Reduction of root fillet stress by alternative root fillet profileeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Reduction of root fillet stress by alternative root fillet profileeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Front-end Tooling - Dicas de ferramentas para melhorar a produtividadeHerson Leite
Algumas dicas sobre tools ( ferramentas ) para o desenvolvimento de front-end, falo um pouco sobre plugins para o Atom e task runners como Gulp, Grunt e NPM Scripts.
https://youtu.be/nVpKO0OYLBM
Evento: DeepDay 2016
Local: DeVry Facimp ( Faculdade de Imperatriz )
A hybrid bacterial foraging and modified particle swarm optimization for mode...IJECEIAES
This paper study the model reduction procedures used for the reduction of large-scale dynamic models into a smaller one through some sort of differential and algebraic equations. A confirmed relevance between these two models exists, and it shows same characteristics under study. These reduction procedures are generally utilized for mitigating computational complexity, facilitating system analysis, and thence reducing time and costs. This paper comes out with a study showing the impact of the consolidation between the Bacterial-Foraging (BF) and Modified particle swarm optimization (MPSO) for the reduced order model (ROM). The proposed hybrid algorithm (BF-MPSO) is comprehensively compared with the BF and MPSO algorithms; a comparison is also made with selected existing techniques.
An Ear Recognition Method Based on Rotation Invariant Transformed DCT IJECEIAES
Human recognition systems have gained great importance recently in a wide range of applications like access, control, criminal investigation and border security. Ear is an emerging biometric which has rich and stable structure and can potentially be implemented reliably and cost efficiently. Thus human ear recognition has been researched widely and made greatly progress. High recognition rates which are reported in most existing methods can be reached only under closely controlled conditions. Actually a slight amount of rotation and translation which is inescapable would be injurious for system performance. In this paper, a method that uses a transformed type of DCT is implemented to extract meaningful features from ear images. This algorithm is quite robust to ear rotation, translation and illumination. The proposed method is experimented on two popular databases, i.e. USTB II and IIT Delhi II, which achieves significant improvement in the performance in comparison to other methods with good efficiency based on LBP, DSIFT and Gabor. Also because of considering only important coefficients, this method is faster compared to other methods.
A Statistical Approach to Optimize Parameters for Electrodeposition of Indium...Arkansas State University
A Statistical Approach to Optimize Parameters for Electrodeposition of Indium (III) Sulfide Films, Potential Low-Hazard Buffer Layers for Photovoltaic Applications
RESEARCH ON WIND ENERGY INVESTMENT DECISION MAKING: A CASE STUDY IN JILINWireilla
As a clean renewable energy, wind energy has been focused on an alternative source of fossil fuels. Evaluating wind energy investment projects including multiple attributes is a multi-attribute decision making (MADM) problem. Due to the strong ability in expressing the fuzziness and uncertainty, this paper uses intuitionistic trapezoidal fuzzy numbers to describe the evaluation information from decision-makers. Considering the interaction among attributes and the influence of subjective risk, this paper proposes an intuitionistic trapezoidal fuzzy aggregation operator based on Choquet integral and prospect theory. We use the new operator to aggregate the prospect value functions. And then we generate a grey relationprojection pursuit dynamic cluster method to rank the alternatives. At last, an illustrative example has been taken to demonstrate the validity and feasibility of the proposed method.
STUDIES ON INTEGRATED BIO-HYDROGEN PRODUCTION PROCESS-EXPERIMENTAL AND MODELINGArghya_D
In the project “Studies on integrated biohydrogen production process-Experimental and Modeling”,a co-culture (mixture of two microorganisms in a single reactor) study of a dark fermentative and photofermentative microorganism was done to assess its hydrogen production performance. For modeling purpose, Artificial Neural Network and Genetic Algorithm has been used as a stochastic technique. The optimized data from batch study was successfully used to run a photobioreactor in continuous mode. A mechanistic model was developed for a continuous co-culture setup using data from literature and solved using MATLAB.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Front-end Tooling - Dicas de ferramentas para melhorar a produtividadeHerson Leite
Algumas dicas sobre tools ( ferramentas ) para o desenvolvimento de front-end, falo um pouco sobre plugins para o Atom e task runners como Gulp, Grunt e NPM Scripts.
