We are grateful to funding from NIH grant U01 GM087719. ES was also partially supported by the National Institute of General Medical Sciences MIDAS grant 5U54GM088491-02 and by the Vaccine Modeling Initiative (VMI) funded by the Bill and Melinda Gates Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Regarding research developed with smokers and ex- smokers and how people change their smoking behaviour, Prochaska and DiClemente’s Transtheoretical Model is an important theoretical framework (Anatchkova et al., 2006; Boudreaux, Francis, Taylor, Scarinci, & Brantley, 2003; Carosella, Ossip-Klein, & Owens, 1999; Clarke & Aish, 2002; Fava, Velicer, & Prochaska, 1995; Haslam & Draper, 2000; Kristeller, Rossi, Ockene, Goldberg, & Prochaska, 1992; Norman, Velicer, Fava, & Prochaska, 2000; Oakes, Chapman, Borland, Balmford, & Trotter, 2004; Prochaska, DiClemente, & Norcross, 1992; Prochaska, 1996; Prokhorov et al., 2003; Segan, Borland, & Greenwood, 2002, 2005; Snow, Prochaska, & Rossi, 1992). This model explores how people change their behaviour, structuring this changein five stages (precon- templation, contemplation, preparation, action and maintenance), each of them corresponding to a period of time and to intention and behaviour characteristics, common to all individuals in the same stage (Prochaska et al., 1992). The first stage (precontemplation) includes people who are not intending to stop smoking in a fore- seeable future (i.e., 6 months); individuals in contemplation stage express the intention to stop smoking in the following 6 months; the next stage (prepa- ration) is characterised by an intention to stop smoking in the short term (i.e., in 30 days); the action stage includes participants who have stopped smoking in the previous 6 months; and the last stage (maintenance) includes indi- viduals who have been abstinent for more than 6 months. According to this model, people intentionally change their smoking behaviour as they shift from one stage of change to another, until they get to the last stage of change, when the smoking consumption is already absent. The progres- sion throughout the five stages of changes is not always linear. Instead, the progression is made in a spiral move- ment, as in the smoking cessation process relapse is often observed, with the person returning to a prior stage of the model (Petrocelli, 2002; Prochaska et al., 1992; Ruggiero, Tsoh, Everett, Fava, & Guise, 2000; Sutton, 2001).
2. High-resolution paleoclimatic data show that transitions from cold conditions in the North Atlantic region to warm ones often happened very quickly, i.e. on the decadal-scale or even faster (Taylor et al., 1997; Sev- eringhaus and Brook, 1999). The opposite transitions were slower, i.e. on the century-scale (Schulz, 2002), but nevertheless still faster that the characteristic life- time of the cold and warm intervals (which is on the cen- tennial to multi-millennial time scale, compare Fig. 1). The abruptness of the shifts from cold conditions to warm ones has commonly been interpreted as evidence for the existence of a critical threshold in the climate system that needs to be crossed in order to trigger a shift between stadial and interstadial conditions (Alley et al., 2003). Such a threshold could be provided by the THC (more precisely, by the process of deep-water formation): When warm and salty surface water from lower latitudes cools on its way to the North Atlantic, its density increases. If the density increase is large enough (i.e. if the surface gets denser than the deeper ocean water), surface water starts to sink. Otherwise, surface water can freeze instead of sinking. The onset of deep-water formation can thus hinder sea-ice forma- tion and facilitate sea-ice melting (due to the vertical heat transfer between the surface and the deeper ocean). A switch between two fundamentally different modes of deep-water formation can thus dramatically change sea ice cover and can cause large-scale climate shifts. Such nonlinear, threshold-like transitions between different modes of deep-water formation are at present consid- ered as the most likely explanation for DO events (Alley et al., 1999; Ganopolski and Rahmstorf, 2001).
