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Figure 3. Canonical Variates Analysis head conformation diagram for 140 in Head geometric morphometrics of two Chagas disease vectors from Venezuela
Figure 3. Canonical Variates Analysis head conformation diagram for 140 Rhodnius prolixus specimens and thin-plate deformation grids. A. V instar–Adults. B. I instar–Adults. C. II instar–III instar.
A meta-analysis on global change drivers and the risk of infectious disease database and code
<p>Data and code associated with the manuscript "A Meta-analysis on Global change drivers and the risk of infectious disease".</p>
Data for Altered Glia-Neuron Communication in Alzheimer's Disease Affects WNT, p53, and NFkB Signaling Determined by snRNA-seq
<p><strong>data.tar.gz contains all files from the data directory associated with the 230313_TS_CCCinHumanAD GitHub project and includes the following:</strong></p><ul><li><strong>CellRangerCounts/</strong><ul><li><strong>GSE157827/</strong><ul><li><strong>post_soupX/ : </strong>contains 21 directories for 21 samples, which each contain 3 files obtained from ambient RNA removal with soupX. Below is a representative example, but this repo contains 1 directory per sample:<ul><li><strong>SAMN16100290_S01_AD/</strong><ul><li><strong>barcodes.tsv</strong></li><li><strong>genes.tsv</strong></li><li><strong>matrix.mtx</strong></li></ul></li></ul></li><li><strong>pre_soupX/ : </strong>contains 21 directories for 21 samples, which each contain 2 files obtained from Cell Ranger after aligning fastq files to the reference genome. Below is a representative example, but this repo contains 1 directory per sample:<ul><li><strong>SAMN16100290_S01_AD/</strong><ul><li><strong>filtered_feature_ bc_matrix.h5</strong></li><li><strong>Raw_feature_bc_matrix.h5</strong></li></ul></li></ul></li></ul></li><li><strong>GSE174367/ : </strong>contains 19 directories for 19 samples, which contain 3 files each from Cell Ranger alignment of fastq files to the reference genome. Below is a representative example, but this repo contains 1 directory per sample:<ul><li><strong>SAMN19128610_S1_CTRL/</strong><ul><li><strong>barcodes.tsv</strong></li><li><strong>genes.tsv</strong></li><li><strong>Matrix.mtx</strong></li></ul></li></ul></li></ul></li><li><strong>ccc/</strong><ul><li><strong>nichenet_grn/</strong><ul><li><strong>gr_network_human_21122021.rds : </strong>accessed in October 2023, gene regulation network – gene regulatory information from MultiNicheNet</li><li><strong>ligand_tf_matrix_nsga2r_final.rds: </strong>accessed in October 2023, ligand tf matrix for signaling path determination from MultiNicheNet</li><li><strong>signaling_network_human_21122021.rds : </strong>accessed in October 2023, signaling network – protein-protein interaction information from MultiNicheNet</li><li><strong>weighted_networks_nsga2r_final.rds : </strong>accessed in October 2023, networks weighted by literature evidence from MultiNicheNet</li></ul></li><li><strong>nichenet_prior/</strong><ul><li><strong>ligand_target_matrix.rds : </strong>accessed in April 2023, ligand to target matrix from NicheNet</li><li><strong>lr_network.rds : </strong>accessed in April 2023, ligand-receptor matrix from NicheNet</li></ul></li><li><strong>nichenet_v2_prior/</strong><ul><li><strong>ligand_target_matrix_nsga2r_final.rds : </strong>accessed in June 2023, ligand to target matrix from MultiNicheNet used to predict target genes.</li><li><strong>lr_network_human_21122021.rds : </strong>accessed in June 2023, ligand-receptor matrix from MultiNicheNet used to predict ligand-receptor pairs.