scholarly journals Inferring node dates from tip dates in fossil Canidae: the importance of tree priors

2016 ◽  
Author(s):  
Nicholas J. Matzke ◽  
April Wright
Keyword(s):  
Phylogenetic Trees ◽  
Mechanistic Model ◽  
Mechanistic Models ◽  
Dating Methods ◽  
Uniform Tree ◽  
Key Nodes ◽  
Birth Death

AbstractTip-dating methods are becoming popular alternatives to traditional node calibration approaches for building time-scaled phylogenetic trees, but questions remain about their application to empirical datasets. We compared the performance of the most popular methods against a dated tree of fossil Canidae derived from previously published monographs. Using a canid morphology dataset, we performed tip-dating using Beast 2.1.3 and MrBayes 3.2.5. We find that for key nodes (Canis, ~3.2 Ma, Caninae ~11.7 Ma) a non-mechanistic model using a uniform tree prior produces estimates that are unrealistically old (27.5, 38.9 Ma). Mechanistic models (incorporating lineage birth, death, and sampling rates) estimate ages that are closely in line with prior research. We provide a discussion of these two families of models (mechanistic vs. non-mechanistic) and their applicability to fossil datasets.

scholarly journals Inferring node dates from tip dates in fossil Canidae: the importance of tree priors

2016 ◽  
Vol 12 (8) ◽  
pp. 20160328 ◽  
Author(s):  
Nicholas J. Matzke ◽  
April Wright
Keyword(s):  
Phylogenetic Trees ◽  
Mechanistic Model ◽  
Mechanistic Models ◽  
Dating Methods ◽  
Uniform Tree ◽  
Key Nodes ◽  
Birth Death

Tip-dating methods are becoming popular alternatives to traditional node calibration approaches for building time-scaled phylogenetic trees, but questions remain about their application to empirical datasets. We compared the performance of the most popular methods against a dated tree of fossil Canidae derived from previously published monographs. Using a canid morphology dataset, we performed tip-dating using BEAST v. 2.1.3 and M r B ayes v. 3.2.5. We find that for key nodes ( Canis, approx. 3.2 Ma, Caninae approx. 11.7 Ma) a non-mechanistic model using a uniform tree prior produces estimates that are unrealistically old (27.5, 38.9 Ma). Mechanistic models (incorporating lineage birth, death and sampling rates) estimate ages that are closely in line with prior research. We provide a discussion of these two families of models (mechanistic versus non-mechanistic) and their applicability to fossil datasets.

scholarly journals Simulating the Effects of Intensifying Silviculture on Desired Species Yields across a Broad Environmental Gradient

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 755
Author(s):  
Eric B. Searle ◽  
F. Wayne Bell ◽  
Guy R. Larocque ◽  
Mathieu Fortin ◽  
Jennifer Dacosta ◽  
...  
Keyword(s):  
Climate Change ◽  
Forest Management ◽  
Planning Process ◽  
Mechanistic Model ◽  
Natural Disturbance ◽  
Fine Tuning ◽  
Mechanistic Models ◽  
Paradigm Shifts ◽  
Ecological Processes ◽  
Forest Modelling

In the past two decades, forest management has undergone major paradigm shifts that are challenging the current forest modelling architecture. New silvicultural systems, guidelines for natural disturbance emulation, a desire to enhance structural complexity, major advances in successional theory, and climate change have all highlighted the limitations of current empirical models in covering this range of conditions. Mechanistic models, which focus on modelling underlying ecological processes rather than specific forest conditions, have the potential to meet these new paradigm shifts in a consistent framework, thereby streamlining the planning process. Here we use the NEBIE (a silvicultural intervention scale that classifies management intensities as natural, extensive, basic, intensive, and elite) plot network, from across Ontario, Canada, to examine the applicability of a mechanistic model, ZELIG-CFS (a version of the ZELIG tree growth model developed by the Canadian Forest Service), to simulate yields and species compositions. As silvicultural intensity increased, overall yield generally increased. Species compositions met the desired outcomes when specific silvicultural treatments were implemented and otherwise generally moved from more shade-intolerant to more shade-tolerant species through time. Our results indicated that a mechanistic model can simulate complex stands across a range of forest types and silvicultural systems while accounting for climate change. Finally, we highlight the need to improve the modelling of regeneration processes in ZELIG-CFS to better represent regeneration dynamics in plantations. While fine-tuning is needed, mechanistic models present an option to incorporate adaptive complexity into modelling forest management outcomes.

