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FIGURE 22 in Allotanaupodidae, a new family of early derivative Parasitengona (Acari: Prostigmata)
FIGURE 22. Nanotanaupodus andrei sp. n. (adult female). Ventral view of idiosoma.
FIGURE 26 in Allotanaupodidae, a new family of early derivative Parasitengona (Acari: Prostigmata)
FIGURE 26. Nanotanaupodus andrei sp. n. (deutonymph). Dorsal view of idiosoma.
FIGURE 30 in Allotanaupodidae, a new family of early derivative Parasitengona (Acari: Prostigmata)
FIGURE 30. Nanotanaupodus gracehallae sp. n. (adult male). Dorsal view of idiosoma.
FIGURE 7 in Allotanaupodidae, a new family of early derivative Parasitengona (Acari: Prostigmata)
FIGURE 7. Allotanaupodus williamsi sp. n. (adult male). Ventral view of idiosoma.
FIGURE 21 in Allotanaupodidae, a new family of early derivative Parasitengona (Acari: Prostigmata)
FIGURE 21. Nanotanaupodus andrei sp. n. (adult female). Dorsal view of idiosoma.
FIGURE 163 in Dendropaemon Perty, 1830: taxonomy, systematics and phylogeny of the morphologically most derived phanaeine genus (Coleoptera: Scarabaeidae, Scarabaeinae, Phanaeini)
FIGURE 163. Implied weight tree showing current subgeneric organization.
FIGURE 161 in Dendropaemon Perty, 1830: taxonomy, systematics and phylogeny of the morphologically most derived phanaeine genus (Coleoptera: Scarabaeidae, Scarabaeinae, Phanaeini)
FIGURE 161. Known distribution of D. latistriatus, D. denticollis.
Spectral-temporal features over South Africa (2013-2015) derived from multi-sensor Landsat imagery
<p>The three data sets are spectral-temporal features derived from multi-sensor Landsat imagery over South Africa. They were derived as follows:</p> <p>All Landsat-5, -7 and -8 data from 2013 to 2015 covering South Africa (70 tiles) were acquired and pre-processed following the procedure implemented in Hansen et al. (2013), Potapov et al. (2014), Potapov et al. 2012. Note that four tiles (path/row: 176/77, 175/78, 174/78, 174/79) were discarded because no crop is grown there. Four spectral bands were kept: the red, the near-infrared (NIR), and the two short-wave infrared (SWIR) bands. The blue and green bands were discarded due to their sensitivity to atmospheric effects. We applied a three-step procedure to normalize the radiometry. First, Landsat data were converted to top-of-atmosphere reflectance and then normalized by taking the corresponding MODIS top-of-canopy reflectance data as target. Third, we adjusted cross-track surface anisotropy effects by modeling the Landsat reflectance per spectral band as a function of the viewing angle. The above-mentioned processing steps incrementally improved the appearance of the data, providing more spatial coherence and increasing the generalization and internal consistency of the multi-spectral feature space.</p> <p> </p> <p>Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova S, Tyukavina A, et al. High-resolution global maps of 21st-century forest cover change. science. 2013;342(6160):850{853.</p> <p><br> Potapov P, Dempewolf J, Talero Y, Hansen M, Stehman S, Vargas C, et al. National satellite-based humid tropical forest change assessment in Peru in support of REDD+ implementation. Environmental Research Letters. 2014;9(12):124012.</p> <p><br> Potapov PV, Turubanova S, Tyukavina A, Krylov A, McCarty J, Radeloff V, et al. Eastern Europe's forest cover dynamics from 1985 to 2012 quantified from the full Landsat archive. Remote Sensing of Environment. 2015;159:28 43.</p>
Data from: Identification of prokaryotic and eukaryotic virus-derived sequences in virome using deep learning
