scholarly journals A unifying comparative phylogenetic framework including traits coevolving across interacting lineages

2016 ◽  
Author(s):  
Marc Manceau ◽  
Amaury Lambert ◽  
Hélène Morlon
Keyword(s):  
Broad Class ◽  
Interspecific Interactions ◽  
Computation Time ◽  
Comparative Data ◽  
Phenotypic Evolution ◽  
Trait Evolution ◽  
New Era ◽  
Wide Range ◽  
Classical Models ◽  
Phylogenetic Framework

AbstractModels of phenotypic evolution fit to phylogenetic comparative data are widely used to make inferences regarding the tempo and mode of trait evolution. A wide range of models is already available for this type of analysis, and the field is still under active development. One of the most needed developments concerns models that better account for the effect of within- and between-clade interspecific interactions on trait evolution, that can result from processes as diverse as competition, predation, parasitism, or mutualism. Here, we begin by developing a very general comparative phylogenetic framework for (multi)-trait evolution that can be applied to both ultrametric and non-ultrametric trees. This framework not only encapsulates all previous classical models of univariate and multivariate phenotypic evolution, but also paves the way for the consideration of a much broader series of models in which lineages co-evolve, meaning that trait changes in one lineage are influenced by the value of traits in other, interacting lineages. Next, we provide a standard way for deriving the probabilistic distribution of traits at tip branches under our framework. We show that a multivariate normal distribution remains the expected distribution for a broad class of models accounting for interspecific interactions. Our derivations allow us to fit various models efficiently, and in particular greatly reduce the computation time needed to fit the recently proposed phenotype matching model. Finally, we illustrate the utility of our framework by developing a toy model for mutualistic coevolution. Our framework should foster a new era in the study of coevolution from comparative data.

Data-Driven Analysis of Engine Mission Severity Using Non-Dimensional Groups

2021 ◽  
Author(s):  
Tim Brandes ◽  
Stefano Scarso ◽  
Christian Koch ◽  
Stephan Staudacher
Keyword(s):  
Support Vector Machine ◽  
Computation Time ◽  
Principal Component ◽  
General Term ◽  
Support Vector ◽  
Physical Parameters ◽  
Machine Model ◽  
Precision Error ◽  
Operational Conditions ◽  
Wide Range

Abstract A numerical experiment of intentionally reduced complexity is used to demonstrate a method to classify flight missions in terms of the operational severity experienced by the engines. In this proof of concept, the general term of severity is limited to the erosion of the core flow compressor blade and vane leading edges. A Monte Carlo simulation of varying operational conditions generates a required database of 10000 flight missions. Each flight is sampled at a rate of 1 Hz. Eleven measurable or synthesizable physical parameters are deemed to be relevant for the problem. They are reduced to seven universal non-dimensional groups which are averaged for each flight. The application of principal component analysis allows a further reduction to three principal components. They are used to run a support-vector machine model in order to classify the flights. A linear kernel function is chosen for the support-vector machine due to its low computation time compared to other functions. The robustness of the classification approach against measurement precision error is evaluated. In addition, a minimum number of flights required for training and a sensible number of severity classes are documented. Furthermore, the importance to train the algorithms on a sufficiently wide range of operations is presented.

scholarly journals A 15 million-year long record of phenotypic evolution in the heavily calcified coccolithophore <i>Helicosphaera</i> and its biogeochemical implications

2020 ◽  
Author(s):  
Luka Šupraha ◽  
Jorijntje Henderiks
Keyword(s):  
Cell Size ◽  
Resource Limitation ◽  
Tropical Indian Ocean ◽  
Adaptive Strategies ◽  
Cellular Level ◽  
Phenotypic Evolution ◽  
Environmental Forcing ◽  
Modern Species ◽  
Wide Range ◽  
Mean Size

