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opencc-by-4.0Jul 2020View details →

Output of Tax4Fun of predicted functional profiles. KO IDs and a KO abundance in every samples after a process of normalization.

Supplementary Material Chapter 1.   Table # 3: Output of Tax4Fun of predicted functional profiles. KO IDs and a KO abundance in every samples after a process of normalization.

opencc-zeroJun 2020View details →

Data from: Bacterial phylogeny predicts volatile organic compound composition and olfactory response of an aphid parasitoid

There is increasing evidence that microorganisms emit a wide range of volatile compounds (mVOCs, microbial volatile organic compounds) that act as insect semiochemicals, and therefore play an important role in insect behaviour. Although it is generally believed that phylogenetically closely related microbes tend to have similar phenotypic characteristics and therefore may elicit similar responses in insects, currently little is known about whether the evolutionary history and phylogenetic relationships among microorganisms have an impact on insect-microbe interactions. In this study, we tested the hypothesis that phylogenetic relationships among 40 Bacillus strains isolated from diverse environmental sources predicted mVOC composition and the olfactory response of the generalist aphid parasitoid Aphidius colemani. Results revealed that phylogenetically closely related Bacillus strains emitted similar blends of mVOCs and elicited a comparable olfactory response of A. colemani in Y-tube olfactometer bioassays, varying between attraction and repellence. Analysis of the chemical composition of the mVOC blends showed that all Bacillus strains produced a highly similar set of volatiles, but often in different concentrations and ratios. Benzaldehyde was produced in relatively high concentrations by strains that repel A. colemani, while attractive mVOC blends contained relatively higher amounts of acetoin, 2,3-butanediol, 2,3-butanedione, eucalyptol and isoamylamine. Overall, these results indicate that bacterial phylogeny had a strong impact on mVOC compositions and as a result on the olfactory responses of insects.

opencc-zeroJul 2020View details →

Data from: Accumulation curves of environmental DNA sequences predict coastal fish diversity in the Coral Triangle

Environmental DNA (eDNA) has the potential to provide more comprehensive biodiversity assessments particularly for vertebrates in species-rich regions. Yet, this method requires the completeness of a reference database, i.e. a list of DNA sequences attached to each species, which is never met. As an alternative, a diversity of Operational Taxonomic Units (OTUs) can be extracted from eDNA metabarcoding. However, the extent to which the diversity of OTUs provided by a limited eDNA sampling effort can predict regional species diversity is unknown. Here, by modelling OTU accumulation curves of eDNA seawater samples across the Coral Triangle, we obtained an asymptote reaching 1,531 fish OTUs while 1,611 fish species are recorded in the region. Besides, we also accurately predict (R² = 0.92) the distribution of species richness among fish families from OTU-based asymptotes. Thus, the multi-model framework of OTU accumulation curves extends the use of eDNA metabarcoding in ecology, biogeography and conservation.

opencc-zeroDec 2019View details →

Data from: Chemical structure predicts the effect of plant-derived low-molecular weight compounds on soil microbiome structure and pathogen suppression

1. Plant-derived low molecular weight compounds play a crucial role in shaping soil microbiome functionality. While various compounds have been demonstrated to affect soil microbes, most data are case-specific and do not provide generalizable predictions on their effects. Here we show that the chemical structural affiliation of low molecular weight compounds typically secreted by plant roots – sugars, amino acids, organic acids and phenolic acids – can predictably affect microbiome diversity, composition and functioning in terms of plant disease suppression. 2. We amended soil with single or mixtures of representative compounds, mimicking carbon deposition by plants. We then assessed how different classes of compounds, or their combinations, affected microbiome composition and the protection of tomato plants from the soil-borne Ralstonia solanacearum bacterial pathogen. 3. We found that chemical class predicted well the changes in microbiome composition and diversity. Organic and amino acids generally decreased the microbiome diversity compared to sugars and phenolic acids. These changes were also linked to disease incidence, with amino acids and nitrogen-containing compound mixtures inducing more severe disease symptoms connected with a reduction in bacterial community diversity. 4. Together, our results demonstrate that low molecular weight compounds can predictably steer rhizosphere microbiome functioning providing guidelines to engineer microbiomes based on root exudation patterns by specific plant cultivars or crop regimes.

opencc-zeroJul 2020View details →

Data from: Genetic growth potential, rather than phenotypic size, predicts migration phenotype in Atlantic salmon

