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opencc-by-3.0-usJul 2022View details →

Yield Prediction Through Integration of Genetic, Environment, and Management Data Through Deep Learning: Cleaned Data

The included files and script are to allow for reconstruction of the data directory and cleaned data used in "Yield Prediction Through Integration of Genetic, Environment, and Management Data Through Deep Learning" ( https://doi.org/10.1101/2022.07.29.502051 ). Code used is available at 10.5281/zenodo.7401113 . <table> <tbody> <tr> <th>Filename</th> <th>Description</th> </tr> <tr> <td>interim.tar.gz</td> <td>Contains site grouping dictonary</td> </tr> <tr> <td>processed.tar.gz</td> <td>Processed data</td> </tr> <tr> <td>raw.tar.gz</td> <td>Input data</td> </tr> <tr> <td>SetupInstructions.sh</td> <td>Bash script to prepare folders and unzipped data expected by code in 10.5281/zenodo.7401113</td> </tr> <tr> <td>SetupInstructions.txt</td> <td>Instructions for unzipping the data</td> </tr> <tr> <td>Train_Test_Split_Reference_Phenotypes.csv</td> <td>Reference spreadsheet to allow for easily exploring training and test set groupings</td> </tr> </tbody> </table> <ul> </ul> This work was supported through funding from the USDA Agricultural Research Service, ARS project number 5070-21000-041-000-D. Raw data provided by the [Genomes to Field Initiative](https://www.genomes2fields.org/) and the [Daymet database](https://daymet.ornl.gov/).

Data for: Skin bacterial microbiome diversity predicts lower activity levels in female, but not male, guppies, Poecilia reticulata

While the link between the gut microbiome and host behaviour is well established, how the microbiomes of other organs correlate with behaviour remains unclear. Additionally, behaviour–microbiome correlations are likely sex-specific because of sex differences in behaviour and physiology, but this is rarely tested. Here, we tested whether the skin microbiome of the Trinidadian guppy, Poecilia reticulata , predicts fish activity level and shoaling tendency in a sex-specific manner. High-throughput sequencing revealed that the bacterial community richness on the skin (Faith's phylogenetic diversity) was correlated with both behaviours differently between males and females. Females with richer skin-associated bacterial communities spent less time actively swimming. Activity level was significantly correlated with community membership (unweighted UniFrac), with the relative abundances of 16 bacterial taxa significantly negatively correlated with activity level. We found no association between skin microbiome and behaviours among male fish. This sex-specific relationship between the skin microbiome and host behaviour may indicate sex-specific physiological interactions with the skin microbiome. More broadly, sex specificity in host–microbiome interactions could give insight into the forces shaping the microbiome and its role in the evolutionary ecology of the host.

Bias-corrected data from the preoperational MiKlip system for decadal climate predictions used in the PNRA-IPSODES project

This dataset contains a selection of bias-corrected data from the preoperational MiKlip system for decadal climate predictions (Mueller et al., 2018) used within the Italian research project PNRA18_00199-IPSODES. The adopted method for bias correction is described in the file bias_correction.pdf. Also data from the assimilation run are provided. Nomenclature of variables follows that of the original MiKlip output. Mueller, W., et al. A Higher‐resolution Version of the Max Planck Institute Earth System Model (MPI‐ESM1.2‐HR). J. Adv. Model. Earth Syst. 10, 1383-1413 (2018)

Data for: Predicting habitat suitability for Townsend's big-eared bats across California in relation to climate change