<h4>This repository contains the data and Docker image to reproduce the results of our paper: <strong>identification of prokaryotic and eukaryotic virus-derived sequences in virome using deep learning</strong></h4> <p>Authors: Hengchuang Yin, Shufang Wu, Jie Tan, Qian Guo, Mo Li, Jinyuan Guo, Yaqi Wang, Xiaoqing Jiang, and Huaiqiu Zhu*</p> <p><strong>This work has been accepted by GigaScience. </strong></p> <p><strong>Hengchuang Yin, Shufang Wu, Jie Tan, Qian Guo, Mo Li, Jinyuan Guo, Yaqi Wang, Xiaoqing Jiang, and Huaiqiu Zhu. "IPEV: Identification of Prokaryotic and Eukaryotic Virus-Derived Sequences in Virome Using Deep Learning." GigaScience 13 (2024): giae018. <a href="https://doi.org/10.1093/gigascience/giae018" rel="nofollow">https://doi.org/10.1093/gigascience/giae018</a>.</strong></p> <div> </div> <p> </p> <p><strong>Background: </strong>The virome obtained through virus-like particle enrichment contains a mixture of prokaryotic and eukaryotic virus-derived fragments. Accurate identification and classification of these elements are crucial to understanding their roles and functions in microbial communities. However, the rapid mutation rates of viral genomes pose challenges in developing high-performance tools for classification, potentially limiting downstream analyses.</p> <p><strong>Findings: </strong>We present IPEV, a novel method to distinguish prokaryotic and eukaryotic viruses in viromes, with a 2D convolutional neural network combining trinucleotide pair relative distance and frequency. Cross-validation assessments of IPEV demonstrate its state-of-the-art precision, significantly improving the F1-score by approximately 22% on an independent test set compared to existing methods when query viruses share less than 30% sequence similarity with known viruses. Furthermore, IPEV outperforms other methods in accuracy on marine and gut virome samples based on annotations by sequence alignments. IPEV reduces runtime by at most 1,225 times compared to existing methods under the same computing configuration. We also utilized IPEV to analyze longitudinal samples and found that the gut virome exhibits a higher degree of temporal stability than previously observed in persistent personal viromes, providing novel insights into the resilience of the gut virome in individuals. </p> <p><strong>Conclusions: </strong>IPEV is a high-performance, user-friendly tool that assists biologists in identifying and classifying prokaryotic and eukaryotic viruses within viromes. The tool is available at https://github.com/basehc/IPEV.</p> <p> </p> <p><strong>5_fold_cross_validation.zip:</strong> Dataset of cross-validation of IPEV</p> <p><strong>Eukaryotic_virus_CV_Dataset-1.csv:</strong> GI, and accession ID for the cross-validation Dataset-1 (eukaryotic virus)</p> <p><strong>Prokaryotic_virus_CV_Dataset-1.csv:</strong> GI, and accession ID for the cross-validation Dataset-1 (prokaryotic virus)</p> <p><strong>Test_Prokaryotic_virus_Dataset-1.fasta: </strong>An independent test set of IPEV (prokaryotic virus)</p> <p><strong>Test_Eukaryotic_virus_Dataset-1.fasta: </strong>An independent test set of IPEV (eukaryotic virus)</p> <p> </p> <p> </p> <p><strong>Dataset_sequencing_error.zip: </strong>Simulated dataset with sequencing errors</p> <p><strong>Cap_enzyme_sequence.fasta: </strong>Accession IDs of Receptor Binding Proteins (RBPs) in phages collected by our article</p> <p><strong>Dataset_runtime_evaluation.zip: </strong>Dataset for evaluating the runtime of IPEV</p> <p><strong>Receptor_binding_protein_accession_id:</strong> Accession IDs of Receptor Binding Proteins (RBPs) in phages collected by our article</p> <p> </p> <p><strong>archaea_ID.txt</strong> Accession ID information for the reference archaea dataset</p> <p><strong>bacteria_ID.txt</strong> Accession ID information for the reference bacterial dataset</p> <p><strong>marine_virome_id.csv:</strong> Ocean virome data information used in our paper</p> <p><strong>gut_virome.csv</strong>:Gur virome data information used in our paper</p> <p><strong>fungi.txt: </strong>Negative sequence information used to train, validate, and test the model in the decontamination function</p> <p><strong>bacteria.txt: </strong>Negative sequence information used to train, validate, and test the model in the decontamination function</p> <p> </p> <p> </p> <h4><strong>Reproduce the results of our paper from a Docker image.</strong></h4> <p> </p> <p>We also provide a Docker image file that does not require any environment configuration. You can reproduce the results of our paper (e.g., train and test our IPEV model) in a Docker image.