Filling an important gap in this respect is Lewis et al’s  interdependence model of couple communal coping and behav- iour change. Although this model was not developed specifically for HIV, it offers a series of constructs mapping the mechanisms through which health behaviourchange among couples can be understood. Based on interdependence theory and communal coping perspectives, the model identifies interpersonal factors as key to transforming spouses’ motivation to avoid risk behaviours and to act cooperatively in adopting health-enhancing behaviourchange. Interdependence, a core concept in dyad-level social psychological theory, refers to the ways in which bilateral influence between interacting partners affects the outcomes (behaviour or experience) of one or both of them [31–33]. Transformation of motivation is a construct used to account for changes in couple members’ behaviour from self-centred to relationship-oriented and health-enhancing. It occurs where a partner interprets health events as meaningful for the relationship or their spouse, rather than simply for themselves. In other words, the motivation underlying behaviourchange is given a relational explanation, rather than being ascribed to internal, individual factors such as health beliefs or self-efficacy .
This study revisits the question posed by Jakeman and Hornberger (1993): “How much complexity is warranted in a rainfall-runoff model?”. However, where those authors fo- cused on the number of linear or parallel stores that best de- scribed the delayed release of water from a catchment, the current study focuses on the optimal functional form of the set of equations used to estimate what part of event precipi- tation is converted into storm runoff. The scope of this study is limited to hydrological models with process equations that operate on a daily time step and describe the behaviour of catchments rather than (segments of) hillslopes. Many such so-called ‘lumped’ models have been proposed (reviewed in e.g. Beven, 2004; Bl¨oschl, 2005; Maidment, 1992) and are widely used as a comparatively parsimonious, pragmatic ap- proach to estimating streamflow generation under historic, scenario or forecasted conditions.
Dengue fever is currently the most important arthropod-borne viral disease in Brazil. Mathematical modeling of disease dynamics is a very useful tool for the evaluation of control measures. To be used in decision-making, however, a mathematical model must be carefully parameterized and validated with epidemiological and entomological data. In this work, we developed a simple dengue model to answer three questions: (i) which parameters are worth pursuing in the field in order to develop a dengue transmission model for Brazilian cities; (ii) how vector density spatial heterogeneity influences control efforts; (iii) with a degree of uncertainty, what is the invasion potential of dengue virus type 4 (DEN-4) in Rio de Janeiro city. Our model consists of an expression for the basic reproductive number (R 0 ) that incorporates vector density spatial heterogeneity. To deal with the uncertainty regarding parameter values, we parameterized the model using a priori probability density functions covering a range of plausible values for each parameter. Using the Latin Hypercube Sampling procedure, values for the parameters were generated. We conclude that, even in the presence of vector spatial heterogeneity, the two most important entomological parameters to be estimated in the field are the mortality rate and the extrinsic incubation period. The spatial heterogeneity of the vector population increases the risk of epidemics and makes the control strategies more complex. At last, we conclude that Rio de Janeiro is at risk of a DEN-4 invasion. Finally, we stress the point that epidemiologists, mathematicians, and entomologists need to interact more to find better approaches to the measuring and interpretation of the transmission dynamics of arthropod-borne diseases.
Structural health monitoring of civil infrastructures has great practical importance for engineers, owners and stakeholders. Numerous researches have been carried out using long- term monitoring, such as the Rio–Niterói Bridge in Brazil, the former Z24 Bridge in Switzerland and the Millau Bridge in France. In fact, some structures are continuously monitored to supply dynamic measurements that can be used for the identification of structural problems such as the presence of cracks, excessive vibration or even to perform a quite extensive structural evaluation concerning its reliability and life cycle. The outputs of such an analysis, commonly entitled modal identification, are the so-called modal parameters, that is, natural frequencies, damping rations and mode shapes. Therefore, the development and validation of tools for the automatic modal identification during normal operation is fundamental, as the success of subsequent damage detection algorithms depends on the accuracy of the modal parameters’ estimates. This work proposes a novel methodology to perform, automatically, the modal identification based on the modes’ estimates data generated by any parametric system identification method. To assess the proposed methodology, several tests are conducted using numerically generated signals, as well as experimental data obtained from a simply supported beam and from a motorway bridge.