</li></ul></li><li><strong>geo_multinichenet_output.rds </strong>: MultiNicheNet output for Morabito et al., 2021 data</li><li><strong>geo_signaling_igraph_objects.rds </strong>: list of igraph objects for 17 overlapping LRTs and their signaling mediators in the Morabito et al., 2021 dataset. </li><li><strong>gse_multinichenet_output.rds</strong> : MultiNicheNet output for Lau et al., 2020 data</li><li><strong>gse_signaling_igraph_objects.rds</strong> : list of igraph objects for 17 overlapping LRTs and their signaling mediators in the Lau et al., 2020 dataset </li></ul></li><li><strong>seurat_preprocessing/</strong><ul><li><strong>geo_filtered_seurat.rds : </strong>merged and filtered seurat object of Morabito et al., 2021 data</li><li><strong>geo_integrated_seurat.rds :</strong> seurat object integrated using harmony of Morabito et al., 2021 data</li><li><strong>geo_clustered_seurat.rds : </strong>clustered seurat object of Morabito et al., 2021 data</li><li><strong>geo_processed_seurat.rds : </strong>processed seurat object with final cell type assignments at specified resolution of Morabito et al., 2021 data</li><li><strong>gse_filtered_seurat.rds : </strong>merged and filtered seurat object of Lau et al., 2020 data</li><li><strong>gse_integrated_seurat.rds : </strong>seurat object integrated using harmony of Lau et al., 2020 data</li><li><strong>gse_clustered_seurat.rds :</strong> clustered seurat object of Lau et al., 2020 data</li><li><strong>gse_processed_seurat.rds : </strong>processed seurat object with final cell type assignments at specified resolution of Lau et al., 2020 data </li></ul></li></ul>
Rare Diseases hand-annotated news articles and research articles
<p>This dataset was produced in 2023 from the data collected throughout 2022 from MEDLINE (scientific articles) and from Event Registry (news) for the development of the Rare Diseases Mining project (https://idefine-europe.org/medline)</p><p>The data is distributed across 16 diseases supporting the research paper "Automatic text classification and interactive data visualization of published scientific and news articles on Rare Diseases"</p><p>The available data comes in 2 kinds and file formats:<br>CSV - the hand annotation of the news articles in TXT with 5 to 10 MeSH headings<br>JSON - the input file for the evaluation of the classifier, including the title, news article body and MeSH heading IDs (available from https://www.ncbi.nlm.nih.gov/mesh/)</p><p>The CSV files with name starting in "f1_", "pr_", "re_" are the results of the F1/Precision/Recall evaluation for each of the cases.</p><p>This work was prepared by Joao Pita Costa (researcher) and curated by Tanja Zdolšek Draksler (domain expert) </p>
How does parasite environmental transmission stage concentration change before, during, and after disease outbreaks?
<p>Outbreaks of environmentally transmitted parasites require that susceptible hosts encounter transmission stages in the environment and become infected, but we also know that transmission stages can be in the environment without triggering disease outbreaks. One challenge for understanding the relationship between environmental transmission stages and disease outbreaks is that the distribution and abundance of transmission stages outside of their hosts have been difficult to quantify. Thus, we have limited data about how changes in transmission stage abundance influence disease dynamics; moreover, we do not know whether the relationship between transmission stages and outbreaks differs among parasite species. We used digital PCR to quantify environmental transmission stages of five parasites in six lakes in southeastern Michigan every two weeks from June to November 2021. At the same time, we quantified infection prevalence in hosts and host density. Our study focused on eight zooplankton host species (<em>Daphnia</em> spp. and <em>Ceriodaphnia</em> <em>dubia</em>) and five of their parasites from diverse taxonomic groups (bacteria, yeast, microsporidia, and oomycete) with different infection mechanisms. We found that parasite transmission stage concentration increased prior to disease outbreaks for all parasites. However, parasites differed significantly in the relative timing of peaks in transmission stage concentration and infection outbreaks. The 'continuous shedder' parasites had transmission stage peaks at the same time as or slightly after the outbreak peaks. In contrast, parasites relying on host death for transmission ('obligate killers') had transmission stage peaks before outbreak peaks. For most parasites, lakes with outbreaks had higher spore concentrations than those without outbreaks, especially once an outbreak began; the exception was for a parasite, <em>Pasteuria</em> <em>ramosa</em>, with very strong genotypic specificity of infection. Overall, our results show that disease outbreaks are tightly linked to transmission stage concentration; outbreaks were preceded by increases in transmission stage concentration in the environment and then were fueled by the production of more transmission stages during the outbreak itself, with concentrations decreasing to pre-outbreak levels as outbreaks waned. Thus, tracking transmission stages in the environment improves our understanding of the drivers of disease outbreaks and reveals how parasite traits may affect these dynamics.</p>
An exposome atlas of serum reveals risk of chronic diseases in Chinese population
<p>Although adverse environmental exposures are considered to be a major cause to chronic diseases, current studies have provided limited knowledge on real-world chemical exposures and related risks. Here, we collected serum samples from 5696 healthy people and patients, including 12 chronic diseases in China, and completed serum biomonitoring containing 267 chemicals using gas and liquid chromatography-tandem mass spectrometry. 74 high-frequently detected exposures were used for exposure characterization and risk analysis. Results showed that region was the most critical factor influencing human exposure levels, followed by age. Organochlorine pesticides and perfluoroalkyl substances were associated with multiple chronic diseases, and some of them exceeded safe ranges. Mixture effect models showed significant risk effects of exposure on hyperlipidemia, metabolic syndrome and hyperuricemia. Overall, this study provided a comprehensive human serum exposure atlas and its disease risk, which could guide subsequent more in-depth cause-and-effect studies between environmental exposures and human health. The R codes and related example data for statistical analysis and figure production have been deposited to the GitHub (https://github.com/youlei2023/ExposomeAtlas).</p>
PheKnowLator Human Disease Knowledge Graph Benchmarks Archive
<h2><strong>PKT Human Disease KG Benchmark Builds</strong></h2> <p>The PheKnowLator (PKT) Human Disease KG (PKT-KG) was built to model mechanisms of human disease, which includes the Central Dogma and represents multiple biological scales of organization including molecular, cellular, tissue, and organ. The knowledge representation was designed in collaboration with a PhD-level molecular biologist (<a href="https://user-images.githubusercontent.com/8030363/195469903-86598760-40b7-4126-857c-3d6368305a86.png">Figure</a>). </p> <p>The <strong>PKT Human Disease KG</strong> was constructed using 12 OBO Foundry ontologies, 31 Linked Open Data sets, and results from two large-scale experiments (<a href="https://doi.org/10.48550/arXiv.2307.05727">Supplementary Material</a>). The 12 OBO Foundry ontologies were selected to represent