https://youtu.be/nVpKO0OYLBM
Evento: DeepDay 2016
Local: DeVry Facimp ( Faculdade de Imperatriz )
A hybrid bacterial foraging and modified particle swarm optimization for mode...IJECEIAES
This paper study the model reduction procedures used for the reduction of large-scale dynamic models into a smaller one through some sort of differential and algebraic equations. A confirmed relevance between these two models exists, and it shows same characteristics under study. These reduction procedures are generally utilized for mitigating computational complexity, facilitating system analysis, and thence reducing time and costs. This paper comes out with a study showing the impact of the consolidation between the Bacterial-Foraging (BF) and Modified particle swarm optimization (MPSO) for the reduced order model (ROM). The proposed hybrid algorithm (BF-MPSO) is comprehensively compared with the BF and MPSO algorithms; a comparison is also made with selected existing techniques.
An Ear Recognition Method Based on Rotation Invariant Transformed DCT IJECEIAES
Human recognition systems have gained great importance recently in a wide range of applications like access, control, criminal investigation and border security. Ear is an emerging biometric which has rich and stable structure and can potentially be implemented reliably and cost efficiently. Thus human ear recognition has been researched widely and made greatly progress. High recognition rates which are reported in most existing methods can be reached only under closely controlled conditions. Actually a slight amount of rotation and translation which is inescapable would be injurious for system performance. In this paper, a method that uses a transformed type of DCT is implemented to extract meaningful features from ear images. This algorithm is quite robust to ear rotation, translation and illumination. The proposed method is experimented on two popular databases, i.e. USTB II and IIT Delhi II, which achieves significant improvement in the performance in comparison to other methods with good efficiency based on LBP, DSIFT and Gabor. Also because of considering only important coefficients, this method is faster compared to other methods.
A Statistical Approach to Optimize Parameters for Electrodeposition of Indium...Arkansas State University
A Statistical Approach to Optimize Parameters for Electrodeposition of Indium (III) Sulfide Films, Potential Low-Hazard Buffer Layers for Photovoltaic Applications
RESEARCH ON WIND ENERGY INVESTMENT DECISION MAKING: A CASE STUDY IN JILINWireilla
As a clean renewable energy, wind energy has been focused on an alternative source of fossil fuels. Evaluating wind energy investment projects including multiple attributes is a multi-attribute decision making (MADM) problem. Due to the strong ability in expressing the fuzziness and uncertainty, this paper uses intuitionistic trapezoidal fuzzy numbers to describe the evaluation information from decision-makers. Considering the interaction among attributes and the influence of subjective risk, this paper proposes an intuitionistic trapezoidal fuzzy aggregation operator based on Choquet integral and prospect theory. We use the new operator to aggregate the prospect value functions. And then we generate a grey relationprojection pursuit dynamic cluster method to rank the alternatives. At last, an illustrative example has been taken to demonstrate the validity and feasibility of the proposed method.
STUDIES ON INTEGRATED BIO-HYDROGEN PRODUCTION PROCESS-EXPERIMENTAL AND MODELINGArghya_D
In the project “Studies on integrated biohydrogen production process-Experimental and Modeling”,a co-culture (mixture of two microorganisms in a single reactor) study of a dark fermentative and photofermentative microorganism was done to assess its hydrogen production performance. For modeling purpose, Artificial Neural Network and Genetic Algorithm has been used as a stochastic technique. The optimized data from batch study was successfully used to run a photobioreactor in continuous mode. A mechanistic model was developed for a continuous co-culture setup using data from literature and solved using MATLAB.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
MFBLP Method Forecast for Regional Load Demand SystemCSCJournals
Load forecast plays an important role in planning and operation of a power system. The accuracy of the forecast value is necessary for economically efficient operation and also for effective control. This paper describes a method of modified forward backward linear predictor (MFBLP) for solving the regional load demand of New South Wales (NSW), Australia. The method is designed and simulated based on the actual load data of New South Wales, Australia. The accuracy of discussed method is obtained and comparison with previous methods is also reported.