Abstract. The biogeochemical performance of coccolithophores is defined by their overall abundance in the oceans, but also by a wide range in cell size, degree of calcification and carbon production rates between different species. Species’ sensitivity to environmental forcing has been suggested to relate to their cellular PIC : POC ratio and other physiological constraints. Understanding both the short and longer-term adaptive strategies of different coccolithophore lineages, and how these in turn shape the biogeochemical role of the group, is therefore crucial for modeling the ongoing changes in the global carbon cycle. Here we present data on the phenotypic evolution of a large and heavily-calcified genus Helicosphaera (order Zygodiscales) over the past 15 million years (Ma), at two deep-sea drill sites from the tropical Indian Ocean and temperate South Atlantic. The modern species Helicosphaera carteri, which displays eco-physiological adaptations in modern strains, was used to benchmark the use of its coccolith morphology as a physiological proxy in the fossil record. Our results show that, on the single-genotype level, coccolith morphology has no correlation with physiological traits in H. carteri. However, significant correlations of coccolith morphometric parameters with cell size and physiological rates do emerge once multiple genotypes or closely related lineages are pooled together. Using this insight, we interpret the phenotypic evolution in Helicosphaera as a global, resource limitation-driven selection for smaller cells, which appears to be a common adaptive trait among different coccolithophore lineages, from the warm and high-CO2 world of the middle Miocene to the cooler and low-CO2 conditions of the Pleistocene. However, despite a significant decrease in mean size, Helicosphaera kept relatively stable PIC : POC (as inferred from the coccolith aspect ratio) and thus highly conservative biogeochemical output on the cellular level. We argue that this supports its status as an obligate calcifier, like other large and heavily-calcified genera such as Calcidiscus and Coccolithus, and that other adaptive strategies, beyond size-adaptation, must support the persistent, albeit less abundant, occurrence of these taxa. This is in stark contrast with the ancestral lineage of Emiliania and Gephyrocapsa, which not only decreased in mean size but also displayed much higher phenotypic plasticity in degree of calcification while becoming globally more dominant in plankton communities.

scholarly journals Stochastic cycle selection in active flow networks

2016 ◽  
Vol 113 (29) ◽  
pp. 8200-8205 ◽  
Author(s):  
Francis G. Woodhouse ◽  
Aden Forrow ◽  
Joanna B. Fawcett ◽  
Jörn Dunkel
Keyword(s):  
Broad Class ◽  
Generic Model ◽  
Rate Theory ◽  
Flow Networks ◽  
Complex Flow ◽  
Slime Molds ◽  
Arterial Blood ◽  
Wide Range ◽  
Theoretical And Numerical Analysis ◽  
Active Flow

Active biological flow networks pervade nature and span a wide range of scales, from arterial blood vessels and bronchial mucus transport in humans to bacterial flow through porous media or plasmodial shuttle streaming in slime molds. Despite their ubiquity, little is known about the self-organization principles that govern flow statistics in such nonequilibrium networks. Here we connect concepts from lattice field theory, graph theory, and transition rate theory to understand how topology controls dynamics in a generic model for actively driven flow on a network. Our combined theoretical and numerical analysis identifies symmetry-based rules that make it possible to classify and predict the selection statistics of complex flow cycles from the network topology. The conceptual framework developed here is applicable to a broad class of biological and nonbiological far-from-equilibrium networks, including actively controlled information flows, and establishes a correspondence between active flow networks and generalized ice-type models.

