288 research outputs found

    la conservation du Pelagodoxa Henryana

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    Interfertility between Armillaria cepistipes and A. sinapina

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    Des Ă©tudes ont rapportĂ© que des lignĂ©es europĂ©ennes d'Armillaria cepistipes Ă©taient interfertiles avec trois lignĂ©es amĂ©ricaines d'Armillaria dĂ©signĂ©es par les termes espĂšce biologique nord-amĂ©ricaine (NABS) V (A sinapina), NABS X et NABS XI. Une telle interfertilitĂ© entre les espĂšces soulĂšve des doutes au sujet de l'utilisation de binĂŽmes latins distincts pour des espĂšces pouvant se reproduire. Cette interfertilitĂ© a Ă©tĂ© rĂ©-examinĂ©e en mettant 24 isolats haploĂŻdes d'A cepistipes europĂ©en en prĂ©sence de 23 isolats 6'A. sinapina d'AmĂ©rique du Nord et d'Asie. Les appariements individuels ont Ă©tĂ© effectuĂ©s de façon indĂ©pendante au moins une fois Ă  l'UniversitĂ© Laval (Canada) et Ă  NNRA Clermont-Ferrand (France). Des 420 appariements interspĂ©cifiques effectuĂ©s Ă  l'UniversitĂ© Laval, deux Ă©taient positifs et sept Ă©taient ambigus, pour un total de 2,1 % de tous les appariements. Des 506 appariements effectuĂ©s Ă  Clermont-Ferrand, 10 Ă©taient positifs et 24 Ă©taient ambigus pour un total de 6,7 % des appariements. Les diffĂ©rences dans les rĂ©sultats de ces appariements peuvent ĂȘtre expliquĂ©es par les tempĂ©ratures d'incubation, ainsi que par les diffĂ©rents types et concentrations d'extrait de malt utilisĂ©s dans chaque laboratoire. Les bas niveaux d'interfertilitĂ© trouvĂ©s entre A cepistipes et A. sinapina peuvent rĂ©sulter de l'absence de barriĂšres gĂ©nĂ©tiques habituellement prĂ©sentes entre des espĂšces sympatriques. Ce bas niveau d'interfertilitĂ© reflĂšte des diffĂ©rences entre la morphologie, la rĂ©partition et les habitats des deux espĂšces d'Armillaria, et appuie la conservation de dĂ©nominations d'espĂšces distinctes.European strains of Armillaria cepistipes were reported to be interfertile with strains from three American Armillaria species known as North American Biological Species (NABS) V (A sinapina), NABS X and NABS XI. Such interfertility between species raises some doubts about using different Latin binomials for species capable of mating. This interfertility was reinvestigated by mating 24 haploid isolates of European A cepistipes with 23 isolates of A sinapinafrom North America and Asia. Individual pairings were independently performed at least once at Universite Laval, Canada and at INRA Clermont-Ferrand, France. From the 420 interspecific pairings performed at Laval, two were positive and seven were ambiguous for a total of 2.1% of all the pairings. From the 506 pairings made at Clermont-Ferrand, 10 were positive and 24 were ambiguous for a total of 6.7%. The differences in the pairing results may be explained by incubation temperatures, and the different types and concentrations of malt extract used at each laboratory. The low levels of interfertility found between A. cepistipes and A. sinapina may result from the absence of genetic barriers that are usually present between sympatric species. This low level of interfertility reflects differences in morphology, distribution, and habitat for these two species of Armillaria and this supports the retention of different species denominations

    Large-scale knowledge transfer for object localization in ImageNet

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    ImageNet is a large-scale database of object classes with millions of images. Unfortunately only a small fraction of them is manually annotated with bounding-boxes. This prevents useful developments, such as learning reliable object detectors for thousands of classes. In this paper we propose to automatically populate ImageNet with many more bounding-boxes, by leveraging existing manual annotations. The key idea is to localize objects of a target class for which annotations are not available, by transferring knowledge from related source classes with available annotations. We distinguish two kinds of source classes: ancestors and siblings. Each source provides knowledge about the plausible location, appearance and context of the target objects, which induces a probability distribution over windows in images of the target class. We learn to combine these distributions so as to maximize the location accuracy of the most probable window. Finally, we employ the combined distribution in a procedure to jointly localize objects in all images of the target class. Through experiments on 0.5 million images from 219 classes we show that our technique (i) annotates a wide range of classes with boundingboxes; (ii) effectively exploits the hierarchical structure of ImageNet, since all sources and types of knowledge we propose contribute to the results; (iii) scales efficiently. 1