A genetic algorithm for the optimal design of a multistage amplifier IJECEIAES
The optimal sizing of analog circuits is one of the most complicated processes, because of the number of variables taken into, to the number of required objectives to be optimized and to the constraint functions restrictions. The aim is to automate this activity in order to accelerate the circuits design and sizing. In this paper, we deal with the optimization of the three stage bipolar transistor amplifier performances namely the voltage gain (AV), the input impedance (ZIN), the output impedance (ZOUT), the power consumption (P) and the low and the high cutoff frequency (FL,FH), through the Genetic Algorithm (GA). The presented optimization problem is of multi-dimensional parameters, and the trade-off of all parameters. In fact, the passive components (Resistors and Capacitors) are selected from manufactured constant values (E12, E24, E48, E96, E192) for the purpose of reduce the cost of design; also, the intrinsic parameters of transistors (hybrid parameters and the junction capacitances) are considered variables in order not to be limited in design. SPICE simulation is used to validate the obtained result/performances.
Backscatter Working Group Software Inter-comparison ProjectRequesting and Co...Giuseppe Masetti
Backscatter mosaics of the seafloor are now routinely produced from multibeam sonar data, and used in a wide range of marine applications. However, significant differences (up to 5 dB) have been observed between the levels of mosaics produced by different software processing a same dataset. This is a major detriment to several possible uses of backscatter mosaics, including quantitative analysis, monitoring seafloor change over time, and combining mosaics. A recently concluded international Backscatter Working Group (BSWG) identified this issue and recommended that “to check the consistency of the processing results provided by various software suites, initiatives promoting comparative tests on common data sets should be encouraged […]”. However, backscatter data processing is a complex (and often proprietary) sequence of steps, so that simply comparing end-results between software does not provide much information as to the root cause of the differences between results.
In order to pinpoint the source(s) of inconsistency between software, it is necessary to understand at which stage(s) of the data processing chain do the differences become substantial. We have invited willing software developers to discuss this framework and collectively adopt a list of intermediate processing steps. We provided a small dataset consisting of various seafloor types surveyed with the same multibeam sonar system, using constant acquisition settings and sea conditions, and have the software developers generate these intermediate processing results, to be eventually compared. If the experiment proves fruitful, we may extend it to more datasets, software and intermediate results. Eventually, software developers may consider making the results from intermediate stages a standard output as well as adhering to a consistent terminology, as advocated by Schimel et al. (2018). To date, the developers of four software (Sonarscope, QPS FMGT, CARIS SIPS, MB Process) have expressed their interest in collaborating on this project.
Remote sensing data from satellite with high temporal resolution typically have lower spatial resolution, with one pixel often spanning over a square kilometer. The signal recorded by such satellite at a pixel is typically a mixture of reflectance from different types of land covers within
the pixel, resulting in a mixed pixel. In this talk we introduce a couple of parametric and nonparametric statistical approaches to deal with the un-mixing problem which integrate information from multiple sources, and present some preliminary results applying the methodology to data
from the SMOS (Soil Moisture and Ocean Salinity) mission and the OCO-2 (Orbiting Carbon Observatory 2) mission, which motivated this research.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
De-mystifying Zero to One: Design Informed Techniques for Greenfield Innovati...
