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248 results for “Neural development”
Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European Marine Species, developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Native ecological niche models of 1508 European species (894 fish and 614 non fish) developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines, for 2019 and under RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, at 0.5° spatial resolution.</p>
Biodiversity Index, in 2019 and across RCP 4.5, and 8.5 scenarios in 2050 and 2100 of 1508 European Marine Species based on ensemble Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Biodiversity Index in 2019 and across RCP 4.5, and 8.5 scenarios in 2050 and 2100 of 1508 European marine species based on ensemble Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution. The Index counts the number of species (among the 1508) potentially present in each 0.5° cell according to the ensemble models. For each ensemble model, a threshold of at least 3 models agreeing on species presence in the cell was used to indicate species presence.</p>
Ecological Niche Models of 96 European Marine Species, for 2019, developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines at 0.1° Resolution
<p>Native ecological niche models of 96 European marine species of particular commercial and conservation interest developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines, for 2019 at 0.1° spatial resolution.</p>
Ensemble Ecological Niche Models and Biodiversity Index for 2019 of 96 European Marine Species based on Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, AquaMaps, and Support Vector Machines at 0.1° Resolution
<p>Ensemble Ecological Niche Models for 2019 of 96 European marine species of particular commercial and conservation interest, based on Ecological Niche Models developed with (i) Artificial Neural Networks, (ii) Maximum Entropy, (iii) Support Vector Machines, and (iv) AquaMaps at 0.1° Resolution. The data report, for each 0.1° cell, how many models (from 0 to 4) overcome a model-specific decision threshold to assess species presence in the cell. A Biodiversity Index is also provided as the count of the number of species (among the 96) potentially present in each 0.1° cell according to the ensemble models. For each ensemble model, a threshold of at least 3 models agreeing on species presence in the cell was used to indicate species presence.</p>
Srrm4 expression during neural development in zebrafish
<p>Confocal microscope z-stacks recording expression of srrm4 during neural development in zebrafish, ascertained using hybridization chain reaction (HCR) in situ hybridization against srrm4 together with additional genes as indicated in file titles. In each file, srrm4 is in channel 1 (green), and if a second gene is listed, its expression is in channel 2 (red). Ages are indicated in file names: hpf = hours post fertilization ; dpf = days post fertilization.</p> <p>srrm4_elavl3_avg_3dpf.tif : mean of three co-registered images.</p> <p>srrm4_elavl3_avg_5dpf.tif : mean of five co-registered images.</p>
Investigating the role of miRNA in neural crest development and diseases
<p>This is a recording of the poster presentation of Mr Marco Antonaci (UEA) at the Swiss RNA Workshop 2022, January 21st</p>
Are Neural Bug Detectors Comparable to Software Developers on Variable Misuse Bugs?
<p>Artifact for "Are Neural Bug Detectors Comparable to Software Developers on Variable Misuse Bugs?"</p> <p><strong>Abstract:</strong> </p> <p>Debugging, that is, identifying and fixing bugs in software, is a central part of software development. Developers are therefore often confronted with the task of deciding whether a given code snippet contains a bug, and if yes, where. Recently, data-driven methods have been employed to learn this task of bug detection, resulting (amongst others) in so called neural bug detectors. Neural bug detectors are trained on millions of buggy and correct code snippets.</p> <p>Given the “neural learning” procedure, it seems likely that neu- ral bug detectors – on the specific task of finding bugs – have a performance similar to human software developers. For this work, we set out to substantiate or refute such a hypothesis. We report on the results of an empirical study with over 100 software developers, targeting the comparison of humans and neural bug detectors. As detection task, we chose a specific form of bugs (variable misuse bugs) for which neural bug detectors have recently made significant progress. Our study shows that despite the fact that neural bug detectors see millions of such misuse bugs during training, software developers – when conducting bug detection as a majority decision – are slightly better than neural bug detectors on this class of bugs. Altogether, we find a large overlap in the performance, both for classifying code as buggy and for localizing the buggy line in the code. In comparison to developers, one of the two evaluated neural bug detectors, however, raises a higher number of false alarms in our study.