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opencc-zeroMar 2023View details →

Data for: Juvenile leg autotomy predicts adult male morph in a New Zealand harvestman with weapon polymorphism

Intraspecific weapon polymorphisms that arise via conditional thresholds may be affected by juvenile experiences such as predator encounters, yet this idea has rarely been tested. The New Zealand harvestman Forsteropsalis pureora has three male morphs: majors (alphas and betas) are large-bodied with large chelicerae used in male-male contests, while minors (gammas) are small-bodied with small chelicerae and scramble to find mates. Individuals use leg autotomy to escape predators and there is no regeneration of the missing leg. Here, we tested whether juvenile experience affects adult morph using leg autotomy scars as a proxy of predator encounters. Juvenile males that lost at least one leg (with either locomotory or sensory function) had a 45 times higher probability of becoming a minor morph at adulthood than intact juvenile males. Leg loss during development may affect foraging, locomotion, and/or physiology, potentially linking a juvenile's predator encounters to their final adult morph and future reproductive tactic.

opencc-zeroMar 2023View details →

Head shape predicts isotopic diet in anoles and day geckos

<ol> <li>Trophic morphology affects resource acquisition; therefore, species differences in such traits may be informative for inferring resource use overlap and potential species interactions. </li> <li>In lizards, head size and shape determine the size and hardness of prey that can be consumed. Lizards with large differences in head morphology are expected to overlap less in prey use than lizards with more similar traits. </li> <li>Stable isotopes are increasingly being used to describe diet, yet how traditional functional traits affect isotopic diet is often not clear a priori. </li> <li>We measured head size, head shape, 𝛿15N, and 𝛿13C under controlled resource availability in an enclosure experiment using introduced lizards in Hawaiʻi to test whether functional traits predict isotopic diet. </li> <li>Brown anoles (Anolis sagrei) had the tallest and narrowest heads, the highest values of 𝛿13C, and the lowest values of 𝛿15N. Gold dust day geckos (Phelsuma laticauda) had the shortest and widest heads, the lowest values of 𝛿13C, and the highest values of 𝛿15N. Green anoles (Anolis carolinensis) were intermediate in both diet and morphology. </li> <li>As a result of isotopic diet overlap, green anoles have reduced competitor-free resource space in the presence of both of the other lizard species. </li> <li>Head shape was the best predictor of diet and the only trait that explained variation within as well as among species. Head size was sexually dimorphic, and therefore the weaker diet correlations with this trait may be explained by sexual selection.</li> <li>Breadth in morphospace did not correlate with isotopic diet breadth, nor did the amount of overlap in morphospace predict the amount of overlap in isotopic diet space. </li> <li>While lizards were able to locally depress prey in experimental enclosures, no shifts in diet were detected in response to the presence of heterospecifics. </li> <li>The generality of head shape in predicting isotopic diet, and whether it does so independent of habitat use, warrants additional study. Head shape provides a potentially fruitful avenue for trait-based approaches to studying ecology and evolution in lizards.</li> </ol>

opencc-by-4.0Jun 2023View details →

Predicting geographic distribution and habitat suitability of Opuntia streptacantha in paleoclimatic, current, and future scenarios in Mexico

Geographical records. A total of 825 records (Figure 1), representing the natural distribution historically recognized for O. streptacantha. Maps for past, current, and future models in QGIS format

Vector species richness predicts local mortality rates by Chagas disease

Vector species richness may drive the prevalence of vector-borne diseases by influencing pathogen transmission rates. The dilution effect hypothesis predicts that higher biodiversity reduces disease prevalence, but with inconclusive evidence. In contrast, the amplification effect hypothesis suggests that higher vector diversity may result in greater disease transmission by increasing and diversifying the transmission pathways. The relationship between vector diversity and pathogen transmission remains unclear and requires further study. Chagas disease is a vector-borne disease most prevalent in Brazil and transmitted by multiple species of Triatominae insect vectors, yet the drivers of spatial variation in its impact on human populations remain unresolved. We tested whether triatomine species richness, latitude, bioclimatic variables, human host population density, and socioeconomic variables predict Chagas disease mortality rates across over 5000 spatial grid cells covering all of Brazil. Results show that species richness of triatomine vectors is a good predictor of mortality rates caused by Chagas disease, which supports the amplification effect hypothesis. Vector richness and the impact of Chagas disease may also be driven by latitudinal components of climate and human socioeconomic factors. We provide evidence that vector diversity is a strong predictor of disease prevalence and give support to the amplification effect hypothesis.

