The role of mobility in the dynamics of the COVID-19 epidemic in Andalusia

Metapopulation models have been a popular tool for the study of epidemic spread over a network of highly populated nodes (cities, provinces, countries) and have been extensively used in the context of the ongoing COVID-19 pandemic. In the present work, we revisit such a model, bearing a particular case example in mind, namely that of the region of Andalusia in Spain during the period of the summer-fall of 2020 (i.e., between the first and second pandemic waves). Our aim is to consider the possibility of incorporation of mobility across the province nodes focusing on mobile-phone time dependent data, but also discussing the comparison for our case example with a gravity model, as well as with the dynamics in the absence of mobility. Our main finding is that mobility is key towards a quantitative understanding of the emergence of the second wave of the pandemic and that the most accurate way to capture it involves dynamic (rather than static) inclusion of time-dependent mobility matrices based on cell-phone data. Alternatives bearing no mobility are unable to capture the trends revealed by the data in the context of the metapopulation model considered herein

Experimental characterization of a catalytically active flagellin variant in Clostridium haemolyticum

The bacterial flagellum is made up of approximately 20,000 subunits of the monomeric protein, flagellin, and plays a role in cell motility and pathogenesis. The extreme sequence diversity within the hypervariable region of flagellin genes observed across phyla suggests hidden functional diversity. This thesis outlines the discovery of the first family of flagellin variants with proteolytic activity. A multi-faceted approach revealed a conserved HExxH motif within the hypervariable region (HVR) of these flagellin variants. The motif is characteristic of the Gluzincin family of thermolysin-like peptidases and was found to be conserved in 74 bacterial species spanning over 32 genera. Experimental validation began with the recombinant expression and purification of the HVR of the flagellin FliA(H) from the species Clostridium haemolyticum, an animal pathogen. An approach using mass spectrometry and proteomics revealed that the substrate specificity of this flagellin protease is similar to that of zinc-dependant matrix metallopeptidases (MMPs). Furthermore, peptide sequencing of harvested C.haemolyticum flagellar filaments revealed that the proteolytic flagellin was the second most dominant flagellin component and was also shown to have MMP-like protease activity. Considering the expanded functional repertoire of this organelle in the recent years, this flagellin-associated protease may play a role in chemotaxis, biofilm formation, adhesion and pathogenesis

Evidence-based Kernels: Fundamental Units of Behavioral Influence

This paper describes evidence-based kernels, fundamental units of behavioral influence that appear to underlie effective prevention and treatment for children, adults, and families. A kernel is a behavior–influence procedure shown through experimental analysis to affect a specific behavior and that is indivisible in the sense that removing any of its components would render it inert. Existing evidence shows that a variety of kernels can influence behavior in context, and some evidence suggests that frequent use or sufficient use of some kernels may produce longer lasting behavioral shifts. The analysis of kernels could contribute to an empirically based theory of behavioral influence, augment existing prevention or treatment efforts, facilitate the dissemination of effective prevention and treatment practices, clarify the active ingredients in existing interventions, and contribute to efficiently developing interventions that are more effective. Kernels involve one or more of the following mechanisms of behavior influence: reinforcement, altering antecedents, changing verbal relational responding, or changing physiological states directly. The paper describes 52 of these kernels, and details practical, theoretical, and research implications, including calling for a national database of kernels that influence human behavior

HYBRID INSPIRED TRIBOELECTRIC NANOGENERATOR USING CONTACT SEPARATION MODE

The triboelectric nanogenerator (TENG) concept was used to build four contact separation TENG models to harvest mechanical vibration from an aircraft wing on an unmanned air and surface vehicle. The first three models used a hexagonal base structure with cylindrical pegs and solid rectangular bar on top. The last design used a rectangular box structure with a free moving bar to convert mechanical vibration output into electrical power. To simulate the vibrational motion of an unmanned aerial vehicle wing, a linear arm motor was used at various speeds to test each model for harvesting mechanical motion. The experimental results showed that the model that produced the maximum voltage was the attached solid bar design. The free bar structure design allowed the use of two electrodes in one structure. The ability to use two electrodes for one model enhanced the electrical power production. The finite element method analysis showed that the rectangular bar models would produce the best electrical output based on their contact frequency, matching with the experimental results. In conclusion, the results showed that the two rectangular bar TENG models can harvest mechanical vibrational energy and convert it into electrical power. Further research into using additional free bar TENG models together in series would demonstrate the ability to harvest additional voltage to store and use for sensor power.Lieutenant, United States NavyApproved for public release. Distribution is unlimited

Archives sédimentaires : empreintes des contaminations dans le bassin de la Seine, de 1930 à nos jours

International audienc

Backcasting COVID-19: a physics-informed estimate for early case incidence

It is widely accepted that the number of reported cases during the first stages of the COVID-19 pandemic severely underestimates the number of actual cases. We leverage delay embedding theorems of Whitney and Takens and use Gaussian process regression to estimate the number of cases during the first 2020 wave based on the second wave of the epidemic in several European countries, South Korea and Brazil. We assume that the second wave was more accurately monitored, even though we acknowledge that behavioural changes occurred during the pandemic and region- (or country-) specific monitoring protocols evolved. We then construct a manifold diffeomorphic to that of the implied original dynamical system, using fatalities or hospitalizations only. Finally, we restrict the diffeomorphism to the reported cases coordinate of the dynamical system. Our main finding is that in the European countries studied, the actual cases are under-reported by as much as 50%. On the other hand, in South Korea—which had a proactive mitigation approach—a far smaller discrepancy between the actual and reported cases is predicted, with an approximately 18% predicted underestimation. We believe that our backcasting framework is applicable to other epidemic outbreaks where (due to limited or poor quality data) there is uncertainty around the actual cases

The role of mobility in the dynamics of the COVID-19 epidemic in Andalusia

Metapopulation models have been a popular tool for the study of epidemic spread over a network of highly populated nodes (cities, provinces, countries) and have been extensively used in the context of the ongoing COVID-19 pandemic. In the present work, we revisit such a model, bearing a particular case example in mind, namely that of the region of Andalusia in Spain during the period of the summer-fall of 2020 (i.e., between the first and second pandemic waves). Our aim is to consider the possibility of incorporation of mobility across the province nodes focusing on mobile-phone time dependent data, but also discussing the comparison for our case example with a gravity model, as well as with the dynamics in the absence of mobility. Our main finding is that mobility is key towards a quantitative understanding of the emergence of the second wave of the pandemic and that the most accurate way to capture it involves dynamic (rather than static) inclusion of time-dependent mobility matrices based on cell-phone data. Alternatives bearing no mobility are unable to capture the trends revealed by the data in the context of the metapopulation model considered herein