This document presents a methodological pipeline to analyze the morphological diversity and evolution of marine tetrapods using multiple techniques:
1. Morphometric analyses of extinct and living marine tetrapods to numerically describe their diversity and derive a theoretical morphospace.
2. Computational fluid dynamic simulations to quantify functional parameters like lift, drag, and pressure around bodies under different conditions.
3. Phylogenetic comparative methods and morphospace analyses to test hypotheses about contingency vs. determinism in evolution by examining patterns of morphological convergence and disparity.
The goal is to gain insights into marine tetrapod evolution and the degree of determinism or flexibility in exploring morphological design space.
A comprehensive comparison of the original forms of biogeography based optimi...ijscai
Biogeography-based optimization (BBO) is a new population-based evolutionary algorithm and one of meta-heuristic algorithms. This technique is based on an old mathematical study that explains the geographical distribution of biological organisms. The first original form of BBO was introduced in 2008 and known as a partial migration based BBO. After three months, BBO was re-introduced again with additional three other forms and known as single, simplified partial, and simplified single migration based BBOs. Then a lot of modifications and hybridizations were employed to boost-up the performance of BBO and solve its weak exploration. However, the literature lacks the explanations and the reasons on which the modifications of the BBO forms are based on. This paper tries to clarify this issue by making a comparison between the four original BBO algorithms through 23 benchmark functions with different dimensions and complexities. The final judgment is confirmed by evaluating the performance based on the effect of the problem’s dimensions, the side constraints and the population size. The results show that both single and simplified single migration based BBOs are faster, but have less performance as compared to the others. The comparison between the partial and the simplified partial migration based BBOs shows that the preference depends on the population size, problem’s complexity and dimensions, and the values of the upper and lower side constraints. The partial migration model wins when these factors, except the population size, are increased, and vice versa for the simplified partial migration model. The results can be used as a foundation and a first step of modification for enhancing any proposed modification on BBO including the existing modifications that are described in literature.
A robust, agnostic molecular biosignature based on machine learningSérgio Sacani
The search for definitive biosignatures—unambiguous markers of past or present life—is a central goal of paleobiology and astrobiology. We used pyrolysis–gas chromatogra-phy coupled to mass spectrometry to analyze chemically disparate samples, including living cells, geologically processed fossil organic material, carbon- rich meteorites, and laboratory- synthesized organic compounds and mixtures. Data from each sample were employed as training and test subsets for machine- learning methods, which resulted in a model that can identify the biogenicity of both contemporary and ancient geologically processed samples with ~90% accuracy. These machine- learning methods do not rely on precise compound identification: Rather, the relational aspects of chromatographic and mass peaks provide the needed information, which underscores this method’s utility for detecting alien biology.
A comprehensive comparison of the original forms of biogeography based optimi...ijscai
Biogeography-based optimization (BBO) is a new population-based evolutionary algorithm and one of meta-heuristic algorithms. This technique is based on an old mathematical study that explains the geographical distribution of biological organisms. The first original form of BBO was introduced in 2008 and known as a partial migration based BBO. After three months, BBO was re-introduced again with additional three other forms and known as single, simplified partial, and simplified single migration based BBOs. Then a lot of modifications and hybridizations were employed to boost-up the performance of BBO and solve its weak exploration. However, the literature lacks the explanations and the reasons on which the modifications of the BBO forms are based on. This paper tries to clarify this issue by making a comparison between the four original BBO algorithms through 23 benchmark functions with different dimensions and complexities. The final judgment is confirmed by evaluating the performance based on the effect of the problem’s dimensions, the side constraints and the population size. The results show that both single and simplified single migration based BBOs are faster, but have less performance as compared to the others. The comparison between the partial and the simplified partial migration based BBOs shows that the preference depends on the population size, problem’s complexity and dimensions, and the values of the upper and lower side constraints. The partial migration model wins when these factors, except the population size, are increased, and vice versa for the simplified partial migration model. The results can be used as a foundation and a first step of modification for enhancing any proposed modification on BBO including the existing modifications that are described in literature.