Ae abc general_grenoble_29_juin_25_30_min
1. ABC (Approximate Bayesian Computation) methods to
make inference about population history and processes
from molecular data: principles and applications
Les méthodes ABC (Approximate Bayesian Computation)
pour inférer des histoires et processus populationnels à
partir de données moléculaires: principes et applications
Arnaud Estoup
Centre de Biologie pour la Gestion des Populations (CBGP, INRA)
estoup@supagro.inra.fr
Journée “biodiversité et bioinformatique”
Grenoble - 29 juin 2011
2. General context
Natural populations have complex demographic histories: their sizes and
ranges change over time, leading to fission and fusion processes that
leave signatures on their genetic composition. One ‘promise’ of biology is
that molecular data will help us uncover the complex demographic and
adaptive processes that have acted on natural populations
(Czilléry et al. 2010, TREE)
3. « Make inference about population processes / histories using molecular data »
: what does it means ?
Collect samples Produce molecular data in the lab
in Natura (microsatellites, DNA sequences,…)
4. Title line:DIYABC example of microsatellite and mtDNA data file
locus MS6 <A>
locus MS7 <A>
locus COI <M>
POP
BU , 165200 312312 <[CATAAAGATATTGGAACTCTATATTTTATTTTTAGAATTTGAGCAGGTTTGGTAGGAACTGGGATAAGTGTCTTAATTC
BU , 200200 312312 <[CATAAAGATATTGGAACTCTATATTTTATTTTTAGAATTTGAGCAGGTTTGGTAGGAACTGGGATAAGTGTCTTAATTA
BU , 149200 306312 <[CATAAAGATATTGGAACTCTATATTTTATTTTTAGAATTTGAGCAGGTTTGGTAGGAACTGGGATAAGTGTCTTAATTC
BU , 200200 312312 <[CATAAAGATATTGGAACTCTATATTTTATTTTTAGAATTTGAGCAGGTTTGGTAGGAACTGGGATAAGTGTCTTAATTC
BU , 200200 312312 <[CATAAAGATATTGGAACTCTATATTTTATTTTTAGAATTTGAGCAGGTCTGGTAGGAACTGGGATAAGTGTCTTAATTC
BU , 200200 312312 <[CATAAAGATATTGGAACTCTATATTTTATTTTTAGAATTTGAGCAGGTTTGGTAGGAACTGGGATAAGTGTCTTAATTC
POP
MA , 200205 312315 <[CATAAAGATATTGGAACTCTATATTTTATTTTTAGAATTTGAGCAGGTTTGGTAGGAACTGGGATAAGTGTCTTAATTC
MA , 200218 312315 <[CATAAAGATATTGGAACTCTATATTTTATTTTTAGAATTTGAGCAGGTTTGGTAGGAACTGGGATAAGTGTCTTAATTC
MA , 200205 312312 <[CATAAAGATATTGGAACTCTATATTTTATTTTTAGAATTTGAGCAGGTTTGGTAGGAACTGGGATAAGTGTCTTAATTC
MA , 200205 312312 <[CATAAAGATATTGGAACTCTATATTTTATTTTTAGAATTTGAGCAGGTTTGGTAGGAACTGGGATAAGTGTCTTAATTC
MA , 149200 312312 <[CATAAAGATATTGGGACTCTATATTTTATTTTTAGAATTTGAGCAGGTTTGGTAGGAACTGGGATAAGTGTCTTAATTC
MA , 200200 312315 <[CATAAAGATATTGGAACTCTATATTTTATTTTTAGAATTTGAGCAGGTTTGGTAGGAACTGGGATAAGTGTCTTAATTC
MA , 149200 312312 <[CATAAAGATATTGGAACTCTATATTTTATTTTTAGAATTTGAGCAGGTTTGGTAGGAACTGGGATAAGTGTCTTAATTC
MA , 149200 312315 <[CATAAAGATATTGGGACTCTATATTTTATTTTTAGAATTTGAGCAGGTTTGGTAGGAACTGGGATAAGTGTCTTAATTC
MA , 200200 312315 <[CATAAAGATATTGGGACTCTATATTTTATTTTTAGAATTTGAGCAGGTTTGGTAGGAACTGGGATAAGTGTCTTAATTC
POP
BF , 200200 312312 <[CATAAAGATATTGGGACTCTATATTTTATTTTTAGAATTTGAGCAGGTTTGGTAGGAACTGGGATAAGTGTCTTAATTC
BF , 200200 306315 <[CATAAAGATATTGGGACTCTATATTTTATTTTTAGAATTTGAGCAGGTTTGGTAGGAACTGGGATAAGTGTCTTAATTC
