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This letter reports the first measurement of spin alignment, with respect to the helicity axis, for D⁎+ vector mesons and their charge conjugates from charm-quark hadronisation (prompt) and from beauty-meson decays (non-prompt) in hadron collisions. The measurements were performed at midrapidity (|y|<0.8) as a function of transverse momentum (pT) in proton–proton (pp) collisions collected by ALICE at the centre-of-mass energy √s=13TeV. The diagonal spin density matrix element ρ00 of D⁎+ mesons was measured from the angular distribution of the D⁎+→D0(→K−π+)π+ decay products, in the D⁎+ rest frame, with respect to the D⁎+ momentum direction in the pp centre of mass frame. The ρ00 value for prompt D⁎+ mesons is consistent with 1/3, which implies no spin alignment. However, for non-prompt D⁎+ mesons an evidence of ρ00 larger than 1/3 is found. The measured value of the spin density element is ρ00=0.455±0.022(stat.)±0.035(syst.) in the 5<pT<20GeV/c interval, which is consistent with a Pythia 8 Monte Carlo simulation coupled with the EvtGen package, which implements the helicity conservation in the decay of D⁎+ meson from beauty mesons. In non-central heavy-ion collisions, the spin of the D⁎+ mesons may be globally aligned with the direction of the initial angular momentum and magnetic field. Based on the results for pp collisions reported in this letter it is shown that alignment of non-prompt D⁎+ mesons due to the helicity conservation coupled to the collective anisotropic expansion may mimic the signal of global spin alignment in heavy-ion collisions.
We present the first systematic comparison of the charged-particle pseudorapidity densities for three widely different collision systems, pp, pPb, and PbPb, at the top energy of the Large Hadron Collider (√sNN=5.02TeV) measured over a wide pseudorapidity range (−3.5<η<5), the widest possible among the four experiments at that facility. The systematic uncertainties are minimised since the measurements are recorded by the same experimental apparatus (ALICE). The distributions for pPb and PbPb collisions are determined as a function of the centrality of the collisions, while results from pp collisions are reported for inelastic events with at least one charged particle at midrapidity. The charged-particle pseudorapidity densities are, under simple and robust assumptions, transformed to charged-particle rapidity densities. This allows for the calculation and the presentation of the evolution of the width of the rapidity distributions and of a lower bound on the Bjorken energy density, as a function of the number of participants in all three collision systems. We find a decreasing width of the particle production, and roughly a smooth ten fold increase in the energy density, as the system size grows, which is consistent with a gradually higher dense phase of matter.
Multiplicity (Nch) distributions and transverse momentum (pT) spectra of inclusive primary charged particles in the kinematic range of |η|<0.8 and 0.15 GeV/c<pT<10 GeV/c are reported for pp, p–Pb, Xe–Xe and Pb–Pb collisions at centre-of-mass energies per nucleon pair ranging from √sNN=2.76 TeV up to 13 TeV. A sequential two-dimensional unfolding procedure is used to extract the correlation between the transverse momentum of primary charged particles and the charged-particle multiplicity of the corresponding collision. This correlation sharply characterises important features of the final state of a collision and, therefore, can be used as a stringent test of theoretical models. The multiplicity distributions as well as the mean and standard deviation derived from the pT spectra are compared to state-of-the-art model predictions. Providing these fundamental observables of bulk particle production consistently across a wide range of collision energies and system sizes can serve as an important input for tuning Monte Carlo event generators.
The interaction between Λ baryons and kaons/antikaons is a crucial ingredient for the strangeness S=0 and S=−2 sector of the meson–baryon interaction at low energies. In particular, the ΛK‾ might help in understanding the origin of states such as the Ξ(1620), whose nature and properties are still under debate. Experimental data on Λ–K and Λ–K‾ systems are scarce, leading to large uncertainties and tension between the available theoretical predictions constrained by such data. In this Letter we present the measurements of Λ–K⊕+Λ‾–K− and Λ–K⊕−Λ‾–K+ correlations obtained in the high-multiplicity triggered data sample in pp collisions at s=13 TeV recorded by ALICE at the LHC. The correlation function for both pairs is modeled using the Lednický–Lyuboshits analytical formula and the corresponding scattering parameters are extracted. The Λ–K⊕−Λ‾–K+ correlations show the presence of several structures at relative momenta k⁎ above 200 MeV/c, compatible with the Ω baryon, the Ξ(1690), and Ξ(1820) resonances decaying into Λ–K− pairs. The low k⁎ region in the Λ–K⊕−Λ‾–K+ also exhibits the presence of the Ξ(1620) state, expected to strongly couple to the measured pair. The presented data allow to access the ΛK+ and ΛK− strong interaction with an unprecedented precision and deliver the first experimental observation of the Ξ(1620) decaying into ΛK−.
Non-coding variations located within regulatory elements may alter gene expression by modifying Transcription Factor (TF) binding sites and thereby lead to functional consequences like various traits or diseases. To understand these molecular mechanisms, different TF models are being used to assess the effect of DNA sequence variations, such as Single Nucleotide Polymorphisms (SNPs). However, few statistical approaches exist to compute statistical significance of results but they often are slow for large sets of SNPs, such as data obtained from a genome-wide association study (GWAS) or allele-specific analysis of chromatin data.