</p> <p>Pull the<a href="https://hub.docker.com/r/dryinhc/ipev_v1"> <em>dryinhc/ipev_v1</em></a> image from Docker Hub. Open a terminal window and run the following command:</p> <p><em>docker pull dryinhc/ipev_v1</em></p> <p>This will download the image to your local machine.</p> <p>Run the <em>dryinhc/ipev_v1</em> image. In the same terminal window, run the following command:</p> <p><em>docker run -it --rm dryinhc/ipev_v1</em></p> <p>This will start a container based on the image and run the IPEV tool.</p> <p>And you can run cd train or cd other file folders in the container.</p> <p>To exit the container, press <em>Ctrl+D</em> or type <em>exit</em>.</p> <p>It contains 4 directories, namely 5 fold cross validation, independent set, marine virome, and gut virome. The 5-fold cross-validation directory holds the scripts required for implementing the 5-fold cross-validation method. The independent set directory contains scripts necessary for working with an independent set. Lastly, the marine virome and gut virome directories store scripts for analyzing real datasets.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p>We hereby confirm that the dataset associated with the research described in this work is made available to the public under the Creative Commons Zero (CC0) license.</p> <p> </p> <p> </p> <h4><strong>Contact </strong></h4> <p> </p> <p>If you have any questions, please don't hesitate to ask me: yinhengchuang@pku.edu.cn or hqzhu@pku.edu.cn</p>
Friend virus virulence and reproduction data for wild-derived male house mice competing in semi-natural populations
<p>Social status in social animals, including humans, is considered a major determinant of overall health and susceptibility to disease. There is considerable evidence that these effects extend to the standing immune profile and that social status directly influences susceptibility to pathogens. However, the mechanistic underpinnings that link infectious disease and social status remain difficult to disentangle from associated factors. Here we examined the association between dominance status in male wild-derived house mice (<i>Mus musculus</i>) and susceptibility to Friend virus complex in the context of semi-natural populations with high male-male competition and no predation. These enclosures resemble house mouse environments, which have been associated with human architecture since the dawn of agriculture. Such conditions were achieved with large indoor enclosures designed to allow remote monitoring and to encourage mice to compete over optimal territories. Due to an interruption in our facility's heating system, we were unexpectedly presented with the opportunity to assess how reduced temperature influences the association of social status and pathogen susceptibility. <a>We found that pathogen virulence and reproduction were significantly lower in socially dominant hosts compared to non-dominant hosts</a>. Interestingly, when temperature was reduced dominant and non-dominant males experienced similar levels of virulence and viral reproduction. While several hypotheses are considered, these insights may illuminate a new method of experimental manipulation to modify competitive intensity. If confirmed, future experiments may help elucidate the mechanistic underpinnings between social status and disease in the best studied and economical mammalian model available to science.</p>
Supplementary material 1 from: Ruswanto R, Mardianingrum R, Septian AD, Yanuar A (2023) The design and virtual screening of thiourea derivatives as a Sirtuin-1 inhibitor. Pharmacia 70(4): 1335-1344. https://doi.org/10.3897/pharmacia.70.e108012
The structure and docking results of the 1-benzoyl-3-methylthioureas derivatives
Supplementary material 3 from: Khalaf WY, Elias RS, Raheem LA (2023) Design, synthesis and molecular docking study of coumarin pyrazoline derivatives against MCF-7 breast cancer cell line. Pharmacia 70(4): 1497-1492. https://doi.org/10.3897/pharmacia.70.e108670
Data synthesis compounds, physicochemical properties and identification of synthesis compound
