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27 results for “hierarchical clustering”
Supporting dataset for: "Plasma essential amino acid concentration and profile are associated with performance of lactating dairy cows as revealed through meta-analysis and hierarchical clustering"
<p>This dataset was used in the meta-analysis and hierarchical clustering published in "Plasma essential amino acid concentration and profile are associated with performance of lactating dairy cows as revealed through meta-analysis and hierarchical clustering" in the Journal of Dairy Science. We searched Web of Science and Google Scholar databases through March 2020 with the terms “plasma EAA,” “milk urea” or “blood urea,” and “dairy” or lactating dairy”. To be included in our study, the papers must have met the following selection criteria: (1) been published in English in a peer-reviewed journal; (2) reported dietary ingredients on a DM basis and at minimum dietary CP concentration; (3) used treatments based on diet changes (e.g., no infusion trials were included); (4) reported DMI, lactation performance, and milk components yield; (5) reported all individual [EAA]p (excluding Trp); and (6) reported blood urea-N or plasma urea-N. Infusion studies were excluded to avoid possible effects of method of EAA supply (e.g., infusion vs. feeding) and to narrow the scope of application. The final dataset included 22 studies and 96 dietary treatments. For a more complete description of the methods, please refer to the published paper. </p>
Hierarchical binary black hole mergers in globular clusters: Mass function and evolution with redshift
<p>Database of catalogs and numerical results for the paper: Hierarchical binary black hole mergers in globular clusters: Mass function and evolution with redshift.</p> <p> </p> <p>ABSTRACT</p> <p>Hierarchical black hole (BH) mergers are one of the most straightforward mechanisms producing BHs inside and above the pair-instability mass gap. We investigated the impact of globular cluster (GC) evolution on hierarchical mergers, accounting for the uncertainties related to BH mass pairing functions on the predicted primary BH mass, mass ratio, and spin distribution. <br>We find that the evolution of the host GC quenches the hierarchical BH assembly at the third generation, mainly due to cluster expansion powered by a central BH subsystem. Hierarchical mergers match the primary BH mass distribution from GW events for $m_1 > 50 \, \msun$ regardless of the assumed BH pairing function. <br>At lower masses, however, different pairing functions lead to dramatically different predictions on the primary BH mass merger-rate density. <br>We find that the primary BH mass distribution evolves with redshift, with a larger contribution from mergers with $m_1 \geq 30 \, \msun$ for $z\geq{}2$.<br>Finally, we calculate the mixing fraction of binary black holes (BBHs) from GCs and isolated binary systems. Our predictions are very <br>sensitive to the spins, which favor a large fraction ($>0.6$) of BBHs born in GCs in order to reproduce misaligned spin observations.</p> <p> </p> <p>FILES DESCRIPTION:</p> <p>Files Catalogs.zip contain the data used in this paper. </p> <p>The directory Metallicities contains the outputs of the Fastcluster runs at Z=0.0002. For each model and for each GC evolutionary case, we report the populations of BBHs at first ("first_generation.csv") and nth ("nth_generation.csv") generation. </p> <p>The directory Merger_Rate_Density contains the catalogs from Cosmorate+Fastcluster at redshift 0 to 4 ("redshift_*.csv") and the merger rate density as a funcion of redshift ("merger_rate_density.csv"), for different GC models. Also, it contains the mixing fractions for all the models presented in this paper ("mixing_fractions.csv").</p> <p>The Jupyter notebooks generate the Figures in the main body of the paper. </p>
Fig. 2. Hierarchical cluster analysis with 2 in Proliferation of the invasive termite Coptotermes gestroi (Isoptera: Rhinotermitidae) on Grand Cayman and overall termite diversity on the Cayman Islands
Fig. 2. Hierarchical cluster analysis with 2 (a), 3 (b), 4 (c), and 5 (d) clusters for Coptotermes gestroi over Grand Cayman Island.
