scholarly journals A novel Bayesian method for inferring and interpreting the dynamics of adaptive landscapes from phylogenetic comparative data

2014 ◽  
Author(s):  
Josef C Uyeda ◽  
Luke J Harmon
Keyword(s):  
Large Scale ◽  
Bayesian Method ◽  
A Priori ◽  
Model Fitting ◽  
Biological Data ◽  
Comparative Data ◽  
Adaptive Landscape ◽  
Multiple Sources ◽  
Adaptive Landscapes ◽  
Selection Parameters

Our understanding of macroevolutionary patterns of adaptive evolution has greatly increased with the advent of large-scale phylogenetic comparative methods. Widely used Ornstein-Uhlenbeck (OU) models can describe an adaptive process of divergence and selection. However, inference of the dynamics of adaptive landscapes from comparative data is complicated by interpretational difficulties, lack of identifiability among parameter values and the common requirement that adaptive hypotheses must be assigneda priori. Here we develop a reversible-jump Bayesian method of fitting multi-optima OU models to phylogenetic comparative data that estimates the placement and magnitude of adaptive shifts directly from the data. We show how biologically informed hypotheses can be tested against this inferred posterior of shift locations using Bayes Factors to establish whether oura priorimodels adequately describe the dynamics of adaptive peak shifts. Furthermore, we show how the inclusion of informative priors can be used to restrict models to biologically realistic parameter space and test particular biological interpretations of evolutionary models. We argue that Bayesian model-fitting of OU models to comparative data provides a framework for integrating of multiple sources of biological data--such as microevolutionary estimates of selection parameters and paleontological timeseries--allowing inference of adaptive landscape dynamics with explicit, process-based biological interpretations.

Study of Biochemical and Clinical Markers in Steatohepatitis Related to Obesity

2018 ◽  
Vol 69 (6) ◽  
pp. 1501-1505
Author(s):  
Roxana Maria Livadariu ◽  
Radu Danila ◽  
Lidia Ionescu ◽  
Delia Ciobanu ◽  
Daniel Timofte
Keyword(s):  
Liver Disease ◽  
Liver Biopsy ◽  
Large Scale ◽  
Biological Data ◽  
Obese Patients ◽  
Clinical Markers ◽  
Mean Values ◽  
Nonalcoholic Fatty Liver ◽  
Obstructive Sleep ◽  
Increased Risk

Nonalcoholic fatty liver disease (NAFLD) is highly associated to obesity and comprises several liver diseases, from simple steatosis to steatohepatitis (NASH) with increased risk of developing progressive liver fibrosis, cirrhosis and hepatocellular carcinoma. Liver biopsy is the gold standard in diagnosing the disease, but it cannot be used in a large scale. The aim of the study was the assessment of some non-invasive clinical and biological markers in relation to the progressive forms of NAFLD. We performed a prospective study on 64 obese patients successively hospitalised for bariatric surgery in our Surgical Unit. Patients with history of alcohol consumption, chronic hepatitis B or C, other chronic liver disease or patients undergoing hepatotoxic drug use were excluded. All patients underwent liver biopsy during sleeve gastrectomy. NAFLD was present in 100% of the patients: hepatic steatosis (38%), NASH with the two forms: with fibrosis (31%) and without fibrosis (20%), cumulating 51%; 7 patients had NASH with vanished steatosis. NASH with fibrosis statistically correlated with metabolic syndrome (p = 0.036), DM II (p = 0.01) and obstructive sleep apnea (p = 0.02). Waist circumference was significantly higher in the steatohepatitis groups (both with and without fibrosis), each 10 cm increase increasing the risk of steatohepatitis (p = 0.007). The mean values of serum fibrinogen and CRP were significantly higher in patients having the progressive forms of NAFLD. Simple clinical and biological data available to the practitioner in medicine can be used to identify obese patients at high risk of NASH, aiming to direct them to specialized medical centers.

scholarly journals Mapping the adaptive landscape of a major agricultural pathogen reveals evolutionary constraints across heterogeneous environments

