scholarly journals Assessing the durability and efficiency of landscape-based strategies to deploy plant resistance to pathogens

2018 ◽  
Author(s):  
Loup Rimbaud ◽  
Julien Papaïx ◽  
Jean-François Rey ◽  
Luke G. Barrett ◽  
Peter H. Thrall
Keyword(s):  
Quantitative Resistance ◽  
R Package ◽  
Cereal Crops ◽  
Evolutionary Potential ◽  
Sources Of Resistance ◽  
Resistance Sources ◽  
Wide Range ◽  
Resistance Breakdown ◽  
Spatio Temporal ◽  
Period Duration

AbstractGenetically-controlled plant resistance can reduce the damage caused by pathogens. However, pathogens have the ability to evolve and overcome such resistance. This often occurs quickly after resistance is deployed, resulting in significant crop losses and a continuing need to develop new resistant cultivars. To tackle this issue, several strategies have been proposed to constrain the evolution of pathogen populations and thus increase genetic resistance durability. These strategies mainly rely on varying different combinations of resistance sources across time (crop rotations) and space. The spatial scale of deployment can vary from multiple resistance sources occurring in a single cultivar (pyramiding), in different cultivars within the same field (cultivar mixtures) or in different fields (mosaics). However, experimental comparison of the efficiency (i.e. ability to reduce disease impact) and durability (i.e. ability to limit pathogen evolution and delay resistance breakdown) of landscape-scale deployment strategies presents major logistical challenges.Therefore, we developed a spatially explicit stochastic model able to assess the epidemiological and evolutionary outcomes of the four major deployment options described above, including both qualitative resistance (i.e. major genes) and quantitative resistance traits against several components of pathogen aggressiveness: infection rate, latent period duration, propagule production rate, and infectious period duration. This model, implemented in the R package landsepi, provides a new and useful tool to assess the performance of a wide range of deployment options, and helps investigate the effect of landscape, epidemiological and evolutionary parameters.This article describes the model and its parameterisation for rust diseases of cereal crops, caused by fungi of the genus Puccinia. To illustrate the model, we use it to assess the epidemiological and evolutionary potential of the combination of a major gene and different traits of quantitative resistance. The comparison of the four major deployment strategies described above will be the objective of future studies.Author summaryThere are many recent examples which demonstrate the evolutionary potential of plant pathogens to overcome the resistances deployed in agricultural landscapes to protect our crops. Increasingly, it is recognised that how resistance is deployed spatially and temporally can impact on rates of pathogen evolution and resistance breakdown. Such deployment strategies are mainly based on the combination of several sources of resistance at different spatiotemporal scales. However, comparison of these strategies in a predictive sense is not an easy task, owing to the logistical difficulties associated with experiments involving the spread of a pathogen at large spatio-temporal scales. Moreover, both the durability of a strategy and the epidemiological protection it provides to crops must be assessed since these evaluation criteria are not necessarily correlated. Surprisingly, no current simulation model allows a thorough comparison of the different options. Here we describe a spatio-temporal model able to simulate a wide range of deployment strategies and resistance sources. This model, implemented in the R package landsepi, facilitates assessment of both epidemiological and evolutionary outcomes across simulated scenarios. In this work, the model is used to investigate the combination of different sources of resistance against fungal diseases such as rusts of cereal crops.

scholarly journals Fuzzy Quantification of Common and Rare Species in Ecological Communities (FuzzyQ)

2020 ◽  
Author(s):  
Juan A. Balbuena ◽  
Clara Montlleó ◽  
Cristina Llopis-Belenguer ◽  
Isabel Blasco-Costa ◽  
Volodimir L. Sarabeev ◽  
...  
Keyword(s):  
Rare Species ◽  
Clustering Algorithm ◽  
Species Abundance ◽  
R Package ◽  
Ecological Communities ◽  
Wide Range ◽  
Species Abundance Distributions ◽  
The Common ◽  
Spatio Temporal ◽  
The Impact