</p> <p><strong>Content:</strong> The artifact includes the following components:</p> <ul> <li> <p><strong>Web UI:</strong> The developer survey was performed online in the browser of the participants. For this, we created a custom web interface tailored for our study task. We included both the implementation of the frontend (website) and backend implementation (buisness logic and database) in this artifact. Therefore, it is not only possible to replicate our survey with same interface and a new group of participants but it is also possible to extend the interface for future studies. </p> </li> <li> <p><strong>Neural bug detectors: </strong>We evaluate the performance of the developers against two neural bug detectors. In this artifact, we include the bug detectors (implementation + trained models) and the evaluation script used for producing our results. Besides the replication of our bug detector evaluation, the detectors can also be used in future projects for detecting variable misuse bugs in Java methods.</p> </li> <li> <p><strong>Analysis scripts</strong>: After collecting the raw results from the developers and neural bug detectors, we performed several analysis to gain insights how developers and bug detectors compare on the variable misuse task. We include all analysis steps in form of Jupyter notebooks in the artifact. With this, it is possible to reproduce all the figures of our paper. </p> </li> </ul> <p>In addition, we also provide further artifacts that were successfully evaluated at ASE 2022:</p> <p><strong>ASE 2022 Artifact: </strong><a href="https://doi.org/10.5281/zenodo.6958242">10.5281/zenodo.6958242</a></p> <p><strong>Virtual machine: </strong><a href="https://doi.org/10.5281/zenodo.6957849">10.5281/zenodo.6957849</a></p>
Text-fig. 3. "Glossanodon" musceli A – nearly complete specimen NM Pc 02875a; B – caudal skeleton of specimens NM Pc 02871b and NM Pc 02871a (B-1 and B-2 respectively; part and counterpart) and its tentative reconstruction (B-3); C – specimen NM Pc 02873a, general view (C-1) and detail of the head (C-2); D – specimen NM Pc 02874a (the white arrow shows normally developed neural spine on the anterior abdominal vertebra). Abbreviations: ao – antorbitale; d – dentale; epu – epurale; fr – frontale; hp1-6 – hypurals 1-6; io – infraorbitals; mx – maxillare; npu2 – neural spine of second preural vertebra; ph – parhypurale; pu1 – first preural vertebra; pop – preoperculum; psp – parasphenoideum; stu – stegurale; u1 – urale 1; u2 – urale 2. in An Annotated List Of The Oligocene Fish Fauna From The Osíčko Locality (Menilitic Fm.; Moravia, The Czech Republic)
Text-fig. 3. "Glossanodon" musceli A – nearly complete specimen NM Pc 02875a; B – caudal skeleton of specimens NM Pc 02871b and NM Pc 02871a (B-1 and B-2 respectively; part and counterpart) and its tentative reconstruction (B-3); C – specimen NM Pc 02873a, general view (C-1) and detail of the head (C-2); D – specimen NM Pc 02874a (the white arrow shows normally developed neural spine on the anterior abdominal vertebra). Abbreviations: ao – antorbitale; d – dentale; epu – epurale; fr – frontale; hp1-6 – hypurals 1-6; io – infraorbitals; mx – maxillare; npu2 – neural spine of second preural vertebra; ph – parhypurale; pu1 – first preural vertebra; pop – preoperculum; psp – parasphenoideum; stu – stegurale; u1 – urale 1; u2 – urale 2.
Deep Learning Neural Network Development for the Classification of Bacteriocin Sequences Produced by Lactic Acid Bacteria
<p>This project contains the following underlying data:</p> <h3> Software-Related Files</h3> <p><strong> · </strong><strong>BacLABNet_script.ipynb</strong> (Deep Learning Neural Network for classification of Bacteriocin Sequences) </p> <p><strong> · </strong><strong>embed_proteins.py </strong>(Recurrent Neural Network to obtained the embedding vectors)</p> <p> · <strong> </strong><strong>model_I22.h5</strong> (This file contains the trained weights of the trained model)</p> <p> · <strong>model_I22.json</strong> (This file contains the structure of the trained model)</p> <p><strong> · </strong><strong>rnn_gru.pt</strong> (Initial weights of the Recurrent Neural Network to obtain embedding vectors)</p> <p><strong> · </strong><strong>List_kmers.csv</strong> (List of 5-mers and 7-mers obtained from dataset after it filtered sequences shorter than 50 aa and longer than 2000 aa)</p> <p><strong> </strong></p> <p> <strong>Files Used for Training, Testing, and Validation of the Neural Network</strong></p> <p> <strong>· </strong> <strong>data_nonBacLAB.csv</strong> (25000 nonBacLAB amino acid sequences retrieved from Uniprot)</p> <p> <strong>· data_BacLAB.csv</strong> (24964 BacLAB amino acid sequences retrieved from Uniprot)</p> <p> <strong> </strong></p> <p><strong> Additional Files</strong></p> <p><strong> · data_BacLAB_and_nonBacLAB.csv </strong>(Combination of sequences from data_BacLAB.csv and data_nonBacLAB.csv)</p> <p> <strong> · </strong><strong>all k.mers list.xlsx </strong>(Table of all k-mers obtained for k=3,5,7,15,20)</p> <p> </p> <p><strong>Note:</strong> Codes are additionally available on GitHub. </p> <p><span>Data are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication)(http://creativecommons.org/publicdomain/zero/1.0/)</span></p>
Addressing the impact of surface ectoderm and somitic mesoderm development on neural tube morphogenesis
<p>This dataset was generated during the implementation of the MSCA individual fellowship project (<a>101038073</a>).</p>
Co-development of gut microbial metabolism and visual neural circuitry over human infancy
Open the record for dataset details and reuse information.
