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368 results for “Consensus”
Fig. 2. The Bayesian consensus tree basedon 988 in Integrative approach to resolve the Calotes mystaceus Duméril & Bibron, 1837 species complex (Squamata: Agamidae)
Fig. 2. The Bayesian consensus tree basedon 988 bp of mitochondrial DNA (12S rRNA and COI) shows six distinctlineages within Calotes mystaceus. Node support in terms of Bayesian posterior probabilities is indicated by circles at nodes (nodes with a BPP ≥ 0.90 are white, BPP ≥ 0.95 are grey, BPP ≥ 0.99 are black, values <0.90 arenot marked). Outgroup (Calotes versicolor) notshown for clarity. Numbers in parentheses behind taxa refer to localities mapped in Fig. 1.
Fig. 1. Strict consensus tree resulting from equal weighting analysis. Jackknife values over 51 in Pseudocetherinae (Hemiptera: Reduviidae) revisited: phylogeny and taxonomy of the lobe-headed bugs
Fig. 1. Strict consensus tree resulting from equal weighting analysis. Jackknife values over 51 are reported for tree one of eight.
Opinion dynamics in social network under competition: the role of influencing factors in consensus reaching
<p>The profitability of opinion and the finiteness of individual attention have already spawned the extensive competition for individual preferences on social networks. It's quite necessary to investigate the opinion dynamics over social networks in a competitive environment. To this point, this paper develops a novel social network DeGroot model based on competition game (DGCG) to characterize the opinion evolution in a competitive opinion dynamics. Based on the DGCG model, we obtain equilibrium results in the stable state of opinion evolution. Consecutively, we analyze what role relevant factors play in the final consensus and competitive outcomes, including the resource ratio of both contestants, initial opinions and network structure. Theoretical analyses and simulation experiments show that these factors can significantly sway the consensus and even reverse competition outcomes.</p>
Point-of-care monitoring of head and neck cancer treatment response and recurrence development using nanopore-based ctDNA consensus sequencing
<p>Circulating tumor DNA (ctDNA) in blood may become a generic biomarker for non-invasive cancer diagnosis and monitoring. However, detection of ctDNA is challenged by the presence of many circulating DNA molecules from healthy cells. We found that single ctDNA molecules can be sequenced with high accuracy by a three-step process consisting of capturing, copying and concatenation of the original double-stranded ctDNA molecules. This innovative approach - called CyclomicsSeq - is unparalleled by any other method in terms of cost-efficiency and speed, allowing point-of-care cancer diagnostics.</p> <p>Within this CPOC, subsidized by the Oncode institute, we have applied our CyclomicsSeq ctDNA test in patients with advanced head and neck cancer squamous cell carcinoma (HNSCC). Head and neck cancer (HNSCC) accounts for 380,000 cancer-related deaths worldwide. For these patients, determining whether a patient responds to the primary chemoradiation treatment is challenging, and non-responders are sometimes identified when other treatment options are no longer possible. By measuring the ctDNA levels in the blood of these patients prior to and during treatment, we aim to identify non-responders at an earlier stage.</p> <p>This dataset contains base calls of TP53 of 47 nanopore sequencing runs. We included 10 patients and 7 controls. For the patients, we have samples of multiple time points (0 = prior to treatment, 1 = 1 week after treatment initiation, etc).</p>
Consensus of Game Engine Architectures: an Overview of Coupling and Subsystems in Game Engines
<p>The video game industry is one of the most innovative, competitive, and rapidly growing industries. The industry's successes along with the increasing gamers’ expectations result in always larger and more complex games. These games thus must be developed with game engines, which have become correspondingly more sophisticated. Today, game engine developers find game engine development challenging. To support the process of creating and maintaining game engines, we propose applying an approach based on a consensus algorithm to a set of game engine architectures. Our approach generates a model that suggests the most commonly used subsystems in game engine architectures, ranked by their degree of coupling. The model can be used by developers as a starting point when deciding what subsystems to include when building a game engine and points out the most coupled subsystems, which can play an important role towards higher subsystems maintainability and reusability. We evaluate our approach by comparing the results of our approach against a predefined ground truth data. The result of our approach matches the subsystems defined in the ground truth data and it shows that the most coupled subsystems are core, low-level rendering, third-party SDKs and world editor. Additionally, when comparing game engine architectures, we observe that most architectures are composed of nearly the same set of subsystems. </p>
Fig. 3. Consensus maximum likelihood tree for combined 16S in Cryptic multicolored lizards in the Polychrus marmoratus Group (Squamata: Sauria: Polychrotidae) and the status of Leiolepis auduboni Hallowell
Fig. 3. Consensus maximum likelihood tree for combined 16S and COI sequence data from seventeen Polychrus tissue samples (1,035 bp total). Bootstrap support values are indicated at each node, if greater than 50%. Samples are indicated by their museum accession number and country of origin, if known. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. For details of analysis, see text.
Figure 3 in Getting to a decision: using structured decision-making to gain consensus on approaches to invasive species control
Figure 3. Total multi-attribute utility including all weighted objectives for responding to smallmouth bass in Cultus Lake, BC, across all management activities. Each utility was on a scale from 0-1, so the multi-attribute scale also ranges from 0–1.
Figure 1 in Getting to a decision: using structured decision-making to gain consensus on approaches to invasive species control
Figure 1. Steps involved in structured decision-making. Steps can be re-evaluated at any point; responses should be re-evaluated once new data is gathered following implementation. Adapted from Liu and Cook (2016).