    Fast Energy Minimization Using Learned State Filters

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    Pairwise discrete energies defined over graphs are ubiquitous in computer vision. Many algorithms have been proposed to minimize such energies, often concentrating on sparse graph topologies or specialized classes of pairwise potentials. However, when the graph is fully connected and the pairwise potentials are arbitrary, the complexity of even approximate minimization algorithms such as TRW-S grows quadratically both in the number of nodes and in the number of states a node can take. Moreover, recent applications are using more and more computationally expensive pairwise potentials. These factors make it very hard to employ fully connected models. In this paper we propose a novel, generic algorithm to approximately minimize any discrete pairwise energy function. Our method exploits tractable sub-energies to filter the domain of the function. The parameters of the filter are learnt from instances of the same class of energies with good candidate solutions. Compared to existing methods, it efficiently handles fully connected graphs, with many states per node, and arbitrary pairwise potentials, which might be expensive to compute. We demonstrate experimentally on two applications that our algorithm is much more efficient than other generic minimization algorithms such as TRW-S, while returning essentially identical solutions. 1

    The debiased Whittle likelihood

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    The Whittle likelihood is a widely used and computationally efficient pseudolikelihood. However, it is known to produce biased parameter estimates with finite sample sizes for large classes of models. We propose a method for debiasing Whittle estimates for second-order stationary stochastic processes. The debiased Whittle likelihood can be computed in the same O(n log n) operations as the standard Whittle approach. We demonstrate the superior performance of our method in simulation studies and in application to a large-scale oceanographic dataset, where in both cases the debiased approach reduces bias by up to two orders of magnitude, achieving estimates that are close to those of the exact maximum likelihood, at a fraction of the computational cost. We prove that the method yields estimates that are consistent at an optimal convergence rate of n(-1/2) for Gaussian processes and for certain classes of non-Gaussian or nonlinear processes. This is established under weaker assumptions than in the standard theory, and in particular the power spectral density is not required to be continuous in frequency. We describe how the method can be readily combined with standard methods of bias reduction, such as tapering and differencing, to further reduce bias in parameter estimates

    2022 Update of the consensus on the rational use of antithrombotics and thrombolytics in Veterinary Critical Care (CURATIVE) Domain 6: Defining rational use of thrombolytics

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    Objectives To systematically review available evidence and establish guidelines related to the use of thrombolytics for the management of small animals with suspected or confirmed thrombosis. Design PICO (Population, Intervention, Control, and Outcome) questions were formulated, and worksheets completed as part of a standardized and systematic literature evaluation. The population of interest included dogs and cats (considered separately) and arterial and venous thrombosis. The interventions assessed were the use of thrombolytics, compared to no thrombolytics, with or without anticoagulants or antiplatelet agents. Specific protocols for recombinant tissue plasminogen activator were also evaluated. Outcomes assessed included efficacy and safety. Relevant articles were categorized according to level of evidence, quality, and as to whether they supported, were neutral to, or opposed the PICO questions. Conclusions from the PICO worksheets were used to draft guidelines, which were subsequently refined via Delphi surveys undertaken by the Consensus on the Rational Use of Antithrombotics and Thrombolytics in Veterinary Critical Care (CURATIVE) working group. Results Fourteen PICO questions were developed, generating 14 guidelines. The majority of the literature addressing the PICO questions in dogs is experimental studies (level of evidence 3), thus providing insufficient evidence to determine if thrombolysis improves patient-centered outcomes. In cats, literature was more limited and often neutral to the PICO questions, precluding strong evidence-based recommendations for thrombolytic use. Rather, for both species, suggestions are made regarding considerations for when thrombolytic drugs may be considered, the combination of thrombolytics with anticoagulant or antiplatelet drugs, and the choice of thrombolytic agent. Conclusions Substantial additional research is needed to address the role of thrombolytics for the treatment of arterial and venous thrombosis in dogs and cats. Clinical trials with patient-centered outcomes will be most valuable for addressing knowledge gaps in the field

    Assessment of microcirculation variables and endothelial glycocalyx using sidestream dark field videomicroscopy in anesthetized dogs undergoing cardiopulmonary bypass