Results We investigate the distribution of maximal differential TF binding scores for general computational models that assess TF binding. We find that a modified Laplace distribution can adequately approximate the empirical distributions. A benchmark on in vitro and in vivo data sets showed that our new approach improves on an existing method in terms of performance and speed. In applications on large sets of eQTL and GWAS SNPs we could illustrate the usefulness of the novel statistic to highlight cell type specific regulators and TF target genes.
Conclusions Our approach allows the evaluation of DNA changes that induce differential TF binding in a fast and accurate manner, permitting computations on large mutation data sets. An implementation of the novel approach is freely available at https://github.com/SchulzLab/SNEEP.
Human functional brain connectivity can be temporally decomposed into states of high and low cofluctuation, defined as coactivation of brain regions over time. Rare states of particularly high cofluctuation have been shown to reflect fundamentals of intrinsic functional network architecture and to be highly subject-specific. However, it is unclear whether such network-defining states also contribute to individual variations in cognitive abilities – which strongly rely on the interactions among distributed brain regions. By introducing CMEP, a new eigenvector-based prediction framework, we show that as few as 16 temporally separated time frames (< 1.5% of 10min resting-state fMRI) can significantly predict individual differences in intelligence (N = 263, p < .001). Against previous expectations, individual’s network-defining time frames of particularly high cofluctuation do not predict intelligence. Multiple functional brain networks contribute to the prediction, and all results replicate in an independent sample (N = 831). Our results suggest that although fundamentals of person-specific functional connectomes can be derived from few time frames of highest connectivity, temporally distributed information is necessary to extract information about cognitive abilities. This information is not restricted to specific connectivity states, like network-defining high-cofluctuation states, but rather reflected across the entire length of the brain connectivity time series.
The fundamental structure of cortical networks arises early in development prior to the onset of sensory experience. However, how endogenously generated networks respond to the onset of sensory experience, and how they form mature sensory representations with experience remains unclear. Here we examine this ‘nature-nurture transform’ using in vivo calcium imaging in ferret visual cortex. At eye-opening, visual stimulation evokes robust patterns of cortical activity that are highly variable within and across trials, severely limiting stimulus discriminability. Initial evoked responses are distinct from spontaneous activity of the endogenous network. Visual experience drives the development of low-dimensional, reliable representations aligned with spontaneous activity. A computational model shows that alignment of novel visual inputs and recurrent cortical networks can account for the emergence of reliable visual representations.
The fundamental structure of cortical networks arises early in development prior to the onset of sensory experience. However, how endogenously generated networks respond to the onset of sensory experience, and how they form mature sensory representations with experience remains unclear. Here we examine this "nature-nurture transform" using in vivo calcium imaging in ferret visual cortex. At eye-opening, visual stimulation evokes robust patterns of cortical activity that are highly variable within and across trials, severely limiting stimulus discriminability. Initial evoked responses are distinct from spontaneous activity of the endogenous network. Visual experience drives the development of low-dimensional, reliable representations aligned with spontaneous activity. A computational model shows that alignment of novel visual inputs and recurrent cortical networks can account for the emergence of reliable visual representations.
Grasping the meaning of everyday visual events is a fundamental feat of human intelligence that hinges on diverse neural processes ranging from vision to higher-level cognition. Deciphering the neural basis of visual event understanding requires rich, extensive, and appropriately designed experimental data. However, this type of data is hitherto missing. To fill this gap, we introduce the BOLD Moments Dataset (BMD), a large dataset of whole-brain fMRI responses to over 1,000 short (3s) naturalistic video clips and accompanying metadata. We show visual events interface with an array of processes, extending even to memory, and we reveal a match in hierarchical processing between brains and video-computable deep neural networks. Furthermore, we showcase that BMD successfully captures temporal dynamics of visual events at second resolution. BMD thus establishes a critical groundwork for investigations of the neural basis of visual event understanding.
Dendritic spines are considered a morphological proxy for excitatory synapses, rendering them a target of many different lines of research. Over recent years, it has become possible to image simultaneously large numbers of dendritic spines in 3D volumes of neural tissue. In contrast, currently no automated method for spine detection exists that comes close to the detection performance reached by human experts. However, exploiting such datasets requires new tools for the fully automated detection and analysis of large numbers of spines. Here, we developed an efficient analysis pipeline to detect large numbers of dendritic spines in volumetric fluorescence imaging data. The core of our pipeline is a deep convolutional neural network, which was pretrained on a general-purpose image library, and then optimized on the spine detection task. This transfer learning approach is data efficient while achieving a high detection precision. To train and validate the model we generated a labelled dataset using five human expert annotators to account for the variability in human spine detection. The pipeline enables fully automated dendritic spine detection and reaches a near human-level detection performance. Our method for spine detection is fast, accurate and robust, and thus well suited for large-scale datasets with thousands of spines. The code is easily applicable to new datasets, achieving high detection performance, even without any retraining or adjustment of model parameters.