Migration for family and labour market outcomes in Sweden

Using information on stated motives for migrating among working-age individuals in the 2007 Swedish Motives for Migration survey(N = 1,852),we use multinomial logistic regression to examine whether and how moves for family reasons are linked to labour market outcomes in ways that differ from migration initiated for other motives, including more overtly labour-related factors. The results indicate that family-based migration is associated with worse labour market outcomes than migration for employment or other reasons. Additionally, family-motivated migrants with co-resident children are more likely to experience labour market deterioration than those without children. Among those who were unemployed before moving, those who reported family as a motive for moving were significantly more likely to be employed after the move. These results help us better assess how families and social networks impact economic outcomes-negatively in some circumstances and positively in others

Analytical study of non Gaussian fluctuations in a stochastic scheme of autocatalytic reactions

A stochastic model of autocatalytic chemical reactions is studied both numerically and analytically. The van Kampen perturbative scheme is implemented, beyond the second order approximation, so to capture the non Gaussianity traits as displayed by the simulations. The method is targeted to the characterization of the third moments of the distribution of fluctuations, originating from a system of four populations in mutual interaction. The theory predictions agree well with the simulations, pointing to the validity of the van Kampen expansion beyond the conventional Gaussian solution.Comment: 15 pages, 8 figures, submitted to Phys. Rev.

Modelling the Dynamics of an Aedes albopictus Population

We present a methodology for modelling population dynamics with formal means of computer science. This allows unambiguous description of systems and application of analysis tools such as simulators and model checkers. In particular, the dynamics of a population of Aedes albopictus (a species of mosquito) and its modelling with the Stochastic Calculus of Looping Sequences (Stochastic CLS) are considered. The use of Stochastic CLS to model population dynamics requires an extension which allows environmental events (such as changes in the temperature and rainfalls) to be taken into account. A simulator for the constructed model is developed via translation into the specification language Maude, and used to compare the dynamics obtained from the model with real data.Comment: In Proceedings AMCA-POP 2010, arXiv:1008.314

Efficient Parallel Statistical Model Checking of Biochemical Networks

We consider the problem of verifying stochastic models of biochemical networks against behavioral properties expressed in temporal logic terms. Exact probabilistic verification approaches such as, for example, CSL/PCTL model checking, are undermined by a huge computational demand which rule them out for most real case studies. Less demanding approaches, such as statistical model checking, estimate the likelihood that a property is satisfied by sampling executions out of the stochastic model. We propose a methodology for efficiently estimating the likelihood that a LTL property P holds of a stochastic model of a biochemical network. As with other statistical verification techniques, the methodology we propose uses a stochastic simulation algorithm for generating execution samples, however there are three key aspects that improve the efficiency: first, the sample generation is driven by on-the-fly verification of P which results in optimal overall simulation time. Second, the confidence interval estimation for the probability of P to hold is based on an efficient variant of the Wilson method which ensures a faster convergence. Third, the whole methodology is designed according to a parallel fashion and a prototype software tool has been implemented that performs the sampling/verification process in parallel over an HPC architecture

Consideration of within-patient diversity highlights transmission pathways and antimicrobial resistance gene variability in vancomycin-resistant Enterococcus faecium

BackgroundWGS is increasingly being applied to healthcare-associated vancomycin-resistant Enterococcus faecium (VREfm) outbreaks. Within-patient diversity could complicate transmission resolution if single colonies are sequenced from identified cases.ObjectivesDetermine the impact of within-patient diversity on transmission resolution of VREfm.Materials and methodsFourteen colonies were collected from VREfm positive rectal screens, single colonies were collected from clinical samples and Illumina WGS was performed. Two isolates were selected for Oxford Nanopore sequencing and hybrid genome assembly to generate lineage-specific reference genomes. Mapping to closely related references was used to identify genetic variations and closely related genomes. A transmission network was inferred for the entire genome set using Phyloscanner.Results and discussionIn total, 229 isolates from 11 patients were sequenced. Carriage of two or three sequence types was detected in 27% of patients. Presence of antimicrobial resistance genes and plasmids was variable within genomes from the same patient and sequence type. We identified two dominant sequence types (ST80 and ST1424), with two putative transmission clusters of two patients within ST80, and a single cluster of six patients within ST1424. We found transmission resolution was impaired using fewer than 14 colonies.ConclusionsPatients can carry multiple sequence types of VREfm, and even within related lineages the presence of mobile genetic elements and antimicrobial resistance genes can vary. VREfm within-patient diversity could be considered in future to aid accurate resolution of transmission networks