Data from: Advancing mold identification in the routine laboratory: Performance of smartphone-based imaging and a newly developed Convolutional Neural Network
Open the record for dataset details and reuse information.
Development of a neural interface for high-definition, long-term recording in rodents and nonhuman primates
<p>Data set and code used to generate figures in "Development of a neural interface for high-definition, long-term recording in rodents and nonhuman primates".</p> <p> </p>
Data from: Anthropogenic changes in sodium affect neural and muscle development in butterflies
The development of organisms is changing drastically because of anthropogenic changes in once-limited nutrients. Although the importance of changing macronutrients, such as nitrogen and phosphorus, is well-established, it is less clear how anthropogenic changes in micronutrients will affect organismal development, potentially changing dynamics of selection. We use butterflies as a study system to test whether changes in sodium availability due to road salt runoff have significant effects on the development of sodium-limited traits, such as neural and muscle tissue. We first document how road salt runoff can elevate sodium concentrations in the tissue of some plant groups by 1.5–30 times. Using monarch butterflies reared on roadside- and prairie-collected milkweed, we then show that road salt runoff can result in increased muscle mass (in males) and neural investment (in females). Finally, we use an artificial diet manipulation in cabbage white butterflies to show that variation in sodium chloride per se positively affects male flight muscle and female brain size. Variation in sodium not only has different effects depending on sex, but also can have opposing effects on the same tissue: across both species, males increase investment in flight muscle with increasing sodium, whereas females show the opposite pattern. Taken together, our results show that anthropogenic changes in sodium availability can affect the development of traits in roadside-feeding herbivores. This research suggests that changing micronutrient availability could alter selection on foraging behavior for some roadside-developing invertebrates.
Supplementary material 1 from: Behei N, Tryhubchak O, Pryymak B (2022) Development of amlodipine and enalapril combined tablets based on quality by design and artificial neural network for confirming of qualitative composition. Pharmacia 69(3): 779-789. https://doi.org/10.3897/pharmacia.69.e86876
The results of the study of pharmaco-technological parameters of intermediates and amlodipine tablets with enalapril, data of the functions of desirability
Somitic mesoderm morphogenesis is necessary for neural tube closure during Xenopus development
<p>Data generated during the implementation of the MSCA infividual fellowship project 101038073.</p>
Single-cell and spatial transcriptomics of cardiac neural crest reveal a dual role of vinculin in Tgf beta signaling and cell-extracellular matrix interaction during cardiac outflow tract development
<p>single-cell RNA-seq data analysis:</p> <p>all.cncc.combined.EMBO.mapped.Rdata: public CNCC single-cell RNA-seq data integration</p> <p>E13.5_CNCC_merged_updated.Rdata: single-cell RNA-seq data of E13.5 CNCC generated in Elly lab</p> <p>all.seurat.GFP.Rdata: scRNA-seq data with GFP detected</p> <p>visium.merge_AB.control.Rdata: R processed ST data for slice A and B</p> <p>visium.merge_CD.mutant.Rdata: R processed ST data for slice A and B</p> <p> </p>
Ensemble Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European Marine Species based on Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Ensemble Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European marine species based on Ecological Niche Models developed with (i) Artificial Neural Networks, (ii) Maximum Entropy, (iii) Support Vector Machines, and (iv) AquaMaps at 0.5° Resolution. The data report, for each 0.5° cell, how many models (from 0 to 4) overcome a model-specific decision threshold to assess species presence in the cell.</p>
Effects of Fat-soluble Vitamins Supplementation on Common Complications and Neural Development in Very Low Birth Weight Infants
ClinicalTrials.gov study NCT03876704. IPD Sharing: UNDECIDED. Countries: 1. Publications: 5.
Assessment of Autonomic Maturation in Neonatal Period and Early Neural Development From a Longitudinal Prospective Cohort
ClinicalTrials.gov study NCT00951860. IPD Sharing: Not stated. Countries: 1. Publications: 3.
ScienceDex guides
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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.