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    IntroductionTo evaluate microcirculation and endothelial glycocalyx (eGC) variables using sidestream darkfield (SDF) videomicroscopy in canine cardiopulmonary bypass (CPB).MethodsDogs undergoing CPB for surgical correction of naturally-occurring cardiac disease were prospectively included. Variables collected included patient demographics, underlying cardiac disease, red blood cell flow (Flow), 4-25 Όm vessel density (Density), absolute capillary blood volume (CBVabs), relative capillary blood volume (CBVrel) and eGC width assessed by perfused boundary region (PBR). Anesthetized healthy dogs were used as control. Microcirculation and eGC variables were compared at baseline under anesthesia (T0), on CPB prior to cross clamping (T1), after cross clamp removal following surgical correction (T2) and at surgical closure (T3).ResultsTwelve dogs were enrolled, including 10 with a complete dataset. Median Flow was 233.9, 79.9, 164.3, and 136.1 Όm/s at T0, T1, T2, and T3, respectively, (p = 1.00). Median Density was 173.3, 118.4, 121.0 and 155.4 mm/mm2 at T0, T1, T2, and T3, respectively, (p = 1.00). Median CBVabs decreased over time: 7.4, 6.6, 4.8 and 4.7 103ÎŒm3 at T0, T1, T2, and T3, respectively, (p < 0.01). Median CBVrel increased over time: 1.1, 1.5,1.1, and 1.3 103ÎŒm3 at T0, T1, T2, and T3, respectively, (p < 0.001). Median PBR increased over time: 1.8, 2.1, 2.4, 2.1 Όm at T0, T1, T2, and T3, respectively, (p < 0.001). Compared to control dogs (n = 8), CPB dogs had lower CBVabs at T0.ConclusionAlterations in eGC thickness and microvascular occur in dogs undergoing CPB for naturally-occurring cardiac disease

    2022 Update of the Consensus on the Rational Use of Antithrombotics and Thrombolytics in Veterinary Critical Care (CURATIVE) Domain 6: Defining rational use of thrombolytics

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    Objectives: To systematically review available evidence and establish guidelines related to the use of thrombolytics for the management of small animals with suspected or confirmed thrombosis. Design: PICO (Population, Intervention, Control, and Outcome) questions were formulated, and worksheets completed as part of a standardized and systematic literature evaluation. The population of interest included dogs and cats (considered separately) and arterial and venous thrombosis. The interventions assessed were the use of thrombolytics, compared to no thrombolytics, with or without anticoagulants or antiplatelet agents. Specific protocols for recombinant tissue plasminogen activator were also evaluated. Outcomes assessed included efficacy and safety. Relevant articles were categorized according to level of evidence, quality, and as to whether they supported, were neutral to, or opposed the PICO questions. Conclusions from the PICO worksheets were used to draft guidelines, which were subsequently refined via Delphi surveys undertaken by the Consensus on the Rational Use of Antithrombotics and Thrombolytics in Veterinary Critical Care (CURATIVE) working group. Results: Fourteen PICO questions were developed, generating 14 guidelines. The majority of the literature addressing the PICO questions in dogs is experimental studies (level of evidence 3), thus providing insufficient evidence to determine if thrombolysis improves patient-centered outcomes. In cats, literature was more limited and often neutral to the PICO questions, precluding strong evidence-based recommendations for thrombolytic use. Rather, for both species, suggestions are made regarding considerations for when thrombolytic drugs may be considered, the combination of thrombolytics with anticoagulant or antiplatelet drugs, and the choice of thrombolytic agent. Conclusions: Substantial additional research is needed to address the role of thrombolytics for the treatment of arterial and venous thrombosis in dogs and cats. Clinical trials with patient-centered outcomes will be most valuable for addressing knowledge gaps in the field

    What’s the Point: Semantic Segmentation with Point Supervision

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    The semantic image segmentation task presents a trade-off between test time accuracy and training-time annotation cost. Detailed per-pixel annotations enable training accurate models but are very time-consuming to obtain, image-level class labels are an order of magnitude cheaper but result in less accurate models. We take a natural step from image-level annotation towards stronger supervision: we ask annotators to point to an object if one exists. We incorporate this point supervision along with a novel objectness potential in the training loss function of a CNN model. Experimental results on the PASCAL VOC 2012 benchmark reveal that the combined effect of point-level supervision and objectness potential yields an improvement of 12.9% mIOU over image-level supervision. Further, we demonstrate that models trained with point-level supervision are more accurate than models trained with image-level, squiggle-level or full supervision given a fixed annotation budget.Comment: ECCV (2016) submissio
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