chemicals and vaccines (i.e., ChEBI and Vaccine Ontology), cells and cell lines (i.e., Cell Ontology, Cell Line Ontology), gene/gene product attributes (i.e., Gene Ontology), phenotypes and diseases (i.e., Human Phenotype Ontology, Mondo Disease Ontology), proteins, including complexes and isoforms (i.e., Protein Ontology), pathways (i.e., Pathway Ontology), types and attributes of biological sequences (i.e., Sequence Ontology), and anatomical entities (Uberon ontology). The RO is used to provide relationships between the core OBO Foundry ontologies and database entities.</p> <p>The <strong>PKT Human Disease KG</strong> contained 18 node types and 33 edge types. Note that the number of nodes and edge types reflects those that are explicitly added to the core set of OBO Foundry ontologies and does not take into account the node and edge types provided by the ontologies. These nodes and edge types were used to construct 12 different PKT Human Disease benchmark KGs by altering the Knowledge Model (i.e., class- vs. instance-based), Relation Strategy (i.e., standard vs. inverse relations), and Semantic Abstraction (i.e., OWL-NETS (yes/no) with and without Knowledge Model harmonization [OWL-NETS Only vs. OWL-NETS + Harmonization]) parameters. Benchmarks within the PheKnowLator ecosystem are different versions of a KG that can be built under alternative knowledge models, relation strategies, and with or without semantic abstraction. They provide users with the ability to evaluate different modeling decisions (based on the prior mentioned parameters) and to examine the impact of these decisions on different downstream tasks.</p> <p>The Figures and Tables explaining attributes in the builds can be found <a href="https://github.com/callahantiff/PheKnowLator/wiki/Archived-Builds">here</a>.</p> <p> </p> <h3><strong>Build Data Access</strong></h3> <h4><strong>Important Build Information</strong></h4> <p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed this Zenodo-based archive for the builds. While the original GCP resources contained all of the resources needed to generate the builds, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the logs associated with each build.</p> <p>🗂 For additional information on the KG file types please see the following <a href="https://github.com/callahantiff/PheKnowLator/wiki/KG-Construction#table-knowledge-graph-build-output">Wiki page</a>, which is also available as a download from this repository (PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx). </p> <h4><strong>v1.0.0</strong></h4> <ul> <li>KGs: <a href="../doi/10.5281/zenodo.7030200">https://zenodo.org/doi/10.5281/zenodo.7030200</a></li> <li>Embeddings: <a href="../doi/10.5281/zenodo.7030188">https://zenodo.org/doi/10.5281/zenodo.7030188</a></li> </ul> <h4><strong>All Other Build Versions</strong></h4> <p><strong>Class-based Builds</strong></p> <p><em>Standard Relations</em></p> <ul> <li>OWL Build <ul> <li>v2.0.0: <a href="../doi/10.5281/zenodo.7029957">MAY2020</a><a href="../record/8178783">; </a><a href="../doi/10.5281/zenodo.8180239">JAN2021</a>; <a href="../doi/10.5281/zenodo.8180539">FEB2021</a></li> <li>v2.1.0: <a href="../doi/10.5281/zenodo.8180774">MAY2021</a>;<a href="../doi/10.5281/zenodo.8180825"> JUN2021</a>; <a href="../doi/10.5281/zenodo.8180972">JUL2021</a>; <a href="../doi/10.5281/zenodo.8183987">AUG2021</a>;<a href="../doi/10.5281/zenodo.8184090"> SEP2021</a></li> <li>v3.0.2: <a href="../doi/10.5281/zenodo.8184131">OCT2021</a>; <a href="../doi/10.5281/zenodo.8184205">NOV2021</a></li> </ul> </li> <li>OWL-NETS Build <ul> <li>v2.0.0: <a href="../doi/10.5281/zenodo.7029953">MAY2020</a><a href="../record/8178783">; </a><a href="../doi/10.5281/zenodo.8180255">JAN2021</a>; <a