The interplay of intrinsic and extrinsic bounded noises in genetic networks

After being considered as a nuisance to be filtered out, it became recently clear that biochemical noise plays a complex role, often fully functional, for a genetic network. The influence of intrinsic and extrinsic noises on genetic networks has intensively been investigated in last ten years, though contributions on the co-presence of both are sparse. Extrinsic noise is usually modeled as an unbounded white or colored gaussian stochastic process, even though realistic stochastic perturbations are clearly bounded. In this paper we consider Gillespie-like stochastic models of nonlinear networks, i.e. the intrinsic noise, where the model jump rates are affected by colored bounded extrinsic noises synthesized by a suitable biochemical state-dependent Langevin system. These systems are described by a master equation, and a simulation algorithm to analyze them is derived. This new modeling paradigm should enlarge the class of systems amenable at modeling. We investigated the influence of both amplitude and autocorrelation time of a extrinsic Sine-Wiener noise on: $(i)$ the Michaelis-Menten approximation of noisy enzymatic reactions, which we show to be applicable also in co-presence of both intrinsic and extrinsic noise, $(ii)$ a model of enzymatic futile cycle and $(iii)$ a genetic toggle switch. In $(ii)$ and $(iii)$ we show that the presence of a bounded extrinsic noise induces qualitative modifications in the probability densities of the involved chemicals, where new modes emerge, thus suggesting the possibile functional role of bounded noises

Stochastic modelling of reaction-diffusion processes: algorithms for bimolecular reactions

Several stochastic simulation algorithms (SSAs) have been recently proposed for modelling reaction-diffusion processes in cellular and molecular biology. In this paper, two commonly used SSAs are studied. The first SSA is an on-lattice model described by the reaction-diffusion master equation. The second SSA is an off-lattice model based on the simulation of Brownian motion of individual molecules and their reactive collisions. In both cases, it is shown that the commonly used implementation of bimolecular reactions (i.e. the reactions of the form A + B -> C, or A + A -> C) might lead to incorrect results. Improvements of both SSAs are suggested which overcome the difficulties highlighted. In particular, a formula is presented for the smallest possible compartment size (lattice spacing) which can be correctly implemented in the first model. This implementation uses a new formula for the rate of bimolecular reactions per compartment (lattice site).Comment: 33 pages, submitted to Physical Biolog

Drug-related readmissions in older hospitalized adults: External validation and updating of OPERAM DRA prediction tool.

BACKGROUND Drug-related readmissions (DRAs) are defined as rehospitalizations with an adverse drug event as their main or significant contributory cause. DRAs represent a major adverse health burden for older patients. A prediction model which identified older hospitalized patients at high risk of a DRA <1 year was previously developed using the OPERAM trial cohort, a European cluster randomized controlled trial including older hospitalized patients with multimorbidity and polypharmacy. This study has performed external validation and updated the prediction model consequently. METHODS The MedBridge trial cohort (a multicenter cluster randomized crossover trial performed in Sweden) was used as a validation cohort. It consisted of 2516 hospitalized patients aged ≥65 years. Model performance was assessed by: (1) discriminative power, assessed by the C-statistic with a 95% confidence interval (CI); (2) calibration, assessed by visual examination of the calibration plot and use of the Hosmer-Lemeshow goodness-of-fit test; and (3) overall accuracy, assessed by the scaled Brier score. Several updating methods were carried out to improve model performance. RESULTS In total, 2516 older patients were included in the validation cohort, of whom 582 (23.1%) experienced a DRA <1 year. In the validation cohort, the original model showed a good overall accuracy (scaled Brier score 0.03), but discrimination was moderate (C-statistic 0.62 [95% CI 0.59-0.64]), and calibration showed underestimation of risks. In the final updated model, the predictor "cirrhosis with portal hypertension" was removed and "polypharmacy" was added. This improved the model's discriminative capability to a C-statistic of 0.64 (95% CI 0.59-0.70) and enhanced calibration plots. Overall accuracy remained good. CONCLUSIONS The updated OPERAM DRA prediction model may be a useful tool in clinical practice to estimate the risk of DRAs in older hospitalized patients subsequent to discharge. Our efforts lay the groundwork for the future development of models with even better performance