Chief Digital Officers and Their Roles, Agendas, and Competencies

2021 ◽  
pp. 1929-1940
Author(s):  
Vladimir Korovkin
Keyword(s):  
Digital Transformation ◽  
Important Condition ◽  
New Era ◽  
Managerial Skills ◽  
Profound Knowledge ◽  
Wide Range ◽  
The World ◽  
Strategic Role ◽  
Chief Digital Officer

Digital transformation of business is an increasingly pressing issue for top management of the companies across the world. Appointing dedicated executive is a popular measure undertaken to respond to the challenges of the new era. Many view the role of CDO (Chief Digital Officer) to be “the most exciting strategic role in the coming decade”. There is a wide range of views on the CDO's role, agenda, and competencies. Depending on the nature and the environment of a given business, there are three possible strategic approaches to the digital transformation: “fully digital”, “digitally wrapped”, and “digitally spiced”. Each of these requires a CDO, the digital transformation-focused executive, as an important condition for success, yet the range of tasks such a manager handles is profoundly different in each case. The role of CDO is defined by a diverse and demanding set of requirements; the perfect CDO is a manager with a variety of functions who actively interacts with other executives and has profound knowledge and strong managerial skills.

Social Networks and Internet Communities in the Field of Geographic Information and Their Role in Open Data Government Initiatives

2015 ◽  
pp. 1586-1618
Author(s):  
Paula Díaz ◽  
Joan Masó
Keyword(s):  
Open Data ◽  
User Generated Content ◽  
Geospatial Information ◽  
Global Networks ◽  
New Era ◽  
Traditional Procedure ◽  
Wide Range ◽  
Relevant Role ◽  
Feasible Alternative ◽  
User Friendly

Users are playing an increasingly relevant role in geospatial data production. The traditional procedure for creating cartography, mainly by experts in official mapping agencies, has evolved into a more participative process for generating data: neogeography. Technology and the Internet are now user-friendly for a wide range of people who have become active users of global networks, such as GEOSS, INSPIRE, Eye On Earth, and EarthCube, and official producers need to adapt to the new era of openness, collaboration, and hybrid maps by adopting open standards. Although the creation of geospatial information is notably growing worldwide, and is enhanced by user-generated content, we may wonder whether this is a feasible alternative to official cartography. This chapter reviews the main geospatial networks based on both bottom-up and top-down data creation approaches, as well as the potentialities and limitations of user-generated content in the scientific field and in decision-making organisms.

CRISPR-Based Tools in Immunity

2019 ◽  
Vol 37 (1) ◽  
pp. 571-597 ◽  
Author(s):  
Dimitre R. Simeonov ◽  
Alexander Marson
Keyword(s):  
Genome Engineering ◽  
Disease Risk ◽  
Ease Of Use ◽  
Model Organisms ◽  
Mechanistic Studies ◽  
Genetic Circuits ◽  
New Era ◽  
Wide Range ◽  
Health And Disease ◽  
Cancer Immunotherapies

CRISPR technology has opened a new era of genome interrogation and genome engineering. Discovered in bacteria, where it protects against bacteriophage by cleaving foreign nucleic acid sequences, the CRISPR system has been repurposed as an adaptable tool for genome editing and multiple other applications. CRISPR's ease of use, precision, and versatility have led to its widespread adoption, accelerating biomedical research and discovery in human cells and model organisms. Here we review CRISPR-based tools and discuss how they are being applied to decode the genetic circuits that control immune function in health and disease. Genetic variation in immune cells can affect autoimmune disease risk, infectious disease pathogenesis, and cancer immunotherapies. CRISPR provides unprecedented opportunities for functional mechanistic studies of coding and noncoding genome sequence function in immunity. Finally, we discuss the potential of CRISPR technology to engineer synthetic cellular immunotherapies for a wide range of human diseases.

scholarly journals Object Detection in Very High-Resolution Aerial Images Using One-Stage Densely Connected Feature Pyramid Network

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3341 ◽  
Author(s):  
Hilal Tayara ◽  
Kil Chong
Keyword(s):  
High Resolution ◽  
Object Detection ◽  
Computation Time ◽  
Aerial Images ◽  
Feature Maps ◽  
Two Stage ◽  
One Stage ◽  
Wide Range ◽  
Feature Pyramid ◽  
Very High