BF , 149200 312312 <[]>
BF , 200200 312312 <[CATAAAGATATTGGGACTCTATATTTTATTTTTAGAATTTGAGCAGGTTTGGTAGGAACTGGGATAAGTGTCTTAATTC
BF , 200200 312315 <[CATAAAGATATTGGGACTCTATATTTTATTTTTAGAATTTGAGCAGGTTTGGTAGGAACTGGGATAAGTGTCTTAATTC
BF , 200200 312312 <[CATAAAGATATTGGGACTCTATATTTTATTTTTAGAATTTGAGCAGGTTTGGTAGGAACTGGGATAAGTGTCTTAATTC
BF , 200205 312312 <[CATAAAGATATTGGGACTCTATATTTTATTTTTAGAATTTGAGCGGGTTTGGTAGGAACTGGGATAAGTGTCTTAATTC
BF , 200205 312312 <[CATAAAGATATTGGGACTCTATATTTTATTTTTAGAATTTGAGCAGGTTTGGTAGGAACTGGGATAAGTGTCTTAATTC
5. (Non)Independent evolutionary Routes of introduction of
origin of populations adapted to invasive species
new environments
6. Inference: compute likelihood (molecular_data / parameters / model)
(e.g. IS if gene tree) and explore parameter space (e.g. MCMC)
Complex models (scenarios): difficult to compute likelihood + difficult to
explore parameter space
7. Approximate Bayesian Computation (ABC) circumvents
these problems bypass exact likelihood calculations by
using summary statistics and simulations.
Summary statistics are values calculated from the data to
represent the maximum amount of information in the simplest
possible form.
e.g. nbre alleles, nbre of haplotypes, Heterozygosity,
allele size variance, Fst, genetic distance,…
8. Historical background in population genetics – Key papers
Tavaré S, Balding DJ, Griffiths RC, Donnely P (1997) Inferring coalescence times
from DNA sequence data. Genetics 145, 505-518.
Pritchard JK, Seielstad MT, Prez-Lezaum A, Feldman MW (1999) Population growth
of human Y chromosomes, a study of Y chromosome microsatellites. Molecular
Biology and Evolution 16, 1791-1798.
Beaumont MA, Zhang W, Balding DJ (2002) Approximate Bayesian computation in
population genetics. Genetics 162, 2025-2035.
9. “Standard/conventional ABC” Beaumont MA, Zhang W, Balding DJ
(2002) Approximate Bayesian computation in population genetics. Genetics
162, 2025-2035.
Several studies studies have now shown that parameter posterior
distributions inferred by ABC are similar to those provided by full-likelihood
approaches (see e.g. Beaumont et al. 2009; Bortot et al. 2007; Leuenberger &
Wegmann 2009; Marjoram et al. 2003),
The approach is still in its infancy and continues to evolve, and to be
improved.
10. More and more sophisticated developments on ABC
Better exploration of the parameter space
ABC-MCMC (Majoram et al. 2003; Wegmann et al. 2009; Leuenberger &
Wegmann 2010)
ABC-PMC (peculiar Monte Carlo schemes ABC with Population Monte Carlo;
Beaumont et al. 2009)
Choice of statistics to summarise datasets (Joyce & Marjoram 2008; Sousa et al.
2009; Nunez & Balding 2010) and how they can be combined e.g. extraction of a
limited number of orthogonal components (Wegmann et al. 2009) or non-linear
feed-forward neural network (Blum & François, 2009)
More efficient conditional density estimation (Blum & Francois 2009; Wegmann
et al. 2010).