href="../doi/10.5281/zenodo.8180545">FEB2021</a></li> <li>v2.1.0: <a href="../doi/10.5281/zenodo.8180772">MAY2021</a>; <a href="../doi/10.5281/zenodo.8180827">JUN2021</a>; <a href="../doi/10.5281/zenodo.8180974">JUL2021</a>; <a href="../doi/10.5281/zenodo.8183989">AUG2021</a>; <a href="../doi/10.5281/zenodo.8184088">SEP2021</a></li> <li>v3.0.2: <a href="../doi/10.5281/zenodo.8184133">OCT2021</a>; <a href="../doi/10.5281/zenodo.8184208">NOV2021</a></li> </ul> </li> </ul> <p><em>Inverse Relations</em></p> <ul> <li>OWL Build <ul> <li>v2.0.0: <a href="../doi/10.5281/zenodo.7029893">MAY2020</a><a href="../record/8178783">; </a><a href="../doi/10.5281/zenodo.8180269">JAN2021</a>; <a href="../doi/10.5281/zenodo.8180550">FEB2021</a></li> <li>v2.1.0: <a href="../doi/10.5281/zenodo.8180766">MAY2021</a>; <a href="../doi/10.5281/zenodo.8180829">JUN2021</a>; <a href="../doi/10.5281/zenodo.8180976">JUL2021</a>;<a href="../doi/10.5281/zenodo.8183991"> AUG2021</a>; <a href="../doi/10.5281/zenodo.8184086">SEP2021</a></li> <li>v3.0.2: <a href="../doi/10.5281/zenodo.8184135">OCT2021</a>; <a href="../doi/10.5281/zenodo.8184210">NOV2021</a></li> </ul> </li> <li>OWL-NETS Build <ul> <li>v2.0.0: <a href="../doi/10.5281/zenodo.7029921">MAY2020</a><a href="../record/8178783">; </a><a href="../doi/10.5281/zenodo.8180279">JAN2021</a>; <a href="../doi/10.5281/zenodo.8180555">FEB2021</a></li> <li>v2.1.0: <a href="../doi/10.5281/zenodo.8180768">MAY2021</a>; <a href="../doi/10.5281/zenodo.8180833">JUN2021</a>; <a href="../doi/10.5281/zenodo.8180982">JUL2021</a>; <a href="../doi/10.5281/zenodo.8183993">AUG2021</a>; <a href="../doi/10.5281/zenodo.8184084">SEP2021</a></li> <li>v3.0.2: <a href="../doi/10.5281/zenodo.8184137">OCT2021</a>; <a href="../doi/10.5281/zenodo.8184212">NOV2021</a></li> </ul> </li> </ul> <p><strong>Instance-based Builds</strong></p> <p><em>Standard Relations</em></p> <ul> <li>OWL Build <ul> <li>v2.0.0: <a href="../doi/10.5281/zenodo.7029941">MAY2020</a><a href="../record/8178783">; </a><a href="../doi/10.5281/zenodo.8180333">JAN2021</a>;<a href="../doi/10.5281/zenodo.8180558"> FEB2021</a></li> <li>v2.1.0: <a href="../doi/10.5281/zenodo.8180764">MAY2021</a>; <a href="../doi/10.5281/zenodo.8180835">JUN2021</a>; <a href="../doi/10.5281/zenodo.8180984">JUL2021</a>; <a href="../doi/10.5281/zenodo.8183995">AUG2021</a>; <a href="../doi/10.5281/zenodo.8184082">SEP2021 </a></li> <li>v3.0.2: <a href="../doi/10.5281/zenodo.8184139">OCT2021</a>; <a href="../doi/10.5281/zenodo.8184216">NOV2021 </a></li> </ul> </li> <li>OWL-NETS Build <ul> <li>v2.0.0: <a href="../doi/10.5281/zenodo.7029939">MAY2020</a><a href="../record/8178783">; </a><a href="../doi/10.5281/zenodo.8180335">JAN2021</a>;<a href="../doi/10.5281/zenodo.8180564"> FEB2021</a></li> <li>v2.1.0: <a href="../doi/10.5281/zenodo.8180762">MAY2021</a>; <a href="../doi/10.5281/zenodo.8180837">JUN2021</a>; <a href="../doi/10.5281/zenodo.8180986">JUL2021</a>; <a href="../doi/10.5281/zenodo.8183997">AUG2021</a>; <a href="../doi/10.5281/zenodo.8184080">SEP2021</a></li> <li>v3.0.2: <a href="../doi/10.5281/zenodo.8184141">OCT2021</a>; <a href="../doi/10.5281/zenodo.8184218">NOV2021</a></li> </ul> </li> </ul> <p><em>Inverse Relations</em></p> <ul> <li>OWL Build <ul> <li>v2.0.0: <a href="../doi/10.5281/zenodo.7029945">MAY2020</a><a href="../record/8178783">; </a><a href="../doi/10.5281/zenodo.8180338">JAN2021</a>; <a href="../doi/10.5281/zenodo.8180588">FEB2021</a></li> <li>v2.1.0: <a href="../doi/10.5281/zenodo.8180758">MAY2021</a>; <a href="../doi/10.5281/zenodo.8180878">JUN2021</a>; <a href="../doi/10.5281/zenodo.8180992">JUL2021</a>; <a href="../doi/10.5281/zenodo.8184001">AUG2021</a>; <a href="../doi/10.5281/zenodo.8184078">SEP2021 </a></li> <li>v3.0.2: <a