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368 results for “Consensus”
FIGURE 1. The strict consensus tree resulting from the parsimony analysis I in Taxonomy and evolution of asymmetric male genitalia in the subgenus Ashima Chen (Diptera: Drosophilidae: Phortica Schiner), with descriptions of seven new species
FIGURE 1. The strict consensus tree resulting from the parsimony analysis I (PAUP* v4.0a166) of the data matrix of 40 spp. × 66 morphological characters (Appendix 1) for the genus Phortica (especially focusing on the subgenus Ashima). Synapomorphies (solid circle: nonhomoplastic; open circle: homoplastic) inferred from both ACCTRAN and DELTRAN character optimization are indicated on each internal branch along with support values (bootstrap frequency %).
FIGURE 125. Strict consensus cladogram for Ptiloneura species under implied weights for K in An appraisal of the genus Ptiloneura Enderlein (Insecta: Psocodea: Psocomorpha Ptiloneuridae), new species from Colombia and Peru, and a key to the males
FIGURE 125. Strict consensus cladogram for Ptiloneura species under implied weights for K = 2 (L = 190; IC = 31; IR = 46). Brasineura jiboia, Loneura gorgonaensis and L. deibyi are outgroups. Full circles (black, red, or green) indicate unique character changes, black circles (unambiguous), color circles (ambiguous); empty circles indicate parallelisms or reversals. ACCTRAN (green circles) and DELTRAN (red circles) optimization. Character number above and character states below each circle. Bootstrap (numbers on the branches) and Symmetric resampling (numbers under the branches) values>50.
Data set for: Behavioral traits that define social dominance are the same that reduce social influence in a consensus task
<p>Dominant individuals are often most influential in their social groups, affecting movement, opinion, and performance across species and contexts. Yet behavioral traits like aggression, intimidation, and coercion, which are associated with and in many cases define dominance, can be socially aversive. The traits that make dominant individuals influential in one context may therefore reduce their influence in other contexts. Here we examine this association between dominance and influence using the cichlid fish <i>Astatotilapia burtoni</i>, comparing the influence of dominant and subordinate males during normal social interactions and in a more complex group-consensus association task. We find that phenotypically dominant males are aggressive, socially central, and that these males have a strong influence over normal group movement, whereas subordinate males are passive, socially peripheral, and have little influence over normal movement. However, subordinate males have the greatest influence in generating group-consensus during the association task. Dominant males are spatially distant and have lower signal-to-noise ratios of informative behavior in the association task, potentially interfering with their ability to generate group-consensus. In contrast, subordinate males are physically close to other group members, have a high signal-to-noise ratio of informative behavior, and equivalent visual connectedness to their group as dominant males. The behavioral traits that define effective social influence are thus highly context-specific and can be dissociated with social dominance. Thus, processes of hierarchical ascension in which the most aggressive, competitive, or coercive individuals rise to positions of dominance may be counter-productive in contexts where group-performance is prioritized.</p>
PODC 2020 live recording, session: consensus
<p>A recording of the consensus session in PODC 2020. This session took place on Tuesday, 4-Aug-2020, and was chaired by Robert Elsässer.</p>
Robustness of consensus is independent from preference diversity among group members in the American cockroach (Periplaneta american)
Collective choices and consensus resulting from the competition of positive feedbacks, are the subject of numerous studies. However, how the interindividual diversity of preferences among the group members affects these dynamics is usually overlooked apart from vertebrates based studies. Gregarism is a useful model for studying how the interindividual diversity of preferences affects the collective choices and consensus. The decision-making of three types of groups of the cockroach <i>Periplaneta americana</i> is tested in a choice between an odorous shelter and an odourless one: naïves (individuals showing an inherent preference towards the odorous shelter); conditioned (individuals with the preference for the odorous shelter inhibited); and mixed (a combination of conditioned individuals and a minority of naïve ones). We show that while the consensus is robust for all groups (>90% of the total population is in the same shelter), the group composition determines the rate to achieve a consensus and the frequency of selection of the odorous shelter. Indeed, increasing the proportion of naïves in the group leads to an increasing number of consensuses towards the odorous shelter and to an increasing rate to reach them. We also show that the minority of naïve individuals in mixed groups act as leaders or influencers.
