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985 results for “Classics”
Data from: Only rare classical MHC-I alleles are highly expressed in the European house sparrow
<p>The exceptional polymorphism observed within genes of the major histocompatibility complex (MHC), a core component of the vertebrate immune system, has long fascinated biologists. The highly polymorphic <em>classical</em> MHC class-I (MHC-I) genes are maintained by pathogen-mediated balancing selection (PMBS), as shown by many sites subject to positive selection, while the more monomorphic MHC-I genes show signatures of purifying selection. In line with PMBS, at any point in time, rare classical MHC alleles are more likely than common classical MHC alleles to confer a selective advantage in host-pathogen interactions. Combining genomic and expression data from the blood of wild house sparrows <em>Passer domesticus</em>, we found that only rare classical MHC-I alleles were highly expressed, while common classical MHC-I alleles were lowly expressed or not expressed. Moreover, highly expressed rare classical MHC-I alleles had more positively selected sites, indicating exposure to stronger PMBS, compared with lowly expressed classical alleles. As predicted, the level of expression was unrelated to allele frequency in the monomorphic non-classical MHC-I alleles. Going beyond previous studies, we offer a fine-scale view of selection on classical MHC-I genes in a wild population by revealing differences in the strength of PMBS according to allele frequency and expression level.</p>
Supplemental material to "Solving Quantified Modal Logic Problems by Translation to Classical Logics"
<p>These files are associated with the manuscript entitled<br>"Solving Quantified Modal Logic Problems by Translation to Classical Logics"<br>by Alexander Steen, Geoff Sutcliffe, Christoph Benzmüller.</p> <p>Contact: Alexander Steen <alexander.steen@uni-greifswald.de></p> <p>Contents<br>-----------</p> <p> - QMLTP-monomodal-NX0.tar.gz<br> This archive contains the TPTP NX0 representations of the 580 mono-modal<br> problems translated from the QMLTP library [1,2].<br> <br> - QMLTP-monomodal-TF0-embedded-rigid-local.tar.gz<br> This archive contains the embedded TF0 files created <br> from the monomodal NX0 files using the Logic Embedding Tool [3].<br> <br> - QMLTP-monomodal-TH0-embedded-rigid-local.tar.gz<br> This archive contains the embedded TH0 files created <br> from the monomodal NX0 files using the Logic Embedding Tool [3,4].<br> <br> - QMLTP-multimodal-NX0-and-embedded.tar.gz<br> This archive contains the TPTP NX0 representations of the 20 multi-modal<br> problems translated from the QMLTP library [1,2]. Additionally, it<br> contains the 20 embedded TF0 and the 20 embedded THF files created <br> from the NX0 files using the Logic Embedding Tool [3,4].<br> <br> - QMLTP-primary-evaluation-results-QMLTP.zip<br> This archive contains the primary evaluation data creating from<br> running E 3.0.03, Leo-III 1.7.8, Nitpick 2016, Vampire 4.8, <br> MleanCoP 1.3, nanoCoP-M 2.0 on the problem files.<br> All reasoning systems except Nitpick were run on the StarExec Miami cluster with a 60s<br> wall clock and 480 CPU time limit. The StarExec Miami computers have an<br> octa-core Intel Xeon E5-2667 3.20 GHz CPU, 128 GiB memory, and run the<br> CentOS Linux release 7.4.1708 operating system. Nitpick was run on a server<br> with a 60s wall clock time limit. The server has an octa-core Intel Xeon E5-<br> 2609 2.50 GHz CPU, 64 GiB memory, and the CentOS Linux release 7.9.2009<br> operating system.<br> <br> - README<br> This file.<br> <br> <br> <br>[1] T. Raths and J. Otten. The QMLTP Problem Library for First-Order Modal Logics.<br> In B. Gramlich, D. Miller, and U. Sattler, editors, Proceedings of the 6th International Joint Conference on Automated Reasoning,<br> number 7364 in Lecture Notes in Artificial Intelligence, pages 454–461. Springer, 2012.<br>[2] http://www.iltp.de/qmltp/<br>[3] A. Steen. An extensible logic embedding tool for lightweight non-classical reasoning (short paper).<br> In B. Konev, C. Schon, and A. Steen, editors, Proceedings of the 8th Workshop on Practical Aspects of Automated<br> Reasoning, number 3201 in CEUR Workshop Proceedings, 2022.<br>[4] https://github.com/leoprover/logic-embedding</p>
Classical Tibetan Verbal Paradigms
