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Fig. 1 in Lactarius indohirtipes and L. sharmai (Russulales, Basidiomycota): two novel species from Jammu and Kashmir, India

Fig. 1. ML phylogram (RAxML) inferred from the nrITS alignment. Lactarius indohirtipes K.Verma, Uniyal & Mehmood sp. nov. is highlighted in bold blue.

Fig. 2 in Lactarius indohirtipes and L. sharmai (Russulales, Basidiomycota): two novel species from Jammu and Kashmir, India

Fig. 2. ML phylogram (RAxML) inferred from the ITS-rDNA alignment. Lactarius sharmai K.Verma, Uniyal & Mehmood sp. nov. is highlighted in bold blue.

Fig. 3. Lactarius indohirtipes K in Lactarius indohirtipes and L. sharmai (Russulales, Basidiomycota): two novel species from Jammu and Kashmir, India

Fig. 3. Lactarius indohirtipes K.Verma, Uniyal & Mehmood sp. nov. (CAL 1918). A. Basidiomata in the field. B. Basidiomata at the base camp. C. Lamellae showing latex. D. Pleuropseudocsytidia. E, F. Basidia. G. Pleuromacrocystidia. H. Transverse section through pileipellis. I. Lamellae edge. J. Basidiospores under light microscope. K–M. Basidiospores under SEM. Scale bars: A, C = 20 mm; D–J = 10 μm; K–M = 2 μm.

Fig. 5. Lactarius sharmai K in Lactarius indohirtipes and L. sharmai (Russulales, Basidiomycota): two novel species from Jammu and Kashmir, India

Fig. 5. Lactarius sharmai K.Verma, Uniyal & Mehmood sp. nov. (CAL 1919). A. Fresh basidiomata in the field. B. Lamellae showing latex. C. Bruised context. D. Basidiomata at the base camp. E–F. Basidia. G. Pseudocystidia. H–I. Cheiloleptocystidia. J. Transverse section through pileipellis. K. Basidiospores under light microscope. L–M. Basidiospores under SEM. Scale bars: E–K = 10 μm; L–M = 2 μm.

FIGURE 3 in Investigations into the ancestry of the Grape-eye Seabass (Hemilutjanus macrophthalmos) reveal novel limits and relationships for the Acropomatiformes (Teleostei: Percomorpha)

FIGURE 3 | Optimal cladogram resulting from the partitioned-likelihood analysis of the Sanger and UCE dataset of the 54 core taxa and 279.979 nucleotide characters. Clades with ≥95% bootstrap support are identified with a black circle, clades with 70–94% bootstrap support are identified with a gray circle, and clades with ≥50–69% bootstrap support are identified with a white circle.

FIGURE 2 in Investigations into the ancestry of the Grape-eye Seabass (Hemilutjanus macrophthalmos) reveal novel limits and relationships for the Acropomatiformes (Teleostei: Percomorpha)

FIGURE 2 | Hypotheses of relationships among the Acropomatiformes based on the following studies: Smith, Wheeler (2006); Smith, Craig (2007); Betancur-R et al. (2013b); Near et al. (2013, 2015); Thacker et al. (2015); Davis et al. (2016); Mirande (2016); Sanciangco et al. (2016); Ghedotti et al. (2018); Rabosky et al. (2018); Satoh (2018). The asterisk in Mirande refers to the polyphyly of Malakichthyidae, where some members of the family were resolved outside of the Acropomatiformes.

FIGURE 5 in Investigations into the ancestry of the Grape-eye Seabass (Hemilutjanus macrophthalmos) reveal novel limits and relationships for the Acropomatiformes (Teleostei: Percomorpha)

FIGURE 5 | Optimal cladogram resulting from the partitioned-likelihood analysis of the Sanger and UCE dataset of the family-level 57 taxa and 279.979 nucleotide characters. Clades with ≥95% bootstrap support are identified with a black circle, clades with 70–94% bootstrap support are identified with a gray circle, and clades with ≥50–69% bootstrap support are identified with a white circle.

FIGURE 6 in Investigations into the ancestry of the Grape-eye Seabass (Hemilutjanus macrophthalmos) reveal novel limits and relationships for the Acropomatiformes (Teleostei: Percomorpha)

FIGURE 6 | Simplified 57-taxon maximum-likelihood phylogeny of major acropomatiform clades with the maximum-likelihood optimization of depth illustrated as pie charts on the nodes (black: fishes that live in the deep sea; white: fishes that exclusively live in shallow water) and of bioluminescence on the branches (blue: clade includes bioluminescent fishes; black: clade does not include bioluminescent fishes). The depth ranges of the different acropomatiform clades (standard deviation from the mean) are plotted in gray and the depth mean values are represented by the colored silhouettes (blue: families with bioluminescent fishes; pink non-bioluminescent families).

FIGURE 1 in Investigations into the ancestry of the Grape-eye Seabass (Hemilutjanus macrophthalmos) reveal novel limits and relationships for the Acropomatiformes (Teleostei: Percomorpha)

FIGURE 1 | Images of preserved and radiographed specimens of Hemilutjanus macrophthalmos: USNM 77623 (upper); SIO 12- 3086 (middle); LACM 44038 (lower). Scale bars = 10 mm.

