Braess's paradox and programmable behaviour in microfluidic networks.

Microfluidic systems are now being designed with precision as miniaturized fluid manipulation devices that can execute increasingly complex tasks. However, their operation often requires numerous external control devices owing to the typically linear nature of microscale flows, which has hampered the development of integrated control mechanisms. Here we address this difficulty by designing microfluidic networks that exhibit a nonlinear relation between the applied pressure and the flow rate, which can be harnessed to switch the direction of internal flows solely by manipulating the input and/or output pressures. We show that these networks- implemented using rigid polymer channels carrying water-exhibit an experimentally supported fluid analogue of Braess's paradox, in which closing an intermediate channel results in a higher, rather than lower, total flow rate. The harnessed behaviour is scalable and can be used to implement flow routing with multiple switches. These findings have the potential to advance the development of built-in control mechanisms in microfluidic networks, thereby facilitating the creation of portable systems and enabling novel applications in areas ranging from wearable healthcare technologies to deployable space systems.

Random heterogeneity outperforms design in network synchronization.

A widely held assumption on network dynamics is that similar components are more likely to exhibit similar behavior than dissimilar ones and that generic differences among them are necessarily detrimental to synchronization. Here, we show that this assumption does not generally hold in oscillator networks when communication delays are present. We demonstrate, in particular, that random parameter heterogeneity among oscillators can consistently rescue the system from losing synchrony. This finding is supported by electrochemical-oscillator experiments performed on a multielectrode array network. Remarkably, at intermediate levels of heterogeneity, random mismatches are more effective in promoting synchronization than parameter assignments specifically designed to facilitate identical synchronization. Our results suggest that, rather than being eliminated or ignored, intrinsic disorder in technological and biological systems can be harnessed to help maintain coherence required for function.

On Parameter Identifiability in Network-Based Epidemic Models.

Modelling epidemics on networks represents an important departure from classical compartmental models which assume random mixing. However, the resulting models are high-dimensional and their analysis is often out of reach. It turns out that mean-field models, low-dimensional systems of differential equations, whose variables are carefully chosen expected quantities from the exact model provide a good approximation and incorporate explicitly some network properties. Despite the emergence of such mean-field models, there has been limited work on investigating whether these can be used for inference purposes. In this paper, we consider network-based mean-field models and explore the problem of parameter identifiability when observations about an epidemic are available. Making use of the analytical tractability of most network-based mean-field models, e.g. explicit analytical expressions for leading eigenvalue and final epidemic size, we set up the parameter identifiability problem as finding the solution or solutions of a system of coupled equations. More precisely, subject to observing/measuring growth rate and final epidemic size, we seek to identify parameter values leading to these measurements. We are particularly concerned with disentangling transmission rate from the network density. To do this, we give a condition for practical identifiability and we find that except for the simplest model, parameters cannot be uniquely determined, that is, they are practically unidentifiable. This means that there exist multiple solutions (a manifold of infinite measure) which give rise to model output that is close to the data. Identifying, formalising and analytically describing this problem should lead to a better appreciation of the complexity involved in fitting models with many parameters to data.

Towards Inferring Network Properties from Epidemic Data.

Epidemic propagation on networks represents an important departure from traditional mass-action models. However, the high-dimensionality of the exact models poses a challenge to both mathematical analysis and parameter inference. By using mean-field models, such as the pairwise model (PWM), the high-dimensionality becomes tractable. While such models have been used extensively for model analysis, there is limited work in the context of statistical inference. In this paper, we explore the extent to which the PWM with the susceptible-infected-recovered (SIR) epidemic can be used to infer disease- and network-related parameters. Data from an epidemics can be loosely categorised as being population level, e.g., daily new cases, or individual level, e.g., recovery times. To understand if and how network inference is influenced by the type of data, we employed the widely-used MLE approach for population-level data and dynamical survival analysis (DSA) for individual-level data. For scenarios in which there is no model mismatch, such as when data are generated via simulations, both methods perform well despite strong dependence between parameters. In contrast, for real-world data, such as foot-and-mouth, H1N1 and COVID19, whereas the DSA method appears fairly robust to potential model mismatch and produces parameter estimates that are epidemiologically plausible, our results with the MLE method revealed several issues pertaining to parameter unidentifiability and a lack of robustness to exact knowledge about key quantities such as population size and/or proportion of under reporting. Taken together, however, our findings suggest that network-based mean-field models can be used to formulate approximate likelihoods which, coupled with an efficient inference scheme, make it possible to not only learn about the parameters of the disease dynamics but also that of the underlying network.