2021 ◽  
Author(s):  
Anik Dutta ◽  
Fanny E. Hartmann ◽  
Carolina Sardinha Francisco ◽  
Bruce A. McDonald ◽  
Daniel Croll
Keyword(s):  
Life History ◽  
Large Scale ◽  
Life History Traits ◽  
Genetic Correlations ◽  
Stress Factors ◽  
Life History Trait ◽  
Adaptive Landscape ◽  
Heterogeneous Environments ◽  
Growth Responses ◽  
Trade Offs

AbstractThe adaptive potential of pathogens in novel or heterogeneous environments underpins the risk of disease epidemics. Antagonistic pleiotropy or differential resource allocation among life-history traits can constrain pathogen adaptation. However, we lack understanding of how the genetic architecture of individual traits can generate trade-offs. Here, we report a large-scale study based on 145 global strains of the fungal wheat pathogen Zymoseptoria tritici from four continents. We measured 50 life-history traits, including virulence and reproduction on 12 different wheat hosts and growth responses to several abiotic stressors. To elucidate the genetic basis of adaptation, we used genome-wide association mapping coupled with genetic correlation analyses. We show that most traits are governed by polygenic architectures and are highly heritable suggesting that adaptation proceeds mainly through allele frequency shifts at many loci. We identified negative genetic correlations among traits related to host colonization and survival in stressful environments. Such genetic constraints indicate that pleiotropic effects could limit the pathogen’s ability to cause host damage. In contrast, adaptation to abiotic stress factors was likely facilitated by synergistic pleiotropy. Our study illustrates how comprehensive mapping of life-history trait architectures across diverse environments allows to predict evolutionary trajectories of pathogens confronted with environmental perturbations.

scholarly journals Accessible Routes Integrating Data from Multiple Sources

2020 ◽  
Vol 10 (1) ◽  
pp. 7
Author(s):  
Miguel R. Luaces ◽  
Jesús A. Fisteus ◽  
Luis Sánchez-Fernández ◽  
Mario Munoz-Organero ◽  
Jesús Balado ◽  
...  
Keyword(s):  
Information System ◽  
Data Model ◽  
Large Scale ◽  
Heterogeneous Data ◽  
Multiple Sources ◽  
Heterogeneous Data Sources ◽  
Different Types ◽  
Software Sensors ◽  
The City

Providing citizens with the ability to move around in an accessible way is a requirement for all cities today. However, modeling city infrastructures so that accessible routes can be computed is a challenge because it involves collecting information from multiple, large-scale and heterogeneous data sources. In this paper, we propose and validate the architecture of an information system that creates an accessibility data model for cities by ingesting data from different types of sources and provides an application that can be used by people with different abilities to compute accessible routes. The article describes the processes that allow building a network of pedestrian infrastructures from the OpenStreetMap information (i.e., sidewalks and pedestrian crossings), improving the network with information extracted obtained from mobile-sensed LiDAR data (i.e., ramps, steps, and pedestrian crossings), detecting obstacles using volunteered information collected from the hardware sensors of the mobile devices of the citizens (i.e., ramps and steps), and detecting accessibility problems with software sensors in social networks (i.e., Twitter). The information system is validated through its application in a case study in the city of Vigo (Spain).

scholarly journals PEDRERA. Positive Energy District Renovation Model for Large Scale Actions

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2833
Author(s):  
Paolo Civiero ◽  
Jordi Pascual ◽  
Joaquim Arcas Abella ◽  
Ander Bilbao Figuero ◽  
Jaume Salom
Keyword(s):  
Simulation Model ◽  
Performance Indicators ◽  
Large Scale ◽  
Key Performance Indicators ◽  
Positive Energy ◽  
Design Phase ◽  
Multiple Sources ◽  
Reliable Prediction ◽  
Sensitive Analysis ◽  
Web Platform

In this paper, we provide a view of the ongoing PEDRERA project, whose main scope is to design a district simulation model able to set and analyze a reliable prediction of potential business scenarios on large scale retrofitting actions, and to evaluate the overall co-benefits resulting from the renovation process of a cluster of buildings. According to this purpose and to a Positive Energy Districts (PEDs) approach, the model combines systemized data—at both building and district scale—from multiple sources and domains. A sensitive analysis of 200 scenarios provided a quick perception on how results will change once inputs are defined, and how attended results will answer to stakeholders’ requirements. In order to enable a clever input analysis and to appraise wide-ranging ranks of Key Performance Indicators (KPIs) suited to each stakeholder and design phase targets, the model is currently under the implementation in the urbanZEB tool’s web platform.