href="../doi/10.5281/zenodo.8184143">OCT2021</a>; <a href="../doi/10.5281/zenodo.8184220">NOV2021</a></li> </ul> </li> <li>OWL-NETS Build <ul> <li>v2.0.0: <a href="../doi/10.5281/zenodo.7029919">MAY2020</a><a href="../record/8178783">; </a><a href="../doi/10.5281/zenodo.8180340">JAN2021</a>; <a href="../doi/10.5281/zenodo.8180584">FEB2021</a></li> <li>v2.1.0: <a href="../doi/10.5281/zenodo.8180756">MAY2021</a>; <a href="../doi/10.5281/zenodo.8180823">JUN2021</a>; <a href="../doi/10.5281/zenodo.8180996">JUL2021</a>; <a href="../doi/10.5281/zenodo.8184003">AUG2021</a>; <a href="../doi/10.5281/zenodo.8184076">SEP2021 </a></li> <li>v3.0.2: <a href="../doi/10.5281/zenodo.8184145">OCT2021</a>; <a href="../doi/10.5281/zenodo.8184222">NOV2021</a></li> </ul> </li> </ul>
Parkinson's Disease Hoehn&Yahr Dataset
<p><span>The ALAMEDA_PD_HoehnYahr_dataset.csv contains 76 preprocessed features extracted from raw force data. In total, it includes 79 columns:</span></p> <ol> <li> <p><span>The first two columns (ID, Datetime) correspond to the ID of PD patient and Date of the recording.</span></p> </li> <li> <p><span>The next 76 columns correspond to features extracted from raw force data collected with loadsol insoles throughout a short walk assessment during in-clinic visits. The features were extracted from both time and frequency domains of the recorded timeseries. The naming of the features contains information on the channel used to extract each feature. More specifically </span><span><em>l1 </em></span><span>corresponds to left heel, </span><span><em>r1 </em></span><span>to right heel, </span><span><em>l8 </em></span><span>to left forefoot and </span><span><em>r8 </em></span><span>to right forefoot. Features extracted from the frequency domain are denoted by the inclusion of the term </span><span><em>freq </em></span><span>in their name. The full list of the extracted features used to predict Hoehn & Yahr stage is demonstrated in the table below.</span></p> </li> <li> <div> <div> <div> <p><span>The final column, HoehnYahr, indicates the Hoehn & Yahr stage of the patients.</span></p> </div> </div> </div> <div> <div> </div> </div> </li> </ol>
Associated code and data for "A disease network-based deep learning approach for characterizing melanoma (doi:10.1002/IJC.33860)"
<p>This deposit contains the data, code, and analysis to recreate the results in the manuscript - Lai X, Zhou JF, Wissely A, Heppt M, Maier A, Berking C, Vera J, Zhang L. A disease network-based deep learning approach for characterizing melanoma. International Journal of Cancer. 2022; 150(6): 1029- 1044. <a href="http://www.researchgate.net/publication/355774213_A_disease_network-based_deep_learning_approach_for_characterizing_melanoma">doi:10.1002/IJC.33860</a>.</p> <p>If you have used the code for your research, please cite the original publication. Thank you very much.</p> <p> </p> <p> </p>
Tabular and image data of article "Morphing cholinesterase inhibitor amiridine into multipotent drugs for the treatment of Alzheimer's disease"
<p>The search for novel drugs to address the medical needs of Alzheimer’s disease (AD) is an ongoing process relying on the discovery of disease-modifying agents. Given the complexity of the disease, such an aim can be pursued by developing so-called multi-target directed ligands (MTDLs) that will impact the disease pathophysiology more<br>comprehensively. Herewith, we contemplated the therapeutic efficacy of an amiridine drug acting as a cholinesterase inhibitor by converting it into a novel class of novel MTDLs. Applying the linking approach, we have paired amiridine as a core building block with memantine/adamantylamine, trolox, and substituted benzothiazole moieties to generate novel MTDLs endowed with additional properties like N-methyl-D-aspartate (NMDA) receptor affinity, antioxidant capacity, and anti-amyloid properties, respectively. The top-ranked amiridine-based compound 5d was also inspected by in silico to reveal the butyrylcholinesterase binding differences with its close structural analogue 5b. Our study provides insight into the discovery of novel amiridinebased drugs by broadening their target-engaged profile from cholinesterase inhibitors towards MTDLs with potential implications in AD therapy.