Supplementary Data for "Prediction of solar wind speed by applying convolutional neural network to potential field source surface (PFSS) magnetograms"

These are supplementary data for the paper "Prediction of solar wind speed by applying convolutional neural network to potential field source surface (PFSS) magnetograms". They are: - Python code to construct a neural network model - Saved optimal models (for 8-fold validation) - Selected y-label data (solar wind speed) and corresponding dates, which we eliminate the data identified as ICME

opencc-zeroApr 2023View details →

Data from: Prediction of three years of annual rain attenuation statistics at Ka-band in French Guiana using the Numerical Weather Prediction model WRF

This study highlights the interest in using an Atmospheric Numerical Simulator (ANS) relying on a high-resolution weather forecast model coupled with an ElectroMagnetic Module (EMM) to compute Ka-band rain attenuation statistics in an equatorial region. An optimization of the parametrisation of the Weather Research and Forecasting meteorological model (WRF) is carried out using measurements collected from a propagation experiment carried out by CNES and ONERA near Kourou in French Guiana. Both simulated and experimental annual Complementary Cumulative Distribution Functions (CCDF) of rain attenuation are presented in this dataset. More specifically, this dataset includes the statistical distribution from both the WRF-EMM model and from the propagation experiment for the years 2017, 2018, 2020 and the whole three-year period.

Predicting Pulsed Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction - sample untreated_162nm

RHEED intensity image dataset of sample untreated_162nm in work "Predicting Pulsed Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction."

Predicting Pulsed Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction - sample treated_81nm

RHEED intensity image dataset of sample t0.08 in work "Predicting Pulsed Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction."

opencc-by-4.0Mar 2023View details →

Development and validation of metabolic models for predicting survival and immune status of hepatocellular carcinoma patients

Supplementary materials for the article titled “Development and validation of metabolic models for predicting survival and immune status of hepatocellular carcinoma patients”

IRAS ML Predictions

Contained in this repository are the large files required for running the code in the following GitHub repository: https://github.com/zfried/IRAS_ML_Predictions fullDataset.csv contains all of the SMILES strings and length 70 feature vectors of the molecules used to train the mol2vec model. mol2vec_model_final_70.pkl is the trained mol2vec model that produces length 70 molecular feature vectors.  prediction_molecules.csv contains all of the SMILES strings and length 89 feature vectors (including isotopic encoding) of the 84,863 molecules that are inputted into the trained model.

Integrated multi-omics analysis of early lung adenocarcinoma links tumor biological features with predicted indolence or aggressiveness

This is a collection of datasets presented in the manuscript "Multi-omics data analysis identifies correlations between tumor biology features and predicted behaviors in early lung adenocarcinoma". This study provides a comprehensive profiling of LUAD indolence and aggressiveness at the biological bulk and single cell levels, as well as at the clinical and radiomics levels. This hypothesis generating study uncovers several potential future research avenues. It also highlights the importance and power of data integration to improve our systemic understanding of LUAD and to help reduce the gap between basic science research and clinical practice.

A dataset for Customer Churn Prediction for Video Websites Incorporating Behavioral Sequence Features

In order to study the issue of network customer churn, the iQiyi customer dataset was collected. Behavioral sequence features were extracted from it to build a deep learning model and experiments were conducted.Here, we provide the corresponding raw dataset, including all the data we used.

opencc-zeroMay 2023View details →

Does a history of population co-occurrence predict plant performance, community productivity, or invasion resistance?