<p>This is a collection of verb paradigms in Classical Tibetan (Written Tibetan), conforming to the Paralex (https://www.paralex-standard.org) standard. The metadata conforms to the Frictionless (https://frictionlessdata.io/) standard. Datafiles are encoded as csv files.</p>
Distributing quantum correlations through local operations and classical resources
<p>Text files containing data of the figures shown in "Distributing quantum correlations through local operations and classical resources". Where some variables do not affect the plot value, for instance the values of φ in Figures 4a and 4b, fewer plot points of these variables are used in the final heatmaps to allow more detail in the other variables which do affect the function values. https://arxiv.org/abs/2408.05490. </p>
Indian Semi-Classical Music Dataset
<p>This dataset is a collection of mel-spectrogram features extracted from Indian semi-classical music containing the following 9 semi-classical styles:<br> Bhajan, Chaiti, Dadra, Ghazal, Kajri, Natya Sangeet, Qawwali, Tappa, Thumri.</p> <p>The number of recordings varies from 25 (for Chaiti) to 50 in the mentioned styles representing the scarcity of availability of given folk styles on the Internet. There are at least 5 artists and a maximum of 13. Overall there are 48 artists (36 female + 12 male) in these 9 semi-classical styles. <br> There is a total of 425 recordings in the dataset, with a total duration of 54.69 hrs.<br> Mel-spectrogram is extracted from a 3-second segment with each song's 1/2 second sliding window. Extracted mel-spectrogram for each segment is annotated with the genre, artist, gender, song, source, no_of_artists, genre_id, artist_id, gender_id.<br> _________________________________________________________________________________________________________<br> This project was funded under the grant number: ECR/2018/000204 by the Science & Engineering Research Board (SERB).</p>
All-atom molecular dynamics simulations of phenylalanine-4-hydroxylase (PAH) tetramer to investigate the impact of two novel heterozygous mutations, p.Y198N and p.Y204F, observed in a classical phenylketonuria patient
<p>Phenylalanine-4-hydroxylase (PAH) tetramer system (Robetta modelling to complete the structure with template PDB ID: 6hyc) with parametrised BH<sub>4</sub> ligand (parameters are available in the dataset) and Fe(II) metal ions in a TIP3P water box ionised with 0.15 M KCl were presented as wild-type and carrying two novel mutations as Y198N on dimeric chains A and B, and Y204F on dimeric chains C and D. In addition, E353 and E422 are protonated as predicted by PROPKA. BH<sub>4</sub> molecule parametrization was performed by using GAFF, Antechamber and “amb2chm_par.py” program of Amber2018.</p> <p>5,000-step minimization and 1 ns equilibration were performed by fixing the protein to relax the system. Then, another 5,000-step minimization and 1 ns equilibration were performed without any constraints, except the SHAKE algorithm on water molecules, to relax the protein and system. The production simulations were performed along 100 ns trajectory at 310 K collected under NpT ensemble.</p> <p>All system preparation and simulation details for this dataset is available with the related background, results and conclusions in the following article:</p> <p>Tolga Aslan, Aslı Yenenler-Kutlu, Umut Gerlevik, Ayşe Çiğdem Aktuğlu Zeybek, Ertuğrul Kıykım, Osman Uğur Sezerman & Necla Birgul Iyison (2021) Identifying and elucidating the roles of Y198N and Y204F mutations in the PAH enzyme through molecular dynamic simulations, Journal of Biomolecular Structure and Dynamics, DOI: <a href="https://doi.org/10.1080/07391102.2021.1921619">10.1080/07391102.2021.1921619</a></p>
Research data for `Quantifying information scrambling via Classical Shadow Tomography on Programmable Quantum Simulators'
<p>Research data associated with the paper `Quantifying information scrambling via Classical Shadow Tomography on Programmable Quantum Simulators'. Contains raw data obtained from simulations run on the IBM quantum device ibm_lagos.</p>
FIGURE 1 in Comparisons of two cryptic Ampedus species (Coleoptera: Elateridae) by using classical systematics, ecological niche modeling, and DNA barcoding
FIGURE 1. Habitus photos and aedeagi drawings of examined species. A-B. Ampedus platiai, C-D. A. samedovi, E-F. A. pomonae (Aedeagi of A. platiai and A. samedovi are redrawn from Kabalak 2010 and aedeagus of A. pomonae is redrawn from Platia 1994.). BML: Basal struts of median lobe, BP: Basal piece, ML: Median Lobe, PDT: Paramere distal tooth, PR: Paramere.