</p> <p><strong>Table 1. </strong>hBChE inhibitory activities of 5c-d, 7c and 7 g and reference compounds (amiridine hydrochloride and THA); their cytotoxicity profile on SH-SY5Y cell line, and predictions of BBB penetration.</p> <p>T<strong>able 2</strong>. Relative inhibitions (RIs) of 5c-d and 7 m and reference compound memantine at recombinant human GluN1/GluN2B NMDA receptor expressed in HEK293 cells.</p> <p><strong>Fig_1</strong>. Chemical structures of rivastigmine, galantamine, and tacrine as representatives of cholinesterase inhibitors. Approaches to novel drugs for AD treatment on the selected candidates are displayed.</p> <p><strong>Fig_2</strong>. Examples of previously published amiridine-based derivatives and design strategy applied in the current study below, using various pharmacophores.</p> <p><strong>Fig_3</strong>. Top scored docking pose of 5b (A) and 5d (B) highlighting the key findings responsible for compound activity/inactivity. For the sake of clarity, superimposed ligands are aligned in the Fig. C with respect to key amino acid residue W82 to demonstrate the binding difference. Compounds 5b and 5d are colored in salmon and yellow, respectively. Essential amino acid residues responsible for ligand anchoring are rendered in green. Important interactions of different origin are displayed with dashed black lines. The figure was created with The PyMOL Molecular Graphics System, v. 2.5.2.</p> <p><strong>Scheme 1</strong>. Preparation of the amiridine-based compounds 5a-d. Reagents and conditions: <strong>a)</strong> 2-chloroacetyl chloride (4 eq.), CHCl3, 90 ◦C, overnight, 8, 54%, 11, 90%, 12, 52%; <strong>b)</strong> CH3CN, K2CO3, KI, reflux, 3 h, 5a, 54%, 5b, 54%; <strong>c)</strong> amiridine (1.1 eq), CH3CN, K2CO3, KI, reflux, overnight, 5c, 48%, 5d, 41%.</p> <p><strong>Scheme 2</strong>. Preparation of intermediate 13 and final compound 6. Reagents and conditions: <strong>a)</strong> potassium phthalimide, CH3CN, reflux, 3 h, then an excess of NH2NH2⋅H2O, reflux, overnight, 57%; <strong>b)</strong> DMF, TEA, BOP, room temperature, 2 days, 86%.</p> <p><strong>Scheme 3</strong>. Preparation of amiridine-benzothiazole derivatives 7a-m. Reagents and conditions: <strong>a)</strong> for 7a: 2-chlorobenzothiazole, 110 ◦C, overnight, 32%; for 7b-m: corresponding 2-chlorobenzothiazole, DIPEA, 100 ◦C, overnight, 21–77%.</p>
Fig. 4 in Hamatospiculum flagellispiculosum (Nematoda: Diplotriaenidae) causing severe disease in a new host from Argentine Patagonia: Campephilus magellanicus (Aves: Picidae)
Fig. 4. Optical microscope micrograph of histopathological assessment of muscular tissues dissected from articulations affected by a parasitic infection in Campephilus magellanicus: (A) Sample from the right knee exhibiting loss of the skeletal muscle architecture and nematode eggs (dark dots at lower half), contiguous to muscle fibers with normal tissue architecture (upper half), Bar = 200 μm. (B) Sample from the left tibiotarsus mass showing myofibers of variable shape and size, diffuse mononuclear infiltration, and several eggs, Bar = 100 μm. (C) Two thin-shelled eggs with fully differentiated L1 at the centre of the image, surrounded by mononuclear cells, Bar = 20 μm.