A history of species co-occurrence in plant communities is hypothesized to lead to greater niche differentiation, more efficient resource partitioning, and more productive, resistant communities as a result of evolution in response to biotic interactions. A similar question can be asked of co-occurring populations: do individual species or community responses differ when communities are founded with plants sharing a history of population co-occurrence (sympatric) or originating from different locations (allopatric)? Using shrub, grass, and forb species from six locations in the western Great Basin, USA, we compared establishment, productivity, reproduction, phenology, and resistance to invaders for experimental communities with either sympatric or allopatric population associations. Each community type was planted with six taxa in outdoor mesocosms, measured over three growing seasons, and invaded with the annual grass Bromus tectorum in the final season. For most populations, the allopatric or sympatric status of neighbors was not important. However, in some cases, it was beneficial for some species from some locations to be planted with allopatric neighbors, while others benefited from sympatric neighbors, and some of these responses had large effects. For instance, the Elymus population that benefited the most from allopatry grew 50% larger with allopatric neighbors than in single origin mesocosms. This response affected invasion resistance, as B. tectorum biomass was strongly affected by productivity and phenology of Elymus spp., as well as Poa secunda. Our results demonstrate that while community composition can affect plant performance in semi-arid plant communities, assembling communities from sympatric populations is not sufficient to ensure high productivity and invasion resistance. Instead, we observed an idiosyncratic interaction between sampling effects and evolutionary history, with the potential for seed source of individual populations to have community-level effects.

openmit-licenseMay 2023View details →

StreamFlow run of digital pathology tissue/tumor prediction workflow

This dataset is an <a href="https://www.researchobject.org/ro-crate/">RO-Crate</a> representation of an execution of the tissue/tumor prediction workflow for digital pathology from <a href="https://github.com/crs4/deephealth-pipelines/tree/c54840df08742e3aa454394e0e74d15fbd640f07">crs4/deephealth-pipelines</a>. It follows the <a href="https://w3id.org/ro/wfrun/provenance/0.1">Provenance Run Crate</a> profile. The workflow has been run with <a href="http://streamflow.di.unito.it">StreamFlow</a>, using the following commands to produce an RO-Crate bundle: <pre><code class="language-bash"># Run the pipeline streamflow run \ --name ml-predict-pipeline-streamflow \ streamflow.yml # Generate the RO-Crate bundle streamflow prov \ --add-file src=README.md,dst=/README.md,about="{\"@id\":\"./\"}",encodingFormat=text/markdown \ --add-property \./.license=https://spdx.org/licenses/MIT \ --add-property \./.name="DeepHealth Pipeline" \ --add-property \./.description="Run of digital pathology tissue/tumor prediction workflow" \ --file streamflow.yml \ ml-predict-pipeline-streamflow</code></pre> The input dataset is <a href="https://openslide.cs.cmu.edu/download/openslide-testdata/Mirax/Mirax2-Fluorescence-2.zip">Mirax2-Fluorescence-2</a> by Yves Sucaet, from the <a href="https://openslide.cs.cmu.edu/download/openslide-testdata/Mirax/">MIRAX test data</a>.

Freebase Datasets for Robust Evaluation of Knowledge Graph Link Prediction Models