Abstract1. Most species in ecological communities are rare whereas only a few are common. This distributional paradox has intrigued ecologists for decades but the interpretation of species abundance distributions remains elusive.2. We present Fuzzy Quantification of Common and Rare Species in Ecological Communities (FuzzyQ) as an R package. FuzzyQ shifts the focus from the prevailing species-categorization approach to develop a quantitative framework that seeks to place each species along a rare-commonness gradient. Given a community surveyed over a number of sites, quadrats, or any other convenient sampling unit, FuzzyQ uses a fuzzy clustering algorithm that estimates a probability for each species to be common or rare based on abundance-occupancy information. Such as probability can be interpreted as a commonness index ranging from 0 to 1. FuzzyQ also provides community-level metrics about the coherence of the allocation of species into the common and rare clusters that are informative of the nature of the community under study.3. The functionality of FuzzyQ is shown with two real datasets. We demonstrate how FuzzyQ can effectively be used to monitor and model spatio-temporal changes in species commonness, and assess the impact of species introductions on ecological communities. We also show that the approach works satisfactorily with a wide range of communities varying in species richness, dispersion and abundance currencies.4. FuzzyQ produces ecological indicators easy to measure and interpret that can give both clear, actionable insights into the nature of ecological communities and provides a powerful way to monitor environmental change on ecosystems. Comparison among communities is greatly facilitated by the fact that the method is relatively independent of the number of sites or sampling units considered. Thus, we consider FuzzyQ as a potentially valuable analytical tool in community ecology and conservation biology.

scholarly journals Kinematic Analysis of Lower Limb Joint Asymmetry during Gait in People with Multiple Sclerosis

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 598
Author(s):  
Massimiliano Pau ◽  
Bruno Leban ◽  
Michela Deidda ◽  
Federica Putzolu ◽  
Micaela Porta ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Lower Limb ◽  
Sagittal Plane ◽  
Cross Sectional Study ◽  
Camera Motion ◽  
Cross Sectional ◽  
Temporal Parameters ◽  
Wide Range ◽  
Edss Score ◽  
Spatio Temporal

The majority of people with Multiple Sclerosis (pwMS), report lower limb motor dysfunctions, which may relevantly affect postural control, gait and a wide range of activities of daily living. While it is quite common to observe a different impact of the disease on the two limbs (i.e., one of them is more affected), less clear are the effects of such asymmetry on gait performance. The present retrospective cross-sectional study aimed to characterize the magnitude of interlimb asymmetry in pwMS, particularly as regards the joint kinematics, using parameters derived from angle-angle diagrams. To this end, we analyzed gait patterns of 101 pwMS (55 women, 46 men, mean age 46.3, average Expanded Disability Status Scale (EDSS) score 3.5, range 1–6.5) and 81 unaffected individuals age- and sex-matched who underwent 3D computerized gait analysis carried out using an eight-camera motion capture system. Spatio-temporal parameters and kinematics in the sagittal plane at hip, knee and ankle joints were considered for the analysis. The angular trends of left and right sides were processed to build synchronized angle–angle diagrams (cyclograms) for each joint, and symmetry was assessed by computing several geometrical features such as area, orientation and Trend Symmetry. Based on cyclogram orientation and Trend Symmetry, the results show that pwMS exhibit significantly greater asymmetry in all three joints with respect to unaffected individuals. In particular, orientation values were as follows: 5.1 of pwMS vs. 1.6 of unaffected individuals at hip joint, 7.0 vs. 1.5 at knee and 6.4 vs. 3.0 at ankle (p < 0.001 in all cases), while for Trend Symmetry we obtained at hip 1.7 of pwMS vs. 0.3 of unaffected individuals, 4.2 vs. 0.5 at knee and 8.5 vs. 1.5 at ankle (p < 0.001 in all cases). Moreover, the same parameters were sensitive enough to discriminate individuals of different disability levels. With few exceptions, all the calculated symmetry parameters were found significantly correlated with the main spatio-temporal parameters of gait and the EDSS score. In particular, large correlations were detected between Trend Symmetry and gait speed (with rho values in the range of –0.58 to –0.63 depending on the considered joint, p < 0.001) and between Trend Symmetry and EDSS score (rho = 0.62 to 0.69, p < 0.001). Such results suggest not only that MS is associated with significantly marked interlimb asymmetry during gait but also that such asymmetry worsens as the disease progresses and that it has a relevant impact on gait performances.

scholarly journals BloodGen3Module: Blood transcriptional module repertoire analysis and visualization using R

2021 ◽  
Author(s):  
Darawan Rinchai ◽  
Jessica Roelands ◽  
Mohammed Toufiq ◽  
Wouter Hendrickx ◽  
Matthew C Altman ◽  
...  
Keyword(s):  
Transcript Abundance ◽  
R Package ◽  
Supplementary Information ◽  
Illustrative Case ◽  
Bioinformatic Tools ◽  
Transcriptional Module ◽  
Wide Range ◽  
Downstream Analysis ◽  
Computing Module ◽  
Parallel Workflow

