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1,641 results for “similarity”
Data from: Spatiotemporal-social association predicts immunological similarity in rewilded mice
<p>Environmental influences on immune phenotypes are well-documented, but our understanding of which elements of the environment affect immune systems, and how, remains vague. Behaviors, including socializing with others, are central to an individual's interaction with its environment. We therefore tracked behavior of rewilded laboratory mice of three inbred strains in outdoor enclosures and examined contributions of behavior, including associations measured from spatiotemporal cooccurrences, to immune phenotypes. We found extensive variation in individual and social behavior among and within mouse strains upon rewilding. And we found that the more associated two individuals were, the more similar their immune phenotypes were. Spatiotemporal association was particularly predictive of similar memory T and B cell profiles and was more influential than sibling relationships or shared infection status. These results highlight the importance of shared spatiotemporal activity patterns and/or social networks for immune phenotype and suggest potential immunological correlates of social life.<span><br></span></p>
Temporal Patterns and Trends in Corporate Donations Using PageRank and Node Similarity Graph Algorithm
<p>Corporate donations wield considerable influence within political arenas, shaping policies and influencing decision-making processes. This study uses Neo4j, an advanced graph database tool, to explore a comprehensive company dataset, focusing on unraveling temporal patterns and evolving trends in corporate contributions. Visual representations, such as bar charts, reveal significant fluctuations in donations, indicating potential cyclic patterns occurring every six years. The study explores intricate relationships between donor entities and recipients, highlighting diverse donation patterns—both focused and widespread. The study's derived PageRank scores offer a comprehensive portrayal of the varying degrees of influence among diverse entities receiving donations within the network. Notably, the Conservative and Unionist Party emerges as the most prominent entity, boasting a striking score of 1.86, indicating a substantial influx of financial support likely to significantly shape its political endeavors. Despite a lower score of 0.62, the Labor Party still signifies a noteworthy level of financial backing, albeit less extensive than its counterpart. In contrast, the Liberal Democrats, The In Campaign Ltd, and Network for Animals Ltd exhibit comparatively restrained financial backing, warranting deeper investigation into the factors affecting their funding. Moreover, undisclosed findings regarding 170 similarity scores using Node Similarity algorithm disclose a prevalent similarity trend among entities, notably observed between Company 1 and Company 2, implying potential synergistic partnerships in donation-related endeavors. This high similarity often indicates shared values, highlighting prospects for collaborative initiatives or partnerships to augment positive impacts. Utilizing these insights supports the formulation of targeted donation strategies, circumventing donation redundancies, and ensuring optimal resource allocation for maximal societal benefit within specified sectors.</p> <p>Keywords—Company Dataset, Corporate Donations, Neo4j, Node Similarity, PageRank, Political Influence </p> <p> </p>
Data from: Tree functional traits across Caribbean island dry forests are remarkably similar
<p>Delineation of potential dry forest and estimated actual dry forest on Caribbean islands. Potential dry forest is delineated based on CHELSA climate data (<a href="http://www.chelsa-climate.org/">www.chelsa-climate.org</a>) and the FAO definition of dry forest. Estimated actual dry forest is corrected for land cover using data from Hansen et al. (2022) <a href="https://doi.org/10.1088/1748-9326/ac46ec">https://doi.org/10.1088/1748-9326/ac46ec</a>. Areas of potential dry forest, estimated actual dry forest, and area of built-up land covers are summarized by islands and joined to CHELSA bioclimatic variables for selected islands where data on functional traits are available. Trait values by sites are also included. The package consists of data outputs and R scripts to reproduce the data outputs from identified publicly available data sources.</p>
On the Helpfulness of Answering Developer Questions on Discord with Similar Conversations and Posts from the Past
<p>Replication Package for "On the Helpfulness of Answering Developer Questions on Discord with Similar Conversations and Posts from the Past".</p>
Sex chromosomes and hormones independently influence healthy brain development but act similarly after cranial radiation