Necessary and sufficient conditions for exact closures of epidemic equations on configuration model networks.

We prove that it is possible to obtain the exact closure of SIR pairwise epidemic equations on a configuration model network if and only if the degree distribution follows a Poisson, binomial, or negative binomial distribution. The proof relies on establishing the equivalence, for these specific degree distributions, between the closed pairwise model and a dynamical survival analysis (DSA) model that was previously shown to be exact. Specifically, we demonstrate that the DSA model is equivalent to the well-known edge-based Volz model. Using this result, we also provide reductions of the closed pairwise and Volz models to a single equation that involves only susceptibles. This equation has a useful statistical interpretation in terms of times to infection. We provide some numerical examples to illustrate our results.

Synchronization, clustering, and weak chimeras in a densely coupled transcription-based oscillator model for split circadian rhythms.

The synchronization dynamics for the circadian gene expression in the suprachiasmatic nucleus is investigated using a transcriptional circadian clock gene oscillator model. With global coupling in constant dark (DD) conditions, the model exhibits a one-cluster phase synchronized state, in dim light (dim LL), bistability between one- and two-cluster states and in bright LL, a two-cluster state. The two-cluster phase synchronized state, where some oscillator pairs synchronize in-phase, and some anti-phase, can explain the splitting of the circadian clock, i.e., generation of two bouts of daily activities with certain species, e.g., with hamsters. The one- and two-cluster states can be reached by transferring the animal from DD or bright LL to dim LL, i.e., the circadian synchrony has a memory effect. The stability of the one- and two-cluster states was interpreted analytically by extracting phase models from the ordinary differential equation models. In a modular network with two strongly coupled oscillator populations with weak intragroup coupling, with appropriate initial conditions, one group is synchronized to the one-cluster state and the other group to the two-cluster state, resulting in a weak-chimera state. Computational modeling suggests that the daily rhythms in sleep-wake depend on light intensity acting on bilateral networks of suprachiasmatic nucleus (SCN) oscillators. Addition of a network heterogeneity (coupling between the left and right SCN) allowed the system to exhibit chimera states. The simulations can guide experiments in the circadian rhythm research to explore the effect of light intensity on the complexities of circadian desynchronization.

Finding influential nodes in networks using pinning control: Centrality measures confirmed with electrochemical oscillators.

The spatiotemporal organization of networks of dynamical units can break down resulting in diseases (e.g., in the brain) or large-scale malfunctions (e.g., power grid blackouts). Re-establishment of function then requires identification of the optimal intervention site from which the network behavior is most efficiently re-stabilized. Here, we consider one such scenario with a network of units with oscillatory dynamics, which can be suppressed by sufficiently strong coupling and stabilizing a single unit, i.e., pinning control. We analyze the stability of the network with hyperbolas in the control gain vs coupling strength state space and identify the most influential node (MIN) as the node that requires the weakest coupling to stabilize the network in the limit of very strong control gain. A computationally efficient method, based on the Moore-Penrose pseudoinverse of the network Laplacian matrix, was found to be efficient in identifying the MIN. In addition, we have found that in some networks, the MIN relocates when the control gain is changed, and thus, different nodes are the most influential ones for weakly and strongly coupled networks. A control theoretic measure is proposed to identify networks with unique or relocating MINs. We have identified real-world networks with relocating MINs, such as social and power grid networks. The results were confirmed in experiments with networks of chemical reactions, where oscillations in the networks were effectively suppressed through the pinning of a single reaction site determined by the computational method.

Optimal phase-selective entrainment of heterogeneous oscillator ensembles.