Difficult to implement + in practice modest avantages over “Standard
ABC” at least for a sufficiently large number of simulations
12. -GENERAL PRINCIPLE OF ABC –
ABC is intuitively very easy: millions of simulations (e.g. gene
genealogies) are simulated assuming different parameter
values (drawn into priors) and under different models and the
simulations that produce genetic variation patterns (simulated
summary statistics) close to the observed data (observed
summary statistics) are retained and analysed in detail
14. ABC a la Beaumont et al. (2002)
Basic rejection algorithm: acceptation rate decrease (quickly) with nbre of SS and
parameters algorithm inefficient for complex models
New acceptation / distance criterion to increase the tolerance threshold multi-
statistique Euclidean distance (statistics normalized)
( SS SOi )
2
i
i
ET ( SS i )
Correction step based on regression methods
15. COESTIMATION OF POSTERIOR DISTRIBUTIONS OF PARAMETERS UNDER
A GIVEN SCENARIO (MODEL)
Step 1: Simulate genetic data sets under a scenario (parameter values drawn into
priors) = 106 itérations (i.e. 106 data sets)
106 [values of parameters x summary stat x Euclidian distance]
COALESCENCE
Step 2: Sort data sets according to their Euclidian distances
Step 3: Local LINEAR regression (weighted) on the 1% (10000) “best simulations”
= those with the smallest Euclidian distances
correction of accepted parameter values
17. COMPARAISON OF DIFFERENT SCENARIOS (MODELS): estimation of relative
probabilities for each scenario s
Step1: simulation of genetic data sets (draw from priors)
scenario 1 = 106 data sets
scenario 2 = 106 data sets
COALESCENCE
…
scenario 8 = 106 data sets
8x106 [scenario identifier x Euclidian distances]
Step 2: sort data sets according to their Euclidian distances
Step 3: The scenario number is considered as a discrete parameter do Local
LOGISTIC regression on the 1% (80000) “best simulations” (i.e. with smallest Euclidian
distances) to estimate probabilities of each scenario
18. Local linear regression can be used for
the estimation of a parameter φ
(Beaumont et al. 2002)
Local LOGISTIC regression can be used
for the estimation of model probability
(here, P(M2))
(Fagundes et al. 2007; Cornuet et al. 2008)
19. ABC in practice:
Not as easy/simple as it seems to be ?
1/ SIMULATE MANY DATA SETS (model(s), priors, summary statistics)
2/ KEEP THE « BEST » SIMULATIONS
3/ MAKE « CORRECTIONS » (regression methods) ON RETAINED
SIMULATIONS
22. An example of (used-friendly) integrated software
for inferring population history with ABC: DIYABC
Cornuet J-M, Santos F, Robert PC, Marin J-M, Balding DJ, Guillemaud T,
Estoup A (2008) Inferring population history with DIYABC: a user-friendly
approach to Approximate Bayesian Computation. Bioinformatics, 24, 2713-2719.
Cornuet JM, Ravigné V, Estoup A (2010) Inference on population history and
model checking using DNA sequence and microsatellite data with the software
DIYABC (v1.0). BMC bioinformatics, 11, 401doi:10.1186/1471-2105-11-401.
The software DIYABC (V.0 and V1.) and the companion papers are both freely
available at http://www1.montpellier.inra.fr/CBGP/diyabc.