FIGURE 4 in Investigations into the ancestry of the Grape-eye Seabass (Hemilutjanus macrophthalmos) reveal novel limits and relationships for the Acropomatiformes (Teleostei: Percomorpha)

FIGURE 4 | Optimal cladogram resulting from the species-tree analysis of the Sanger and UCE dataset composed of the 54 core taxa and 466 loci. Clades with ≥95% LPP support are identified with a black circle, clades with 70–94% LPP support are identified with a gray circle, and clades with ≥50–69% LPP support are identified with a white circle.

Analyses of metabolite profiling of Drosophila Parkinson's Disease model for identifying novel glial-based therapeutic targets

Analysis for genetic screening and metabolomics identify glial adenosine metabolism as a therapeutic target in Parkinson’s disease This project contains the analysis of metabolite abundance measurements obtained with four different liquid chromatography mass spectrometry methods of synuclein expressing or control or fly brains in a wilde type or Adk1 knockout background.   <div> <h2>Table of contents</h2> <a href="https://github.com/jravilap/Olsen_Analyses#table-of-contents"></a></div> <div> <h3>Prerequisites</h3> <a href="https://github.com/jravilap/Olsen_Analyses#prerequisites"></a></div> <ul> <li>R (version 4.3.1 or higher)</li> <li>RStudio (optional, but recommended)</li> </ul> <div> <h3>R Packages</h3> <a href="https://github.com/jravilap/Olsen_Analyses#r-packages"></a></div> The following R packages are required. You can install them using the commands below: <div> <pre>install.packages(c("readxl", "calibrate", "dplyr", "ggplot2"))</pre> <div> </div> </div> <div> <h3>Package versions</h3> <a href="https://github.com/jravilap/Olsen_Analyses#package-versions"></a></div> <ul> <li>ggplot2_3.5.1</li> <li>dplyr_1.1.4</li> <li>yaml_2.3.8</li> <li>calibrate_1.7.7</li> <li>readxl_1.4.3</li> </ul> <div> <h2>Project Structure</h2> <a href="https://github.com/jravilap/Olsen_Analyses#project-structure"></a></div> <ul> <li><code>code/</code>: Contains the R scripts for the analysis.</li> <li><code>data/</code>: Processed data files. <ul> <li><code>22_0322_alphaSyn_fly_pilot_Classes.xlsx</code>: metabolite profiling data</li> <li><code>dup_metabs_decision.csv</code>: Table defining which metabolites profiled in more than one method should be used.</li> </ul> </li> <li><code>results/</code>: Output files, including plots and tables.</li> <li><code>common_functions/</code>: Custom R functions used in the analysis.</li> <li><code>config.yml</code>: Configuration file for setting paths.</li> </ul>

Dataset related to article "Distribution of pamiparib, a novel inhibitor of poly(ADP-ribose)-polymerase (PARP), in tumor tissue analyzed by multimodal imaging"

This record contains data related to article "Distribution of pamiparib, a novel inhibitor of poly(ADP-ribose)-polymerase (PARP), in tumor tissue analyzed by multimodal imaging" Pamiparib is a potent and selective oral PARP1/2 inhibitor (PARPi). Pamiparib has good bioavailability and showed greater cytotoxic potency and similar DNA-trapping capacity compared to olaparib. It is not affected by ATP-binding cassette transporters. Consequently, pamiparib may be useful in overcoming drug resistance caused by poor drug distribution in tumor due to overexpression of these efflux pump [1]. Mass spectrometry imaging (MSI) is a powerful technology that allows to study drugs distribution in tissues while maintaining spatial information [2]. Here, MSI was applied to visualize pamiparib in tumor in combination with spatial metabolomics and lipidomics, LC-MS/MS analysis, immunofluorescence analysis, and histological staining to gain a comprehensive understanding of how pamiparib is distributed. The results show that pamiparib was evenly distributed in ovarian tumor models, including those that overexpress P-glycoprotein (P-gp). In contrast, olaparib was not detected by MSI in any of the analyzed tumors, despite the comparable sensitivity of the analytical method. This difference in tumor distribution was confirmed by LC-MS/MS analysis.

opencc-by-4.0Aug 2022View details →

GRM: A Novel Stochastic Model for Real-time GNSS Tropospheric Delay Estimation

The dataset includes the proposed RWPN model (Cal_rwpn_new.m) and related files. The model is built based on ERA5 ZWD products from 2010 to 2019, which can be accessed at  (<a>ftp://ftp.gfz-potsdam.de/pub/home/GNSS/products/gfz-vmf1/</a>). The proposed GRM model can contribute greatly by providing an efficient RWPN value to real-time GNSS ZTD estimation with an accuracy improvement of over 10% compared to fixed RWPN results. In addition, GRM also shows the superiorities of saving computation cost significantly since a large volume of the ERA5-derived RWPN values is modeled with only several parameters.