scholarly journals Modified Mechanistic Model Based on Gaussian Process Adjusting Technique for Cutting Force Prediction in Micro-End Milling

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Xiaoping Liao ◽  
Zhenkun Zhang ◽  
Kai Chen ◽  
Kang Li ◽  
Junyan Ma ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Cutting Force ◽  
Cutting Forces ◽  
End Milling ◽  
Mechanistic Model ◽  
Machining Process ◽  
Mechanistic Models ◽  
Machining Processes ◽  
Model Based ◽  
Micro End Milling

Micro-end milling is in common use of machining micro- and mesoscale products and is superior to other micro-machining processes in the manufacture of complex structures. Cutting force is the most direct factor reflecting the processing state, the change of which is related to the workpiece surface quality, tool wear and machine vibration, and so on, which indicates that it is important to analyze and predict cutting forces during machining process. In such problems, mechanistic models are frequently used for predicting machining forces and studying the effects of various process variables. However, these mechanistic models are derived based on various engineering assumptions and approximations (such as the slip-line field theory). As a result, the mechanistic models are generally less accurate. To accurately predict cutting forces, the paper proposes two modified mechanistic models, modified mechanistic models I and II. The modified mechanistic models are the integration of mathematical model based on Gaussian process (GP) adjustment model and mechanical model. Two different models have been validated on micro-end-milling experimental measurement. The mean absolute percentage errors of models I and II are 7.76% and 6.73%, respectively, while the original mechanistic model’s is 15.14%. It is obvious that the modified models are in better agreement with experiment. And model II performs better between the two modified mechanistic models.

scholarly journals Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models

2019 ◽  
Author(s):  
Marina Esteban ◽  
María Peña-Chilet ◽  
Carlos Loucera ◽  
Joaquín Dopazo
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Fanconi Anemia ◽  
Rare Diseases ◽  
Predictive Models ◽  
Mechanistic Model ◽  
Genomic Data ◽  
Potential Effect ◽  
Mechanistic Models ◽  
Related Cell

AbstractBackgroundIn spite of the abundance of genomic data, predictive models that describe phenotypes as a function of gene expression or mutations are difficult to obtain because they are affected by the curse of dimensionality, given the disbalance between samples and candidate genes. And this is especially dramatic in scenarios in which the availability of samples is difficult, such as the case of rare diseases.ResultsThe application of multi-output regression machine learning methodologies to predict the potential effect of external proteins over the signaling circuits that trigger Fanconi anemia related cell functionalities, inferred with a mechanistic model, allowed us to detect over 20 potential therapeutic targets.ConclusionsThe use of artificial intelligence methods for the prediction of potentially causal relationships between proteins of interest and cell activities related with disease-related phenotypes opens promising avenues for the systematic search of new targets in rare diseases.

scholarly journals Estimating Epidemic Incidence and Prevalence from Genomic Data

2019 ◽  
Vol 36 (8) ◽  
pp. 1804-1816 ◽  
Author(s):  
Timothy G Vaughan ◽  
Gabriel E Leventhal ◽  
David A Rasmussen ◽  
Alexei J Drummond ◽  
David Welch ◽  
...  
Keyword(s):  
Sierra Leone ◽  
Particle Filtering ◽  
Phylogenetic Trees ◽  
Stochastic Dynamics ◽  
Large Population ◽  
Simulated Data ◽  
Model Parameters ◽  
Epidemiological Models ◽  
Case Count ◽  
Birth Death

Abstract Modern phylodynamic methods interpret an inferred phylogenetic tree as a partial transmission chain providing information about the dynamic process of transmission and removal (where removal may be due to recovery, death, or behavior change). Birth–death and coalescent processes have been introduced to model the stochastic dynamics of epidemic spread under common epidemiological models such as the SIS and SIR models and are successfully used to infer phylogenetic trees together with transmission (birth) and removal (death) rates. These methods either integrate analytically over past incidence and prevalence to infer rate parameters, and thus cannot explicitly infer past incidence or prevalence, or allow such inference only in the coalescent limit of large population size. Here, we introduce a particle filtering framework to explicitly infer prevalence and incidence trajectories along with phylogenies and epidemiological model parameters from genomic sequences and case count data in a manner consistent with the underlying birth–death model. After demonstrating the accuracy of this method on simulated data, we use it to assess the prevalence through time of the early 2014 Ebola outbreak in Sierra Leone.

