102 research outputs found

    Harm to self outweighs benefit to others in moral decision making

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    This is the final version. Available from the national Academy of Sciences via the DOI in this record. How we make decisions that have direct consequences for ourselves and others forms the moral foundation of our society. Whereas economic theory contends that humans aim at maximizing their own gains, recent seminal psychological work suggests that our behavior is instead hyperaltruistic: We are more willing to sacrifice gains to spare others from harm than to spare ourselves from harm. To investigate how such egoistic and hyperaltruistic tendencies influence moral decision making, we investigated trade-off decisions combining monetary rewards and painful electric shocks, administered to the participants themselves or an anonymous other. Whereas we replicated the notion of hyperaltruism (i.e., the willingness to forego reward to spare others from harm), we observed strongly egoistic tendencies in participants’ unwillingness to harm themselves for others’ benefit. The moral principle guiding intersubject trade-off decision making observed in our study is best described as egoistically biased altruism, with important implications for our understanding of economic and social interactions in our society

    Study of Bc+B_c^+ decays to the K+Kπ+K^+K^-\pi^+ final state and evidence for the decay Bc+χc0π+B_c^+\to\chi_{c0}\pi^+

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    A study of Bc+K+Kπ+B_c^+\to K^+K^-\pi^+ decays is performed for the first time using data corresponding to an integrated luminosity of 3.0 fb1\mathrm{fb}^{-1} collected by the LHCb experiment in pppp collisions at centre-of-mass energies of 77 and 88 TeV. Evidence for the decay Bc+χc0(K+K)π+B_c^+\to\chi_{c0}(\to K^+K^-)\pi^+ is reported with a significance of 4.0 standard deviations, resulting in the measurement of σ(Bc+)σ(B+)×B(Bc+χc0π+)\frac{\sigma(B_c^+)}{\sigma(B^+)}\times\mathcal{B}(B_c^+\to\chi_{c0}\pi^+) to be (9.83.0+3.4(stat)±0.8(syst))×106(9.8^{+3.4}_{-3.0}(\mathrm{stat})\pm 0.8(\mathrm{syst}))\times 10^{-6}. Here B\mathcal{B} denotes a branching fraction while σ(Bc+)\sigma(B_c^+) and σ(B+)\sigma(B^+) are the production cross-sections for Bc+B_c^+ and B+B^+ mesons. An indication of bˉc\bar b c weak annihilation is found for the region m(Kπ+)<1.834GeV ⁣/c2m(K^-\pi^+)<1.834\mathrm{\,Ge\kern -0.1em V\!/}c^2, with a significance of 2.4 standard deviations.Comment: All figures and tables, along with any supplementary material and additional information, are available at https://lhcbproject.web.cern.ch/lhcbproject/Publications/LHCbProjectPublic/LHCb-PAPER-2016-022.html, link to supplemental material inserted in the reference

    Identifying Cognate Binding Pairs among a Large Set of Paralogs: The Case of PE/PPE Proteins of Mycobacterium tuberculosis

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    We consider the problem of how to detect cognate pairs of proteins that bind when each belongs to a large family of paralogs. To illustrate the problem, we have undertaken a genomewide analysis of interactions of members of the PE and PPE protein families of Mycobacterium tuberculosis. Our computational method uses structural information, operon organization, and protein coevolution to infer the interaction of PE and PPE proteins. Some 289 PE/PPE complexes were predicted out of a possible 5,590 PE/PPE pairs genomewide. Thirty-five of these predicted complexes were also found to have correlated mRNA expression, providing additional evidence for these interactions. We show that our method is applicable to other protein families, by analyzing interactions of the Esx family of proteins. Our resulting set of predictions is a starting point for genomewide experimental interaction screens of the PE and PPE families, and our method may be generally useful for detecting interactions of proteins within families having many paralogs

    Gaze fixation improves the stability of expert juggling

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    Novice and expert jugglers employ different visuomotor strategies: whereas novices look at the balls around their zeniths, experts tend to fixate their gaze at a central location within the pattern (so-called gaze-through). A gaze-through strategy may reflect visuomotor parsimony, i.e., the use of simpler visuomotor (oculomotor and/or attentional) strategies as afforded by superior tossing accuracy and error corrections. In addition, the more stable gaze during a gaze-through strategy may result in more accurate movement planning by providing a stable base for gaze-centered neural coding of ball motion and movement plans or for shifts in attention. To determine whether a stable gaze might indeed have such beneficial effects on juggling, we examined juggling variability during 3-ball cascade juggling with and without constrained gaze fixation (at various depths) in expert performers (n = 5). Novice jugglers were included (n = 5) for comparison, even though our predictions pertained specifically to expert juggling. We indeed observed that experts, but not novices, juggled significantly less variable when fixating, compared to unconstrained viewing. Thus, while visuomotor parsimony might still contribute to the emergence of a gaze-through strategy, this study highlights an additional role for improved movement planning. This role may be engendered by gaze-centered coding and/or attentional control mechanisms in the brain