scholarly journals Graph Convolutional Network for Drug Response Prediction Using Gene Expression Data

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 772
Author(s):  
Seonghun Kim ◽  
Seockhun Bae ◽  
Yinhua Piao ◽  
Kyuri Jo
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Large Scale ◽  
Drug Response ◽  
Response Prediction ◽  
Biological Data ◽  
Expression Data ◽  
Convolutional Network ◽  
Essential Information ◽  
Protein Protein Interaction

Genomic profiles of cancer patients such as gene expression have become a major source to predict responses to drugs in the era of personalized medicine. As large-scale drug screening data with cancer cell lines are available, a number of computational methods have been developed for drug response prediction. However, few methods incorporate both gene expression data and the biological network, which can harbor essential information about the underlying process of the drug response. We proposed an analysis framework called DrugGCN for prediction of Drug response using a Graph Convolutional Network (GCN). DrugGCN first generates a gene graph by combining a Protein-Protein Interaction (PPI) network and gene expression data with feature selection of drug-related genes, and the GCN model detects the local features such as subnetworks of genes that contribute to the drug response by localized filtering. We demonstrated the effectiveness of DrugGCN using biological data showing its high prediction accuracy among the competing methods.

scholarly journals Developing a vocabulary and ontology for modeling insect natural history data: example data, use cases, and competency questions

2019 ◽  
Vol 7 ◽  
Author(s):  
Brian Stucky ◽  
James Balhoff ◽  
Narayani Barve ◽  
Vijay Barve ◽  
Laura Brenskelle ◽  
...  
Keyword(s):  
Natural History ◽  
Large Scale ◽  
Use Cases ◽  
Data Systems ◽  
Multiple Sources ◽  
History Data ◽  
Insect Ecology ◽  
Many Sources ◽  
Multicellular Organisms ◽  
Initial Results

Insects are possibly the most taxonomically and ecologically diverse class of multicellular organisms on Earth. Consequently, they provide nearly unlimited opportunities to develop and test ecological and evolutionary hypotheses. Currently, however, large-scale studies of insect ecology, behavior, and trait evolution are impeded by the difficulty in obtaining and analyzing data derived from natural history observations of insects. These data are typically highly heterogeneous and widely scattered among many sources, which makes developing robust information systems to aggregate and disseminate them a significant challenge. As a step towards this goal, we report initial results of a new effort to develop a standardized vocabulary and ontology for insect natural history data. In particular, we describe a new database of representative insect natural history data derived from multiple sources (but focused on data from specimens in biological collections), an analysis of the abstract conceptual areas required for a comprehensive ontology of insect natural history data, and a database of use cases and competency questions to guide the development of data systems for insect natural history data. We also discuss data modeling and technology-related challenges that must be overcome to implement robust integration of insect natural history data.

scholarly journals A trait-based approach to assess climate change sensitivity of freshwater invertebrates across Swedish ecoregions

2014 ◽  
Vol 60 (2) ◽  
pp. 221-232 ◽  
Author(s):  
Leonard Sandin ◽  
Astrid Schmidt-Kloiber ◽  
Jens-Christian Svenning ◽  
Erik Jeppesen ◽  
Nikolai Friberg
Keyword(s):  
Climate Change ◽  
Global Climate Change ◽  
Large Scale ◽  
Global Climate ◽  
Species Traits ◽  
Freshwater Ecosystems ◽  
Biological Data ◽  
Conservation Strategies ◽  
Measured Temperature ◽  
Buffer Strips