scholarly journals Improved multi-type birth-death phylodynamic inference in BEAST 2

2020 ◽  
Author(s):  
Jérémie Scire ◽  
Joëlle Barido-Sottani ◽  
Denise Kühnert ◽  
Timothy G. Vaughan ◽  
Tanja Stadler
Keyword(s):  
Influenza A ◽  
Phylogenetic Trees ◽  
Source Code ◽  
Structured Populations ◽  
Parameter Estimates ◽  
Migration Patterns ◽  
Global Migration ◽  
Initial Release ◽  
Past Population ◽  
Birth Death

AbstractThe multi-type birth-death model with sampling is a phylodynamic model which enables quantification of past population dynamics in structured populations, based on phylogenetic trees. The BEAST 2 package bdmm implements an algorithm for numerically computing the probability density of a phylogenetic tree given the population dynamic parameters under this model. In the initial release of bdmm, analyses were limited to trees consisting of up to approximately 250 genetic samples for numerical reasons. We implemented important algorithmic changes to bdmm which dramatically increase the number of genetic samples that can be analyzed, and improve the numerical robustness and efficiency of the calculations. Being able to use bigger datasets leads to improved precision of parameter estimates. Furthermore, we report on several model extensions to bdmm, inspired by properties common to empirical datasets. We apply this improved algorithm to two partly overlapping datasets of Influenza A virus HA sequences sampled around the world, one with 500 samples, the other with only 175, for comparison. We report and compare the global migration patterns and seasonal dynamics inferred from each dataset.AvailabilityThe latest release with our updates, bdmm 0.3.5, is freely available as an open access package of BEAST 2. The source code can be accessed at https://github.com/denisekuehnert/bdmm.

scholarly journals A versatile workflow to integrate RNA-seq genomic and transcriptomic data into mechanistic models of signaling pathways

2021 ◽  
Vol 17 (2) ◽  
pp. e1008748
Author(s):  
Martín Garrido-Rodriguez ◽  
Daniel Lopez-Lopez ◽  
Francisco M. Ortuno ◽  
María Peña-Chilet ◽  
Eduardo Muñoz ◽  
...  
Keyword(s):  
State Of The Art ◽  
Mechanistic Model ◽  
Cell Activity ◽  
Mechanistic Models ◽  
Rna Seq ◽  
Whole Process ◽  
Functional Profiling ◽  
Biological Interpretation ◽  
Signaling Circuit ◽  
Gene Expression Levels

MIGNON is a workflow for the analysis of RNA-Seq experiments, which not only efficiently manages the estimation of gene expression levels from raw sequencing reads, but also calls genomic variants present in the transcripts analyzed. Moreover, this is the first workflow that provides a framework for the integration of transcriptomic and genomic data based on a mechanistic model of signaling pathway activities that allows a detailed biological interpretation of the results, including a comprehensive functional profiling of cell activity. MIGNON covers the whole process, from reads to signaling circuit activity estimations, using state-of-the-art tools, it is easy to use and it is deployable in different computational environments, allowing an optimized use of the resources available.

scholarly journals Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models (Preprint)

2020 ◽  
Author(s):  
Canelle Poirier ◽  
Dianbo Liu ◽  
Leonardo Clemente ◽  
Xiyu Ding ◽  
Matteo Chinazzi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
News Media ◽  
Data Augmentation ◽  
Mechanistic Model ◽  
Digital Data ◽  
Mechanistic Models ◽  
Search Activity ◽  
Current Time ◽  
Aid Decision