<h2><strong>Description</strong></h2> <p>Biological sex influences prevalence of developmental disorders through sex hormones and sex chromosomes. However, our understanding of their impacts in neurodevelopment and response to injury remains limited. In this project, we use high resolution magnetic resonance imaging (MRI) to investigate the four core genotype mouse model (FCG) that separates the influences of sex hormones and sex chromosomes during normal brain development and after cranial radiation therapy. </p> <p>Sex differences are attributed to either sex hormones or sex chromosomes. This can be distinguished by the FCG model which decouples the sex determining region (SRY) from the Y chromosome by moving SRY onto an autosome. This gives us four core sex genotypes: XX NULL, XY NULL, XX SRY, and XY SRY.</p> <p>This dataset represents the <em>most comprehensive mouse brain imaging study</em> employing the FCG model to date with 5 timepoints (P14, P23, P42, P63, P98), Ccl2 wildtype (+/+) and knockouts (-/-), irradiation (7Gy) and sham (0Gy) mice. All in all, a total of <strong>1071 images</strong>! The results presented here is published in PNAS.</p> <p>In vivo MRI scans were obtained using a 7-T MRI scanner (Bruker BioSpin, Ettlingen, Germany) equipped with four cryocoils for simultaneous imaging of four mice. The scans were performed with the following settings: T1-weighted, 3D-gradient echo sequence, 75μm isotropic resolution, TR=26ms, TE=8.25ms, flip angle=26°, field of view=25×22×22mm, and matrix size=334×294×294.</p> <p>All structural MR images are stored in <strong>images.tar.gz</strong>. Images were segmented and registered using an automated pipeline which are stored in <strong>labels.tar.gz</strong>. The consensus average and labels are <strong>final_average.mnc </strong>and <strong>final_labels.mnc</strong>, respectively. Extracted structure volumes alongside the metadata are included in <strong>df_micevolumes.csv</strong>. Structural MRIs are in MINC format and the <strong>readme.txt</strong> provides further information on this dataset. </p> <p>The authors express their sincere gratitude for the research funding recieved from the Canadian Institutes of Health Research (158622, 168037) and the Ontario Institute for Cancer Research (IA-024) with funding from the Government of Ontario and Restracomp from the SIckKids Research Training Centre.</p> <p><strong>Publication</strong>: https://www.pnas.org/doi/10.1073/pnas.2404042121</p> <h2><strong>Code/Software </strong></h2> <p><strong>MINC</strong><br>https://www.bic.mni.mcgill.ca/ServicesSoftware/MINC</p> <p><strong>RMINC</strong><br>https://github.com/Mouse-Imaging-Centre/RMINC</p> <p><strong>PydPiper</strong><br>https://github.com/Mouse-Imaging-Centre/pydpiper/tree/v2.0.19.1</p>
LIKE-A-PRO_T6.4_Projects and initiatives covering similar topics to LIKE-A-PRO
<p>Dataset is a spreadsheet-file with an overview of existing projects (funded by national, regional <br>or European government) that cover similar topics to LIKE A PRO. Besides projects, also other <br>relevant initiatives and events are listed. The data is sources from public data sources such as <br>CORDIS and other databases. Listed projects, event and initiatives are all recent and/or ongoing. <br>The goal of this dataset is to map the landscape of alternative protein activities and identify <br>activities and actors that are relevant for the LIKE A PRO to connect with to explore synergies.</p>
SIMpat: a synthetic benchmark for similarity metrics on patient representations
<div> <h2>Introduction</h2> <p>We used Synthea to generate six cohorts of patients with certain specified disease. Please refer to Synthea documentation for the generation process.</p> <p>We selected 6 different diseases that could be generated by Synthea, that were deemed by a medical professional as “different enough”. The goal of this simulation is to find a metric that can differentiate between patients.</p> <p>The six conditions are:</p> <ul> <li>Cerebral Palsy (SNOMED-CT code : 128188000 - Cerebral palsy (disorder))</li> <li>Colorectal Cancer (SNOMED-CT code : 93761005 - Primary malignant neoplasm of colon (disorder))</li> <li>Dialisys (SNOMED-CT code : 265764009 - Renal dialysis (procedure))</li> <li>Hypertension (SNOMED-CT code : 59621000 - Essential hypertension (disorder))</li> <li>Breast Cancer (SNOMED-CT code : 254837009 - Malignant neoplasm of breast (disorder))</li> <li>Prostate Cancer (SNOMED-CT code : 126906006 - Neoplasm of prostate (disorder))</li> </ul> <p>NB:</p> <ul> <li>Dialisys is not a disorder, but a condition, but is used here as a proxy for renal issue</li> <li>Synthea doesn’t have a module to generate prostate cancer in men, but only prostate cancer in veteran, hence this is the module used here (all men with prostate cancer are veterans)</li> </ul> <p>We use those cohort to compare the ability of 12 different distance metrics to separate patients.