Figure 4 in Classical and geometric morphometric methods reveal differences between specimens of Varroa destructor (Mesostigmata: Varroidae) from seven provinces of Iran
Figure 4. Dendrogram plotted by on UPGMA method based on morphometric measurement. The vertical line is the
Figure 3 in Classical and geometric morphometric methods reveal differences between specimens of Varroa destructor (Mesostigmata: Varroidae) from seven provinces of Iran
Figure 3. Distribution of morphometric characters in PCA analysis. This graph is based on the average size of the characters, is drawn.
Figure 2 in Classical and geometric morphometric methods reveal differences between specimens of Varroa destructor (Mesostigmata: Varroidae) from seven provinces of Iran
Figure 2. Distribution of six landmarks on the ventral surface of varroa mite for geometric measurement.
Figure 5 in Classical and geometric morphometric methods reveal differences between specimens of Varroa destructor (Mesostigmata: Varroidae) from seven provinces of Iran
Figure 5. Distribution of varroa mite based on a landmark in PCA analysis. Weight matrices data are used for this analysis. Circles show the closer groups.
Figure 1 in Classical and geometric morphometric methods reveal differences between specimens of Varroa destructor (Mesostigmata: Varroidae) from seven provinces of Iran
Figure 1. Morphometric parameters measured on the ventral surface varroa mite – a: body width, b: body length, c: length of the epigynal shield, d: length of the anal shield, e: metapodal shield's width, f: metapodal shield's length.
Datasets for the Next Release Problem (agile and classic costs)
<p>Datasets synthetically sampled for experimentation for the (multi-objective) Next Release Problem. </p> <p>aX datasets have cost values sampled from fibonacci scale.</p> <p>cX and dX datasets have cost values sampled from Function Points size of real datasets from the ISBSG 2015 dataset.</p> <p> </p> <table> <tbody> <tr> <td><strong>Dataset</strong></td> <td><strong>#Stakeholders</strong></td> <td><strong>|R|</strong></td> <td><strong>#(ri⇒D⊆R)</strong></td> <td><strong>%(r_i⇒D⊆R)</strong></td> <td><strong>Avg|D|</strong></td> </tr> <tr> <td>a1</td> <td>5</td> <td>50</td> <td>18</td> <td>0.360</td> <td>2.222</td> </tr> <tr> <td>a2</td> <td>15</td> <td>50</td> <td>18</td> <td>0.360</td> <td>2.722</td> </tr> <tr> <td>a3</td> <td>5</td> <td>200</td> <td>74</td> <td>0.370</td> <td>1.946</td> </tr> <tr> <td>a4</td> <td>15</td> <td>200</td> <td>75</td> <td>0.375</td> <td>2.253</td> </tr> <tr> <td>c1</td> <td>15</td> <td>50</td> <td>20</td> <td>0.400</td> <td>2.400</td> </tr> <tr> <td>c2</td> <td>100</td> <td>50</td> <td>17</td> <td>0.340</td> <td>3.529</td> </tr> <tr> <td>c3</td> <td>15</td> <td>200</td> <td>69</td> <td>0.345</td> <td>1.942</td> </tr> <tr> <td>c4</td> <td>100</td> <td>200</td> <td>75</td> <td>0.375</td> <td>2.093</td> </tr> <tr> <td>d1</td> <td>15</td> <td>200</td> <td>88</td> <td>0.440</td> <td>3.352</td> </tr> <tr> <td>d2</td> <td>50</td> <td>200</td> <td>88</td> <td>0.440</td> <td>4.852</td> </tr> <tr> <td>d3</td> <td>15</td> <td>300</td> <td>131</td> <td>0.437</td> <td>3.771</td> </tr> <tr> <td>d4</td> <td>50</td> <td>300</td> <td>145</td> <td>0.483</td> <td>3.697</td> </tr> </tbody> </table>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 5. Differences in Validation Criteria between Classical Engineering Sciences and the Field of Brain- Like Artificial Intelligence for Automation