Figure 3. Different domain-Hybridization of Fuzzy Clustering and Hierarchical Method for Link Discovery
<p>In this paper, we propose a new hybrid algorithm, which combines the features of fuzzy<br> algorithm and hierarchical algorithm. Our algorithm decreases the number of comparisons on link<br> discovery. Using hierarchical algorithm in the first level, the data is divided into two groups. In the<br> second level the worst cluster is determined by matrix memberships and then it split. This stage is<br> repeated until the optimal number of clusters is achieved.Creating typed links, between the entities<br> of different datasets is one of the key challenges on web of data .We presented the clustering<br> approach, which decreases the number of comparisons on link discovery. The results of linking the<br> movies in LinkedMDB to corresponding movies in DBpedia and also linking the places in<br> LinkedGeoData to the places of DBpedia show the it reduces the number of comparisons without<br> loss of recall and precision. Hopefully in the future, we will be able to elevate the proposed method<br> recall to 100 % using the membership matrix.</p>
Figure 1. Workflow of approach-Hybridization of Fuzzy Clustering and Hierarchical Method for Link Discovery
<p>In this section, we present our model in more detail. Figure 1 gives an overview of the<br> workflow. Our approach is organized in two phases: first, the division of data in two clusters; thenthe determination of the worst cluster and splitting. The number of clusters is unknown, but our<br> algorithms can find this parameter based on the complexity of cluster structure.</p>
Figure 2. Decrease comparisons.-Hybridization of Fuzzy Clustering and Hierarchical Method for Link Discovery
<p>In this paper, we propose a new hybrid algorithm, which combines the features of fuzzy<br> algorithm and hierarchical algorithm. Our algorithm decreases the number of comparisons on link<br> discovery. Using hierarchical algorithm in the first level, the data is divided into two groups. In the<br> second level the worst cluster is determined by matrix memberships and then it split. This stage is<br> repeated until the optimal number of clusters is achieved.Creating typed links, between the entities<br> of different datasets is one of the key challenges on web of data .We presented the clustering<br> approach, which decreases the number of comparisons on link discovery. The results of linking the<br> movies in LinkedMDB to corresponding movies in DBpedia and also linking the places in<br> LinkedGeoData to the places of DBpedia show the it reduces the number of comparisons without<br> loss of recall and precision. Hopefully in the future, we will be able to elevate the proposed method<br> recall to 100 % using the membership matrix.</p>
BRAIN Journal-Personality Questionnaires as a Basis for Improvement of University Courses in Applied Computer Science and Informatics-Figure 5. Hierarchical clustering by scores across the EPQ–R scales for data about all the participants
<p>The clusters were generated using an implementation of a hierarchical clustering algorithm available in the R environment (R, n.d.). The top three clusters were extracted from a hierarchical cluster tree shown in Figure 5, while the color of data points in the visualization shown in figure 4 was determined based on cluster labels. Hierarchical clusters could be used when investigating which students in the analyzed sample share similar personality traits. This could be especially useful for smaller student groups as the teacher may manually inspect the cluster tree and its leaves, which designate individual students. For instance, there are three students in cluster 3, who are represented within the tree in Figure 5 by identifiers 14, 22, and 24. The students with identifiers 14 and 22 are more closely linked and more similar to each other than to the student with identifier 24. </p>
Benchmark dataset for CATH hierarchical clustering tools (GeMMA/FunFHMMEr, MARC, FRAN and eMMA)
<p>Benchmark dataset for CATH SuperFamily 3.40.50.620 (HUPS).</p> <p>Contains Functional Families alignments and Hidden Markov Models generated by GeMMA/FunFHMMER, MARC, FRAN and CATH-eMMA and Python code used to assess their quality (EC purity, DOPS, Neff) and intermediate steps by the MARC and FRAN pipelines (pooling, randomisation, renaming).</p> <p>3.4.50.620_full_superfamily_sequences.fasta contains all HUPs superfamily sequences, the FunFams are a subset of these.</p> <p>all_starting_clusters_sequences.fasta contain the sequences included in the starting clusters used in the analyses.</p> <p>3.40.50.620_embedded.pt includes embeddings for the HUPs superfamily generated using the ESM2 Protein Language Model.</p> <p> </p>
Deciphering the explanatory potential of blood pressure variables on post-operative length of stay through hierarchical clustering: A retrospective monocentric study