We develop a framework to design optimal entrainment signals that entrain an ensemble of heterogeneous nonlinear oscillators, described by phase models, at desired phases. We explicitly take into account heterogeneity in both oscillation frequency and the type of oscillators characterized by different Phase Response Curves. The central idea is to leverage the Fourier series representation of periodic functions to decode a phase-selective entrainment task into a quadratic program. We demonstrate our approach using a variety of phase models, where we entrain the oscillators into distinct phase patterns. Also, we show how the generalizability gained from our formulation enables us to meet a wide range of design objectives and constraints, such as minimum-power, fast entrainment, and charge-balanced controls.

MiRNAs in Lung Adenocarcinoma: Role, Diagnosis, Prognosis, and Therapy.

Lung cancer has emerged as a significant public health challenge and remains the leading cause of cancer-related mortality worldwide. Among various types of lung malignancies, lung adenocarcinoma (LUAD) stands as the most prevalent form. MicroRNAs (miRNAs) play a crucial role in gene regulation, and their involvement in cancer has been extensively explored. While several reviews have been published on miRNAs and lung cancer, there remains a gap in the review regarding miRNAs specifically in LUAD. In this review, we not only highlight the potential diagnostic, prognostic, and therapeutic implications of miRNAs in LUAD, but also present an inclusive overview of the extensive research conducted on miRNAs in this particular context.

Optimal timing of one-shot interventions for epidemic control.

The interventions and outcomes in the ongoing COVID-19 pandemic are highly varied. The disease and the interventions both impose costs and harm on society. Some interventions with particularly high costs may only be implemented briefly. The design of optimal policy requires consideration of many intervention scenarios. In this paper we investigate the optimal timing of interventions that are not sustainable for a long period. Specifically, we look at at the impact of a single short-term non-repeated intervention (a "one-shot intervention") on an epidemic and consider the impact of the intervention's timing. To minimize the total number infected, the intervention should start close to the peak so that there is minimal rebound once the intervention is stopped. To minimise the peak prevalence, it should start earlier, leading to initial reduction and then having a rebound to the same prevalence as the pre-intervention peak rather than one very large peak. To delay infections as much as possible (as might be appropriate if we expect improved interventions or treatments to be developed), earlier interventions have clear benefit. In populations with distinct subgroups, synchronized interventions are less effective than targeting the interventions in each subcommunity separately.

Maternal overnutrition elevates offspring's blood pressure-A systematic review and meta-analysis.

BACKGROUND: Maternal overnutrition during pregnancy predisposes the offspring to cardiometabolic diseases. OBJECTIVES: This systematic review and meta-analysis aimed to investigate the association between maternal overnutrition and offspring's blood pressure (BP) and the effect of offspring's obesity on this association. DATA SOURCES: PubMed, EMBASE, Clinicaltrials.gov, CENTRAL. STUDY SELECTION AND DATA EXTRACTION: Human studies published in English before October 2021 were identified that presented quantitative estimates of association between maternal overnutrition just before or during pregnancy and the offspring's BP. SYNTHESIS: Random-effect model with the DerSimonian and Laird weighting method was used to analyse regression coefficients or mean differences. RESULTS: After selection, 17 observational studies (140,517 mother-offspring pairs) were included. Prepregnancy body mass index (ppBMI) showed positive correlation with BP in offspring (regression coefficient for systolic: 0.38 mmHg per kg/m2 , 95% confidence interval (CI) 0.17, 0.58; diastolic: 0.10 mmHg per kg/m2 , 95% CI 0.05, 0.14). These indicate 1.9 mmHg increase in systolic and 0.5 mmHg increase in diastolic BP of offspring with every 5 kg/m2 gain in maternal ppBMI. Results on coefficients adjusted for offspring's BMI also showed association (systolic: 0.08 mmHg per kg/m2 , 95% CI 0.04, 0.11; diastolic: 0.03 mmHg per kg/m2 , 95% CI 0.01, 0.04). Independent from ppBMI, gestational weight gain (GWG) showed positive correlation with systolic BP (systolic BP: 0.05 mmHg per kg, 95% CI 0.01, 0.09), but not after adjustment for offspring's BMI. Mean systolic BP was higher in children of mothers with excessive GWG than in those of mothers with optimal GWG (difference: 0.65 mmHg, 95% CI 0.25, 1.05). CONCLUSIONS: Independent from offspring's BMI, higher prepregnancy BMI may increase the risk for hypertension in offspring. The positive association between GWG and offspring's systolic BP is indirect via offspring's obesity. Reduction in maternal obesity and treatment of obesity in children of obese mothers are needed to prevent hypertension.