</p> <p>Those 12 metrics are split in three groups :</p> <p>Sementic based metrics:</p> <ul> <li>AvgEmb* method encodes text by averaging the pre-trained word embeddings of all the words present in it.</li> <li>BERT* uses bidirectional transformer based neural model to solve the task of masked language modeling.</li> <li>Universal Sentence Encoders (USE)* use transformer based encoders to encode sentences into embedding vectors.</li> <li>Embeddings from Language Models (ELMo)* uses bi-directional LSTM based encoders to encode a sentence into a fixed size representation</li> </ul> <p>Graph based metrics:</p> <ul> <li>DeepWalk* uses random walks to generate sequences of vertices (vertex sentences) which are subsequently fed to a skip-gram model to learn the embeddings corresponding to the vertices.</li> <li>Node2Vec* uses biased random walks to optimize a neighborhood preserving objective function such that the nodes which are highly interconnected and the nodes with similar roles in the graph are closer in the embedding space.</li> <li>LINE* tries to directly optimize the vertex embeddings based on one hop and two hop random walk probabilities.</li> <li>HARP* proposes a meta-strategy for embedding vertices of a graph such that they preserve the higher-order structural features.</li> <li>Bags of findings^</li> <li>Average Links^</li> <li>Average Links Weighted by Information Content (IC)^</li> <li>Path Distance weighted by IC^</li> </ul> <p>Concept followed by a * are extracted from <a href="https://proceedings.mlr.press/v116/pattisapu20a/pattisapu20a.pdf">this paper</a> and can be downloaded <a href="../records/3842143">here</a></p> <p>Concept followed by a ^ were develloped by Jean-Virgile Voegeli (SIMED)</p> </div> <div> <div> </div> <h2>Descriptive analysis of the sample</h2> <p>We will first look at the cohort that were created by Synthea. The cohorts were created using the seed 123456789 for reproducibility.</p> <p>For this first experiment, Synthea was asked to generate 100 alive individuals for each specific disease. We asked Synthea to keep only 10 years of history. Each individual was set to be between the age of 18 and 80 years old. Except for specific sex-disease such as breast cancer and prostate cancer, all cohorts contains both male and female individuals. We used the default location, which is Massachussetts.</p> <p>One important note on age. The Synthea modules sometimes specify a minimum age to onset a certain condition / disease. For example, colorectal cancer can only onset after 50 years old, and prostate cancer after 60 years old.</p> <p>Each Synthea run was set to run 10.000 times. If after 10.000 tries, the software didn’t manage to generate a patient that fit the criterion (here, a specific snomed code), the run would fail. Synthea can also generate patients that dies before the “run date”, and if this happens will simulate another patient.</p> <p>This explains why we have cohorts of more than 100 individuals but less than 100 alive individuals. We can also have in certain cases a little above 100 individuals. This is due to the fact that the synthea generator is multicore, and patients are generated simultaneously.</p> </div>
Text-fig. 5. Lauraceae, Platanaceae, Cercidiphyllaceae/Trochodendraceae. a: Sassafras hespera with 2 lobes, UAPC-ALTA S6556. b: cf. Lindera leaf. UAPC-ALTA S 67687. c: Macginitiea gracilis, UAPC-ALTA S 25748. d: Macginicarpa capitulum showing florets grouped in fives, UAPC-ALTA S 59507. e, g: Platanaceous fruitlets with basal tufts of dispersal hairs, UAPC-ALTA S 25748B, S S275238. f: Macginicarpa infructesence with five attached capitula, UAPC-ALTA S 59507A. h: Leaf similar to Populus and Trochodendroides, BBM-PAL-P000010. i: Leaf similar to Populus and Trochodendroides, UAPC-ALTA S 59516. j: cf. Trochodendroides, UAPC-ALTA S 59516. k: Jenkinsella infructesence; Figured in Penhallow 1908, plate 33. l: cf. Leaf similar to Cercidiphyllum and Trochodendroides, BBM-PAL-P000010. Scale bars: a–c, f, h, j, l = 2 cm, d, i, k = 1 cm, e, g = 0.5 cm. in The Early Eocene Flora Of Horsefly, British Columbia, Canada And Its Phytogeographic Significance
Text-fig. 5. Lauraceae, Platanaceae, Cercidiphyllaceae/Trochodendraceae. a: Sassafras hespera with 2 lobes, UAPC-ALTA S6556. b: cf. Lindera leaf. UAPC-ALTA S 67687. c: Macginitiea gracilis, UAPC-ALTA S 25748. d: Macginicarpa capitulum showing florets grouped in fives, UAPC-ALTA S 59507. e, g: Platanaceous fruitlets with basal tufts of dispersal hairs, UAPC-ALTA S 25748B, S S275238. f: Macginicarpa infructesence with five attached capitula, UAPC-ALTA S 59507A. h: Leaf similar to Populus and Trochodendroides, BBM-PAL-P000010. i: Leaf similar to Populus and Trochodendroides, UAPC-ALTA S 59516. j: cf. Trochodendroides, UAPC-ALTA S 59516. k: Jenkinsella infructesence; Figured in Penhallow 1908, plate 33. l: cf. Leaf similar to Cercidiphyllum and Trochodendroides, BBM-PAL-P000010. Scale bars: a–c, f, h, j, l = 2 cm, d, i, k = 1 cm, e, g = 0.5 cm.
