Sequence-independent characterization of viruses based on the pattern of viral small RNAs produced by the host. [Corrigendum]

published erratum2016 Apr 202016 01 21importedErratum for : Sequence-independent characterization of viruses based on the pattern of viral small RNAs produced by the host. [Nucleic Acids Res. 2015

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Observation of the Λb0→χc1(3872)pK−\Lambda_b^0\rightarrow \chi_{c1}(3872)pK^- decay

International audienceUsing proton-proton collision data, collected with the LHCb detector and corresponding to 1.0, 2.0 and 1.9 fb$^{−1}$ of integrated luminosity at the centre-of-mass energies of 7, 8, and 13 TeV, respectively, the decay {\Lambda}_{\mathrm{b}}^0\to {\upchi}_{\mathrm{c}1} (3872)pK$^{−}$ with χ$_{c1}$(3872) → J/ψ π$^{+}$π$^{−}$ is observed for the first time. The significance of the observed signal is in excess of seven standard deviations. It is found that (58 ± 15)% of the decays proceed via the two-body intermediate state χ$_{c1}$(3872)Λ(1520). The branching fraction with respect to that of the ${\Lambda}_{\mathrm{b}}^0$ → ψ(2S)pK$^{−}$ decay mode, where the ψ(2S) meson is reconstructed in the J/ψ π$^{+}$π$^{−}$ final state, is measured to be: $ \frac{\beta \left({\Lambda}_{\mathrm{b}}^0\to {\upchi}_{\mathrm{c}1}(3872){\mathrm{pK}}^{-}\right)}{\beta \left({\Lambda}_{\mathrm{b}}^0\to \uppsi \left(2\mathrm{S}\right){\mathrm{pK}}^{-}\right)}\times \frac{\beta \left({\upchi}_{\mathrm{c}1}(3872)\to \mathrm{J}/\uppsi {\uppi}^{+}{\uppi}^{-}\right)}{\beta \left(\uppsi \left(2\mathrm{S}\right)\to \mathrm{J}/\uppsi {\uppi}^{+}{\uppi}^{-}\right)}=\left(5.4\pm 1.1\pm 0.2\right)\times {10}^{-2},

Measurement of fs/fuf_s / f_u Variation with Proton-Proton Collision Energy and BB-Meson Kinematics

International audienceThe ratio of the Bs0 and B+ fragmentation fractions fs and fu is studied with Bs0→J/ψϕ and B+→J/ψK+ decays using data collected by the LHCb experiment in proton-proton collisions at 7, 8, and 13 TeV center-of-mass energies. The analysis is performed in bins of B-meson momentum, longitudinal momentum, transverse momentum, pseudorapidity, and rapidity. The fragmentation-fraction ratio fs/fu is observed to depend on the B-meson transverse momentum with a significance of 6.0σ. This dependency is driven by the 13 TeV sample (8.7σ), while the results for the other collision energies are not significant when considered separately. Furthermore, the results show a 4.8σ evidence for an increase of fs/fu as a function of collision energy