A robust, agnostic molecular biosignature based on machine learningSérgio Sacani
The search for definitive biosignatures—unambiguous markers of past or present life—is a central goal of paleobiology and astrobiology. We used pyrolysis–gas chromatogra-phy coupled to mass spectrometry to analyze chemically disparate samples, including living cells, geologically processed fossil organic material, carbon- rich meteorites, and laboratory- synthesized organic compounds and mixtures. Data from each sample were employed as training and test subsets for machine- learning methods, which resulted in a model that can identify the biogenicity of both contemporary and ancient geologically processed samples with ~90% accuracy. These machine- learning methods do not rely on precise compound identification: Rather, the relational aspects of chromatographic and mass peaks provide the needed information, which underscores this method’s utility for detecting alien biology.
El equipo de investigadores formado por dos físicos (Jayanth Banavarr y Amos Maritan), un botánico (Todd Cooke) y un hidrólogo (Andrea Rinaldo) sugiere que las plantas y los animales llegaron a soluciones similares (evolutivamente hablando) para resolver el problema del uso eficiente de la energía, y que evolucionaron en respuesta a los mismos principios físicos.
An In Vivo Look at Vertebrate Liver Architecture: Three-Dimensional Reconstructions from Medaka (Oryzias latipes)
RON C. HARDMAN,* DAVE C. VOLZ, SETH W. KULLMAN, AND DAVID E. HINTON
Duke University, Nicholas School of the Environment and Earth Sciences,
Durham, North Carolina
Almost the same as the talk given to Ph.D. students one year ago. It covers the problem of research reproducibility and the tools for doing it. First comes some "theoretical" arguments, then the enumeration of some tools.
Biogeography-based optimization (BBO) is a new popu
lation-based evolutionary algorithm and
one of meta-heuristic algorithms. This technique is
based on an old mathematical study that
explains the geographical distribution of biologica
l organisms. The first original form of BBO
was introduced in 2008 and known as a partial migra
tion based BBO. Few months later, BBO
was re-introduced again with additional three other
forms and known as single, simplified
partial, and simplified single migration based BBOs
. Then a lot of modifications were employed
to enhance the performance of BBO. However, the lit
erature lacks the explanations and the
reasons on which the modifications are based on. Th
is paper tries to clarify this issue by making
a comparison between the four original BBO algorith
ms through a variety of benchmark
functions with different dimensions and complexitie
s. The results show that both single and
simplified single migration based BBOs are faster,
but have less performance as compared to
the others. The comparison between the partial and
the simplified partial migration based BBOs
shows that the preference depends on the population
size, problem’s complexity and dimensions
and the values of the upper and lower side constrai
nts. The partial migration model wins when
these factors, except population size, are increase
d, and vice versa for the simplified partial
migration model. The results can be used as a found
ation and a first step of modification for
enhancing any proposed modification on BBO includin
g the existing modifications that are
described in literature.
PERFORMANCE COMPARISON BETWEEN THE ORIGINAL FORMS OF BIOGEOGRAPHY-BASED OPTIM...csandit
Biogeography-based optimization (BBO) is a new population-based evolutionary algorithm and
one of meta-heuristic algorithms. This technique is based on an old mathematical study that
explains the geographical distribution of biological organisms. The first original form of BBO
was introduced in 2008 and known as a partial migration based BBO. Few months later, BBO
was re-introduced again with additional three other forms and known as single, simplified
partial, and simplified single migration based BBOs. Then a lot of modifications were employed
to enhance the performance of BBO. However, the literature lacks the explanations and the
reasons on which the modifications are based on. This paper tries to clarify this issue by making
a comparison between the four original BBO algorithms through a variety of benchmark
functions with different dimensions and complexities. The results show that both single and
simplified single migration based BBOs are faster, but have less performance as compared to
the others. The comparison between the partial and the simplified partial migration based BBOs
shows that the preference depends on the population size, problem’s complexity and dimensions
and the values of the upper and lower side constraints. The partial migration model wins when
these factors, except population size, are increased, and vice versa for the simplified partial
migration model. The results can be used as a foundation and a first step of modification for
enhancing any proposed modification on BBO including the existing modifications that are
described in literature.