Freebase is amongst the largest public cross-domain knowledge graphs. It possesses three main data modeling idiosyncrasies. It has a strong type system; its properties are purposefully represented in reverse pairs; and it uses mediator objects to represent multiary relationships. These design choices are important in modeling the real-world. But they also pose nontrivial challenges in research of embedding models for knowledge graph completion, especially when models are developed and evaluated agnostically of these idiosyncrasies. We make available several variants of the Freebase dataset by inclusion and exclusion of these data modeling idiosyncrasies. This is the first-ever publicly available full-scale Freebase dataset that has gone through proper preparation.  Dataset DetailsThe dataset consists of the four variants of Freebase dataset as well as related mapping/support files. For each variant, we made three kinds of files available:<ul><li>Subject matter triples file<ul><li>fb+/-CVT+/-REV One folder for each variant. In each folder there are 5 files: train.txt, valid.txt, test.txt, entity2id.txt, relation2id.txt Subject matter triples are the triples belong to subject matters domains—domains describing real-world facts.<ul><li>Example of a row in train.txt, valid.txt, and test.txt: <ul><li>2, 192, 0</li></ul></li><li>Example of a row in entity2id.txt:<ul><li>/g/112yfy2xr, 2</li></ul></li><li>Example of a row in relation2id.txt:<ul><li>/music/album/release_type, 192</li></ul></li><li>Explaination<ul><li>"/g/112yfy2xr" and "/m/02lx2r" are the MID of the subject entity and object entity, respectively. "/music/album/release_type" is the realtionship between the two entities. 2, 192, and 0 are the IDs assigned by the authors to the objects.</li></ul></li></ul></li></ul></li><li>Type system file<ul><li>freebase_endtypes: Each row maps an edge type to its required subject type and object type.<ul><li>Example<ul><li>92, 47178872, 90</li></ul></li><li>Explanation<ul><li>"92" and "90" are the type id of the subject and object which has the relationship id "47178872".</li></ul></li></ul></li></ul></li><li>Metadata files<ul><li>object_types: Each row maps the MID of a Freebase object to a type it belongs to.<ul><li>Example<ul><li>/g/11b41c22g, /type/object/type, /people/person</li></ul></li><li>Explanation<ul><li>The entity with MID "/g/11b41c22g" has a type "/people/person"</li></ul></li></ul></li><li>object_names: Each row maps the MID of a Freebase object to its textual label.<ul><li>Example<ul><li>/g/11b78qtr5m, /type/object/name, "Viroliano Tries Jazz"@en</li></ul></li><li>Explanation<ul><li>The entity with MID "/g/11b78qtr5m" has name "Viroliano Tries Jazz" in English.</li></ul></li></ul></li><li>object_ids: Each row maps the MID of a Freebase object to its user-friendly identifier.<ul><li>Example<ul><li>/m/05v3y9r, /type/object/id, "/music/live_album/concert"</li></ul></li><li>Explanation<ul><li>The entity with MID "/m/05v3y9r" can be interpreted by human as a music concert live album.</li></ul></li></ul></li><li>domains_id_label: Each row maps the MID of a Freebase domain to its label.<ul><li>Example<ul><li>/m/05v4pmy, geology, 77</li></ul></li><li>Explanation<ul><li>The object with MID "/m/05v4pmy" in Freebase is the domain "geology", and has id "77" in our dataset.</li></ul></li></ul></li><li>types_id_label: Each row maps the MID of a Freebase type to its label.<ul><li>Example<ul><li>/m/01xljxh, /government/political_party, 147</li></ul></li><li>Explanation<ul><li>The object with MID "/m/01xljxh" in Freebase is the type "/government/political_party", and has id "147" in our dataset.</li></ul></li></ul></li><li>entities_id_label: Each row maps the MID of a Freebase entity to its label.<ul><li>Example<ul><li>/g/11b78qtr5m, Viroliano Tries Jazz, 2234</li></ul></li><li>Explanation<ul><li>The entity with MID "/g/11b78qtr5m" in Freebase is "Viroliano Tries Jazz", and has id "2234" in our dataset.</li></ul></li><li>properties_id_label: Each row maps the MID of a Freebase property to its label.<ul><li>Example<ul><li>/m/010h8tp2, /comedy/comedy_group/members, 47178867</li></ul></li><li>Explanation<ul><li>The object with MID "/m/010h8tp2" in Freebase is a property(relation/edge), it has label "/comedy/comedy_group/members" and has id "47178867" in our dataset.</li></ul></li></ul></li><li>uri_original2simplified and uri_simplified2original: The mapping between original URI and simplified URI and the mapping between simplified URI and original URI repectively.<ul><li>Example<ul><li>uri_original2simplified<ul><li>"<a href="http://rdf.freebase.com/ns/type.property.unique">http://rdf.freebase.com/ns/type.property.unique</a>": "/type/property/unique"</li></ul></li><li>uri_simplified2original<ul><li>"/type/property/unique": "<a href="http://rdf.freebase.com/ns/type.property.unique">http://rdf.freebase.com/ns/type.property.unique</a>"</li></ul></li></ul></li><li>Explanation<ul><li>The URI "<a href="http://rdf.freebase.com/ns/type.property.unique">http://rdf.freebase.com/ns/type.property.unique</a>" in the original Freebase RDF dataset is simplified into "/type/property/unique" in our dataset.</li><li>The identifier "/type/property/unique" in our dataset has URI <a href="http://rdf.freebase.com/ns/type.property.unique">http://rdf.freebase.com/ns/type.property.unique</a> in the original Freebase RDF dataset.</li></ul></li></ul></li></ul></li></ul></li></ul>