International consensus guidance for management of myasthenia gravis
<p>OBJECTIVE: To update the 2016 formal consensus-based guidance for the management of myasthenia gravis (MG) based on the latest evidence in the literature.</p> <p>METHODS: In October 2013, the Myasthenia Gravis Foundation of America appointed a Task Force to develop treatment guidance for MG, and a panel of 15 international experts was convened. The RAND/UCLA appropriateness method was used to develop consensus recommendations pertaining to seven treatment topics. In February 2019, the international panel was reconvened with the addition of one member to represent South America. All prior recommendations were reviewed for currency, and new consensus recommendations were developed on topics that required inclusion or updates based on recent literature. Up to three rounds of anonymous e-mail votes were used to reach consensus, with modifications to recommendations between rounds based on panel input. A simple majority vote (80% of panel members voting 'yes') was used to approve minor changes in grammar and syntax to improve clarity.</p> <p>RESULTS: The previous recommendations for thymectomy were updated. New recommendations were developed for the use of rituximab, eculizumab and methotrexate as well as for the following topics: early immunosuppression in ocular MG and MG associated with immune checkpoint inhibitor treatment.</p> <p>CONCLUSION: This updated formal consensus guidance of international MG experts, based on new evidence, provides recommendations to clinicians caring for MG patients worldwide.</p>
mtDNA from consensus motif paper
<p>Mitochondrial DNA (mtDNA) dataset from https://sites.google.com/site/consensusmotifs/ as backup for tutorial on consensus motifs</p>
ACE: Abstract Consensus Encapsulation for Liveness Boosting of State Machine Replication (video)
Full video presentation of the paper: ACE: Abstract Consensus Encapsulation for Liveness Boosting of State Machine Replication.<br><br>Appears in Session 3 of the 24th International Conference on Principles of Distributed Systems OPODIS 2020<br><a href="https://opodis2020.unistra.fr">https://opodis2020.unistra.fr</a>
Multi-Threshold Asynchronous Reliable Broadcast and Consensus
Full video presentation of the paper: Multi-Threshold Asynchronous Reliable Broadcast and Consensus.<br><br>Appears in Session 2 of the 24th International Conference on Principles of Distributed Systems OPODIS 2020<br><a href="https://opodis2020.unistra.fr">https://opodis2020.unistra.fr</a>
Effects of Network Topology On the Performance of Consensus and Distributed Learning of SVMs Using ADMM
<p>The Alternating Direction Method Of Multipliers (ADMM) is a popular and promising distributed framework for solving large-scale machine learning problems. We consider decentralized consensus-based ADMM in which nodes may only communicate with one-hop neighbors. This may cause slow convergence. We investigate the impact of network topology on the performance of an ADMM-based learning of Support Vector Machine (SVM) using expander, and mean-degree graphs, and additionally some of the common modern network topologies. In particular, we investigate to which degree the expansion property of the network influences the convergence in terms of iterations, training and communication time. We furthermore suggest which topology is preferable. Additionally, we provide an implementation that makes these theoretical advances easily available. The results show that the performance of decentralized ADMM-based learning of SVMs in terms of convergence is improved using graphs with large spectral gaps, higher and homogeneous degrees.</p>
FIGURE 20. Majority-rule consensus tree from Bayesian analysis using 16S in Revision of the French Polycirridae (Annelida, Terebelliformia), with descriptions of eight new species
FIGURE 20. Majority-rule consensus tree from Bayesian analysis using 16S. Asterisks indicate posterior probability> 90 %. Text in red refers to specimens sequenced during this study.
FIGURE 50. Preferred topology with ACCTRAN character optimisations. Strict consensus tree from 7 trees using implied weights K in The new Southeast Asian goblin spider genus Aposphragisma (Araneae, Oonopidae): diversity and phylogeny
FIGURE 50. Preferred topology with ACCTRAN character optimisations. Strict consensus tree from 7 trees using implied weights K=1–5 (L=149, CI=0.63, RI=0.77). Squares show synapomorphic (black) and homoplastic (white) character state changes. Numbers above the squares correspond to the character numbers, those below to character state changes. The ingroup Aposphragisma gen. nov. is marked in shades of grey: species showing completely ornamented sterna in light grey and those possessing sterna with a glabrous middle stripe ('stripe-clade') in dark grey.
Data from: Consensus forecasting of intertidal seagrass habitat in the Wadden Sea
After the dramatic eutrophication-induced decline of intertidal seagrasses in the 1970s, the Wadden Sea has shown diverging developments. In the northern Wadden Sea, seagrass beds have expanded and become denser, while in the southern Wadden Sea, only small beds with low shoot densities are found. A lack of documentation of historical distributions hampers conservation management. Yet, the recovery in the northern Wadden Sea provides opportunity to construct robust habitat suitability models to support management. We tuned habitat distribution models based on 17 years of seagrass surveys in the northern Wadden Sea and high-resolution hydrodynamics and geomorphology for the entire Wadden Sea using five machine learning approaches. To obtain geographically transferable models, hyperparameters were tuned on the basis of prediction accuracy assessed by non-random, spatial cross-validation. The spatial cross-validation methodology was combined with a consensus modelling approach. The predicted suitability scores correlated amongst each other and with the hold-out observations in the training area indicating that the models converged and were transferable across space. Prediction accuracy was improved by averaging the predictions of the best models. We graphically examined the relationship between the consensus suitability score and independent presence-only data from outside the training area using the area-adjusted seagrass frequency per suitability class (continuous Boyce index). The Boyce index was positively correlated with the suitability score indicating the adequacy of the prediction methodology. We used the plot of the continuous Boyce index against habitat suitability score to demarcate three habitat classes – unsuitable, marginal and suitable – for the entire international Wadden Sea. This information is valuable for habitat conservation and restoration management. Divergence between predicted suitability and actual distributions from the recent past indicates that unaccounted factors limit seagrass development in the southern Wadden Sea. Synthesis and applications. Our methodology and data enabled us to produce a robust and validated consensus habitat suitability model. We identified highly suitable areas where intertidal seagrass meadows may establish and persist. Our work provides scientific underpinning for effective conservation planning in a dynamic landscape and sets monitoring priorities.