Synchronization of Belousov-Zhabotinsky oscillators with electrochemical coupling in a spontaneous process.

A passive electrochemical coupling approach is proposed to induce spontaneous synchronization between chemical oscillators. The coupling exploits the potential difference between a catalyst redox couple in the Belousov-Zhabotinsky (BZ) reaction, without external feedback, to induce surface reactions that impact the kinetics of the bulk system. The effect of coupling in BZ oscillators under batch condition is characterized using phase synchronization measures. Although the frequency of the oscillators decreases nonlinearly over time, by a factor of 2 or more within 100 cycles, the coupling is strong enough to maintain synchronization. In such a highly drifting system, the Gibbs-Shannon entropy of the cyclic phase difference distribution can be used to quantify the coupling effect. We extend the Oregonator BZ model to account for the drifting natural frequencies in batch condition and for electrochemical coupling, and numerical simulations of the effect of acid concentration on synchronization patterns are in agreement with the experiments. Because of the passive nature of coupling, the proposed coupling scheme can open avenues for designing pattern recognition and neuromorphic computation systems using chemical reactions in a spontaneous process.

Main reasons and predictive factors of cancer-related emergency department visits in a Hungarian tertiary care center.

BACKGROUND: Identifying the reasons for the Emergency Department (ED) visit of patients with cancer would be essential for possibly decreasing the burden of ED use. The aim of our study was to analyze the distribution of the demographic and clinical parameters of patients with cancer based on the reasons for the ED visits and to identify possible predictive factors for their visits. METHODS: This retrospective study, carried out at a large, public tertiary hospital in Hungary, involved all patients 18 years or over, who had received a cancer diagnosis latest within five years of their visit to the ED in 2018. Demographic and clinical characteristics were collected partly via automated data collection and partly through the manual chart review by a team of experts, including six emergency physicians and an oncologist. Five main reasons for the ED visit were hypothesized, pilot-tested, then identified, including those with cancer-related ED visits (whose visit was unambiguously related to their cancer illness) and those with non-cancer-related ED visits (whose visit to the ED was in no way associated with their cancer illness.) A descriptive approach was used for data analysis and binary logistic regression was used to determine predictive factors for patients with cancer visiting the ED. RESULTS: 23.2% of the altogether 2383 ED visits were directly cancer-related, and these patients had a significantly worse overall survival than patients with non-cancer related ED visits. Age 65 or below (Odds Ratio: 1.51), presence of two more comorbidities (OR: 7.14), dyspnea as chief complaint (OR: 1.52), respiratory cancer (OR: 3.37), any prior chemotherapy (OR: 1.8), any prior immune/biological treatment (OR: 2.21), any prior Best Supportive Care/palliative care (OR: 19.06), or any prior hospice care (OR: 9.43), and hospitalization (OR:2.88) were independent risk factors for the ED visit to be cancer-related. CONCLUSIONS: Our study is the first to identify independent predictive factors of ED use by patients with cancer based on the chief cause of their visit in the Central and Eastern European region. These results may provide important information for the development of algorithms intended to identify the needs of care of patients with cancer at the ED.

Genome sequencing of a novel variant of fowl adenovirus B reveals mosaicism in the pattern of homologous recombination events.

We determined the genomic sequence of a Ukrainian strain of fowl adenovirus B (FAdV-B). The isolate (D2453/1) shared 97.2% to 98.4% nucleotide sequence identity with other viruses belonging to the species Fowl aviadenovirus B. Marked genetic divergence was seen in the hexon, fiber, and ORF19 genes, and phylogenetic analysis suggested that recombination events had occurred in these regions. Our analysis revealed mosaicism in the recombination patterns, a finding that has also been described in the genomes of strains of FAdV-D and FAdV-E. The shared recombination breakpoints, affecting the same genomic regions in viruses belonging to different species, suggest that similar selection mechanisms are acting on the key neutralization antigens and epitopes in viruses of different FAdV species.