Identifying the environmental drivers of corridors and predicting connectivity between seasonal ranges in multiple populations of Alpine ibex (Capra ibex) as tools for conserving migration

# GPS locations of Alpine ibex This dataset contains migratory tracks of Alpine ibex identified using the application Migration Mapper (https://migrationinitiative.org/content/migration-mapper) and used in the work Identifying the environmental drivers of corridors and predicting connectivity between seasonal ranges in multiple populations of Alpine ibex (Capra ibex) as tools for conserving migration # Dataset structure Each row of the dataset represents a GPS location with its coordinates contained in the x (longitude) and y(latitude) columns. Coordinates are given in wgs84 (epsg 4326). The column t1_ informs on the date and time the location was recorded. The id and pop columns provide information about the identity of the animal and the population to which it belongs.

opencc-zeroMay 2023View details →

Condition dependence of (un)predictability in escape behavior of a grasshopper species

(Un)predictability has only recently been recognized as an important dimension of animal behavior. Currently, we neither know if (un)predictability encompasses one or multiple traits nor how (un)predictability is dependent on individual conditions. Knowledge about condition dependence, in particular, could inform us about whether predictability or unpredictability is costly in a specific context. Here, we study the condition dependence of (un)predictability in the escape behavior of the steppe grasshopper Chorthippus dorsatus. Predator–prey interactions represent a behavioral context in which we expect unpredictability to be particularly beneficial. By exposing grasshoppers to an immune challenge, we explore if individuals in poor condition become more or less predictable. We quantified three aspects of escape behavior (flight initiation distance, jump distance, and jump angle) in a standardized setup and analyzed the data using a multivariate double-hierarchical generalized linear model. The immune challenge did not affect (un)predictability in flight initiation distance and jump angle, but decreased unpredictability in jump distances, suggesting that unpredictability can be costly. Variance decomposition shows that 3–7% of the total phenotypic variance was explained by individual differences in (un)predictability. Covariation between traits was found both among averages and among unpredictabilities for one of the three trait pairs. The latter might suggest an (un)predictability syndrome, but the lack of (un)predictability correlation in the third trait suggests modularity. Our results indicated condition dependence of (un)predictability in grasshopper escape behavior in one of the traits, and illustrate the value of mean and residual variance decomposition for analyzing animal behavior.

Predicting Intra-Abdominal Adhesion in Repeat C-Sections: Scar Tissue Characteristics, Striae Gravidarum, and Corset Use

We aimed to evaluate the relationship between scar characteristics in abdominal skin, the history of corset use, and the presence and severity of intra-abdominal adhesions in repeated caesarean sections (CS).

opencc-by-4.0Jul 2022View details →

Development of Distress Index Prediction Models for Rehabilitation Treatments in Louisiana Using Advanced Machine Learning Techniques