Abstract Freshwater habitats and organisms are among the most threatened on Earth, and freshwater ecosystems have been subject to large biodiversity losses. We developed a Climate Change Sensitivity (CCS) indicator based on trait information for a selection of stream- and lake-dwelling Ephemeroptera, Plecoptera and Trichoptera taxa. We calculated the CCS scores based on ten species traits identified as sensitive to global climate change. We then assessed climate change sensitivity between the six main ecoregions of Sweden as well as the three Swedish regions based on Illies. This was done using biological data from 1, 382 stream and lake sites where we compared large-scale (ecoregional) patterns in climate change sensitivity with potential future exposure of these ecosystems to increased temperatures using ensemble-modelled future changes in air temperature. Current (1961~1990) measured temperature and ensemble-modelled future (2100) temperature showed an increase from the northernmost towards the southern ecoregions, whereas the predicted temperature change increased from south to north. The CCS indicator scores were highest in the two northernmost boreal ecoregions where we also can expect the largest global climate change-induced increase in temperature, indicating an unfortunate congruence of exposure and sensitivity to climate change. These results are of vital importance when planning and implementing management and conservation strategies in freshwater ecosystems, e.g., to mitigate increased temperatures using riparian buffer strips. We conclude that traits information on taxa specialization, e.g., in terms of feeding specialism or taxa having a preference for high altitudes as well as sensitivity to changes in temperature are important when assessing the risk from future global climate change to freshwater ecosystems.

scholarly journals A Systematic Framework for Drug Repositioning from Integrated Omics and Drug Phenotype Profiles Using Pathway-Drug Network

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Erkhembayar Jadamba ◽  
Miyoung Shin
Keyword(s):  
Breast Cancer ◽  
Drug Repositioning ◽  
Expression Profiles ◽  
A Priori ◽  
Gene Expression Profiles ◽  
Biological Data ◽  
Genomic Knowledge ◽  
High Level ◽  
Systematic Framework ◽  
Phenotype Expression

Drug repositioning offers new clinical indications for old drugs. Recently, many computational approaches have been developed to repurpose marketed drugs in human diseases by mining various of biological data including disease expression profiles, pathways, drug phenotype expression profiles, and chemical structure data. However, despite encouraging results, a comprehensive and efficient computational drug repositioning approach is needed that includes the high-level integration of available resources. In this study, we propose a systematic framework employing experimental genomic knowledge and pharmaceutical knowledge to reposition drugs for a specific disease. Specifically, we first obtain experimental genomic knowledge from disease gene expression profiles and pharmaceutical knowledge from drug phenotype expression profiles and construct a pathway-drug network representing a priori known associations between drugs and pathways. To discover promising candidates for drug repositioning, we initialize node labels for the pathway-drug network using identified disease pathways and known drugs associated with the phenotype of interest and perform network propagation in a semisupervised manner. To evaluate our method, we conducted some experiments to reposition 1309 drugs based on four different breast cancer datasets and verified the results of promising candidate drugs for breast cancer by a two-step validation procedure. Consequently, our experimental results showed that the proposed framework is quite useful approach to discover promising candidates for breast cancer treatment.

EARRINGS: an efficient and accurate adapter trimmer entails no a priori adapter sequences

2021 ◽  
Author(s):  
Ting-Hsuan Wang ◽  
Cheng-Ching Huang ◽  
Jui-Hung Hung
Keyword(s):  
Open Source Software ◽  
Large Scale ◽  
A Priori ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Comparable Accuracy ◽  
Meta Analyses ◽  
Next Generation Sequencing Ngs ◽  
Adapter Trimming ◽  
Generation Sequencing

Abstract Motivation Cross-sample comparisons or large-scale meta-analyses based on the next generation sequencing (NGS) involve replicable and universal data preprocessing, including removing adapter fragments in contaminated reads (i.e. adapter trimming). While modern adapter trimmers require users to provide candidate adapter sequences for each sample, which are sometimes unavailable or falsely documented in the repositories (such as GEO or SRA), large-scale meta-analyses are therefore jeopardized by suboptimal adapter trimming. Results Here we introduce a set of fast and accurate adapter detection and trimming algorithms that entail no a priori adapter sequences. These algorithms were implemented in modern C++ with SIMD and multithreading to accelerate its speed. Our experiments and benchmarks show that the implementation (i.e. EARRINGS), without being given any hint of adapter sequences, can reach comparable accuracy and higher throughput than that of existing adapter trimmers. EARRINGS is particularly useful in meta-analyses of a large batch of datasets and can be incorporated in any sequence analysis pipelines in all scales. Availability and implementation EARRINGS is open-source software and is available at https://github.com/jhhung/EARRINGS. Supplementary information Supplementary data are available at Bioinformatics online.

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