    Measurement of the B0s →J/ψη lifetime

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    Using a data set corresponding to an integrated luminosity of 3 fb−1, collected by the LHCb experiment in pp collisions at centre-of-mass energies of 7 and 8 TeV, the effective lifetime in the Bs0→J/ψη decay mode, τeff, is measured to be τeff=1.479±0.034 (stat)±0.011 (syst) ps. Assuming CP conservation, τeff corresponds to the lifetime of the light Bs0 mass eigenstate. This is the first measurement of the effective lifetime in this decay mode

    Measurement of D s <sup>±</sup> production asymmetry in pp collisions at √s=7 and 8 TeV

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    The inclusive Ds±D_s^{\pm} production asymmetry is measured in pppp collisions collected by the LHCb experiment at centre-of-mass energies of s=7\sqrt{s} =7 and 8 TeV. Promptly produced Ds±D_s^{\pm} mesons are used, which decay as Ds±ϕπ±D_s^{\pm}\to\phi\pi^{\pm}, with ϕK+K\phi\to K^+K^-. The measurement is performed in bins of transverse momentum, pTp_{\rm T}, and rapidity, yy, covering the range 2.5<pT<25.02.5<p_{\rm T}<25.0 GeV/c/c and 2.0<y<4.52.0<y<4.5. No kinematic dependence is observed. Evidence of nonzero Ds±D_s^{\pm} production asymmetry is found with a significance of 3.3 standard deviations.Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2018-010.htm

    Search for CP violation in Λb0→pK− and Λb0→pπ− decays

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    A search for CP violation in Λb0→pK− and Λb0→pπ− decays is presented using a sample of pp collisions collected with the LHCb detector and corresponding to an integrated luminosity of 3.0fb−1. The CP -violating asymmetries are measured to be ACPpK−=−0.020±0.013±0.019 and ACPpπ−=−0.035±0.017±0.020, and their difference ACPpK−−ACPpπ−=0.014±0.022±0.010, where the first uncertainties are statistical and the second systematic. These are the most precise measurements of such asymmetries to date

    Is implicit motor learning preserved after stroke? A systematic review with meta-analysis

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    © 2016 Kal et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Many stroke patients experience difficulty with performing dual-tasks. A promising intervention to target this issue is implicit motor learning, as it should enhance patients' automaticity of movement. Yet, although it is often thought that implicit motor learning is preserved poststroke, evidence for this claim has not been systematically analysed yet. Therefore, we systematically reviewed whether implicit motor learning is preserved post-stroke, and whether patients benefit more from implicit than from explicit motor learning. We comprehensively searched conventional (MEDLINE, Cochrane, Embase, PEDro, PsycINFO) and grey literature databases (BIOSIS, Web of Science, OpenGrey, British Library, trial registries) for relevant reports. Two independent reviewers screened reports, extracted data, and performed a risk of bias assessment. Overall, we included 20 out of the 2177 identified reports that allow for a succinct evaluation of implicit motor learning. Of these, only 1 study investigated learning on a relatively complex, whole-body (balance board) task. All 19 other studies concerned variants of the serial-reaction time paradigm, with most of these focusing on learning with the unaffected hand (N = 13) rather than the affected hand or both hands (both: N = 4). Four of the 20 studies compared explicit and implicit motor learning post-stroke. Meta-analyses suggest that patients with stroke can learn implicitly with their unaffected side (mean difference (MD) = 69 ms, 95% CI[45.1, 92.9], p < .00001), but not with their affected side (standardized MD = -.11, 95% CI[-.45, .25], p = .56). Finally, implicit motor learning seemed equally effective as explicit motor learning post-stroke (SMD = -.54, 95% CI[-1.37, .29], p = .20). However, overall, the high risk of bias, small samples, and limited clinical relevance of most studies make it impossible to draw reliable conclusions regarding the effect of implicit motor learning strategies post-stroke. High quality studies with larger samples are warranted to test implicit motor learning in clinically relevant contexts

    Analysis of shared heritability in common disorders of the brain

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    Paroxysmal Cerebral Disorder

    Model-independent measurement of mixing parameters in D0 → K S 0 π+π− decays

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    The first model-independent measurement of the charm mixing parameters in the decay D 0 → K S 0 π + π − is reported, using a sample of pp collision data recorded by the LHCb experiment, corresponding to an integrated luminosity of 1.0 fb−1 at a centre-of-mass energy of 7 TeV. The measured values are x=(−0.86±0.53±0.17)×10−2,y=(+0.03±0.46±0.13)×10−2, x=(−0.86±0.53±0.17)×10−2,y=(+0.03±0.46±0.13)×10−2, where the first uncertainties are statistical and include small contributions due to the external input for the strong phase measured by the CLEO collaboration, and the second uncertainties are systematic
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