The Impact of Contact Structure and Mixing on Control Measures and Disease-Induced Herd Immunity in Epidemic Models: A Mean-Field Model Perspective.

The contact structure of a population plays an important role in transmission of infection. Many 'structured models' capture aspects of the contact pattern through an underlying network or a mixing matrix. An important observation in unstructured models of a disease that confers immunity is that once a fraction [Formula: see text] has been infected, the residual susceptible population can no longer sustain an epidemic. A recent observation of some structured models is that this threshold can be crossed with a smaller fraction of infected individuals, because the disease acts like a targeted vaccine, preferentially immunising higher-risk individuals who play a greater role in transmission. Therefore, a limited 'first wave' may leave behind a residual population that cannot support a second wave once interventions are lifted. In this paper, we set out to investigate this more systematically. While networks offer a flexible framework to model contact patterns explicitly, they suffer from several shortcomings: (i) high-fidelity network models require a large amount of data which can be difficult to harvest, and (ii) very few, if any, theoretical contact network models offer the flexibility to tune different contact network properties within the same framework. Therefore, we opt to systematically analyse a number of well-known mean-field models. These are computationally efficient and provide good flexibility in varying contact network properties such as heterogeneity in the number contacts, clustering and household structure or differentiating between local and global contacts. In particular, we consider the question of herd immunity under several scenarios. When modelling interventions as changes in transmission rates, we confirm that in networks with significant degree heterogeneity, the first wave of the epidemic confers herd immunity with significantly fewer infections than equivalent models with less or no degree heterogeneity. However, if modelling the intervention as a change in the contact network, then this effect may become much more subtle. Indeed, modifying the structure disproportionately can shield highly connected nodes from becoming infected during the first wave and therefore make the second wave more substantial. We strengthen this finding by using an age-structured compartmental model parameterised with real data and comparing lockdown periods implemented either as a global scaling of the mixing matrix or age-specific structural changes. Overall, we find that results regarding (disease-induced) herd immunity levels are strongly dependent on the model, the duration of the lockdown and how the lockdown is implemented in the model.

Breast cancer screening knowledge among Hungarian women: a cross-sectional study.

BACKGROUND: Breast cancer (BC) is the leading malignant tumor among women worldwide. Although attending regular BC screening effectively reduces cancer-related mortality, surveys testify that screening knowledge is critically low among women. We aimed to conduct a comparative cross-sectional survey to assess BC and BC screening-related knowledge in Hungary. METHODS: Women between 25 and 65 years of age without a previous history of malignant tumors were included with non-probability sampling in 2017. Respondents were recruited either from primary care (laywomen) or from the waiting rooms of mammography (screening attendees). A self-completion questionnaire was constructed with questions about BC (risk factors, signs and symptoms, curability, and mortality), BC screening (mammography and breast self-examination), and BC-related information sources to assess knowledge among laywomen and screening attendees. In addition to descriptive statistics, odds ratios with 95% confidence intervals were calculated in univariate analysis and logistic regression was used in multivariate analysis. RESULTS: Altogether, 480 women completed the questionnaire, of which 429 (227 laywomen and 202 screening attendees) were eligible for inclusion. Laywomen and screening attendees knew the recommended age at first mammography in 35.2% and 86.6%, the recommended frequency of screening in 33.9% and 12.9%, the recommended age at first breast-self examination in 38.8% and 51.2%, had sufficient knowledge of the risk factors of BC in 7.0% and 5.9%, and that of signs and symptoms of BC in 16.7% and 28.9%, respectively. A higher proportion of screening attendees correctly identified the recommended age of first BC screening correctly than that of laywomen (86.6% vs. 35.2%; p < 0.001). The most popular information sources were television among laywomen and general practitioners or specialists among screening attendees. In multivariate analysis, older age, higher education, and place of residency were significant predictors of the right answers. CONCLUSIONS: Although knowledge was insufficient in almost all fields of the questionnaire, the most prominent gap was observed concerning risk factors and signs and symptoms of BC both in laywomen and, unexpectedly, screening attendees. Most laywomen were lacking knowledge of screening protocol. These results urge breast health and BC knowledge interventions in Hungary.