opencc-by-4.0Oct 2024View details →

MultiFranceFences: A novel deep learning dataset for automated fence detection from multimodal aerial imagery

The MultiFranceFences dataset is a large-scale, multimodal remote sensing benchmark for the semantic segmentation of fences across various landscapes in France. This dataset integrates high-resolution orthophotographs (RGB through BDOrtho) and Digital Surface Models (DSM) derived from LiDARHD data.  MultiFranceFences is suitable for deep learning models in semantic segmentation, including state-of-the-art models like UNet, D-LinkNet, and the newly proposed H-IncepUNet, which integrates handcrafted features and multi-scale feature extraction modules for enhanced fence detection. Dataset features: <ul> <li>Multimodal imagery: Combines orthophotographs and DSM data from LiDARHD for fences semantic segmentation (folders ortho and lidar).</li> <li>Buffer options: 2-meter and 3-meter buffer fence annotations to fit varying detection requirements (folders fences_2m and fences_3m).</li> <li>Diverse landscapes: Covers rural, and natural environments across France.</li> <li>Validated dataset: Manually cleaned and validated to remove erroneous fence labels under tree canopies or areas with limited visibility.</li> </ul> Each patch is named according to the nomenclature of the original BDOrtho tile, followed by the specific x and y coordinates of the patch within that tile.

opencc-zeroJan 2024View details →

Linked collectors and determiners for: The new spider genus Palindroma, featuring a novel synapomorphy for the Zodariidae (Araneae).

Natural history specimen data linked to collectors and determiners held within, "The new spider genus Palindroma, featuring a novel synapomorphy for the Zodariidae (Araneae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/efa2a512-3b55-4cd7-83ff-b391ec49c058">https://bionomia.net/dataset/efa2a512-3b55-4cd7-83ff-b391ec49c058</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/efa2a512-3b55-4cd7-83ff-b391ec49c058">https://gbif.org/dataset/efa2a512-3b55-4cd7-83ff-b391ec49c058</a>. Formatted as a Frictionless Data package.

opencc-by-4.0Oct 2024View details →

Extensive data mining uncovers novel diversity among members of the rare biosphere within the Thermoplasmatota

This repository contains all EX4484-6 MAGs and additional raw data files used to create main figures and supplementary figures of the project: "Extensive data mining uncovers novel diversity among members of the rare biosphere within the Thermoplasmatota" (https://github.com/Microbial-Ecophysiology/EX4484-6_data_mining).

opencc-by-4.0Oct 2024View details →

Integrated Omics-Based Discovery of Novel Genes, Secondary Metabolites Clusters, and Small Molecules in Penicillium spp. with Disparate Fungal Isolates

Penicillium expansum is a ubiquitous postharvest pathogen of pome fruit that causes blue mold decay of apple fruit while another member of the genus, P. chrysogenum, is a well-studied saprophyte used for antibiotic and small molecule production. While these two fungi have been investigated individually, the recent discovery of P. chrysogenum hindering P. expansum apple fruit infection has not been well studied. To shed light on this interaction between the two species, we conducted a comparative transcriptomic, metabolomic, and genomic study. Global transcriptional and metabolomic outputs were disparate between the species, nearly identical for the P. chrysogenum isolates, and different between P. expansum isolates. Further, the two P. chrysogenum genomes revealed secondary metabolite gene clusters that differed from P. expansum. This included the absence of an intact patulin gene cluster in P. chrysogenum, which corroborates the metabolomic data regarding the species’ inability to produce patulin. Additionally, P. expansum virulence gene homologues were identified in P. chrysogenum and were similarly transcriptionally regulated in vitro. Molecules with potential antimicrobial activity, and phytohormones like indole-3-acetic acid (IAA), were detected for the first time in P. expansum while pharmacological compounds like the well-studied antibiotic penicillin G were identified in P. chrysogenum culture supernatants. Our findings provide new omics-based resources that enable the study of small molecule production of interest, the potential of Penicillium-derived antimicrobials for postharvest decay control, and P. expansum’s metabolites roles in host-pathogen interactions.

opencc-by-4.0Jul 2021View details →

R code to accompany 'A novel initialisation technique for decadal climate predictions'

R code for the diagnostic published in 'A novel initialisation technique for decadal climate predictions' <a href="https://doi.org/10.3389/fclim.2021.681127">https://doi.org/10.3389/fclim.2021.681127</a> The data loaded follow the cmor standard.

opencc-by-4.0Jul 2021View details →

7 T MRI rawdata for (part 1): A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry

MRI raw data from three different magnetic field strength (1.5 T, 3 T, 7T; 7T data are in separate datasets) for the publication 'A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry', in which a phantom was presented that allows for an experimental evaluation of QSM reconstruction algorithms. The phantom contains susceptibility producing particles with dia- and paramagnetic properties embedded in an MRI visible medium (gelatin and agarose gel) and is suitable to assess the performance of algorithms that attempt to separate isotropic dia- and paramagnetic susceptibility at the sub-voxel level. This dataset additionally contains raw data for a phantom that only contains diamagnetic and paramagnetic particles, respectively, for magnetic field strengths of 7 T.