<p><em>Objective:</em> Mean arterial pressure is widely used as the variable to monitor during anesthesia. But there are many other variables proposed to define intraoperative arterial hypotension. The goal of the present study was to search arterial pressure variables linked with prolonged postoperative length of stay (pLOS).</p> <p><em>Design: </em>Retrospective cohort study of adult patients having received general for a scheduled non cardiac surgical procedure between 15<sup>th</sup> July 2017 and 31st December 2019.</p> <p><em>Methods:</em> pLOS was defined as a stay longer than the median (main outcome), adjusted for surgery type and duration. 330 arterial pressure variables were analyzed and organized through a clustering approach. An unsupervised hierarchical aggregation method for optimal cluster determination, employing Kendall's tau coefficients and a penalized Bayes information criterion was used. Variables were ranked using the absolute standardized mean distance (aSMD) to measure their effect on pLOS. Finally, after multivariate independence analysis, the number of variables was reduced to three.</p> <p><em>Results:</em> Our study examined 9,516 patients. When LOS is defined as strictly greater than the median, 34% of patients experienced pLOS. Key arterial pressure variables linked with this definition of pLOS included the difference between the highest and lowest pulse pressure values computed throughout the surgery (aSMD[95%CI] =0.39[0.31-0.40], p<0.001), the accumulated time pulse pressure above 61mmHg (aSMD = 0.21[0.17-0.25], p<0.001), and the lowest MAP during surgery (aSMD= 0.20[0.16-0.24], p<0.001).</p> <p><em>Conclusions: </em>By applying a clustering approach, three arterial pressure variables were associated with pLOS. This scalable method can be applied to various dichotomized outcomes.</p>
FT-IR data set for hierarchical cluster-based deep learning
<p>FT-IR of raw materials described in Kristoffersen et al. (2019) doi:<a href="https://doi.org/10.1016/j.talanta.2019.06.084">10.1016/j.talanta.2019.06.084</a>. Predicts average molecular weight after hydrolysis. The data is pre-processed using Savitzky-Golay 2nd derivative smoothing (with window width 11 pt and 3rd order polynomial smoothing) followed by extended multiplicative signal correction (EMSC). </p>
Deciphering the explanatory potential of blood pressure variables on post-operative length of stay through hierarchical clustering: A retrospective monocentric study
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A Comparison Between Hierarchical and Non-Hierarchical Software Clustering
<p>Dataset for "A Comparison Between Hierarchical and Non-Hierarchical Software Clustering" paper results.</p>
Superficial white matter bundle atlas based on hierarchical fiber clustering over probabilistic tractography data
<p>Superficial white matter bundle atlas constructed using fiber clustering algorithms, based on 100 subjects of HCP database (probabilistic tractography). The ROIs of Desikan-Killiany atlas where used to label the final clusters.</p> <p>The atlas is composed of 525 fascicles, 267 bundles in the left hemisphere and 258 bundles in the right hemisphere. It has 384 bundles connecting pairs of different ROIs and 141 bundles connecting portions of the same ROI.</p>
Supplemental Material for Paper 'Taxonomy Extraction Using Knowledge Graph Embeddings and Hierarchical Clustering'
<p>Contains input data and gold standard for the non-expressive extraction task, as well as examples of extracted taxonomies for both the non-expressive and expressive cases. Extracted taxonomies can also be found at <a href="http://labowest.ca/sdb2020/">labowest.ca</a>.</p>
The code and dataset for the paper "Hierarchical Clustering for Consistent Inverse Rendering of Indoor Scenes"
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Hierarchical ZIF-8@Au cluster Nanocarriers for In Situ CAR-M programming and Immunotherapy in Prostate Cancer
GEO Series GSE298594. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Unsupervised hierarchical clustering of iNPCs induced by 6 or 5 TFs
GEO Series GSE29724. Mus musculus. 2 samples. Type: Expression profiling by array.
Genetic deletion of microRNA biogenesis in muscle cells reveals a hierarchical non- clustered network that controls focal adhesion signaling during muscle regeneration
GEO Series GSE154254. Homo sapiens. 20 samples. Type: Expression profiling by high throughput sequencing.
Using the Hierarchical Cluster Method for Foot Deformities
ClinicalTrials.gov study NCT03954301. IPD Sharing: NO. Countries: 1. Publications: 0.
Auricular Malformations Driven by Copy Number Variations in a Hierarchical Enhancer Cluster and Dominant Enhancer Recapitulates Human Pathogenesis [RNA-seq]
GEO Series GSE263086. Mus musculus. 10 samples. Type: Expression profiling by high throughput sequencing.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.