Knowledge of the relative importance of genetic versus environmental determinants of major developmental transitions is pertinent to understanding phenotypic evolution. In salmonid fishes, a major developmental transition enables a risky seaward migration that provides access to feed resources. In Atlantic salmon, initiation of the migrant phenotype, and thus age of migrants, is presumably controlled via thresholds of a quantitative liability, approximated by body size expressed long before the migration. However, how well size approximates liability, both genetically and environmentally, remains uncertain. We studied 32 Atlantic salmon families in two temperatures and feeding regimes (fully fed, temporarily restricted) to completion of migration status at age 1 year. We detected a lower migrant probability in the cold (0.42) than the warm environment (0.76), but no effects of male maturation status or feed restriction. By contrast, body length in late summer predicted migrant probability and its control reduced migrant probability heritability by 50–70%. Furthermore, migrant probability and length showed high heritabilities and between-environment genetic correlations, and were phenotypically highly correlated with stronger genetic than environmental contributions. Altogether, quantitative estimates for the genetic and environmental effects predicting the migrant phenotype indicate, for a given temperature, a larger importance of genetic than environmental size effects.

Data from: Long-term mechanistic hindcasts predict the structure of experimentally-warmed intertidal communities

Increases in global temperatures are expected to have dramatic effects on the abundance and distribution of species in the coming years. Intertidal organisms, which already experience temperatures at or beyond their thermal limits, provide a model system in which to investigate these effects. We took advantage of a previous study in which experimental plates were deployed in the intertidal zone and passively warmed for 12 years to a daily maximum temperature on average 2.7°C higher than control plots on the adjacent bedrock. We compared the composition of the biological communities on each experimental plate with its neighboring bedrock control. Plate communities showed decreased richness of taxa and percent cover of filamentous algae, mussels and mobile grazers relative to bedrock, and increased percent cover of biofilm. We then used short-term time-series measurements of plate and bedrock temperatures and a mechanistic heat-budget model to hindcast those temperatures back 12 years. Greater differences in long-term average temperature between the experimental plates and bedrock controls were correlated with lower similarity in community composition. Additionally, years with higher average differences between plate and bedrock temperatures were more predictive of current compositional similarity between plate and bedrock communities, even though they occurred farther in the past than did more recent, but cooler, years. We conclude that current intertidal communities reflect their long-term, rather than short-term, thermal histories. Mechanistic heat-budget models based on short-term measurements can provide this valuable, long-term information.

Climate more important than soils for predicting forest biomass at the continental scale

Above-ground biomass in forests is critical to the global carbon cycle as it stores and sequesters carbon from the atmosphere. Climate change will disrupt the carbon cycle hence understanding how climate and other abiotic variables determine forest biomass at broad spatial scales is important for validating and constraining Earth System models and predicting the impacts of climate change on forest carbon stores. We examined the importance of climate and soil variables to explaining above-ground biomass distribution across the Australian continent using publicly available biomass data from 3130 mature forest sites, in 6 broad ecoregions, encompassing tropical, subtropical, and temperate biomes. We used the Random Forest algorithm to test the explanatory power of 14 abiotic variables (8 climate, 6 soil) and to identify the best-performing models based on climate-only, soil-only, and climate plus soil. The best performing models explained ~50% of the variation (climate-only: R2 = 0.47 ± 0.04, and climate plus soils: R2 = 0.49 ± 0.04). Mean temperature of the driest quarter was the most important climate variable, and bulk density was the most important soil variable. Climate variables were consistently more important than soil variables in combined models, and model predictive performance was not substantively improved by the inclusion of soil variables. This result was also achieved when the analysis was repeated at the ecoregion scale. Predicted forest above-ground biomass ranged from 18 to 1066 Mg ha-1, often under-predicting measured above-ground biomass, which ranged from 7 to 1500 Mg ha-1. This suggested that other non-climate, non-edaphic variables impose a substantial influence on forest above-ground biomass, particularly in the high biomass range. We conclude that climate is a strong predictor of above-ground biomass at broad spatial scales and across large environmental gradients, yet to predict forest above-ground biomass distribution under future climates, other non-climatic factors must also be identified.