Abstract Motivation We previously described the construction and characterization of generic and reusable blood transcriptional module repertoires. More recently we released a third iteration (“BloodGen3” module repertoire) that comprises 382 functionally annotated gene sets (modules) and encompasses 14,168 transcripts. Custom bioinformatic tools are needed to support downstream analysis, visualization and interpretation relying on such fixed module repertoires. Results We have developed and describe here a R package, BloodGen3Module. The functions of our package permit group comparison analyses to be performed at the module-level, and to display the results as annotated fingerprint grid plots. A parallel workflow for computing module repertoire changes for individual samples rather than groups of samples is also available; these results are displayed as fingerprint heatmaps. An illustrative case is used to demonstrate the steps involved in generating blood transcriptome repertoire fingerprints of septic patients. Taken together, this resource could facilitate the analysis and interpretation of changes in blood transcript abundance observed across a wide range of pathological and physiological states. Availability The BloodGen3Module package and documentation are freely available from Github: https://github.com/Drinchai/BloodGen3Module Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals MUREN: a robust and multi-reference approach of RNA-seq transcript normalization

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yance Feng ◽  
Lei M. Li
Keyword(s):  
Biological Significance ◽  
Housekeeping Genes ◽  
R Package ◽  
Data Sets ◽  
Statistical Regression ◽  
Rna Seq ◽  
Least Trimmed Squares ◽  
Standard Data ◽  
Wide Range ◽  
Multiple References

Abstract Background Normalization of RNA-seq data aims at identifying biological expression differentiation between samples by removing the effects of unwanted confounding factors. Explicitly or implicitly, the justification of normalization requires a set of housekeeping genes. However, the existence of housekeeping genes common for a very large collection of samples, especially under a wide range of conditions, is questionable. Results We propose to carry out pairwise normalization with respect to multiple references, selected from representative samples. Then the pairwise intermediates are integrated based on a linear model that adjusts the reference effects. Motivated by the notion of housekeeping genes and their statistical counterparts, we adopt the robust least trimmed squares regression in pairwise normalization. The proposed method (MUREN) is compared with other existing tools on some standard data sets. The goodness of normalization emphasizes on preserving possible asymmetric differentiation, whose biological significance is exemplified by a single cell data of cell cycle. MUREN is implemented as an R package. The code under license GPL-3 is available on the github platform: github.com/hippo-yf/MUREN and on the conda platform: anaconda.org/hippo-yf/r-muren. Conclusions MUREN performs the RNA-seq normalization using a two-step statistical regression induced from a general principle. We propose that the densities of pairwise differentiations are used to evaluate the goodness of normalization. MUREN adjusts the mode of differentiation toward zero while preserving the skewness due to biological asymmetric differentiation. Moreover, by robustly integrating pre-normalized counts with respect to multiple references, MUREN is immune to individual outlier samples.

Quantitative Resistance to witches’ broom disease in progenies of different sources of resistance.

2021 ◽  
pp. 105678
Author(s):  
Alezania Silva Pereira ◽  
Uilson Vanderlei Lopes ◽  
José Luís Pires ◽  
Mariana Araujo Barreto ◽  
Lindolfo Pereira dos Santos ◽  
...  
Keyword(s):  
Quantitative Resistance ◽  
Sources Of Resistance ◽  
Broom Disease ◽  
Different Sources

Forest path condition monitoring based on crowd-based trajectory data analysis

2020 ◽  
pp. 1-18
Author(s):  
Francisco Arcas-Tunez ◽  
Fernando Terroso-Saenz
Keyword(s):  
Information Acquisition ◽  
Road Network ◽  
Road Networks ◽  
Trajectory Data ◽  
Country Road ◽  
Wide Range ◽  
Sport Activities ◽  
Path Condition ◽  
Spatio Temporal ◽  
The City

The development of Road Information Acquisition Systems (RIASs) based on the Mobile Crowdsensing (MCS) paradigm has been widely studied for the last years. In that sense, most of the existing MCS-based RIASs focus on urban road networks and assume a car-based scenario. However, there exist a scarcity of approaches that pay attention to rural and country road networks. In that sense, forest paths are used for a wide range of recreational and sport activities by many different people and they can be also affected by different problems or obstacles blocking them. As a result, this work introduces SAMARITAN, a framework for rural-road network monitoring based on MCS. SAMARITAN analyzes the spatio-temporal trajectories from cyclists extracted from the fitness application Strava so as to uncover potential obstacles in a target road network. The framework has been evaluated in a real-world network of forest paths in the city of Cieza (Spain) showing quite promising results.