Performance Comparison Between the Original Forms of Biogeography-Based Optim...XperiaZ1
Biogeography-based optimization (BBO) is a new population-based evolutionary algorithm and one of meta-heuristic algorithms. This technique is based on an old mathematical study that explains the geographical distribution of biological organisms. The first original form of BBO was introduced in 2008 and known as a partial migration based BBO. Few months later, BBO was re-introduced again with additional three other forms and known as single, simplified partial, and simplified single migration based BBOs. Then a lot of modifications were employed to enhance the performance of BBO. However, the literature lacks the explanations and the reasons on which the modifications are based on. This paper tries to clarify this issue by making a comparison between the four original BBO algorithms through a variety of benchmark functions with different dimensions and complexities. The results show that both single and simplified single migration based BBOs are faster, but have less performance as compared to the others. The comparison between the partial and the simplified partial migration based BBOs shows that the preference depends on the population size, problem’s complexity and dimensions and the values of the upper and lower side constraints. The partial migration model wins when these factors, except population size, are increased, and vice versa for the simplified partial migration model. The results can be used as a foundation and a first step of modification for enhancing any proposed modification on BBO including the existing modifications that are described in literature.
El equipo de investigadores formado por dos físicos (Jayanth Banavarr y Amos Maritan), un botánico (Todd Cooke) y un hidrólogo (Andrea Rinaldo) sugiere que las plantas y los animales llegaron a soluciones similares (evolutivamente hablando) para resolver el problema del uso eficiente de la energía, y que evolucionaron en respuesta a los mismos principios físicos.
An In Vivo Look at Vertebrate Liver Architecture: Three-Dimensional Reconstructions from Medaka (Oryzias latipes)
RON C. HARDMAN,* DAVE C. VOLZ, SETH W. KULLMAN, AND DAVID E. HINTON
Duke University, Nicholas School of the Environment and Earth Sciences,
Durham, North Carolina
Almost the same as the talk given to Ph.D. students one year ago. It covers the problem of research reproducibility and the tools for doing it. First comes some "theoretical" arguments, then the enumeration of some tools.
Biogeography-based optimization (BBO) is a new popu
lation-based evolutionary algorithm and
one of meta-heuristic algorithms. This technique is
based on an old mathematical study that
explains the geographical distribution of biologica
l organisms. The first original form of BBO
was introduced in 2008 and known as a partial migra
tion based BBO. Few months later, BBO
was re-introduced again with additional three other
forms and known as single, simplified
partial, and simplified single migration based BBOs
. Then a lot of modifications were employed
to enhance the performance of BBO. However, the lit
erature lacks the explanations and the
reasons on which the modifications are based on. Th
is paper tries to clarify this issue by making
a comparison between the four original BBO algorith
ms through a variety of benchmark
functions with different dimensions and complexitie
s. The results show that both single and
simplified single migration based BBOs are faster,
but have less performance as compared to
the others. The comparison between the partial and
the simplified partial migration based BBOs
shows that the preference depends on the population
size, problem’s complexity and dimensions
and the values of the upper and lower side constrai
nts. The partial migration model wins when
these factors, except population size, are increase
d, and vice versa for the simplified partial
migration model. The results can be used as a found
ation and a first step of modification for
enhancing any proposed modification on BBO includin
g the existing modifications that are
described in literature.
PERFORMANCE COMPARISON BETWEEN THE ORIGINAL FORMS OF BIOGEOGRAPHY-BASED OPTIM...csandit
Biogeography-based optimization (BBO) is a new population-based evolutionary algorithm and
one of meta-heuristic algorithms. This technique is based on an old mathematical study that
explains the geographical distribution of biological organisms. The first original form of BBO
was introduced in 2008 and known as a partial migration based BBO. Few months later, BBO
was re-introduced again with additional three other forms and known as single, simplified
partial, and simplified single migration based BBOs. Then a lot of modifications were employed
to enhance the performance of BBO. However, the literature lacks the explanations and the
reasons on which the modifications are based on. This paper tries to clarify this issue by making
a comparison between the four original BBO algorithms through a variety of benchmark
functions with different dimensions and complexities. The results show that both single and
simplified single migration based BBOs are faster, but have less performance as compared to
the others. The comparison between the partial and the simplified partial migration based BBOs
shows that the preference depends on the population size, problem’s complexity and dimensions
and the values of the upper and lower side constraints. The partial migration model wins when
these factors, except population size, are increased, and vice versa for the simplified partial
migration model. The results can be used as a foundation and a first step of modification for
enhancing any proposed modification on BBO including the existing modifications that are
described in literature.