Performance prediction models are used by state agencies to predict future trends in distress indices, hence, determining the required maintenance and/or rehabilitation treatment as well as the deterioration rate and remaining pavement service life. However, most of these models are based on a limited number of parameters and cannot predict the performance distress indices reliably. Such limitation resulted in having, most of the time, a maximum prediction period of five years. As a solution and coping with the ever-increasing size of pavement data, machine learning techniques have become a promising alternative. The objective of this study was to develop a machine-learning-based framework for states with a hot and humid climate that can predict the long-term field performance (for 11 years) of their asphalt (AC) overlays based on their key project conditions. Two machine learning algorithms were examined, namely Random Forest (RF) and CatBoost, and the one yielding a higher accuracy was considered. In this study, the well-known pavement condition index (PCI) was used as the pavement performance indicator. A total of 892 log miles of AC overlay data were obtained from the Louisiana Department of Transportation and Development (LaDOTD) Pavement Management System (PMS) database.  Based on the collected data, six models were trained (for each algorithm) and validated to predict the future PCI of AC overlays for up to 11 years. Results indicated that the RF algorithm yielded higher accuracy than the CatBoost Algorithm and thus the RF-based models were considered in the proposed decision-making framework.

MEDDOPROF Silver Standard: Predictions for Occupations, Working Statuses and Activities in Clinical Case Reports

MEDDOPROF Silver Standard This repository contains the MEDDOPROF Silver Standard, a collection of clinical case reports of multiple specialties that have been automatically annotated with occupations, working statuses and activities using named entity recognition models trained on the MEDDOPROF corpus. MEDDOPROF and this Silver Standard have been created by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis. About the MEDDOPROF Corpus <a href="https://temu.bsc.es/meddoprof/">MEDDOPROF</a> is a corpus and shared task focused on the detection and normalization of occupations and related information such as working statuses and non-paid activities in clinical texts in Spanish. All mentions in the corpus have been normalized to SNOMED CT, with occupations also being normalized to the European Skills, Competences, and Occupations classification ESCO. MEDDOPLACE was released as part of the IberLEF 2021 workshop, which in turn was held within the SEPLN 2021 conference. About this Silver Standard The Silver Standard includes 13,357 documents and a total of 5,568,844 tokens. The documents belong to over 10 different medical specialties, including occupational health, oncology, radiology, infectology, dermatology, psichiatry and more. The corpus includes three different labels: PROFESION (occupation), SITUACION-LABORAL (working status) and ACTIVIDAD (activity/hobby). There is a total of 4,332 predictions (1,325 unique), with the most common label being PROFESION with 3,599 predictions (1,181 unique), followed by SITUACION-LABORAL with 682 (180 unique) and ACTIVIDAD with 51 (44 unique). The annotations are presented in .ann format, which is the format used by the annotation tool brat. For more information, please check the attached README file. Citation If you use this Silver Standard or any MEDDOPROF materials, please cite the task's overview paper: Lima-López, Salvador, Eulàlia Farré-Maduell, Antonio Miranda-Escalada, Vicent Brivá-Iglesias, & Martin Krallinger. “NLP applied to occupational health: MEDDOPROF shared task at IberLEF 2021 on automatic recognition, classification and normalization of professions and occupations from medical texts.” Procesamiento del Lenguaje Natural [Online], 67 (2021): 243-256. License This work is licensed under a Creative Commons Attribution 4.0 International License. Contact If you have any questions or suggestions, please contact us at: - Salvador Lima-López (<salvador [dot] limalopez [at] gmail [dot] com>) - Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>) Acknowledgements MEDDOPROF was promoted by the Spanish government's 2020 Proyectos de I+D+i RTI Tipo A (AI4ProfHealth - DESCIFRANDO EL PAPEL DE LAS PROFESIONES EN LA SALUD DE LOS PACIENTES A TRAVES DE LA MINERIA DE TEXTOS (PID2020-119266RA-I00)). Related Links - MEDDOPROF website: <a href="https://temu.bsc.es/meddoprof/">https://temu.bsc.es/meddoprof/</a> - MEDDOPROF Gold Standard: <a href="https://doi.org/10.5281/zenodo.5070540">https://doi.org/10.5281/zenodo.5070540</a> - MEDDOPROF Annotation Guidelines: <a href="https://doi.org/10.5281/zenodo.4694675">https://doi.org/10.5281/zenodo.4694675</a>