scholarly journals Gene-drive suppression of mosquito populations in large cages as a bridge between lab and field

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Andrew Hammond ◽  
Paola Pollegioni ◽  
Tania Persampieri ◽  
Ace North ◽  
Roxana Minuz ◽  
...  
Keyword(s):  
Approximate Bayesian Computation ◽  
Field Studies ◽  
Gene Drive ◽  
Suppressive Activity ◽  
Vector Ecology ◽  
Reproductive Capability ◽  
Age Structured ◽  
Approximate Bayesian ◽  
One Year ◽  
Gene Drives

AbstractCRISPR-based gene-drives targeting the gene doublesex in the malaria vector Anopheles gambiae effectively suppressed the reproductive capability of mosquito populations reared in small laboratory cages. To bridge the gap between laboratory and the field, this gene-drive technology must be challenged with vector ecology.Here we report the suppressive activity of the gene-drive in age-structured An. gambiae populations in large indoor cages that permit complex feeding and reproductive behaviours.The gene-drive element spreads rapidly through the populations, fully supresses the population within one year and without selecting for resistance to the gene drive. Approximate Bayesian computation allowed retrospective inference of life-history parameters from the large cages and a more accurate prediction of gene-drive behaviour under more ecologically-relevant settings.Generating data to bridge laboratory and field studies for invasive technologies is challenging. Our study represents a paradigm for the stepwise and sound development of vector control tools based on gene-drive.

scholarly journals The COVID-19 epidemic in the Czech Republic: retrospective analysis of measures (not) implemented during the spring first wave

2020 ◽  
Author(s):  
Luděk Berec ◽  
Jan Smyčka ◽  
René Levínský ◽  
Eva Hromádková ◽  
Michal Šoltés ◽  
...  
Keyword(s):  
Czech Republic ◽  
Approximate Bayesian Computation ◽  
Protective Measures ◽  
The Czech Republic ◽  
Alternative Intervention ◽  
Movement Data ◽  
Age Structured ◽  
Approximate Bayesian ◽  
Age Structured Model ◽  
Second Wave

The Czech Republic (or Czechia) is facing the second wave of COVID-19 epidemic, with the rate of growth in the number of confirmed cases (among) the highest in Europe. Learning from the spring first wave, when many countries implemented interventions that effectively stopped national economics (i.e., a form of lockdown), political representations are now unwilling to do that again, at least until really necessary. Therefore, it is necessary to look back and assess efficiency of each of the first wave restrictions, so that interventions can now be more finely tuned. We develop an age-structured model of COVID-19 epidemic, distinguish several types of contact, and divide the population into 206 counties. We calibrate the model by sociological and population movement data and use it to analyze the first wave of COVID-19 epidemic in Czechia, through assessing effects of applied restrictions as well as exploring functionality of alternative intervention schemes that were discussed later. To harness various sources of uncertainty in our input data, we apply the Approximate Bayesian Computation framework. We found that (1) personal protective measures as face masks and increased hygiene are more effective than reducing contacts, (2) delaying the lockdown by four days led to twice more confirmed cases, (3) implementing personal protection and effective testing as early as possible is a priority, and (4) tracing and quarantine or just local lockdowns can effectively compensate for any global lockdown if the numbers of confirmed cases not exceedingly high.

General practitioners and clinical pharmacology

1989 ◽  
Vol 4 (4) ◽  
pp. 241-244
Author(s):  
P. Lemoine
Keyword(s):  
Clinical Pharmacology ◽  
General Practitioners ◽  
Field Studies ◽  
Discussion Group ◽  
Group Method ◽  
Study Group ◽  
Test Drug ◽  
Monthly Basis ◽  
One Year ◽  
The One

SummaryIt is difficult to undertake field studies with non marketed psychotropic drugs because of two apparently contradictory conditions : on the one hand, the methodology has to be rigorously controlled, and on the other hand, such studies have to be carried out in their future environment by general practitioners (GPs). Bearing in mind the lack of training and experience regarding this kind of approach, the author adopted a discussion group method according to the techniques developed by M. Balint. The study group comprised five GPs, a clinical pharmacology expert and a doctor from the pharmaceutical laboratory which had developed the test drug. These persons met on a monthly basis over a one year period. In the present paper, the author indicates the benefits of such a methodology, based on six years’ experience and several trials, with special emphasis placed on the pedagogical aspects.

scholarly journals Weighted approximate Bayesian computation via Sanov’s theorem

2021 ◽  
Author(s):  
Cecilia Viscardi ◽  
Michele Boreale ◽  
Fabio Corradi
Keyword(s):  
Large Deviations ◽  
Posterior Distribution ◽  
Approximate Bayesian Computation ◽  
Bayesian Computation ◽  
Information Theoretic ◽  
Discrete Random Variables ◽  
Positive Weights ◽  
Approximate Bayesian ◽  
Information Theoretic Method ◽  
Computational Resources

AbstractWe consider the problem of sample degeneracy in Approximate Bayesian Computation. It arises when proposed values of the parameters, once given as input to the generative model, rarely lead to simulations resembling the observed data and are hence discarded. Such “poor” parameter proposals do not contribute at all to the representation of the parameter’s posterior distribution. This leads to a very large number of required simulations and/or a waste of computational resources, as well as to distortions in the computed posterior distribution. To mitigate this problem, we propose an algorithm, referred to as the Large Deviations Weighted Approximate Bayesian Computation algorithm, where, via Sanov’s Theorem, strictly positive weights are computed for all proposed parameters, thus avoiding the rejection step altogether. In order to derive a computable asymptotic approximation from Sanov’s result, we adopt the information theoretic “method of types” formulation of the method of Large Deviations, thus restricting our attention to models for i.i.d. discrete random variables. Finally, we experimentally evaluate our method through a proof-of-concept implementation.