23. In 2008 we wrote (Cornuet et al. 2008) “ In its current state, the ABC approach
remains inaccessible to most biologists because there is not yet a simple software
solution”……less and less true
Bertorelle et al. Mol Ecol 2010
24. DIYABC: Inferences on complex scenarios
Historical events = population divergence, admixture, effective size
fluctuation
Large sample sizes (populations, individuals, loci)
Diploid or haploid individuals
Different sampling times
Microsatellite and/or sequence data, no gene flow between populations
Program:
- writen in Delphi
- running under a 32-bit Windows operating system
- multi-processor
- user-friendly graphical interface
25. Examples of (complex) scenarios that can be formalised and treated with
DIYABC: looking for the source of an invasive population case of the ladybird
Harmonia axyridis
N1 N2 N3 N4 N5
0 sample 1
0 sample 2
0 sample 3
0 sample 4
50 sample 5
t1-DB1 VarNe 1 NF1
t1 split 1 5 3 ra
t2-DB2 VarNe 2 NF2
t2 VarNe 2 Nbc2
t2lb merge 4 2
t3-DB3 VarNe 3 NF3
t3 VarNe 3 Nbc3
t3lb merge 4 3
t4 merge 4 5
31. Evaluate confidence in scenario choice (4)
Define a set of compared scenarios (e.g. S1, S2, S3)
Define a target scenario (e.g. S1)
Type I error on the target scenario (e.g. S1): proportion of cases in which the target
scenario is not chosen while it is “true”
Simulation of « pseudo-observed data sets » under the target scenario - drawing
parameter values into (prior) distributions
Compute probabilities of each scenario S1, S2, S3 count p(S1) is not the highest
Type II error on the target scenario (e.g. S1): proportion of cases in which the target
scenario is chosen while it is “wrong”
Simulation of « pseudo-observed data sets » under another scenario than the target one
(e.g. S2) and drawing parameter values into (prior) distributions
Compute probabilities of each scenario S1, S2, S3 p(S1) > count p(S1) is the highest
32. Compute bias and precision on parameter estimation (7)
Define a target scenario (e.g. S1)
Simulation of « pseudo-observed data sets » under the target scenario - drawing
parameter values into (prior) distributions (« true parameter values »)
Compute posterior distributions for each parameter under the target scenario
True and estimated parameter values Bias, RMSE,…for each parameter
34. Scenario 1 Scenario 2 Scenario 3
Scenarios to illustrate model-posterior checking
The three presented scenarios are often compared when making ABC inferences on the routes of introduction of invasive
species. S is the source population in the native area, and U, the unsampled population in the introduced area that is
actually the source of populations 1 and 2 in the scenario 3. The stars indicate the bottleneck events occurring in the first
few generations following introductions. We here considered that the dates of first observation were well known so that
divergence times could be fixed at 5, 10, 15 and 20 generations for t1, t2, t3 and t4, respectively. The datasets consisted of
simulated genotypes at 20 statistically independent microsatellite loci obtained from a sample of diploid individuals collected
from the invasive and source populations (30 individuals per population). The “pseudo-observed” test data set analyzed
here to illustrate model-posterior checking was simulated under scenario 3 with an effective population size (NS) of 10,000
diploid individuals in all populations except during the bottleneck events corresponding to an effective population size (NFi)
of 10 diploid individuals for 5 generations. Prior distributions for ABC analyses (discrimination of scenarios and estimation of
posterior distribution of parameters) were as followed: Uniform[1000; 20000] for NS and logUniform[2;100] for NFi. The
prior distributions of the microsatellite markers were the same than those described in the legend of Figure 1.
35.
36.
37.
38. Model-posterior checking for the introduction scenarios 1, 2 and 3
The scenarios 1, 2 and 3 are detailed in Figure 3. The “pseudo-observed” test data set analyzed here was simulated under scenario 3.
The probability (simulated<observed) given for each test quantities was computed from 1,000 data tests simulated from the posterior
distributions of parameters obtained under a given scenario. Corresponding tail-area probabilities, or p-values, of the test quantities can
be easily obtained as probability(simulated<observed) and |1.0 – probability(simulated<observed) | for
probability(simulated<observed) ≤ 0.5 and > 0.5, respectively (Gelman et al. 1995). The test quantities correspond to the summary
statistics used to compute the posterior distributions of parameters (a) or to other statistics (b). NAL_i = mean number of alleles in
population i, HET_i = mean expected heterozygosity in population i (Nei 1987), MGW_i = mean ratio of the number of alleles over the
range of allele sizes (Excoffier et al. 2005), FST_i_j = FST value between populations i and j (Weir and Cockerham 1984), VAR_i =
mean allelic size variance in population i, LIK_i_j = mean individual assignment likelihoods of population i assigned to population j
(Pascual et al. 2007), H2P_i_j = mean expected heterozygosity pooling samples from populations i and j, DAS_i_j = shared allele
distance between populations i and j (Chakraborty and Jin, 1993). *, **, *** = tail-area probability < 0.05, < 0.01 and < 0.001,
respectively.
