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13,621 results for “Biologics”
Training data composition determines machine learning generalization and biological rule discovery
<p>Github: https://github.com/csi-greifflab/negative-class-optimization</p> <p>Preprint: https://www.biorxiv.org/content/10.1101/2024.06.17.599333v1</p>
Figure 1 in Spectral characterization and biological evaluation of biomolecules from the peels of three orange fruits: a comparative study
Figure 1. Schematic representation of experimental protocol.
Figure 9 in Spectral characterization and biological evaluation of biomolecules from the peels of three orange fruits: a comparative study
Figure 9. FT-IR spectroscopy of RM of African Navel orange peel.
Figure 10 in Spectral characterization and biological evaluation of biomolecules from the peels of three orange fruits: a comparative study
Figure 10. The dose-response curve of RM of orange peels against MCF-7 cells.
Figure 7 in Spectral characterization and biological evaluation of biomolecules from the peels of three orange fruits: a comparative study
Figure 7. FT-IR spectroscopy of RM of Valencia orange peel.
Figure 4 in First information on biology of the needlefish Tylosurus acus imperialis (Belonidae) off the Tunisian coast (Central Mediterranean)
Figure 4. - Length at first maturity of females of T. a. imperialis.
Figure 1 in First information on biology of the needlefish Tylosurus acus imperialis (Belonidae) off the Tunisian coast (Central Mediterranean)
Figure 1. - Length distribution of needlefishes T. a. imperialis from Tunisian coast.
Figure 6 in First information on biology of the needlefish Tylosurus acus imperialis (Belonidae) off the Tunisian coast (Central Mediterranean)
Figure 6. - Otolith of Tylosurus acus imperialis.
Figure 5 in First information on biology of the needlefish Tylosurus acus imperialis (Belonidae) off the Tunisian coast (Central Mediterranean)
Figure 5. - Length at first maturity of males of T. a. imperialis.
Figure 1 in Reproductive biology of Lophius budegassa (Lophiidae) in the North Aegean Sea
Figure 1. - Map of the study area; Saros Bay, in the North Aegean Sea. ● Sampling stations.
Code and data from: Towards a more dynamic metabolic theory of ecology to predict climate change effects on biological systems
<p>This repository contains data and code used to produce Figures 1, 2, and S1 in <em>Towards a more dynamic metabolic theory of ecology to predict climate change effects on biological systems.</em></p> <p>The file "empirical_mte_database.csv" contains a database of peer-reviewed articles pulled from a Web of Science search of papers that empirically tested the temperature dependence predictions of the metabolic theory of ecology (Brown et al. 2004) between 2004 and 2024. </p> <p>The .zip file contains a jupyter notebook, julia project toml file and a README in order to reproduce the simulations illustrating how different temperature dynamics can lead to different inferred thermal performance curves in populations.</p> <p> </p>
Importance of non-consumptive effects of Nabis americoferus in biological control strategies including trap crops against the Tarnished plant bug, Lygus lineolaris
<p>Dataset on<span> polyphagous tarnished plant bug (TPB), <em>Lygus lineolaris </em>(Palisot de Beauvois) (Hemiptera: Miridae) </span><span> host preferences and reproductive behaviors under various integrated pest management approaches. </span></p>
Data and scripts used in: "Exploring Biological Neuronal Correlations with Quantum Generative Models"
<div>Data and script for the manuscript "Exploring Biological Neuronal Correlations with Quantum Generative Models", by Vinicius Hernandes and Eliska Greplova.</div> <h3>main scripts</h3> <div> <p><strong><em>generate_activity_dataset.py</em></strong></p> <p>reshape data in <em>neuronData.npy</em> to 50k samples of (neurons, timesteps) shape, saved in <em>activity_data.npy</em></p> <p><strong><em>create_target_distributions.py</em></strong></p> <p>based on the dataset, makes dicionary with the the target distribution for each (neurons, timesteps) pair, saved in <em>distribution_target_dictionary.pkl</em></p> <p><strong><em>create_hyperparameters_file.py</em></strong></p> <p>generates <em>hyperparameters.csv</em>, containing:</p> </div> <ul> <li>number of neurons</li> <li>number of timesteps</li> <li>number of auxiliary_qubits</li> <li>batch_size</li> <li>learning rate of generator</li> <li>learning rate of critic</li> <li>number parametrized layers</li> <li>number of training iterations</li> <li>loss type</li> </ul> <p>for each run</p> <p><strong><em>train_qgan.py</em></strong></p> <div> <p>trains models defined <em>models.py</em> using <em>activity_data.npy</em> dataset, and for the hyperparameters defined in <em>hyperparameters.csv</em></p> </div> <div>saves loss functions, and the trained models for each 10 iterations, in specific folders indexed by the run specified in the hyperparameters file</div> <div> </div> <div><strong><em>generate_fake_activity.py</em></strong></div> <div> </div> <div>uses trained models saved in <em>output/models/run{run}/i{training_step}.pth</em> for a specific <em>training_step</em> and <em>run</em> to generate fake data, and save them in <em>output/generated_data/run{run}/i{training_step}.npy</em> files</div> <div> </div> <div><strong><em>analyze_error.py</em></strong></div> <div> </div> <div>uses generated data saved in <em>output/generated_data/run{run}/i{training_step}.npy</em> to generate two statistical quantities (k-probs and firing rate), using the function in <em>metrics.py</em>, and compare the errors in those quantities between the models using k-loss and standard-loss</div> <div> </div> <div><strong><em>analyze_stats.py</em></strong></div> <div> </div> <div>uses generated data saved in <em>output/generated_data/run{run}/i{training_step}.npy</em> to generate:</div> <ul> <li>js diverge for each training step, and final distribution of generated states, stored in <em>distribution_target_dictionary.pkl</em></li> <li>other statistical quantities, using the function in <em>metrics.py</em> file</li> </ul> <h3>auxiliary scripts</h3> <div><strong><em>metrics.py</em></strong></div> <div> </div> <div>functions to calculate neuronal statistics</div> <div> </div> <div><strong><em>aux.py</em></strong></div> <div> </div> <div>auxiliary functions:</div> <ul> <li>to generate states distribution given a dataset</li> <li>custom js divergence</li> </ul> <h3>Data</h3> <p><strong><em>neuronData.npy</em></strong></p> <p>neuronal data from Marre et al., Multi-electrode array recording from salamander retinal ganglion cells (2017)</p> <p><strong><em>activity_data.npy</em></strong></p> <p>dataset obtained from <em>neuronData.npy</em>, taking 50 thousand samples of shape (neurons, timesteps)</p> <p><strong><em>output</em></strong></p> <p>results obtained from <em>train_qgan.py</em> and <em>generate_fake_activity.py</em> </p> <p>contains:</p> <ul> <li><strong><em>losses</em></strong></li> </ul> <p>generator and critic loss for all training runs and steps</p> <ul> <li><strong><em>models</em></strong></li> </ul> <p>saved torch models every 10 training steps, for all training runs</p> <ul> <li><strong><em>generated_data</em></strong></li> </ul> <p>generated data for all models saved in <em>models</em></p>
Impact of Interval Censoring on Data Accuracy and Machine Learning Performance in Biological High-Throughput Screening
<div> <h2>Overview</h2> <div>Data and Results used in the publication entitled "Impact of Interval Censoring on Data Accuracy and Machine Learning Performance in Biological High-Throughput Screening"</div> </div> <div> <h3><strong>Data</strong></h3> <div>This folder contains the raw data used during this work.</div> <div>`EvoEF.csv` contains information on the library used (sequences, number of mutations, etc.) and the fitness (energy) used as continuous mean values. `mut.csv` contains the information about the combinatorial scaling (N vs N_norm), the number of mutations (m) and the probability of each variant using different distributions (uniform and binomial) at different $p_{WT}$.</div> <div>For further details on how the fitness values were calculated and how the combinatorial scale works, please refer to our prevoius [Paper](https://arxiv.org/abs/2405.05167).</div> <div> </div> <h3><strong>Results</strong></h3> <div> <div>This folder contains the results (outputs) of all scripts used. Such results are included in the form of `.npy` and `.npz` files. To load such files with numpy you should include the option `allow_pickle=True`.</div> </div> </div>
Supplementary tables for the paper: "Comprehensive Mapping of the AOP-Wiki Database: Identifying Biological and Disease Gaps"
<p><strong>Supplementary tables for the paper: "Comprehensive Mapping of the AOP-Wiki Database: Identifying Biological and Disease Gaps"</strong></p> <p><em>Original Research Article</em><br><strong>Frontiers in Toxicology</strong>, March 8, 2024<br>Section: Regulatory Toxicology<br><strong>Volume 6 - 2024</strong> | <a href="https://doi.org/10.3389/ftox.2024.1285768" target="_new" rel="noopener">https://doi.org/10.3389/ftox.2024.1285768</a></p> <p><strong>Authors</strong>:<br>Thomas Jaylet, Thibaut Coustillet, Nicola M. Smith, Barbara Viviani, Birgitte Lindeman, Lucia Vergauwen, Oddvar Myhre, Nurettin Yarar, Johanna M. Gostner, Pablo Monfort-Lanzas, Florence Jornod, Henrik Holbech, Xavier Coumoul, Dimosthenis A. Sarigiannis, Philipp Antczak, Anna Bal-Price, Ellen Fritsche, Eliska Kuchovska, Antonios K. Stratidakis, Robert Barouki, Min Ji Kim, Olivier Taboureau, Marcin W. Wojewodzic, Dries Knapen, Karine Audouze</p>
Biological nitrogen fixation in cropland
<p>Methods are documented in the README.md of the source code repository:</p> <p><a href="https://doi.org/10.5281/zenodo.7133336">https://doi.org/10.5281/zenodo.7133336</a></p> <p>The version number of this dataset corresponds to the version number of the source code.</p>
Demarcation of boundaries are for biological references only. in SUHAIB FIRDOUS YATOO, AIJAZ AHMAD WACHKOO, KAVYA G. PILLAI & CHRIS M. COHEN (2024) Catalog of the Robber Flies of India (Diptera: Asilidae). Zootaxa, 5509 (1): 001-088.