Predicted secondary structures (2-D representation of this self-folding) of the terminal untranslated regions (UTR) of FvMV1 and FaMV1-162

Schemes (a) and (c) represent the 5`-UTR and 3`-UTR of FvMV1 with dG = -82.52 and dG = -29.55, respectively. Schemes (b) and (d) represent the 5`-UTR and 3`-UTR of FaMV1-162 with dG = -73.96 and dG = -13.93, respectively. The +ssRNA molecules were folded, and the free energy was calculated with the RNA Folding Form V 2.3 Energies (MFOLD) program. For these calculations, the following conditions were sectioned: 25ºC, 1M NaCl and 0M divalent ions. The rendering of the structures has been defined with natural angles and annotated using colored base characters, based on p-num information. Colors are ranged from red to black representing the probability as well-determined (1) to poorly determined (0), respectively.

Application of spectral library prediction for parallel reaction monitoring of viral peptides_DDA_data

Project description: A major part of the analysis of parallel reaction monitoring (PRM) data is the comparison of observed fragment ion intensities to a library spectrum. Classically, these libraries are generated by data-dependent acquisition (DDA). Here we test Prosit, a published deep neural network algorithm, for its applicability in predicting spectral libraries for PRM. For this purpose, we targeted 1,529 precursors derived from synthetic viral peptides and analyzed the data with Prosit and DDA-derived libraries. Additionally, we used a spectral library predicted by Prosit and a DDA library to identify SARS-CoV-2 peptides from a simulated oropharyngeal swab.   Sample processing protocol: A total of 1,569 crude synthetic viral peptides were ordered in six pools from JPT (Berlin, Germany). Synthetic peptides were separated on a 200 cm μPAC™ column (PharmaFluidics) by using an EASY-nLC1200 system (Thermo Fisher Scientific) equipped with a μPAC™ trapping column (PharmaFluidics). The flow rate was set to 300 nL/min and a stepped linear 160 min gradient was applied: 3-10% B in 22 min, 10-33%B in 95 min, 33-49% B in 23 min, 49-80% B in 10 min and 80% B for 10 min. Solvent A was 0.1% (v/v) formic acid (FA) in water, solvent B consisted of 80% (v/v) acetonitrile in 0.1% (v/v) FA. The column temperature was set to 50 °C. The Q Exactive Plus (Thermo Fisher Scientific) operated in Full MS/dd-MS2 or unscheduled PRM mode. For MS/dd-MS2 the following parameters were used. MS1 resolution was 70.000 with an AGC target of 3x106, max. injection time of 20 ms and a scan range of 300-1650 m/z. MS2 resolution was 17.500 with an AGC target of 105, max. injection time of 50 ms and an isolation window of 2 m/z. The analysis parameters in PRM mode were set as follows. MS1 parameters were identical to DDA. MS2 resolution was 17.500 with an AGC target of 106, max. injection time of 55 ms and an isolation window of 1.4 m/z. Potential SARS-CoV-2 target peptides belonging to the N protein were identified by DDA of SARS-CoV-2 infected Calu-3 cells. Peptides were diluted in 0.1% TFA (0.2 µg/µL) and 5 µL were separated on a 50 cm μPAC™ column (PharmaFluidics) using an EASY-nLC1200 system (Thermo Fisher Scientific). The flow rate was set to 800 nL/min and a stepped 30 min gradient was applied: 6-11% B in 2:58 min, 11-30% B in 17:10 min, 30-35% B in 2:41 min, 35-47% B in 3:11 min, 47-80% B for 0:10 min, 80% B for 1:50 min, 80-0% B in 0:10 min and 100% A for 1:50 min. Solvent A was 0.1% (v/v) formic acid (FA) in water, solvent B consisted of 80% (v/v) acetonitrile in 0.1% (v/v) FA. The column temperature was set to 50 °C. The Q Exactive HF (Thermo Fisher Scientific) operated in Full MS/dd-MS2 (Top20) using the following parameters. MS1 resolution was 60.000 with an AGC target of 3x106, max. injection time of 20 ms and a scan range of 300-1650 m/z. MS2 resolution was 17.500 with an AGC target of 105, max. injection time of 50 ms and an isolation window of 2 m/z.   To simulate a SARS-CoV-2 positive patient sample, we spiked cell-culture derived virus in a negative oropharyngeal swab and targeted the N protein by PRM. LC parameters were identical to DDA analysis of SARS-CoV-2 infected Calu-3 cells. The PRM parameters of the The Q Exactive HF (Thermo Fisher Scientific) were set as follows. MS1 parameters were identical to DDA. MS2 resolution was 45.000 with an AGC target of 106, max. injection time of 100 ms and an isolation window of 1.4 m/z.     Data processing protocol: DDA Raw files were searched with MaxQuant against the respective virus database (UniProt) with a peptide FDR of 1%. Detailed MaxQuant parameters can be found in the parameters.txt files of the according results. MaxQuant .msms output files were used to generate spectral libraries with BiblioSpec implemented in the Skyline environment using a cut-off score of 0.95. Peptide identification of PRM runs was done in Skyline using the top 6 fragment ions of the DDA spectral library or according Prosit derived library (Prosit_2020_intensity_model).