Aim: Effective management decisions depend on knowledge of species distribution and habitat use. Maps generated from species distribution models are important in predicting previously unknown occurrences of protected species. However, if populations are seasonally dynamic or locally adapted, failing to consider population level differences could lead to erroneous determinations of occurrence probability and ineffective management. The study goal was to model the distribution of a species of special concern, Townsend's big-eared bats (Corynorhinus townsendii), in California. We incorporate seasonal and spatial differences to estimate the distribution under current and future climate conditions. Methods: We built species distribution models using all records from statewide roost surveys and by subsetting data to seasonal colonies, representing different phenological stages, and to Environmental Protection Agency Level III Ecoregions to understand how environmental needs vary based on these factors. We projected species' distribution for 2061-2080 in response to low and high emissions scenarios and calculated the expected range shifts. Results: The estimated distribution differed between the combined (full dataset) and phenologically-explicit models, while ecoregion-specific models were largely congruent with the combined model. Across the majority of models, precipitation was the most important variable predicting the presence of C. townsendii roosts. Under future climate scnearios, distribution of C. townsendii is expected to contract throughout the state, however suitable areas will expand within some ecoregions. Main conclusion: Comparison of phenologically-explicit models with combined models indicate the combined models better predict the extent of the known range of C. townsendii in California. However, life history-explicit models aid in understanding of different environmental needs and distribution of their major phenological stages. Differences between ecoregion-specific and statewide predictions of habitat contractions highlight the need to consider regional variation when forecasting species' responses to climate change. These models can aid in directing seasonally explicit surveys and predicting regions most vulnerable under future climate conditions.

Protein structure model predictions for secreted fungal proteins

Dataset A - Alphafold2 prediction output data for 753 secreted proteins of Rhizophagus irregularis DAOM197198. Gene IDs are taken from the annotation by Yildirir et al. 2021, <a href="https://doi.org/10.1111/nph.17842">doi.org/10.1111/nph.17842</a> Dataset B - Alphafold2 prediction output data for 10 fungal effectors. These are nine effectors from Fusarium oxysporum f. sp. lycopersici and RiSLM from Rhizophagus irregularis as well as their amino acid sequences. Signal peptides and sequences preceding a predicted Kex2 processing site were removed. Dataset C - Alphafold2 prediction output data for 454 matches of a MycFOLD-HMM search across the Mycocosm genome database (<a href="https://mycocosm.jgi.doe.gov/mycocosm/home">https://mycocosm.jgi.doe.gov/mycocosm/home</a>) and 36 Glomeromycotina fungal genomes.

opencc-by-4.0Dec 2017View details →

DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data

Growing concerns about increasing rates of antibiotic resistance call for expanded and comprehensive global monitoring. Advancing methods for monitoring of environmental media (e.g., wastewater, agricultural waste, food, and water) is especially needed for identifying potential resources of novel antibiotic resistance genes (ARGs), hot spots for gene exchange, and as pathways for the spread of ARGs and human exposure. Next-generation sequencing now enables direct access and profiling of the total metagenomic DNA pool, where ARGs are typically identified or predicted based on the “best hits” of sequence searches against existing databases. Unfortunately, this approach produces a high rate of false negatives. To address such limitations, we propose here a deep learning approach, taking into account a dissimilarity matrix created using all known categories of ARGs. Two deep learning models, DeepARG-SS and DeepARG-LS, were constructed for short read sequences and full gene length sequences, respectively. Evaluation of the deep learning models over 30 antibiotic resistance categories demonstrates that the DeepARG models can predict ARGs with both high precision (> 0.97) and recall (> 0.90). The models displayed an advantage over the typical best hit approach, yielding consistently lower false negative rates and thus higher overall recall (> 0.9). As more data become available for under-represented ARG categories, the DeepARG models’ performance can be expected to be further enhanced due to the nature of the underlying neural networks. Our newly developed ARG database, DeepARG-DB, encompasses ARGs predicted with a high degree of confidence and extensive manual inspection, greatly expanding current ARG repositories. The deep learning models developed here offer more accurate antimicrobial resistance annotation relative to current bioinformatics practice. DeepARG does not require strict cutoffs, which enables identification of a much broader diversity of ARGs. The DeepARG models and database are available as a command line version and as a Web service at <a href="http://bench.cs.vt.edu/deeparg">http://bench.cs.vt.edu/deeparg</a>.