Flow parameter estimation using laser absorption spectroscopy and approximate Bayesian computation

2021 ◽  
Vol 62 (2) ◽  
Author(s):  
Jason D. Christopher ◽  
Olga A. Doronina ◽  
Dan Petrykowski ◽  
Torrey R. S. Hayden ◽  
Caelan Lapointe ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Absorption Spectroscopy ◽  
Approximate Bayesian Computation ◽  
Flow Parameter ◽  
Bayesian Computation ◽  
Laser Absorption Spectroscopy ◽  
Laser Absorption ◽  
Approximate Bayesian

scholarly journals Approximate Bayesian Computation for Discrete Spaces

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 312
Author(s):  
Ilze A. Auzina ◽  
Jakub M. Tomczak
Keyword(s):  
Approximate Bayesian Computation ◽  
Likelihood Function ◽  
Real Life ◽  
Random Variables ◽  
Bayesian Computation ◽  
Inference Problem ◽  
Markov Kernel ◽  
Inference Problems ◽  
Binary Neural Network ◽  
Approximate Bayesian

Many real-life processes are black-box problems, i.e., the internal workings are inaccessible or a closed-form mathematical expression of the likelihood function cannot be defined. For continuous random variables, likelihood-free inference problems can be solved via Approximate Bayesian Computation (ABC). However, an optimal alternative for discrete random variables is yet to be formulated. Here, we aim to fill this research gap. We propose an adjusted population-based MCMC ABC method by re-defining the standard ABC parameters to discrete ones and by introducing a novel Markov kernel that is inspired by differential evolution. We first assess the proposed Markov kernel on a likelihood-based inference problem, namely discovering the underlying diseases based on a QMR-DTnetwork and, subsequently, the entire method on three likelihood-free inference problems: (i) the QMR-DT network with the unknown likelihood function, (ii) the learning binary neural network, and (iii) neural architecture search. The obtained results indicate the high potential of the proposed framework and the superiority of the new Markov kernel.

scholarly journals Complex genetic admixture histories reconstructed with Approximate Bayesian Computation

2021 ◽  
Author(s):  
Cesar A. Fortes‐Lima ◽  
Romain Laurent ◽  
Valentin Thouzeau ◽  
Bruno Toupance ◽  
Paul Verdu
Keyword(s):  
Approximate Bayesian Computation ◽  
Genetic Admixture ◽  
Bayesian Computation ◽  
Approximate Bayesian

scholarly journals Novel combination of CRISPR-based gene drives eliminates resistance and localises spread

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nicky R. Faber ◽  
Gus R. McFarlane ◽  
R. Chris Gaynor ◽  
Ivan Pocrnic ◽  
C. Bruce A. Whitelaw ◽  
...  
Keyword(s):  
Driving Forces ◽  
Spatial Models ◽  
Cost Effective ◽  
Biodiversity Loss ◽  
Invasive Pest ◽  
Economic Losses ◽  
Sciurus Carolinensis ◽  
Gene Drive ◽  
Grey Squirrel ◽  
Gene Drives

AbstractInvasive species are among the major driving forces behind biodiversity loss. Gene drive technology may offer a humane, efficient and cost-effective method of control. For safe and effective deployment it is vital that a gene drive is both self-limiting and can overcome evolutionary resistance. We present HD-ClvR in this modelling study, a novel combination of CRISPR-based gene drives that eliminates resistance and localises spread. As a case study, we model HD-ClvR in the grey squirrel (Sciurus carolinensis), which is an invasive pest in the UK and responsible for both biodiversity and economic losses. HD-ClvR combats resistance allele formation by combining a homing gene drive with a cleave-and-rescue gene drive. The inclusion of a self-limiting daisyfield gene drive allows for controllable localisation based on animal supplementation. We use both randomly mating and spatial models to simulate this strategy. Our findings show that HD-ClvR could effectively control a targeted grey squirrel population, with little risk to other populations. HD-ClvR offers an efficient, self-limiting and controllable gene drive for managing invasive pests.

scholarly journals Cophylogeny Reconstruction via an Approximate Bayesian Computation

2014 ◽  
Vol 64 (3) ◽  
pp. 416-431 ◽  
Author(s):  
C. Baudet ◽  
B. Donati ◽  
B. Sinaimeri ◽  
P. Crescenzi ◽  
C. Gautier ◽  
...  
Keyword(s):  
Approximate Bayesian Computation ◽  
Bayesian Computation ◽  
Approximate Bayesian
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