We perform sensitivity analysis on the parameters of a networked Susceptible-Exposed-Infectious-Recovered (SEIR) dis- ease model. The four disease states in the SEIR model (Figure 1) are used in describing within host disease progression and between host influenza transmission in the social network . To simplify the disease process, three parameters are used: transmissibility, incubation period distribution and infectious period distribution. The transmissibility is the diffusion intensity of a disease through a population. The transmissibility is usually measured using the reproductive number - the number of secondary cases for each primary case. The incubation period is the interval during which infected individuals cannot spread the disease and usually lasts between 1–4 days for seasonal influenza . The infectious period duration is the period during which infected individuals can transmit the disease to susceptible individuals. During typical seasonal influenza epidemics, infectious individuals can shed the virus a day before onset ‘‘through 5–10 days after illness onset’’ . In this model, the incubation and infectious periods are described using discrete probability distributions since individuals in the population tend to have different incubation and infectious period durations based on their age and health status. The initial (base case) parameters based on the natural history of seasonal influenza have been used in several studies [15,19,21,22]. The base case incubation period distribution is defined as follows: t E?I = 1,2, or 3 days with probability 0 :3,0:5 or 0:2, respectively. This implies an infected individual can have an incubation period duration of 1,2, or 3 days with probability 0:3,0:5 or 0:2. Likewise, the infectious period distribution is given by: t I ?R = 3,4,5 or 6 days with probability 0 :3,0:4,0:2 or 0 :1, respectively. To our knowledge, there is no defined standard for performing sensitivity analysis on parameters which are non-parameterized discrete distributions (in contrast to parameterized distributions like the Poisson or the binomial), especially not in individual-based models. Therefore, we use a combination of statistical methods and present a sensitivity analysis study which provides a framework for future studies.
Due to the scarcity of data that address gender differences in daily activities, there is a tendency to simplify the transport on offer on the basis of standard passengers/ commuters, as well using the same function distribution probability for both genders in transport modelling structures. Similarly, there is a gap in terms of systematic studies on locally expressed time use. In effect, perceiving human spatial behaviour, particularly restrictions and implications in the allocation of limited time between activities in space, is a powerful conceptual framework for understanding the activity of women and men in their everyday lives (Miller, 2005). Transport promotes higher efficiency in exchanging time for space when traveling to participate in activities in certain locations. According to Miller (2005), the constraints that limit travel include the ability to trade time for space in movement (e.g. access to public or private transport), the need to be with others at particular locations for specific time periods (e.g. meetings), thus limiting activities elsewhere, and some authorities’ ability to restrict physical presence in certain locations for a specified time (e.g. gated communities, shopping centres). These constraints are also differentiated according to gender perspective.
As expected, when we set all bond lengths and angles to exactly the values observed in the reference crystal structure, we could reconstruct the entire backbone with an RMSD close to zero. We did see an accumulation of rounding errors in longer proteins, but these rounding errors amounted to an RMSD of less than 0.01 ˚A even for a protein of over 600 residues. Hence they are negligible in practice. By contrast, reconstructions relying on just backbone dihedral angles performed poorly. We found that we had to adjust all backbone bond angles, inlcuding planar angles, to obtain accurate reconstructions. Bond lengths, on the other hand, could be left at their default values. Table 1 summarizes our findings for all 10 structures, and Fig. 1 shows the results of the four different methods of reconstruction for one example structure. The python script to generate these reconstructions is provided as part of Supplemental Information 1.
The Field is located South-West of Port Harcourt and has initial oil and free gas in place of about 1200 MMstb and 4730 Bscf respectively. Cumulative oil produced stands at about 200 MMstb from 50 wells completed on 22 reservoirs. Reservoir depths ranged between 7500 and 12800 ft in a stacked series of anticlinal or dip and fault bounded structures. The gravity of the oils varies between 20 o and 55 o API. Porosity range between 21 and 28 %, and average permeability is about 2000 mD.