The presence of territorial damselfish predicts choosy client species richness at cleaning stations

Mutualisms are driven by partners deciding to interact with one another to gain specific services or rewards. As predicted by biological market theory, partners should be selected based on the likelihood, quality, reward level, and or services each partner can offer. Third-party species that are not directly involved in the interaction, however, may indirectly affect the occurrence and or quality of the services provided, thereby affecting which partners are selected or avoided. We investigated how different clients of the sharknose goby (Elacatinus evelynae) cleaner fish were distributed across cleaning stations, and asked what characteristics, relating to biological market theory, affected this distribution. Through quantifying the visitation and cleaning patterns of client fish that can choose which cleaning station(s) to visit, we found that the relative species richness of visiting clients at stations was negatively associated with the presence of disruptive territorial damselfish at the station. Our study highlights, therefore, the need to consider the indirect effects of third-party species and their interactions (e.g. agonistic interactions) when attempting to understand mutualistic interactions between species. Moreover, we highlight how cooperative interactions may be indirectly governed by external partners.

Predicting mobility and research performance of the faculty members in the economics departments at Turkish public universities

The data used for the regression analysis is in Table 5 on page 17 of the paper.

iDPP@CLEF 2022 - Participants' repositories for the Intelligent Disease Prediction Progression Challenge

<a href="https://brainteaser.health/open-evaluation-challenges/idpp-2022/">iDPP@CLEF 2022</a> (Intelligent Disease Progression Prediction at CLEF) is a challenge organised by the <a href="https://brainteaser.health/">BRAINTEASER</a> Horizon 2020 project and co-located with <a href="https://clef2022.clef-initiative.eu/">CLEF 2022</a> (Conference and Labs of the Evaluation Forum).  BRAINTEASER is a data science project that seeks to exploit the value of big data, including those related to health, lifestyle habits, and environment, to support patients with amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS) and their clinicians. Taking advantage of cost-efficient sensors and apps, BRAINTEASER will integrate large, clinical datasets that host both patient-generated and environmental data. The goal of iDPP@CLEF is to design and develop an evaluation infrastructure for AI algorithms able to: <ul> <li> Better describe disease mechanisms. </li> <li> Stratify patients according to their phenotype assessed all over the disease evolution. </li> <li> Predict disease progression in a probabilistic, time dependent fashion. </li> </ul> iDPP@CLEF 2022 offered the following tasks: <ul> <li> Pilot Task 1 – Ranking Risk of Impairment: It focuses on ranking of patients based on the risk of impairment in specific domains. More in detail, we will use the ALSFRS-R scale to monitor speech, swallowing, handwriting, dressing/hygiene, walking and respiratory ability in time and will ask participants to rank patients based on time to event risk of experiencing impairment in each specific domain. </li> <li> Pilot Task 2 – Predicting Time of Impairment: It refines Task 1 asking participants to predict when specific impairments will occur (i.e. in the correct time-window). In this regard, we assess model calibration in terms of the ability of the proposed algorithms to estimate a probability of an event close to the true probability within a specified time-window. </li> <li> Position Papers Task 3 – Explainability of AI algorithms: We call for proposals of different visualization frameworks able to show the multivariate nature of the data and the model predictions in an explainable, possibly interactive, way. </li> </ul>   This dataset contains the repositories of the participants to iDPP@CLEF 2022. These repositories contain the output, i.e. the predictions, produced by the participating systems as well as the performance scores for those systems. For additional information about iDPP@CLEF 2022, please see: <ul> <li> Guazzo, A., Trescato, I., Longato, E., Hazizaj, E., Dosso, D., Faggioli, G., Di Nunzio, G. M., Silvello, G., Vettoretti, M., Tavazzi, E., Roversi, C., Fariselli, P., Madeira, S. C., de Carvalho, M., Gromicho, M., Chiò, A., Manera, U., Dagliati, A., Birolo, G., Aidos, H., Di Camillo, B., and Ferro, N. (2022). Intelligent Disease Progression Prediction: Overview of iDPP@CLEF 2022. In Barr ́on-Cedeno, A., Da San Martino, G., Degli Es- posti, M., Sebastiani, F., Macdonald, C., Pasi, G., Hanbury, A., Potthast, M., Faggioli, G., and Ferro, N., editors, Experimental IR Meets Multilinguality, Multimodality, and Interaction. Proceedings of the Thirteenth International Conference of the CLEF Association (CLEF 2022), pages 395–422. Lecture Notes in Computer Science (LNCS) 13390, Springer, Heidelberg, Germany. </li> <li> Guazzo, A., Trescato, I., Longato, E., Hazizaj, E., Dosso, D., Faggioli, G., Di Nunzio, G. M., Silvello, G., Vettoretti, M., Tavazzi, E., Roversi, C., Fariselli, P., Madeira, S. C., de Carvalho, M., Gromicho, M., Chiò, A., Manera, U., Dagliati, A., Birolo, G., Aidos, H., Di Camillo, B., and Ferro, N. (2022). Overview of iDPP@CLEF 2022: The Intelligent Disease Progression Prediction Challenge. In Faggioli, G., Ferro, N., Hanbury, A., and Potthast, M., editors, CLEF 2022 Working Notes, pages 1130– 1210. CEUR Workshop Proceedings (CEUR-WS.org), ISSN 1613-0073. <a href="https://ceur-ws.org/Vol-3180/paper-88.pdf">http://ceur-ws.org/Vol-3180/</a>. </li> </ul>