FIG. 2 in Nematode community structure of forest woodlots. I. Relationships based on similarity coefficients of nematode species
FIG. 2. Dendrogram of forest sites in Tippecanoe County, Ind., based on similarity indices of nematode species.
FIG. 1 in Nematode community structure of forest woodlots. I. Relationships based on similarity coefficients of nematode species
FIG. 1. Influence of the number of soil cores taken at Tippecanoe County, Ind., at site P on the number of nematode species recovered.
Similarity data set used to test Synchronous Growth Changes (SGC) on dendrochronological data using tree-ring series from the ITRDB
<p>Dataset used to test the SGC, SSGC and AGC in:</p> <div> <div>Visser, RM. 2021 On the similarity of tree-ring patterns: Assessing the influence of semi-synchronous growth changes on the Gleichläufigkeitskoeffizient for big tree-ring data sets. <em>Archaeometry</em> 63(1): 204–215. DOI: <a href="https://doi.org/10.1111/arcm.12600">https://doi.org/10.1111/arcm.12600</a>.</div> </div> <p>The dataset contains the database used in this study</p> <ul> <li><em>itrdb_structure.sql</em> described the structure of the database (PostgreSQL/PostGIS)</li> <li>Tables <ul> <li><em>GC_??_tbl</em> are tables with ?? denoting the continent (see below) containg the comparisons between tree-ring series and the growth changes <ul> <li>The following columns are present: <ul> <li>ID1 and ID2: These are the ID's of the series compared.</li> <li>SGC: Synchronous Growth Changes</li> <li>SSGC: Semi Synchronous Growth Changes</li> <li>Overlap: the number of tree-rings compared</li> </ul> </li> <li>Data files with values in each table. The continents are as defined in the ITRDB (https://www.ncei.noaa.gov/access/paleo-search/?dataTypeId=18) <ul> <li>GC_af_tbl_202005 (Africa)</li> <li>GC_as_tbl_202005 (Asia)</li> <li>GC_au_tbl_202005 (Australia)</li> <li>GC_ca_tbl_202005 (Canada)</li> <li>GC_eu_tbl_202005 (Europe)</li> <li>GC_mx_tbl_202005 (Mexico)</li> <li>GC_sa_tbl_202005 (South America)</li> <li>GC_us_tbl_202005 (North America)</li> </ul> </li> </ul> </li> <li><em>headers</em>: <ul> <li>The following columns are present: <ul> <li>continent: two letter code of the continent (ITRDB)</li> <li>filename: orginal filename as deposited in the ITRDB</li> <li>line_nr: line number of the header</li> <li>header_text: text of the header related to the line number</li> </ul> </li> <li>Datafile: headers_201905222007.csv</li> </ul> </li> <li><em>names</em>: <ul> <li>The following columns are present: <ul> <li>filename: orginal filename as deposited in the ITRDB</li> <li>name_orig: orginal name of the tree-ring series as deposited in the ITRDB</li> <li>name_new: the IDs of the tree-ring series were replaced with a two‐letter code for the continent (AF, AS, AU, CA, EU, SA, US) and a sequence code to prevent duplicate IDs. These are used as ID1 and ID2 in the tables <em>GC_??_tbl</em></li> </ul> </li> <li>Datafile: names_201905240643.csv</li> </ul> </li> </ul> </li> <li>file: <em>geo_location_201906250635.csv</em> <ul> <li>Contains the locations related to each site in the database</li> <li>The following columns: <ul> <li>filename: orginal filename as deposited in the ITRDB</li> <li>continent: two letter code of the continent (ITRDB)</li> <li>lat: latitude</li> <li>long: longitude</li> <li>geom_point: WGS84 coordinates expressed as well-known text (WKT)</li> </ul> </li> </ul> </li> </ul> <p>For the related code, see also: </p> <p>Ronald Visser. (2022). Code and data related to semi-synchronous growth changes and the similarity of tree-ring patterns (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7157738</p> <p>Or: https://github.com/RonaldVisser/SGC</p>
F I G U R E 3 in Larval stages of the Antarctic dragonfish Akarotaxis nudiceps (Waite, 1916), with comments on the larvae of the morphologically similar species Prionodraco evansii Regan 1914 (Notothenioidei: Bathydraconidae)
F I G U R E 3 Comparison of (a) left lateral view and (b) dorsal view of lower tail of Akarotaxis nudiceps [VIMS 22788a, 22.7 mm total length ðLT)] to (c) left lateral view and (d) dorsal view of lower tail of Prionodraco evansii (VIMS 43603, 19.4 mm LT). Anterior faces left in both (b) and (d)