Application of spectral library prediction for parallel reaction monitoring of viral peptides_PRM_NCE_data

Data from: Heat tolerance in ectotherms scales predictably with body size

This study analyses how heat stress varies with body size in ectothermic organisms. The analytical approach is based on thermal death time (TDT) curves, which take into consideration both the intensity and the duration of a thermal stress, and result in a linear relationship between temperature and the logarithm of time. We analyzed two separate heat tolerance datasets measured in mollusk, arthropod, fish, amphibian and reptile species, covering nearly 9 orders of magnitude in size. Datasets ware intrinsically different but ultimately convey the same type of information on thermal tolerance. The first dataset comprised individual measurements of heat stress (i.e., data points expressed in temperature and time coordinates) whereas the second dataset described the intercept and slope of TDT curves. In both cases, smaller organisms exhibited a higher tolerance to an acute heat stress than larger ones, but their tolerance declined faster in time.We then combined the results from our scaling analyses with geographic information to quantify thermal safety margins standardized by mass and time.

Reliably predicting pollinator abundance: challenges of calibrating process-based ecological models

1. Pollination is a key ecosystem service for global agriculture but evidence of pollinator population declines is growing. Reliable spatial modelling of pollinator abundance is essential if we are to identify areas at risk of pollination service deficit and effectively target resources to support pollinator populations. Many models exist which predict pollinator abundance but few have been calibrated against observational data from multiple habitats to ensure their predictions are accurate. 2. We selected the most advanced process-based pollinator abundance model available and calibrated it for bumblebees and solitary bees using survey data collected at 239 sites across Great Britain. We compared three versions of the model: one parameterised using estimates based on expert opinion, one where the parameters are calibrated using a purely data-driven approach and one where we allow the expert opinion estimates to inform the calibration process. 3. All three model versions showed significant agreement with the survey data, demonstrating this model's potential to reliably map pollinator abundance. However, there were significant differences between the nesting/floral attractiveness scores obtained by the two calibration methods and from the original expert opinion scores. 4. Our results highlight a key universal challenge of calibrating spatially-explicit, process-based ecological models. Notably, the desire to reliably represent complex ecological processes in finely mapped landscapes necessarily generates a large number of parameters, which are challenging to calibrate with ecological and geographical data that is often noisy, biased, asynchronous and sometimes inaccurate. Purely data-driven calibration can therefore result in unrealistic parameter values, despite appearing to improve model-data agreement over initial expert opinion estimates. We therefore advocate a combined approach where data-driven calibration and expert opinion are integrated into an iterative Delphi-like process, which simultaneously combines model calibration and credibility assessment. This may provide the best opportunity to obtain realistic parameter estimates and reliable model predictions for ecological systems with expert knowledge gaps and patchy ecological data.

Supplemental Material to Article "A practical approach for the peel stress prediction in the trailing-edge adhesive joint of wind turbine blades"

This set supplements the figure data to the article "A practical approach for the peel stress prediction in the trailing-edge adhesive joint of wind turbine blades", DOI: .

opencc-by-4.0Sep 2020View details →

Seafloor Density Measurements, Prediction, and Associated Uncertainty for "Predicting global marine sediment density using the random forest regressor machine learning algorithm"

Global seafloor density prediction results using the random forest regressor machine learning algorithm.  Dataset S1. Seafloor density measurements.  Columns are labeled with a header and include associated drilling project and measurement type for each sample.  File format: CSV text file Dataset S2. Seafloor density prediction results from the random forest regressor machine learning algorithm at 5×5-arc minute resolution.  Units are g/cm^3.  File format: netCDF (.nc) Dataset S3. Seafloor density prediction standard deviation from the random forest regressor machine learning algorithm at 5×5-arc minute resolution.  Units are g/cm^3.  File format: netCDF (.nc)

opencc-by-4.0Sep 2020View details →

Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations - data

Data from the paper: Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations. Preprint: https://www.biorxiv.org/content/10.1101/840256v1 Marek A. Pedziwiatr marek.pedziwi@gmail.com September 2020

opencc-zeroSep 2020View details →

Data from: Predicting evolutionary responses to interspecific interference in the wild