Data for: Predicting m7G sites used by autoBioSeqpy

As a vital post-transcriptional RNA modification, N7-methylguanosine plays a key role in the regulation of gene expression. Precise identification of the m7G sites is a crucial first step toward understanding its biological function and regulatory mechanisms. Although whole-genome sequencing is the gold standard for RNA modification site detection, it is time-consuming, expensive, and relatively complex. In recent years, computational approaches have become increasingly popular in achieving this goal, especially with the rise of deep learning techniques. Some deep learning algorithms, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have emerged as versatile approaches for modeling biological sequence data. However, designing a successful network architecture with excellent performance remains a challenge because it requires substantial professional knowledge and commitment of time and effort. We previously developed a tool called autoBioSeqpy to efficiently design and apply deep learning networks for the classification of biological sequences. autoBioSeqpy has many unique features that make it a practical tool for a broad range of biological questions. In this work, we used autoBioSeqpy to develop, train, evaluate and analyze sequence-level deep learning models for predicting the m7G sites. We here provided a detailed description of the various models it implements, as well as a step-by-step guide on how to execute them. The same strategy can also be applied in other systems to address similar biological questions.

Accurate Prediction of Enzyme Thermostabilization with Rosetta using AlphaFold Ensembles

DTM vs DGf,mut values for scoring LovD, LipA, p-nitrobenzyl esterase, xylanase A and tryptophan 6-halogenase variants (DTM_vs_DDGf_mut.xlsx). AlphaFold predicted structures in PDB and Pymol sessions formats for top scoring LovD, LovD6, LovD9, LipA WT, LipA 6B, p-nitrobenzyl esterase WT, xylanase A WT and tryptophan 6-halogenase WT decoys (mAF-min_ensembles.zip). Rosetta energies for all calculations (Rosetta_scores.zip).

opencc-zeroJan 2023View details →

Data from: Predicting primate-parasite associations with exponential random graph models

Ecological associations between hosts and parasites are influenced by host exposure and susceptibility to parasites, and by parasite traits, such as transmission mode. Advances in network analysis allow us to answer questions about the causes and consequences of traits in ecological networks in ways that could not be addressed in the past. We used a network-based framework (exponential random graph models, or ERGMs) to investigate the biogeographic, phylogenetic, and ecological characteristics of hosts and parasites characteristics that affect the probability of interactions among nonhuman primates and their parasites. Parasites included arthropods, bacteria, fungi, protozoa, viruses, and helminths. We investigated existing hypotheses, along with new predictors and an expanded host-parasite database that included 213 primate nodes, 763 parasite nodes, and 2,319 edges among them. Analyses also investigated phylogenetic relatedness, sampling effort, and spatial overlap among hosts. In addition to supporting some previous findings, our ERGM approach demonstrated that more threatened hosts had fewer parasites, and notably, that this effect was independent of threatened hosts also having a smaller geographic range. Despite having fewer parasites, threatened host species shared more parasites with other hosts, consistent with the loss of specialist parasites and threats arising from generalist parasites that can be maintained in other, non-threatened hosts. Viruses, protozoa, and helminths had broader host ranges than bacteria or fungi, and parasites that infect non-primates had a higher probability of infecting more primate species. The value of the ERGM approach for investigating the processes structuring host-parasite networks provided a more complete view of the biogeographic, phylogenetic, and ecological traits that influence parasite species richness and parasite sharing among hosts. The results supported some previous analyses and revealed new associations that warrant future research, thus revealing how hosts and parasites interact to form ecological networks.