<p>A usual validation procedure in classical fields of engineering and computer sciences as well as in Applied AI, which is currently the dominant AI research domain, is to analyze and implement different potential methods to solve a given problem and to then compare their performance. What is thus usually desired are comparable, quantifiable results. In comparison, the starting situation is<br> different in the field of Brain-Like AI (see Figure 5).</p>
Figure 6. A in Classical taxonomy, molecular phylogeny and genetic analysis of the genus Exitianus Ball, 1929 (Hemiptera: Cicadellidae: Deltocephalinae) from Egypt
Figure 6. A. Amino acids variations of the COX1 gene generated by WebLogo3 server. B. Multiple amino acids alignments for selected Exitianus isolates generated by MultAlin server.
Figure 2. Exitianus nanus. A in Classical taxonomy, molecular phylogeny and genetic analysis of the genus Exitianus Ball, 1929 (Hemiptera: Cicadellidae: Deltocephalinae) from Egypt
Figure 2. Exitianus nanus. A. Habitus, dorsal view; B. Habitus, female ventral view; C. Habitus, male ventral view; D. Pronotum & scutellum; E. Face; F. Male genitalia (pygofer, subgenital plate, valva, styles and connective, aedeagus).
Figure 1. Exitianus capicola. A in Classical taxonomy, molecular phylogeny and genetic analysis of the genus Exitianus Ball, 1929 (Hemiptera: Cicadellidae: Deltocephalinae) from Egypt
Figure 1. Exitianus capicola. A. Habitus, dorsal view; B. Habitus, female ventral view; C. Habitus, male ventral view; D. Pronotum
Figure 3. Exitianus pondus. A in Classical taxonomy, molecular phylogeny and genetic analysis of the genus Exitianus Ball, 1929 (Hemiptera: Cicadellidae: Deltocephalinae) from Egypt
Figure 3. Exitianus pondus. A. Habitus, dorsal view; B. Habitus, female ventral view; C. Habitus, male ventral view; D. Pronotum & scutellum; E. Face; F. Male genitalia (pygofer, subgenital plate, valva, styles and connective, aedeagus); G. Aedeagus, lateral view.
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 12. Affinities and Differences of Neuro-Symbolic Networks in Comparison to Classical Neural Networks
<p>After having briefly illustrated the basic function principle of neuro-symbolic networks, this<br> section aims at reviewing their affinities and differences to standard neural networks like for<br> example multi-layer perceptrons (MLPs) [58]. A summary of these affinities and differences is<br> given in Figure 12. The affinities concern certain functions of individual nodes of the networks. In<br> both cases, weighted input information is summed up and an activation function is applied to this<br> sum. In both cases, the individual nodes are interconnected to form networks. Much larger than the<br> number of affinities between neuro-symbolic networks and neural network is however the number<br> of differences. The first difference consists in the application domain. Neuro-symbolic networks<br> have so far mainly been applied for complex, large-scale sensor data processing of multimodal data<br> – an application which can so far barely be handled by neural networks.</p>
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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.