Inferring dynamic topology for decoding spatiotemporal structures in complex heterogeneous networks.

Extracting complex interactions (i.e., dynamic topologies) has been an essential, but difficult, step toward understanding large, complex, and diverse systems including biological, financial, and electrical networks. However, reliable and efficient methods for the recovery or estimation of network topology remain a challenge due to the tremendous scale of emerging systems (e.g., brain and social networks) and the inherent nonlinearity within and between individual units. We develop a unified, data-driven approach to efficiently infer connections of networks (ICON). We apply ICON to determine topology of networks of oscillators with different periodicities, degree nodes, coupling functions, and time scales, arising in silico, and in electrochemistry, neuronal networks, and groups of mice. This method enables the formulation of these large-scale, nonlinear estimation problems as a linear inverse problem that can be solved using parallel computing. Working with data from networks, ICON is robust and versatile enough to reliably reveal full and partial resonance among fast chemical oscillators, coherent circadian rhythms among hundreds of cells, and functional connectivity mediating social synchronization of circadian rhythmicity among mice over weeks.

Synchronization in populations of electrochemical bursting oscillators with chaotic slow dynamics.

We investigate the synchronization of coupled electrochemical bursting oscillators using the electrodissolution of iron in sulfuric acid. The dynamics of a single oscillator consisted of slow chaotic oscillations interrupted by a burst of fast spiking, generating a multiple time-scale dynamical system. A wavelet analysis first decomposed the time series data from each oscillator into a fast and a slow component, and the corresponding phases were also obtained. The phase synchronization of the fast and slow dynamics was analyzed as a function of electrical coupling imposed by an external coupling resistance. For two oscillators, a progressive transition was observed: With increasing coupling strength, first, the fast bursting intervals overlapped, which was followed by synchronization of the fast spiking, and finally, the slow chaotic oscillations synchronized. With a population of globally coupled 25 oscillators, the coupling eliminated the fast dynamics, and only the synchronization of the slow dynamics can be observed. The results demonstrated the complexities of synchronization with bursting oscillations that could be useful in other systems with multiple time-scale dynamics, in particular, in neuronal networks.

Tobacco Smoking and Smokeless Tobacco Use among Domestic and International Medical Students in Hungary.

BACKGROUND: Medical students as future physicians will have an important role in tobacco control; therefore, their tobacco use behavior is of particular interest. Consumption of combustible tobacco (cigarettes, waterpipes, cigars, and pipes) is prevalent throughout Europe, whereas smokeless tobacco use is common mainly in the Nordic countries. Objectives: Aim of our study is to assess tobacco use among medical students from different countries studying in Hungary with special focus on students from Norway where smokeless tobacco is widely used. A self-administered questionnaire survey was carried out to measure current tobacco use. Results: The survey included 1337 students from Hungary, Norway, Germany, and from other countries (Multinational group). The lowest prevalence of cigarette smoking was found among students from Norway (13.0%) when compared with students from Hungary (21.5%), Germany (34.2%), or with students in the Multinational group (29.5%). Conversely, prevalence of smokeless tobacco use was the highest among students from Norway (40.9%) when compared with students from Hungary (1.4%), Germany (2.6%), or with students in the Multinational group (6.2%). Waterpipes, cigars, and pipes were rarely used, mostly only 1-3 times a month in all groups. More than half of Norwegian students used some form of tobacco (smokeless and/or combustible tobacco). Conclusions: Considering the impending role of medical students in tobacco control, faculties of medicine should sensitize their students on the topic of possible health risks associated with combustible and smokeless tobacco products. Culturally tailored tobacco cessation programs need to be offered to medical students coming from different cultural backgrounds.

Key questions for modelling COVID-19 exit strategies.

Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.