US-XGB Models for Defect Prediction

These files contain two pickle models featuring thousands of machine learning models drawn from the Bug Prediction and Jureczko datasets. These models were trained on extensive data and are suitable for use in bug prediction research data. The models are easy to employ and can be integrated into existing systems with minimal effort. We believe that these models will be a valuable resource for researchers and practitioners working in the field of machine learning and software engineering.

openother-openSep 2022View details →

Dataset for: "Reducing OpenMP to FPGA Round-trip Times with Predictive Modelling"

This archive contains the samples generated for the conference paper "Reducing OpenMP to FPGA Round-trip Times with Predictive Modelling" (In Proc. 18th Intl. Workshop on OpenMP (IWOMP), Chattanooga, TN, Sept. 2022, Springer LNCS vol. 13527, pp. 94–108, <a href="https://doi.org/10.1007/978-3-031-15922-0_7">https://doi.org/10.1007/978-3-031-15922-0_7</a>). Abstract: Recent works aimed at expanding the target offloading capabilities of OpenMP to FPGA platforms. While enabling the easy construction of heterogeneous systems, the approach has to face a major hurdle: by blurring the line between software and hardware development, it forces software developers to consider hardware limitations. This can be difficult through the abstractions that OpenMP introduces over the generated hardware. The high level synthesis tools used by OpenMP compilers to generate hardware already offer predictions on hardware usage. Their value for OpenMP offloading however is questionable. This paper is based on the data mining we conducted on thousands of kernel variations. It demonstrates and proofs under which circumstances these predictions can be trusted in the context of OpenMP to FPGA offloading and concludes by showing how to derive runtime performance predictions from them. The model we present can be used without experience in hardware development and quickly predicts runtime on our benchmarks with an average Pearson correlation of 0.897. This knowledge allows developers to make fast, informed design decisions.

opencc-by-4.0Dec 2019View details →

Fig. 4. Predicted suitable habitats for S in Discovery of a new crocodile lizard population in Vietnam: Population trends, future prognoses and identification of key habitats for conservation

Fig. 4. Predicted suitable habitats for S. crocodilurus in the period between 2020 to 2080, based on bioclimatic data and elevation. Habitat suitability increases from yellow to dark brown.

Arthropod food webs predicted from body length ratios are improved by incorporating prey defensive properties

This dataset was used to run for Van de Walle et al. (2023). Arthropod food webs predicted from body length ratios are improved by incorporating prey defensive properties. Journal of Animal Ecology, 92(4), 913-924. <a href="https://doi.org/10.1111/1365-2656.13905">https://doi.org/10.1111/1365-2656.13905</a> "Arthropod_feeding_trials.csv" contains the results of the experimental feeding trials. Each row contains information on species taxonomy, body size and hunting strategy within a single trial. Abstract Trophic interactions are often deduced from body size differences between predators and potential prey, assuming predators prefer prey smaller than themselves because larger prey are more difficult to subdue. This hypothesis has mainly been confirmed in aquatic ecosystems, but rarely in terrestrial ecosystems, especially in arthropods. Our goal was to validate whether body size ratios can accurately predict trophic interactions in a terrestrial, plant-associated arthropod community. Additionally, we tested whether predator hunting strategy and prey taxonomy could explain possible deviations from this general rule. We collected arthropods from marram grass in coastal dunes and conducted pairwise feeding trials to explicitly test whether two individuals, of the same or different species, would predate each other. From the trial results, we constructed one of the most complete, empirically derived food webs for terrestrial arthropods associated with a single plant species. We contrasted this empirical food web with a theoretical web based on body size ratios, literature and expert knowledge. In our feeding trials, predator-prey interactions were indeed largely size-based. Moreover, the theoretical food web based on body size, activity period, microhabitat and expert knowledge converged quite well with the food web based on experimental feeding trials for both predator and prey species. However, predator hunting strategy, but mainly prey taxonomy improved predictions of predation events. Well-defended taxa, such as hard-bodied beetles, were less frequently consumed than expected based on their body size. Body size ratios predict trophic interactions among plant-associated arthropods fairly well. However, traits such as hunting strategy and anti-predator defences can explain why certain trophic interactions do not adhere to size-based rules. Feeding trials can generate insights into multiple traits underlying real-life trophic interactions among arthropods. Such insights are much needed as the dramatic global decline in arthropod species richness and abundance is knocking out many trophic interactions on which services such as pest control and nutrient cycling depend.