scholarly journals In-field screening for host plant resistance to Delia radicum and Brevicoryne brassicae within selected rapeseed cultivars and new interspecific hybrids

2020 ◽  
Vol 15 (1) ◽  
pp. 711-720
Author(s):  
Janetta Niemann ◽  
Justyna Szwarc ◽  
Jan Bocianowski ◽  
Dorota Weigt ◽  
Marek Mrówczyński
Keyword(s):  
Life Sciences ◽  
Field Trials ◽  
Brevicoryne Brassicae ◽  
Delia Radicum ◽  
Sources Of Resistance ◽  
Cabbage Aphid ◽  
Hybrid Combination ◽  
Resistance To Insects ◽  
Wide Range ◽  
High Level

AbstractRapeseed (Brassica napus) can be attacked by a wide range of pests, for example, cabbage root fly (Delia radicum) and cabbage aphid (Brevicoryne brassicae). One of the best methods of pest management is breeding for insect resistance in rapeseed. Wild genotypes of Brassicaceae and rapeseed cultivars can be used as a source of resistance. In 2017, 2018, and 2019, field trials were performed to assess the level of resistance to D. radicum and B. brassicae within 53 registered rapeseed cultivars and 31 interspecific hybrid combinations originating from the resources of the Department of Genetics and Plant Breeding of Poznań University of Life Sciences (PULS). The level of resistance varied among genotypes and years. Only one hybrid combination and two B. napus cultivars maintained high level of resistance in all tested years, i.e., B. napus cv. Jet Neuf × B. carinata – PI 649096, Galileus, and Markolo. The results of this research indicate that resistance to insects is present in Brassicaceae family and can be transferred to rapeseed cultivars. The importance of continuous improvement of rapeseed pest resistance and the search for new sources of resistance is discussed; furthermore, plans for future investigations are presented.

Predicting Human Mobility with Reinforcement-Learning-Based Long-Term Periodicity Modeling

2021 ◽  
Vol 12 (6) ◽  
pp. 1-23
Author(s):  
Shuo Tao ◽  
Jingang Jiang ◽  
Defu Lian ◽  
Kai Zheng ◽  
Enhong Chen
Keyword(s):  
Reinforcement Learning ◽  
Human Mobility ◽  
Recurrent Network ◽  
Mobility Prediction ◽  
Learning Framework ◽  
Temporal Features ◽  
Wide Range ◽  
Spatio Temporal ◽  
Historical Trajectory

Mobility prediction plays an important role in a wide range of location-based applications and services. However, there are three problems in the existing literature: (1) explicit high-order interactions of spatio-temporal features are not systemically modeled; (2) most existing algorithms place attention mechanisms on top of recurrent network, so they can not allow for full parallelism and are inferior to self-attention for capturing long-range dependence; (3) most literature does not make good use of long-term historical information and do not effectively model the long-term periodicity of users. To this end, we propose MoveNet and RLMoveNet. MoveNet is a self-attention-based sequential model, predicting each user’s next destination based on her most recent visits and historical trajectory. MoveNet first introduces a cross-based learning framework for modeling feature interactions. With self-attention on both the most recent visits and historical trajectory, MoveNet can use an attention mechanism to capture the user’s long-term regularity in a more efficient way. Based on MoveNet, to model long-term periodicity more effectively, we add the reinforcement learning layer and named RLMoveNet. RLMoveNet regards the human mobility prediction as a reinforcement learning problem, using the reinforcement learning layer as the regularization part to drive the model to pay attention to the behavior with periodic actions, which can help us make the algorithm more effective. We evaluate both of them with three real-world mobility datasets. MoveNet outperforms the state-of-the-art mobility predictor by around 10% in terms of accuracy, and simultaneously achieves faster convergence and over 4x training speedup. Moreover, RLMoveNet achieves higher prediction accuracy than MoveNet, which proves that modeling periodicity explicitly from the perspective of reinforcement learning is more effective.