A series of econometric models are used in the macro-economical analyses in the European Union states, and we can appreciate that these are already standard. We shall emphasize the theoretical analysis regarding the simple and multiple regression, and the linear correlation quotient.
The prevalence of A. compactum in the present study is low, with exception of the prevalence in E. trilineata and P. squamosissimus. Studies of the infection of A. compactum in P. squamosissimus showed a prevalence greater than 90% and a mean intensity of infection greater than 20 (KOHN et al., 1995; SANTOS et al., 2002, 2012; MACHADO et al., 2005; PAES et al., 2010a, b), whereas in the current study, the prevalence was 66.6% and the mean intensity of infection was 13.1±6.1. According to Karvonen et al. (2006), the infection dynamics of D. spathaceum in fish is related to the snail population variance. Voutilainen et al. (2009) found a positive correlation between Lymnaea stagnalis (Linnaeus, 1758) density (an intermediate host for Diplostomum spp. in Finland) and Diplostomum sp. prevalence in lakes and ponds in Finland. Additionally, Martins et al. (2002) and Santos et al. (2002, 2012) suggested that high infection rates of A. compactum are dependent on high temperatures. According to Berrie (1960), Diplostomum cercariae emerge in waters only at temperatures greater than 10 °C. Despite the high water temperature observed in the current study location (CARVALHO et al., 2012a), which may lead to a high intensity of infection, the values of water transparency, conductivity and chlorophyll a (Table 1), suggest a small quantity of available nutrients in the local aquatic ecosystem. This can limit the size of the snail populations (first intermediate hosts), leading to the low values observed for the prevalence, mean intensity of infection and mean abundance for the majority fish species analysed.
In the present paper we have tried to draw an analogy be- tween time and temperature for the simplest possible physical system without collective interaction of the objects constitut- ing the system, in order to show the difference in the defini- tion of time for unique objects and for whole systems. One should consider this case as a basic simplified example of the system where the discrete-continuum properties of time may be observed. Thus one should consider it as a rather artifi- cial case since there are no physical objects without field-like interactions between them.
tion zones, depths and velocities due to surcharged water. It is also examine for some of the important hydrodynamic behavior, such as boundary conditions, 2-D flood wave propagation, cell drying and wetting, and flow interaction with topographic obstacles. Since the inundation model is based on detailed spatial information i.e. land use and topography, its output will be more realistic to determine flood damage. The flood dam-
The Covid-19 pandemic has materialised in the context of growing health inequality in Europe (and beyond). While life expectancy had been rising overall before the financial crisis of 2007-08 and its aftermath, it has now begun to slow across the rich world (while inequalities within and between countries and regions remain marked) , something which has been linked to austerity policies. To take an example, for some of the period 2010–2020, women’s life expectancy declined in the most deprived neighbourhoods of the UK (and in some regions of the country for men), and overall socioeconomic inequalities increased . It is in this condition of health and socioeconomic inequality that the prevailing public health ethos that we describe here as being ‘together apart’ has taken hold.
The hadronization of the quark-gluon plasma (QGP) possibly produced in the early universe or in high-energy heavy-ion collisions may proceed in a number of differ- ent ways, depending on the nature of the QCD phase tran- sition. In the heavy-ion case, some results from CERN- SPS and BNL-RHIC suggest what has been called sudden hadronization  or explosive behavior [2, 3]. From the theoretical side, this phenomenon has been associated as- sociated with deep supercooling of the QGP followed by spinodal decomposition, and also to rapid changes in the ef- fective potential of QCD near the critical temperature, such as predicted, for instance, by the Polyakov loop model . Clearly, an understanding of the interplay between the typi- cal space and time scales of the expanding plasma is wel- come. Some attempts in this direction can be found in Refs. [5, 6, 7, 8, 9, 10, 11, 12, 13].