Supplementary Material for Automated Kinetic Models to Predict the Flame Speeds of Halocarbons

Supplementary material to accompany the paper "Automated Kinetic Models to Predict the Flame Speeds of Halocarbons" by Nora Khalil, Sevy Harris, Richard H. West, submitted to the 13th U. S. National Combustion Meeting, Organized by the Central States Section of the Combustion Institute, March 19–22, 2023, College Station, Texas. RMG input files.

opencc-zeroJan 2023View details →

Data from: Beaver activity and red squirrel presence predict bird assemblages in boreal Canada

Wetlands and predation in boreal ecosystems play essential roles throughout the breeding season for bird assemblages. We found a positive association of beaver activity and a negative influence of American red squirrels (Tamiasciurus hudsonicus) on bird assemblages. We used a multispecies hierarchical model to investigate whether bird communities differ between two major wetland habitats in boreal Canada: beaver ponds and peatland ponds. In addition to including variables such as forest cover and latitude, we adopted a structural equation model approach to estimate the occupancy of American red squirrels and its potential influence on bird communities. Using automated recording stations deployed at 50 ponds, we detected 96 bird species in 2018 and 2019. Bird species were grouped into four taxonomic guilds according to their habitat successional requirements: early successional species, late successional species, generalists, and wetland species. Beaver ponds harbored higher species richness, a pattern driven primarily by early successional species. The occupancy of almost a quarter of the species was lower in the presence of red squirrels. Late successional species responded positively to the cover of forest surrounding the pond. Our results highlight the value of considering acoustic data of red squirrels to quantify habitat quality in boreal forests. We conclude that beaver activity shapes bird assemblages through modification of their habitat, and that some bird guilds are associated negatively with the presence of American red squirrels.

opencc-zeroJan 2023View details →

Differences in mating system and predicted parental conflict affect post-pollination reproductive isolation in a flowering plant

Mating system shifts from outcrossing to selfing are frequent in plant evolution. Relative to outcrossing, selfing is associated with reduced parental conflict over seed provisioning, which may result in postzygotic, asymmetric, reproductive isolation in crosses between populations of different mating systems. To test the hypothesis that post-pollination reproductive isolation between populations increases with increasing differences in mating system and predicted parental conflict, we performed a crossing experiment involving all combinations of three self-compatible populations (with low outcrossing rates), and three self-incompatible populations (with high outcrossing rates) of the arctic-alpine herb Arabis alpina, assessing fitness-related seed and plant traits of the progeny. Predicted levels of parental conflict ("genome strength") were quantified based on strength of self-incompatibility and estimates of outcrossing rates. Crosses between self-compatible and self-incompatible populations yielded very small seeds of low viability, resulting in strong reproductive isolation. In 14 of 15 reciprocal between-population crosses, seeds were heavier when the paternal plant had the stronger genome, and seed mass differences between cross directions increased with an increased difference in parental conflict. Overall, our results suggest that, when sufficiently large, differences in mating system and hence in expected parental conflict may result in strong post-pollination reproductive barriers contributing to speciation.

Data supplement for the paper "An integrated computational strategy to predict personalized cancer drug combinations by reversing drug resistance signatures"

This dataset contains the the following data created for the paper "An integrated computational strategy to predict personalized cancer drug combinations by reversing drug resistance signatures". Data listing: Cell line-specific drug resistance signatures (CDRSR) Patient-specific drug resistance signatures (CTR-DB)