Performance Comparison Between the Original Forms of Biogeography-Based Optim...XperiaZ1
Biogeography-based optimization (BBO) is a new population-based evolutionary algorithm and one of meta-heuristic algorithms. This technique is based on an old mathematical study that explains the geographical distribution of biological organisms. The first original form of BBO was introduced in 2008 and known as a partial migration based BBO. Few months later, BBO was re-introduced again with additional three other forms and known as single, simplified partial, and simplified single migration based BBOs. Then a lot of modifications were employed to enhance the performance of BBO. However, the literature lacks the explanations and the reasons on which the modifications are based on. This paper tries to clarify this issue by making a comparison between the four original BBO algorithms through a variety of benchmark functions with different dimensions and complexities. The results show that both single and simplified single migration based BBOs are faster, but have less performance as compared to the others. The comparison between the partial and the simplified partial migration based BBOs shows that the preference depends on the population size, problem’s complexity and dimensions and the values of the upper and lower side constraints. The partial migration model wins when these factors, except population size, are increased, and vice versa for the simplified partial migration model. The results can be used as a foundation and a first step of modification for enhancing any proposed modification on BBO including the existing modifications that are described in literature.
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About Hector Del Castillo
Hector is VP of Professional Development at the PMI Silver Spring Chapter, and CEO of Bold PM. He's a mid-market growth product executive and changemaker. He works with mid-market product-driven software executives to solve their biggest growth problems. He scales product growth, optimizes ops and builds loyal customers. He has reduced customer churn 33%, and boosted sales 47% for clients. He makes a significant impact by building and launching world-changing AI-powered products. If you're looking for an engaging and inspiring speaker to spark creativity and innovation within your organization, set up an appointment to discuss your specific needs and identify a suitable topic to inspire your audience at your next corporate conference, symposium, executive summit, or planning retreat.
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New Explore Careers and College Majors 2024.pdfDr. Mary Askew
Explore Careers and College Majors is a new online, interactive, self-guided career, major and college planning system.
The career system works on all devices!
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This comprehensive program covers essential aspects of performance marketing, growth strategies, and tactics, such as search engine optimization (SEO), pay-per-click (PPC) advertising, content marketing, social media marketing, and more
1. A methodological framework to test competing hypotheses
on the nature of organismal evolution
H. G. Ferron1,2,*, P. C. J. Donoghue2, B. Figueirido3
1Institut Cavanilles de Biodiversitat I Biologia Evolutiva, Paterna, Spain
2School of Earth Sciences, University of Bristol, Bristol, UK
3Universidad de Málaga, Málaga, Spain
*humberto.ferron@uv.es, humberto.ferron@bristol.ac.uk
Macroevolution and Functional morphology research group (https://macrofun.es/)
@Macro_Fun
The long-term patterns and processes of evolution is a key topic in evolutionary research and
the debate over the contingency vs. determinism in evolution has occupied both biologists
and palaeontologists alike for decades. Evolutionary history is replete with parallel natural
evolutionary experiments from which general nomothetic principles can be gleaned. Among
the most powerful of these natural experimental systems is the evolutionary transition to
life in water by tetrapods, a phenomenon that has happened more than 30 times
independently over different lineages. Here, we present a methodological pipeline based on
a novel combination of state-of-the-art techniques in palaeobiology in order to address the
morphological diversity and disparity of extinct and living marine tetrapods from a
functional, ecological and developmental point of view within temporal and phylogenetic
frameworks. The ultimate goal of this methodological framework is to test competing
hypotheses (contingency vs. determinism) on the nature of organismal evolution in marine
tetrapods.
Ancestral character state reconstruction of
ontogenetic trajectories
Figure 1. Methodological pipeline.
2. Methodological pipeline (Fig. 1).
- Summarizing and numerically describing the morphological diversity
of extinct and living marine tetrapod lineages, their closest terrestrial
relatives, and a sample of primitively aquatic vertebrates.
- Deriving a theoretical morphospace to sample both realised and
theoretical morphologies and characterising the evolutionary
exploration of this design space using phylomorphospace methods
to infer ancestral morphologies and plot the path of evolution onto
the theoretical morphospace.
- Interrogating this theoretical morphospace in functional terms to
create an adaptive/performance landscape.