Figure 2 in Getting to a decision: using structured decision-making to gain consensus on approaches to invasive species control
Figure 2. Calculated utility of avoiding fish species at-risk and not at-risk of extirpation in Cultus Lake, BC, across all combinations of smallmouth bass control. Utility is presented on a scale from 0–1.
Fig. 21. Simplified strict consensus trees resulting from a in Taxonomic and stratigraphic update of the material historically attributed to Megalosaurus from Portugal
Fig. 21. Simplified strict consensus trees resulting from a cladistic analysis performed on a dentition-based data matrix and forcing the constrains defined by Hendrickx et al. (2020a) pruning a priori all morphotypes from the Lusitanian Basin but Morphotype 2 (A), simplified strict consensus tree resulting from the analysis pruning a priori all morphotypes from the Lusitanian Basin but MG 15 (B), and simplified strict consensus tree resulting from the analysis pruning a priori all morphotypes from the Lusitanian Basin but Morphotype 3 (C).
Fig. 18. Strict consensus trees resulting from a in Taxonomic and stratigraphic update of the material historically attributed to Megalosaurus from Portugal
Fig. 18. Strict consensus trees resulting from a cladistic analysis performed on a dentition-based data matrix and forcing the constrains defined by Hendrickx et al. (2020a) with the five tooth morphotypes from the Lusitanian Basin (A), simplified strict consensus tree resulting from the cladistic analysis pruning a priori all morphotypes from the Lusitanian Basin but Morphotype 1 (B), and simplified strict consensus tree resulting from the analysis pruning a priori all morphotypes from the Lusitanian Basin but MNHN/UL.EPt.023 (C).
Fig. 2. Consensus trees for the Cyathaspididae. A in A phylogenetic analysis of the heterostracan jawless vertebrate family Cyathaspididae
Fig. 2. Consensus trees for the Cyathaspididae. A, after Lundgren and Blom (2013); B, after Randle and Sansom (2017); C, after Denison (1964). Taxa not used in the analysis in this paper are denoted by an asterisk (*).
Dataset: Consensus Cloud Solutions, Inc. (CCSI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Fig. 3. Majority with bootstrap support consensus trees for 12S in MITOCHONDRIAL 16S AND 12S rRNA SEQUENCE ANALYSIS IN FOUR SALMONID SPECIES FROM ROMANIA
Fig. 3. Majority with bootstrap support consensus trees for 12S rRNA. (a) 12S rRNA Maximum Parsimony tree; (b) 12S rRNA Neighbor Joining tree, distance model Kimura 2 Parameters, transi-
Fig. 2. Majority with bootstrap support consensus trees for 16S in MITOCHONDRIAL 16S AND 12S rRNA SEQUENCE ANALYSIS IN FOUR SALMONID SPECIES FROM ROMANIA
Fig. 2. Majority with bootstrap support consensus trees for 16S rRNA. (a) 16S rRNA Neighbor Joining tree, distance model Kimura 2 Parameters, transition/transversion ratio 2.3; (b) 16S rRNA Maximum Parsimony tree; (c) 16S rRNA Maximum Likelihood tree
Fig. 6. Strict consensus tree resulting from 20 in A new species of Afrolaophonte (Copepoda, Harpacticoida, Laophontidae) from Korea and cladistic tests of species-groups
Fig. 6. Strict consensus tree resulting from 20 equally parsimonious trees from an analysis of 15 weighted morphological characters (Table 1) for 13 species of Afrolaophonte Chappuis, 1960 and one outgroup, Arenolaophonte stygia Lang, 1965. Characters 0, 2-4, and 11-14 were down-weighted to 0.5, while others were left at the default weight of 1. Full circles represent presumed synapomorphies, empty circles presumed plesiomorphies or homoplasies, Arabic numerals above circles characters, and Arabic numerals below circles character states.
Fig. 1 Strict consensus cladogram obtained from 45 in Phylogenetic analysis of the tribe Neanurini questions tribal classification of the subfamily Neanurinae (Collembola: Neanuridae)
Fig. 1 Strict consensus cladogram obtained from 45 most parsimonious trees under equal weights. Values of Jackknife support and symmetric resampling are indicated on and below branches, respectively. Only values above 40 are indicated to facilitate the visualisation of the most internal branches. The main clades are indicated with letters (a–e) on branches
ROC_all SINTEF workflow results with consensus steady state from CCLE
<p>This dataset has the input data + result dataset that was the product of using the <strong>DrugLogics</strong> computational pipeline with the <strong>rbbt</strong> workflow system to predict synergistic drug combinations across 8 cell lines that were also tested in SINTEF. The <strong>atopo topology</strong> was used and the logical models were trained to a <strong>consensus steady state derived from ~1000 cell lines from CCLE</strong>.</p>
Figure 3. Neighbor-joining consensus tree for 23 in Morphology and phylogeny of the sea anemone Stichodactyla haddoni (Cnidaria: Anthozoa: Actiniaria) from Chabahar Bay, Iran
Figure 3. Neighbor-joining consensus tree for 23 species, including Iranian sea anemone species (CHIAS1 and CHIAS2), based on 18S rDNA sequences. Cl1: Stichodactylidae; Cl2: Actiniidae; Cl3: Hormathiidae; Cl4: Aiptasiidae; Cl5: Actinostolidae. The numbers beside the branches are bootstrap values with 1000 replications. Bootstrap supports under 50% are not shown in this analysis.
Fig. 9. Strict consensus tree from phylogenetic analysis under extended implied weighting with 21 in New postcranial remains of large toxodontian notoungulates from the late Oligocene of Mendoza, Argentina and their systematic implications
Fig. 9. Strict consensus tree from phylogenetic analysis under extended implied weighting with 21 different values of k.
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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.