An open-source R-package and web application for high-quality probabilistic predictions in hydrology

2021 ◽  
Author(s):  
Jason Hunter ◽  
Mark Thyer ◽  
Dmitri Kavetski ◽  
David McInerney
Keyword(s):  
Open Source ◽  
Web Application ◽  
R Package ◽  
Error Model ◽  
Objective Functions ◽  
High Quality ◽  
Wide Range ◽  
Probabilistic Error

&lt;p&gt;Probabilistic predictions provide crucial information regarding the uncertainty of hydrological predictions, which are a key input for risk-based decision-making. However, they are often excluded from hydrological modelling applications because suitable probabilistic error models can be both challenging to construct and interpret, and the quality of results are often reliant on the objective function used to calibrate the hydrological model.&lt;/p&gt;&lt;p&gt;We present an open-source R-package and an online web application that achieves the following two aims. Firstly, these resources are easy-to-use and accessible, so that users need not have specialised knowledge in probabilistic modelling to apply them. Secondly, the probabilistic error model that we describe provides high-quality probabilistic predictions for a wide range of commonly-used hydrological objective functions, which it is only able to do by including a new innovation that resolves a long-standing issue relating to model assumptions that previously prevented this broad application. &amp;#160;&lt;/p&gt;&lt;p&gt;We demonstrate our methods by comparing our new probabilistic error model with an existing reference error model in an empirical case study that uses 54 perennial Australian catchments, the hydrological model GR4J, 8 common objective functions and 4 performance metrics (reliability, precision, volumetric bias and errors in the flow duration curve). The existing reference error model introduces additional flow dependencies into the residual error structure when it is used with most of the study objective functions, which in turn leads to poor-quality probabilistic predictions. In contrast, the new probabilistic error model achieves high-quality probabilistic predictions for all objective functions used in this case study.&lt;/p&gt;&lt;p&gt;The new probabilistic error model and the open-source software and web application aims to facilitate the adoption of probabilistic predictions in the hydrological modelling community, and to improve the quality of predictions and decisions that are made using those predictions. In particular, our methods can be used to achieve high-quality probabilistic predictions from hydrological models that are calibrated with a wide range of common objective functions.&lt;/p&gt;

RESISTANCE TO DIEBACK DISEASE CAUSED BY FUSARIUM AND LASIODIPLODIA SPECIES IN CACAO (THEOBROMA CACAO L.) GENOTYPES

2011 ◽  
Vol 48 (1) ◽  
pp. 85-98 ◽  
Author(s):  
RICHARD ADU-ACHEAMPONG ◽  
SIMON ARCHER ◽  
SIMON LEATHER
Keyword(s):  
Biological Sciences ◽  
Theobroma Cacao ◽  
Genotypic Differences ◽  
Sources Of Resistance ◽  
Cross Sectional ◽  
Assessment Scores ◽  
Sahlbergella Singularis ◽  
Wide Range ◽  
Dieback Disease ◽  
Moderately Resistant

SUMMARYFusarium and Lasiodiplodia species invade feeding lesions caused by mirid bugs (Distantiella theobroma [Dist.] and Sahlbergella singularis Hagl.) and inflict serious damage and yield loss to susceptible cacao (Theobroma cacao L.) varieties in West Africa. As it is the fungal invasion rather than the physical feeding injury by mirids that cause dieback and tree death in cacao, a dieback resistance strategy in cacao crop must take into account resistance to these causal agents. Twenty-nine and 15 cacao genotypes were screened in the laboratory and the greenhouse, respectively, for resistance to isolates of Fusarium decemcellulare and Lasiodiplodia theobromae at Imperial College London's Biological Sciences Campus, UK. Resistance was assessed as the size of necrotic lesions, distance of fungal colonisation in the stem and the proportion of seedlings with dieback symptoms. Genotypic differences were found in both laboratory and greenhouse tests among various cacao genotypes, and the clones showed a wide range of disease reactions from highly resistant to very susceptible. The pathogenicity of F. decemcellulare and L. theobromae were similar in this study, which suggests that a breeding programme for controlling one of the pathogens can have benefit against the other. Direct significant correlations (r = 0.7) were obtained between visual dieback assessment scores and the percentage cross-sectional area of stem necrosis. Moreover, the response of inoculated stem segments corresponded to the reaction of intact plants despite the variation in the used methodology. Three cacao genotypes (CATIE 1000, T85/799 and MXC 67) were resistant or moderately resistant to F. decemcellulare and L. theobromae. These genotypes could be useful sources of resistance to both pathogens and other wilt causing pathogens in cacao.

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