Supplementary material 1 from: Khalaf WY, Elias RS, Raheem LA (2023) Design, synthesis and molecular docking study of coumarin pyrazoline derivatives against MCF-7 breast cancer cell line. Pharmacia 70(4): 1497-1492. https://doi.org/10.3897/pharmacia.70.e108670
Synthesis compounds
Supplementary material 2 from: Khalaf WY, Elias RS, Raheem LA (2023) Design, synthesis and molecular docking study of coumarin pyrazoline derivatives against MCF-7 breast cancer cell line. Pharmacia 70(4): 1497-1492. https://doi.org/10.3897/pharmacia.70.e108670
Docking study
Intertidal topography at 10m Resolution Derived from Sentinel-2 Multi-Spectral Imagery for the Bengal Delta Coastline
<p>This repository contains the intertidal topography (e.g., digital elevation model) at 10m spatial resolution and the source-code of the toolbox used in deriving this dataset from Sentinel-2 Level-2A spectral imagery and a regional tidal model. The dataset covers the coastal Bangladesh and West Bengal located in the northern Bay of Bengal. The method and its performance is described in detail in the following paper - </p><p> </p><p>Khan, M.J.U.; Ansary, M.N.; Durand, F.; Testut, L.; Ishaque, M.; Calmant, S.; Krien, Y.; Islam, A.K.M.S.; Papa, F. High-Resolution Intertidal Topography from Sentinel-2 Multi-Spectral Imagery: Synergy between Remote Sensing and Numerical Modeling. Remote Sens. 2019, 11, 2888. https://doi.org/10.3390/rs11242888</p><p> </p><p>The repository has the following files and directory - </p><p> </p><p>1. `DEM_intertidal.dat`: This text file contains the extracted intertidal DEM as space separated x (column 1), y (column 2), z (column 3) points. The spatial location are given in longitude (x), latitude (y), and the vertical height (z) is given in meters (upward positive).</p><p>2. `pyIntertidalDEM.zip`: This compressed zip folder contains a snapshot of the toolbox (https://github.com/jamal919/pyIntertidalDEM/tree/f05de55d726fd4356f9fb8979a2cf0f42f0ec9b1) used to extract the instantaneous shorelines for the current version of the intertidal DEM. The code is also accessible in its most updated version from the github repository - https://github.com/jamal919/pyIntertidalDEM.</p><p>3. `README.txt`: This text file.</p><p> </p><p>The dataset is published under a Creative Commons Attribution 4.0 International license. The pyIntertidalDEM toolbox is published under a Apache License 2.0.</p>
Figure 7 from: Sivakumar B, Kaliappan I (2023) Lead drug discovery from imidazolinone derivatives with Aurora kinase inhibitors. Pharmacia 70(4): 1529-1540. https://doi.org/10.3897/pharmacia.70.e114935
Figure 7 Two-dimensional diagram of Compounds 1, 7, 9, 13, 19 interactions (top) during 100 ns MD simulation and hit molecule compound 19_1MQ4 interaction (bottom).
Figure 5 from: Sivakumar B, Kaliappan I (2023) Lead drug discovery from imidazolinone derivatives with Aurora kinase inhibitors. Pharmacia 70(4): 1529-1540. https://doi.org/10.3897/pharmacia.70.e114935
Figure 5 The root mean square fluctuation (RMSF) of 1MQ4 protein during 100 ns MD, representing local changes along the protein chain for Compounds 1, 7, 9, 13, 19.
Figure 4 from: Sivakumar B, Kaliappan I (2023) Lead drug discovery from imidazolinone derivatives with Aurora kinase inhibitors. Pharmacia 70(4): 1529-1540. https://doi.org/10.3897/pharmacia.70.e114935
Figure 4 The root mean square deviation (RMSD) of 1MQ4 protein during 100 ns MD, Compounds 1, 7, 9, 13, 19.
Figure 6 from: Sivakumar B, Kaliappan I (2023) Lead drug discovery from imidazolinone derivatives with Aurora kinase inhibitors. Pharmacia 70(4): 1529-1540. https://doi.org/10.3897/pharmacia.70.e114935
Figure 6 The plot represents the hydrogen bonding interactions of compounds 1, 7, 9, 13, 19 with esteem to deposits of 1MQ4 throughout 100 ns MD simulation.
Figure 3 from: Sivakumar B, Kaliappan I (2023) Lead drug discovery from imidazolinone derivatives with Aurora kinase inhibitors. Pharmacia 70(4): 1529-1540. https://doi.org/10.3897/pharmacia.70.e114935
Figure 3 Leads generated from QSAR and docking studies (compounds 1, 7, 9, 13, 19) structural stability analysis upon ligand docking.
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.