BACKGROUND The inherent difficulty of identifying and monitoring emerging outbreaks caused by novel pathogens can lead to their rapid spread; and if left unchecked, they may become major public health threats to the planet. The ongoing coronavirus disease (COVID-19) outbreak, which has infected over 2,300,000 individuals and caused over 150,000 deaths, is an example of one of these catastrophic events. OBJECTIVE We present a timely and novel methodology that combines disease estimates from mechanistic models and digital traces, via interpretable machine learning methodologies, to reliably forecast COVID-19 activity in Chinese provinces in real time. METHODS Our method uses the following as inputs: (a) official health reports, (b) COVID-19–related internet search activity, (c) news media activity, and (d) daily forecasts of COVID-19 activity from a metapopulation mechanistic model. Our machine learning methodology uses a clustering technique that enables the exploitation of geospatial synchronicities of COVID-19 activity across Chinese provinces and a data augmentation technique to deal with the small number of historical disease observations characteristic of emerging outbreaks. RESULTS Our model is able to produce stable and accurate forecasts 2 days ahead of the current time and outperforms a collection of baseline models in 27 out of 32 Chinese provinces. CONCLUSIONS Our methodology could be easily extended to other geographies currently affected by COVID-19 to aid decision makers with monitoring and possibly prevention.

scholarly journals Using mechanistic models for the clinical interpretation of complex genomic variation

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
María Peña-Chilet ◽  
Marina Esteban-Medina ◽  
Matias M. Falco ◽  
Kinza Rian ◽  
Marta R. Hidalgo ◽  
...  
Keyword(s):  
Mechanistic Model ◽  
Complex Diseases ◽  
Holistic Approach ◽  
Genomic Variation ◽  
Mechanistic Models ◽  
Loss Of Function ◽  
Expression Of Genes ◽  
Causal Genes ◽  
Functional Consequences

AbstractThe sustained generation of genomic data in the last decade has increased the knowledge on the causal mutations of a large number of diseases, especially for highly penetrant Mendelian diseases, typically caused by a unique or a few genes. However, the discovery of causal genes in complex diseases has been far less successful. Many complex diseases are actually a consequence of the failure of complex biological modules, composed by interrelated proteins, which can happen in many different ways, which conferring a multigenic nature to the condition that can hardly be attributed to one or a few genes. We present a mechanistic model, Hipathia, implemented in a web server that allows estimating the effect that mutations, or changes in the expression of genes, have over the whole system of human signaling and the corresponding functional consequences. We show several use cases where we demonstrate how different the ultimate impact of mutations with similar loss-of-function potential can be and how the potential pathological role of a damaged gene can be inferred within the context of a signaling network. The use of systems biology-based approaches, such as mechanistic models, allows estimating the potential impact of loss-of-function mutations occurring in proteins that are part of complex biological interaction networks, such as signaling pathways. This holistic approach provides an elegant alternative to gene-centric approaches that can open new avenues in the interpretation of the genomic variability in complex diseases.

scholarly journals 4D Micro-Computed X-ray Tomography as a Tool to Determine Critical Process and Product Information of Spin Freeze-Dried Unit Doses

Pharmaceutics ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 430 ◽  
Author(s):  
Brecht Vanbillemont ◽  
Joris Lammens ◽  
Wannes Goethals ◽  
Chris Vervaet ◽  
Matthieu N. Boone ◽  
...  
Keyword(s):  
Thermal Imaging ◽  
Time Distribution ◽  
Freeze Drying ◽  
Product Information ◽  
Mechanistic Model ◽  
Physical Stability ◽  
Mechanistic Models ◽  
Drying Time ◽  
Transfer Resistance ◽  
Freeze Dried

Maintaining chemical and physical stability of the product during freeze-drying is important but challenging. In addition, freeze-drying is typically associated with long process times. Therefore, mechanistic models have been developed to maximize drying efficiency without altering the chemical or physical stability of the product. Dried product mass transfer resistance ( R p ) is a critical input for these mechanistic models. Currently available techniques to determine R p only provide an estimation of the mean R p and do not allow measuring and determining essential local (i.e., intra-vial) R p differences. In this study, we present an analytical method, based on four-dimensional micro-computed tomography (4D- μ CT), which enables the possibility to determine intra-vial R p differences. Subsequently, these obtained R p values are used in a mechanistic model to predict the drying time distribution of a spin-frozen vial. Finally, this predicted primary drying time distribution is experimentally verified via thermal imaging during drying. It was further found during this study that 4D- μ CT uniquely allows measuring and determining other essential freeze-drying process parameters such as the moving direction(s) of the sublimation front and frozen product layer thickness, which allows gaining accurate process knowledge. To conclude, the study reveals that the variation in the end of primary drying time of a single vial could be predicted accurately using 4D- μ CT as similar results were found during the verification using thermal imaging.

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