Demarcation of boundaries are for biological references only.
BOCK: Biological networks and Oligogenic Combinations as a Knowledge graph 2.0
<p>BOCK is a knowledge graph integrating oligogenic disease information (originally from the Oligogenic Disease Database (Natchtegael et al. 2022)) together with multiple biological networks and ontologies.</p> <p>Compared to more generic knowledge graphs, we selected specifically networks relevant to understand the molecular mechanisms of epistasis, placing genes as the central entities, and focused on trusted resources describing a large set of human genes and their interactions.</p> <p>All entities in the KG are linked to their source database entry via an URI (Uniform Resource Identifier) to facilitate integrations within larger bioinformatics linked data repositories.</p> <p>BOCK 2.0 integrates recent versions of the used ontologies and databases, as well as additional pathway-specific (The Reactome Pathway Knowledgebase 2024, Milacic et al.) and tissue-specific information (COXPRESdb v8, Obayashi et al.). Additionally the database used for the coexpression relation between genes, has been replaced by COXPRESdb v8.</p> <p>We provide BOCK 2.0 in three formats:</p> <ol> <li><strong>GraphML (Graph Markup Language)</strong>: a network format enabling the fast import of the KG by multiple libraries (e.g networkx) and tools (e.g Cytoscape).</li> <li><strong>XML (Extensible Markup</strong> <strong>Language)</strong>: a text-encoding system that is human-readable and compatible with many systems.</li> <li><strong>Neo4J import files</strong>: tab-separated files that can be easily imported into Neo4J using the neo4j-admin utils.</li> </ol>
Dataset for: FAG in buckwheat flowers and its possible biological relevance
<p>The file " PFAG and FAG raw data" contains raw data as well as data that has been processed to remove outliers, used for analyzing PFAG and FAG content in different parts of buckwheat flowers and in response to applied LED light.</p> <div> <div> <div> <div> <div> <div> <p>The file "Data for Correlations" contains data used for correlation analyses; it includes log10-transformed data without outliers, as well as raw flower count data.</p> <div> <div> <div> <div> <div> <div> <p>The file "Correlation Analyses PFAG, FAG & Flowers" contains completed correlation analyses.</p> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>
Data from: Potential limits to the benefits of admixture during biological invasion
Species introductions often bring together genetically divergent source populations, resulting in genetic admixture. This geographic reshuffling of diversity has the potential to generate favorable new genetic combinations, facilitating the establishment and invasive spread of introduced populations. Observational support for the superior performance of admixed introductions has been mixed, however, and the broad importance of admixture to invasion questioned. Under most underlying mechanisms, admixture's benefits should be expected to increase with greater divergence among and lower genetic diversity within source populations, though these effects have not been quantified in invaders. We experimentally crossed source populations differing in divergence in the invasive plant Centaurea solstitialis. Crosses resulted in many positive (heterotic) interactions, but fitness benefits declined and were ultimately negative at high source divergence, with patterns suggesting cyto-nuclear epistasis. We explored the literature to assess whether such negative epistatic interactions might be impeding admixture at high source population divergence. Admixed introductions reported for plants came from sources with a wide range of genetic variation, but were disproportionately absent where there was high genetic divergence among native populations. We conclude that while admixture is common in species introductions and often happens under conditions expected to be beneficial to invaders, these conditions may be constrained by predictable negative genetic interactions, potentially explaining conflicting evidence for admixture's benefits to invasion.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.