Many interspecifically territorial species interfere with each other reproductively, and in some cases, aggression toward heterospecifics may be an adaptive response to interspecific mate competition. This hypothesis was recently formalized in an agonistic character displacement (ACD) model which predicts that species should evolve to defend territories against heterospecific rivals above a threshold level of reproductive interference. To test this prediction, we parameterized the model with field estimates of reproductive interference for 32 sympatric damselfly populations and ran evolutionary simulations. Asymmetries in reproductive interference made the outcome inherently unpredictable in some cases, but 80% of the model's stable outcomes matched levels of heterospecific aggression in the field, significantly exceeding chance expectations. In addition to bolstering the evidence for ACD, this paper introduces a new, predictive approach to testing character displacement theory that, if applied to other systems, could help resolve longstanding questions about the importance of character displacement processes in nature.

opencc-by-4.0Dec 2019View details →

Floral signals evolve in a predictable way under artificial and pollinator selection in Brassica rapa

You find here the R scripts and data necessary to reproduce the results published in "Floral signals evolve in a predictable way under artificial and pollinator selection in Brassica rapa" by Zu et al. 2020 BMC Evolutionary Biology. The whole analysis is in the script "G-analysis.R". The associated data is loaded from the ".Rdata" and ".txt" files associated. The scripts "randG...R" are required to generate bootstrap replicates of G-matrix estimates. This is best done on an HPC cluster. To avoid having to run those randomizations, we provide the summary data we have generated from randomizations to run the full analysis. In Zu et al., we used data from an artificial selection and a pollinator (bumblebee, hoverfly) evolution experiment with fast cycling Brassica rapa plants to predict evolutionary changes of 12 floral volatiles and 4 morphological floral traits in response to selection. Using the observed selection gradients and the genetic variance-covariance matrix (G-matrix) of the traits, we showed that the observed responses of most floral traits including volatiles were predicted in the right direction in both artificial- and bumblebee-selection experiment. Genetic covariance had a mix of constraining and facilitating effects on evolutionary responses. We further revealed that G-matrices also evolved in the selection processes. We are depositing here the raw phenotypic data used to estimate the G-matrices in the artificial selection experiment along with the R scripts used to run the analyses. The phenotypic data of the pollinator experimental evolution experiment have been published elsewehere (Gervasi & Schiestl, 2017, Nature Communications 8:14691; doi:10.1038/ncomms14691).

opencc-zeroSep 2020View details →

Simple muscle-lever systems are not so simple: The need for dynamic analyses to predict lever mechanics that maximize speed

Here we argue that quasi-static analyses are insufficient to predict the speed of an organism from its skeletal mechanics alone (i.e. lever arm mechanics). Using a musculoskeletal numerical model we specifically demonstrate that 1) a single lever morphology can produce a range of output velocities, and 2) a single output velocity can be produced by a drastically different set of lever morphologies. These two sets of simulations quantitatively demonstrate that it is incorrect to assume a one-to-one relationship between lever arm morphology and organism maximum velocity. We then use a statistical analysis to quantify what parameters are determining output velocity, and find that muscle physiology, geometry, and limb mass are all extremely important. Lastly we argue that the functional output of a simple lever is dependent on the dynamic interaction of two opposing factors: those decreasing velocity at low mechanical advantage (low torque and muscle work) and those decreasing velocity at high mechanical advantage (muscle force-velocity effects). These dynamic effects are not accounted for in static analyses and are inconsistent with a force-velocity tradeoff in lever systems. Therefore, we advocate for a dynamic, integrative approach that takes these factors into account when analyzing changes in skeletal levers.

opencc-by-4.0Nov 2018View details →

Data and code for training and evaluating machine learning models for thunderstorm prediction from reanalysis data

FIXED Data and Python code for training and evaluating machine learning models for predicting thunderstorms, associated with the paper: "Evaluation of machine learning classifiers for predicting deep convection" by Peter Ukkonen and Antti Mäkelä (to appear in JAMES) The data (preprocessed inputs and outputs) is stored as netCDF files and .mat files which can be loaded with Python.