- Testing alternative hypotheses on the nature of organismal evolution
using phylogenetic comparative methods (PCMs).
Ancestral character state reconstruction of
ontogenetic trajectories
Figure 1. Methodological pipeline.
3. Morphometric and multivariate analyses.
Several morphometric analyses focused on the whole body by capturing
morphological information in different regions (i.e., trunk, caudal
fins/tails, forelimbs and crania) can be performed with geometric and
classical morphometrics, as well as contour analysis (Fig. 2). With this
information, a theoretical morphospace can be derived (Fig. 1).
Inclusion of developmental stages (semaphoronts) for each of the fossil
and extant lineages to obtain an insight into how accessible design
space is to developmental evolution (Fig. 1).
Ancestral character state reconstruction of
ontogenetic trajectories
Figure 1. Methodological pipeline.
4. Morphometric and multivariate analyses.
Figure 2. Proof of concept of (A) landmark configuration for geometric morphometrics
analysis on the crania of ichthyopterygians and (B) principal component analysis
results and phylomorphospace.
Ancestral character state reconstruction of
ontogenetic trajectories
Figure 1. Methodological pipeline.
5. Computational fluid dynamic simulations (CFD).
CFD is a tool for simulating fluid flow and its interaction with solid
surfaces, and it has been used widely in engineering for decades. A
wide range of experimental conditions can be tested including different
swimming speeds and angles of attack as well as considering the
models positioned at different distances to the substrate (i.e., benthic
and pelagic scenarios). This could allow to quantify and visualize several
parameters with functional and ecological relevance such as lift and
drag, pressure distribution around the body, turbulence generation and
vorticity, among others, providing a comprehensive overview of the
hydrodynamics and lifestyle of each species (Fig. 3).
Ancestral character state reconstruction of
ontogenetic trajectories
Figure 1. Methodological pipeline.
6. Computational fluid dynamic simulations (CFD).
Figure 3. Proof of concept of (A) 3D virtual model of Ophthalmosaurus, (B) model
mesh after discretization, and (C, D) distribution of fluid pressure and velocity around
the body, resulting from CFD analysis.
Ancestral character state reconstruction of
ontogenetic trajectories
Figure 1. Methodological pipeline.
7. Macroevolutionary analysis.
PCMs can be used to determine whether secondarily aquatic tetrapod
lineages are more similar to one another than any are to their
immediate terrestrial relatives, whether they are attracted to the same
predictable regions of design space, whether these converge on optimal
fish designs (functional ‘attractors’) or discrete but distinct regions of
design space (Riedl’s phylogenetic burden).
Ancestral character state reconstruction of
ontogenetic trajectories
Figure 1. Methodological pipeline.
8. Macroevolutionary analysis.
Morphological disparity and phylomorphospace techniques to explore
the path of evolution onto the theoretical morphospace.
Ancestral character state reconstruction and morphospace occupation
analyses of ontogenetic trajectories to ascertain the presence of fixed
developmental pathways constraining the diversity of potential
‘designs’.
Mantel tests and Stayton metrics to determine the degree of
morphological convergence
Adaptive/performance landscapes to evaluate the relative functional
optimality of both occupied and unoccupied regions of the
morphospace.
Ancestral character state reconstruction of
ontogenetic trajectories
Figure 1. Methodological pipeline.
9. Macroevolutionary analysis.
Pareto optimality theory to better characterize the trade-offs between
the different functional traits (Fig. 1).
Phylogenetic generalized least squares analysis (PGLS) to evaluate the
functional/biomechanical components of morphology in convergent
taxa. This will allow detecting functional convergences (Fig. 4).
This can allow testing test whether evolution has explored all functional
optimal morphologies, whether many species are functionally
suboptimal, whether unrealised morphologies are functionally poor and
whether some optimal morphologies have never been achieved in
evolutionary history.
Ancestral character state reconstruction of
ontogenetic trajectories
Figure 1. Methodological pipeline.
10. Macroevolutionary analysis.
Figure 4. Proof of concept of (A) heatmaps plotted over the phylomorphospaces (drag
and lift coefficients, CD and CL) and (B) morphofunctional correlations.
Ancestral character state reconstruction of
ontogenetic trajectories
Figure 1. Methodological pipeline.