Uncertainty quantification in Covid-19 spread: Lockdown effects.

We develop a Bayesian inference framework to quantify uncertainties in epidemiological models. We use SEIJR and SIJR models involving populations of susceptible, exposed, infective, diagnosed, dead and recovered individuals to infer from Covid-19 data rate constants, as well as their variations in response to lockdown measures. To account for confinement, we distinguish two susceptible populations at different risk: confined and unconfined. We show that transmission and recovery rates within them vary in response to facts, and that the diagnose rate is quite low, which leads to large amounts of undiagnosed infective individuals. A key unknown to predict the evolution of the epidemic is the fraction of the population affected by the virus, including asymptomatic subjects. Our study tracks its time evolution with quantified uncertainty from available official data, limited, however, by the data quality. We exemplify the technique with data from Spain, country in which late drastic lockdowns were enforced for months during the first wave of the current pandemic. In late actions and in the absence of other measures, spread is delayed but not stopped unless a large enough fraction of the population is confined until the asymptomatic population is depleted. To some extent, confinement can be replaced by strong distancing through masks in adequate circumstances.

Tracking collective cell motion by topological data analysis.

By modifying and calibrating an active vertex model to experiments, we have simulated numerically a confluent cellular monolayer spreading on an empty space and the collision of two monolayers of different cells in an antagonistic migration assay. Cells are subject to inertial forces and to active forces that try to align their velocities with those of neighboring ones. In agreement with experiments in the literature, the spreading test exhibits formation of fingers in the moving interfaces, there appear swirls in the velocity field, and the polar order parameter and the correlation and swirl lengths increase with time. Numerical simulations show that cells inside the tissue have smaller area than those at the interface, which has been observed in recent experiments. In the antagonistic migration assay, a population of fluidlike Ras cells invades a population of wild type solidlike cells having shape parameters above and below the geometric critical value, respectively. Cell mixing or segregation depends on the junction tensions between different cells. We reproduce the experimentally observed antagonistic migration assays by assuming that a fraction of cells favor mixing, the others segregation, and that these cells are randomly distributed in space. To characterize and compare the structure of interfaces between cell types or of interfaces of spreading cellular monolayers in an automatic manner, we apply topological data analysis to experimental data and to results of our numerical simulations. We use time series of data generated by numerical simulations to automatically group, track and classify the advancing interfaces of cellular aggregates by means of bottleneck or Wasserstein distances of persistent homologies. These techniques of topological data analysis are scalable and could be used in studies involving large amounts of data. Besides applications to wound healing and metastatic cancer, these studies are relevant for tissue engineering, biological effects of materials, tissue and organ regeneration.

Incorporating Cellular Stochasticity in Solid-Fluid Mixture Biofilm Models.

The dynamics of cellular aggregates is driven by the interplay of mechanochemical processes and cellular activity. Although deterministic models may capture mechanical features, local chemical fluctuations trigger random cell responses, which determine the overall evolution. Incorporating stochastic cellular behavior in macroscopic models of biological media is a challenging task. Herein, we propose hybrid models for bacterial biofilm growth, which couple a two phase solid/fluid mixture description of mechanical and chemical fields with a dynamic energy budget-based cellular automata treatment of bacterial activity. Thin film and plate approximations for the relevant interfaces allow us to obtain numerical solutions exhibiting behaviors observed in experiments, such as accelerated spread due to water intake from the environment, wrinkle formation, undulated contour development, and the appearance of inhomogeneous distributions of differentiated bacteria performing varied tasks.

Dynamics of Pseudomonas putida biofilms in an upscale experimental framework.

Exploitation of biofilms for industrial processes requires them to adopt suitable physical structures for rendering them efficient and predictable. While hydrodynamics could be used to control material features of biofilms of the platform strain Pseudomonas putida KT2440 there is a dearth of experimental data on surface-associated growth behavior in such settings. Millimeter scale biofilm patterns formed by its parental strain P. putida mt-2 under different Reynolds numbers (Re) within laminar regime were analyzed using an upscale experimental continuous cultivation assembly. A tile-scan image acquisition process combined with a customized image analysis revealed patterns of dense heterogeneous structures at Re = 1000, but mostly flattened coverings sparsely patched for Re < 400. These results not only fix the somewhat narrow hydrodynamic regime under which P. putida cells form stable coatings on surfaces destined for large-scale processes, but also provide useful sets of parameters for engineering catalytic biofilms based on this important bacterium as a cell factory.

Stenosis triggers spread of helical Pseudomonas biofilms in cylindrical flow systems.

Biofilms are multicellular bacterial structures that adhere to surfaces and often endow the bacterial population with tolerance to antibiotics and other environmental insults. Biofilms frequently colonize the tubing of medical devices through mechanisms that are poorly understood. Here we studied the helicoidal spread of Pseudomonas putida biofilms through cylindrical conduits of varied diameters in slow laminar flow regimes. Numerical simulations of such flows reveal vortical motion at stenoses and junctions, which enhances bacterial adhesion and fosters formation of filamentous structures. Formation of long, downstream-flowing bacterial threads that stem from narrowings and connections was detected experimentally, as predicted by our model. Accumulation of bacterial biomass makes the resulting filaments undergo a helical instability. These incipient helices then coarsened until constrained by the tubing walls, and spread along the whole tube length without obstructing the flow. A three-dimensional discrete filament model supports this coarsening mechanism and yields simulations of helix dynamics in accordance with our experimental observations. These findings describe an unanticipated mechanism for bacterial spreading in tubing networks which might be involved in some hospital-acquired infections and bacterial contamination of catheters.

Prevalence of clinical attachment loss in adolescents in Santo Domingo, Dominican Republic.

BACKGROUND: Data on periodontal conditions in adolescents in the Dominican Republic are scarce. The aim of the present cross-sectional study was to estimate the prevalence of periodontal attachment loss among Dominican adolescents. This study did not attempt to classify the disease into aggressive and chronic periodontitis. METHODS: A random sample of 2,007 Dominican adolescents was obtained. A probability, weighted sample was selected using a complex, multi-stage probability sampling design. The study was clustered in 26 schools and 106 classes. The study subjects were clinically examined under field conditions by a single calibrated examiner who measured gingival recession and probing depth at six sites per tooth, with subsequent calculation of clinical periodontal attachment level for each site. RESULTS: The prevalence of clinical attachment loss > or = 1 mm was 49.5%, with the prevalence ranging between 48.7% and 50.2%, depending on age and gender. Clinical attachment loss > or = 2 mm was found in 15% of the students and attachment loss > or = 3 mm in 4.0% of the students. Logistic regression model revealed that only age significantly increased the probability of having clinical attachment loss. CONCLUSION: We conclude that clinical attachment loss is common in adolescents in Santo Domingo, Dominican Republic, suggesting the necessity for improved standards of prevention, diagnosis, and treatment of these lesions.