F I G U R E 4 in Larval stages of the Antarctic dragonfish Akarotaxis nudiceps (Waite, 1916), with comments on the larvae of the morphologically similar species Prionodraco evansii Regan 1914 (Notothenioidei: Bathydraconidae)
F I G U R E 4 Comparison of (a) Akarotaxis nudiceps [VIMS 22788a, 22.7 mm total length ðLT)] and (b) Prionodraco evansii (VIMS 43603, 19.4 mm LT). Dorsal view
F I G U R E 1 in Larval stages of the Antarctic dragonfish Akarotaxis nudiceps (Waite, 1916), with comments on the larvae of the morphologically similar species Prionodraco evansii Regan 1914 (Notothenioidei: Bathydraconidae)
F I G U R E 1 Map of a portion of the western Antarctic Peninsula showing the capture sites of the 14 larval specimens of Akarotaxis nudiceps examined herein with depth contours in meters. The inset shows Antarctica with the grey box indicating the map region. The specimens were collected by the Palmer Antarctica Long-Term Ecological Research (Palmer LTER) programme during austral summer (January–February). The corresponding VIMS catalogue numbers to each of the shortened labels are given in Table 1
F I G U R E 2 in Larval stages of the Antarctic dragonfish Akarotaxis nudiceps (Waite, 1916), with comments on the larvae of the morphologically similar species Prionodraco evansii Regan 1914 (Notothenioidei: Bathydraconidae)
F I G U R E 2 Development of Akarotaxis nudiceps in left lateral view. (a) VIMS 43571, 10.8 mm total length (LT), preflexion. (b) VIMS 41368, 14.9 mm LT, postflexion. (c) VIMS 22690, 19.7 mm LT, postflexion. (d) VIMS 22788a, 22.7 mm LT, postflexion
Figure 7 in Looks can be deceiving: contrasting temperature characteristics of two morphologically similar kelp species co-occurring in the Arctic
Figure 7: Effect of temperature on the relative abundance of different ontogenetic stages during gametogenesis of Laminaria digitata (left) and Hedophyllum nigripes (right) in a temperature gradient after seven days (above) and 14 days (below; mean of n = 3–4; SD not shown for clarity). Only the most developed stage was counted per female gametophyte. †All gametophytes died.
Figure 6 in Looks can be deceiving: contrasting temperature characteristics of two morphologically similar kelp species co-occurring in the Arctic
Figure 6: Sex ratio (female:male) of gametophytes of Laminaria digitata (left) and Hedophyllum nigripes (right) after 14 days in temperature gradients between 0 and 25 °C (L. digitata) and 22 °C (H. nigripes) (n = 4, mean ± SD). Different letters denote significant differences among temperatures within each species (L. digitata: Kruskal–Wallis test with multiple p-value comparison; H. nigripes: one-way ANOVA with Tukey's post hoc test). Please note that the marked deviation from an expected initial 50:50 ratio was due to applied seeding methods. †All gametophytes died.
Figure 4 in Looks can be deceiving: contrasting temperature characteristics of two morphologically similar kelp species co-occurring in the Arctic
Figure 4: Optimal quantum yield (FV/FM) of Laminaria digitata (top) and Hedophyllum nigripes (bottom) sporophytes in a temperature gradient (two weeks; left graph) and post-cultivation at 10 °C (one week; right graph). Horizontal lines represent the median; boxes, the interquartile range; whiskers, 1.5× of inter-quartile range (n = 5).
Figure 3 in Looks can be deceiving: contrasting temperature characteristics of two morphologically similar kelp species co-occurring in the Arctic
Figure 3: Photographic documentation of Laminaria digitata and Hedophyllum nigripes sporophytes exposed to a temperature gradient after post-cultivation at 10 °C. Images are not to scale. Triangular cuts marked individual sporophytes per replicate.
Figure 2 in Looks can be deceiving: contrasting temperature characteristics of two morphologically similar kelp species co-occurring in the Arctic
Figure 2: Relative growth rates (RGR; % d−1) of Laminaria digitata (top) and Hedophyllum nigripes (bottom) sporophytes in a temperature gradient over the experimental time (14 days; left side of the dotted line) and recovery at 10 °C (one week; right side of the dotted line; n = 5, mean ± SD). Each value denotes the RGR between the indicated time point and the measuring day before.
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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.