Fig. 2 in Hamatospiculum flagellispiculosum (Nematoda: Diplotriaenidae) causing severe disease in a new host from Argentine Patagonia: Campephilus magellanicus (Aves: Picidae)
Fig. 2. Scanning electron micrograph (SEM) of female Hamatospiculum flagellispiculosum: (A) Detail of epaulette in anterior end (frontal view): a: amphid, b: cephalic papilla in inner circle, c: cephalic papilla in outer circle, d: tooth. (B) Anterior end with vulva (ventral view), Bar = 100 μm. (C) Anterior end (lateral view), Bar = 20 μm. (D) Detail of anal region (caudal view) with atrophied anus, Bar = 200 μm.
Fig. 1 in Hamatospiculum flagellispiculosum (Nematoda: Diplotriaenidae) causing severe disease in a new host from Argentine Patagonia: Campephilus magellanicus (Aves: Picidae)
Fig. 1. Parasitic infections at joints of a necropsied Magellanic woodpecker (Campephilus magellanicus) adult female from Argentine Patagonia: (A) Dissected distocranial extremity of the left tibiotarsus. (B) Urogygial gland area increased in size. Arrows show roundworms present in the tissues extracted from the affected locations.
Fig. 5 in Hamatospiculum flagellispiculosum (Nematoda: Diplotriaenidae) causing severe disease in a new host from Argentine Patagonia: Campephilus magellanicus (Aves: Picidae)
Fig. 5. Hamatospiculum flagellispiculosum, optical microscope micrograph of eggs: (A) Egg with first-stage larvae (L1), Bar = 15 μm. (B) Larvae hatching, Bar = 15 μm.
Fig. 7. Box plots comparing average counts per 10 in Trypanosomiasis: An emerging disease in Alpine swift (Tachymarptis melba) nestlings in Switzerland?
Fig. 7. Box plots comparing average counts per 10 HPF of granulocytes, mononuclear cells, and thrombocytes between positive (n = 20) and negative (n = 20) 45- day-old Alpine swift nestlings sampled in 2022.
Fig. 5 in Trypanosomiasis: An emerging disease in Alpine swift (Tachymarptis melba) nestlings in Switzerland?
Fig. 5. Skeletal musculature of a nestling Alpine swift showing infiltrations of mononuclear inflammatory cells (A, B) and presumably extracellular, amastigote-like structures (C). Bursa fabricii of a nestling Alpine swift with depletion of the medullary follicle with lymphocytolysis (asterisk) and a distinct epithelium (arrows) (D).
Fig. 6 in Trypanosomiasis: An emerging disease in Alpine swift (Tachymarptis melba) nestlings in Switzerland?
Fig. 6. Blood smears of nestling Alpine swifts with high (A) and moderate (B) trypomastigote burdens. Close-up of a trypomastigote between erythrocytes (C).
Fig. 4 in Trypanosomiasis: An emerging disease in Alpine swift (Tachymarptis melba) nestlings in Switzerland?
Fig. 4. Distribution of louse flies on an Alpine swift nestling (A) compared with the distribution of bruising on post-mortem examination with plumage removed (B).
Fig. 3 in Trypanosomiasis: An emerging disease in Alpine swift (Tachymarptis melba) nestlings in Switzerland?
Fig. 3. Missing (left wing) and poor quality (right wing) primary feathers on a 45-day-old nestling.
Fig. 1 in Trypanosomiasis: An emerging disease in Alpine swift (Tachymarptis melba) nestlings in Switzerland?
Fig. 1. Map of Switzerland with the locations and appearance of the three evaluated colonies (A, B, C).
ScienceDex guides
Understand access before you commit
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.