Object detection in very high-resolution (VHR) aerial images is an essential step for a wide range of applications such as military applications, urban planning, and environmental management. Still, it is a challenging task due to the different scales and appearances of the objects. On the other hand, object detection task in VHR aerial images has improved remarkably in recent years due to the achieved advances in convolution neural networks (CNN). Most of the proposed methods depend on a two-stage approach, namely: a region proposal stage and a classification stage such as Faster R-CNN. Even though two-stage approaches outperform the traditional methods, their optimization is not easy and they are not suitable for real-time applications. In this paper, a uniform one-stage model for object detection in VHR aerial images has been proposed. In order to tackle the challenge of different scales, a densely connected feature pyramid network has been proposed by which high-level multi-scale semantic feature maps with high-quality information are prepared for object detection. This work has been evaluated on two publicly available datasets and outperformed the current state-of-the-art results on both in terms of mean average precision (mAP) and computation time.

scholarly journals Gamma-Ray Burst Observations with BATSE

1998 ◽  
Vol 188 ◽  
pp. 159-162
Author(s):  
Gerald J. Fishman
Keyword(s):  
Gamma Rays ◽  
Gamma Ray ◽  
Gamma Ray Bursts ◽  
Gamma Ray Burst ◽  
New Era ◽  
X Ray ◽  
Time Profiles ◽  
Random Direction ◽  
Wide Range ◽  
New Phenomena

Gamma-ray bursts (GRBs) will be recorded as one of the outstanding new phenomena discovered in astronomy this century. About once per day, a burst of gamma rays appears from a random direction on the sky. Often, the burst outshines all other sources of gamma-rays in the sky, combined. This paper reviews some of the key observed phenomenon of bursts in the hard x-ray/gamma-ray region, as observed with the BATSE experiment on the Compton Gamma Ray Observatory. The observed time profiles, spectral properties and durations of gamma-ray bursts cover a wide range. Recent breakthroughs in the observation of gamma-ray burst counterparts and afterglows in other wavelength regions have marked the beginning of a new era in gamma-ray burst research. Those observations are described in following papers in these proceedings.

scholarly journals ETHOS – an effective parametrization and classification for structure formation: the non-linear regime at z ≳ 5

2020 ◽  
Vol 498 (3) ◽  
pp. 3403-3419
Author(s):  
Sebastian Bohr ◽  
Jesús Zavala ◽  
Francis-Yan Cyr-Racine ◽  
Mark Vogelsberger ◽  
Torsten Bringmann ◽  
...  
Keyword(s):  
Power Spectrum ◽  
Structure Formation ◽  
Parameter Space ◽  
Broad Class ◽  
High Redshift ◽  
Acoustic Oscillations ◽  
Effective Parameters ◽  
Matter Power Spectrum ◽  
Wide Range ◽  
Non Linear

ABSTRACT We propose two effective parameters that fully characterize galactic-scale structure formation at high redshifts (z ≳ 5) for a variety of dark matter (DM) models that have a primordial cutoff in the matter power spectrum. Our description is within the recently proposed ETHOS framework and includes standard thermal warm DM (WDM) and models with dark acoustic oscillations (DAOs). To define and explore this parameter space, we use high-redshift zoom-in simulations that cover a wide range of non-linear scales from those where DM should behave as CDM (k ∼ 10 h Mpc−1), down to those characterized by the onset of galaxy formation (k ∼ 500 h Mpc−1). We show that the two physically motivated parameters hpeak and kpeak, the amplitude and scale of the first DAO peak, respectively, are sufficient to parametrize the linear matter power spectrum and classify the DM models as belonging to effective non-linear structure formation regions. These are defined by their relative departure from cold DM (kpeak → ∞) and WDM (hpeak = 0) according to the non-linear matter power spectrum and halo mass function. We identify a region where the DAOs still leave a distinct signature from WDM down to z = 5, while a large part of the DAO parameter space is shown to be degenerate with WDM. Our framework can then be used to seamlessly connect a broad class of particle DM models to their structure formation properties at high redshift without the need of additional N-body simulations.

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