39. Example of inferences on a complex population history:
the case of pygmy populations in Western Africa
Verdu P, Austerlitz F, Estoup A, Vitalis R, Georges M, Théry S, Alain Froment, Lebomin S,
Gessain A, Hombert J-M, Van der Veen L, Quintana-Murci L, Bahuchet S, Heyer E (2009)
Origins and Genetic Diversity of Pygmy Hunter-Gatherers from Western Central Africa.
Current Biology. 19, 312 – 318. http://dx.doi.org/10.1016/j.cub.2008.12.049.
40. - 604 individuals
- 12 non pygmy
and 9 neighbouring
pygmy populations
- 28 microsatellite loci
No genetic structure between
non pygmy populations
Substantial genetic structure
between pygmy populations
and between pygmy – non
pygmy populations
Substantial socio-culturelles
differences between pygmy
populations
44. Power study: 100 simulated test datasets for each scenario
(parameter values drawn into priors)
focal scenario = 1a
Logistic regression
- Type I error rate = 0.26
- Type II error rates: mean = 0.046 [min=0.00; max=0.09]
48. GENERAL CONCLUSIONS
ABC allows making inferences on complex problems good option
when using a likelihood-based approach is not possible
More and more accessible to biologists because of the constant
development of (more or less) simple software solutions
Not as easy as it seems to be
needs some practice and reading (formalization of scenarios,
prior distributions,…)
needs some “validation” through simulations (possible with
some softwares; e.g. DIYABC)
Does not replace but rather complement more “traditional” statistical
approaches: raw statistics (Fst, heterozygosity,…), NJ-tree, Clustering
Bayesian methods (CBM) developed to address questions related to
genetic structure (STRUCTURE, BAPS, GENELAND, TESS, GESTE…)
CBM minimize the number of “population units” to reduce the
number of possible population topologies to compare with ABC
49. Some key references (non-exhaustive list)
Inferences using « standard ABC »
Pritchard JK, Seielstad MT, Prez-Lezaum A, Feldman MW (1999) Population growth of human Y
chromosomes, a study of Y chromosome microsatellites. Molecular Biology and Evolution 16, 1791-1798.
Beaumont MA, Zhang W, Balding DJ (2002) Approximate Bayesian computation in population genetics.
Genetics 162, 2025-2035.
Verdu P, Austerlitz F, Estoup A, Vitalis R, Georges M, Thery S, Froment A, Le Bomin S, Gessain A, Hombert
JM, et al. (2009) Origins and genetic diversity of pygmy hunter-gatherers from Western Central Africa. Current
Biology, 19, 312-318.
DIYABC (http://www1.montpellier.inra.fr/CBGP/diyabc)
Cornuet J-M, Santos F, Robert PC, Marin J-M, Balding DJ, Guillemaud T, Estoup A (2008) Inferring population
history with DIYABC: a user-friendly approach to Approximate Bayesian Computation. Bioinformatics, 24,
2713-2719.
Cornuet JM, Ravigné V, Estoup A (2010) Inference on population history and model checking using DNA
sequence and microsatellite data with the software DIYABC (v1.0). BMC bioinformatics, 11,
401doi:10.1186/1471-2105-11-401.
Reviews
Csilléry K, Blum MGB, Gaggiotti O, François O (2010) Approximate Bayesian Computation (ABC) in practice.
Trends in Ecology and Evolution, 25, 411-417.
Bertorelle G, Bonazzo A, Mona S (2010) ABC as a flexible framework to estimate demography over space and
time: some cons, many pros. Molecular Ecology, 19, 2609-2625.