunity for intervention on risk factors and early determinants to prevent the development of MLTC-M. Patient and public involvement is integrated throughout.

This book, originally published in 1983, demonstrates the importance of seaports in the growth of less-developed countries. The author focuses on the character of port activity within the context of transport systems and regional economic planning. General principles of port development are illustrated by detailed reference to one Third World port group, that of the Indian Ocean coasts of Kenya and Tanzania. The objective is not merely to illustrate the character of one specific group of ports, but to demonstrate methods of analysis and to underline the crucial role of ports in the development process.

Phenotypic differences of genetically identical cells under the same environmental conditions have been attributed to the inherent stochasticity of biochemical processes. Various mechanisms have been suggested, including the existence of alternative steady states in regulatory networks that are reached by means of stochastic fluctuations, long transient excursions from a stable state to an unstable excited state, and the switching on and off of a reaction network according to the availability of a constituent chemical species. Here we analyse a detailed stochastic kinetic model of two-component system signalling in bacteria, and show that alternative phenotypes emerge in the absence of these features. We perform a bifurcation analysis of deterministic reaction rate equations derived from the model, and find that they cannot reproduce the whole range of qualitative responses to external signals demonstrated by direct stochastic simulations. In particular, the mixed mode, where stochastic switching and a graded response are seen simultaneously, is absent. However, probabilistic and equation-free analyses of the stochastic model that calculate stationary states for the mean of an ensemble of stochastic trajectories reveal that slow transcription of either response regulator or histidine kinase leads to the coexistence of an approximate basal solution and a graded response that combine to produce the mixed mode, thus establishing its essential stochastic nature. The same techniques also show that stochasticity results in the observation of an all-or-none bistable response over a much wider range of external signals than would be expected on deterministic grounds. Thus we demonstrate the application of numerical equation-free methods to a detailed biochemical reaction network model, and show that it can provide new insight into the role of stochasticity in the emergence of phenotypic diversity.