FIGURE 4. Strict consensus from 2010 in Redescription and phylogenetic analysis of the type species of the giant pill-millipede genus Sphaeropoeus Brandt, 1833 (Diplopoda, Sphaerotheriida, Zephroniidae)
FIGURE 4. Strict consensus from 2010 minimum-length trees obtained by the maximum parsimony analysis under the TBR tree search algorithm. Statistical support values obtained by the Bootstrap (left) and Jackknife (right) analysis given above, Bremer support (Decay) values below branches. Support values are not listed for splits below genus level. Highlighted in grey: species newly added to the dataset. Colour codes represent geographical area of origin and family: Green = Arthrosphaeridae, Madagascar & India; Blue = Sphaerotheriidae, South Africa; Dark yellow = Cyliosomatidae, Australia; Red = Zephroniidae, SE Asia (& Seychelles); Light yellow = Procyliosomatidae, New Zealand & Eastern Australia. Tree length = 320.
FIGURE 1. Maximum likelihood phylogram derived from a Bayesian backbone constraint consensus tree constructed using only taxa for which 12S, 16S, cytochrome b and cytochrome oxidase I in A new species of Dendrobates (Anura: Dendrobatidae) from the Amazonian lowlands in Perú
FIGURE 1. Maximum likelihood phylogram derived from a Bayesian backbone constraint consensus tree constructed using only taxa for which 12S, 16S, cytochrome b and cytochrome oxidase I sequence data were available. Numbers indicate posterior probabilities from the Bayesian analysis. Species of the Ventrimaculatus group are denoted with s.s. (sensu stricto), s.l. (sensu lato) and sp. aff (species affinis).
FIGURE 58. Maximum parsimony strict consensus tree. Bootstrap proportions from 1000 in Taxonomic revision and phylogeny of the ant genus Prenolepis (Hymenoptera: Formicidae)
FIGURE 58. Maximum parsimony strict consensus tree. Bootstrap proportions from 1000 replicates are presented along the branches.
FIGURE 1. Majority consensus tree produced from 70 in The position of the Leptoscelini and other taxonomic changes within the family Coreidae (Hemiptera: Heteroptera)
FIGURE 1. Majority consensus tree produced from 70 equally parsimonious trees during analysis of leptosceline (underlined) and anisosceline genera. Numbers on branches = percentage of trees in which the branch occurs.
FIGURE 4. Consensus tree under a in A new species of Cnesterodon (Cyprinodontiformes: Poeciliidae) from a small tributary of arroyo Cuñá-Pirú, río Paraná basin, Misiones, Argentina
FIGURE 4. Consensus tree under a- equal weight; b- implied weighting (K=3-10); c- implied weighting (K= 11-16). The GC/relative Bremer support values are shown. Question marks in GC values represent negatives values, which are an artifact of the method, assigned to weakly supported nodes. Unsupported nodes are shown as collapsed.
FIGURE 9. Strict component consensus tree obtained following a in Cranial anatomy of Jeholosaurus shangyuanensis (Dinosauria: Ornithischia) from the Early Cretaceous of China
FIGURE 9. Strict component consensus tree obtained following a posteriori deletion of Talenkauen, Yandusaurus and Zephyrosaurus. Numbers at nodes indicate bootstrap proportions> 50% (1,000 replicates). See text for further details.
FIGURE 1. Bayesian consensus tree inferred from D2 in Nematodes from galls on Myrtaceae. IV. Fergusobia from flat leaf galls on Eucalyptus and Corymbia, with descriptions of two new species
FIGURE 1. Bayesian consensus tree inferred from D2/D3 under TVM+I+G model (lnL=4053.8611; freqA=0.2908; freqC=0.1437; freqG=0.2386; freqT=0.3269; R(a)=0.8687; R(b)=3.4319; R(c)=1.9944; R(d)=0.4487; R(e)=3.4319; R(f)=1; Pinva=0.5164; Shape=0.5896). Posterior probability values exceeding 50% are given on appropriate clades.
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Allen Brain Atlas
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International Brain Laboratory public data
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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.