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The e+e−→D+sDs1(2536)− and e+e−→D+sD∗s2(2573)− processes are studied using data samples collected with the BESIII detector at center-of-mass energies from 4.530 to 4.946~GeV. The absolute branching fractions of Ds1(2536)−→D¯∗0K− and D∗s2(2573)−→D¯0K− are measured for the first time to be (35.9±4.8±3.5)% and (37.4±3.1±4.6)%, respectively. The measurements are in tension with predictions based on the assumption that the Ds1(2536) and D∗s2(2573) are dominated by a bare cs¯ component. The e+e−→D+sDs1(2536)− and e+e−→D+sD∗s2(2573)− cross sections are measured, and a resonant structure at around 4.6~GeV with a width of 50~MeV is observed for the first time with a statistical significance of 15σ in the e+e−→D+sD∗s2(2573)− process. It could be the Y(4626) found by the Belle collaboration in the D+sDs1(2536)− final state, since they have similar masses and widths. There is also evidence for a structure at around 4.75~GeV in both processes.
Using a sample of 448.1×106 ψ(2S) events collected with the BESIII detector, we perform a study of the decay J/ψ→K+K− via ψ(2S)→π+π−J/ψ.
The branching fraction of J/ψ→K+K− is determined to be BK+K−=(3.072±0.023(stat.)±0.050(syst.))×10−4, which is consistent with previous measurements but with significantly improved precision.
Muller's ratchet, in its prototype version, models a haploid, asexual population whose size~N is constant over the generations. Slightly deleterious mutations are acquired along the lineages at a constant rate, and individuals carrying less mutations have a selective advantage. The classical variant considers {\it fitness proportional} selection, but other fitness schemes are conceivable as well. Inspired by the work of Etheridge et al. ([EPW09]) we propose a parameter scaling which fits well to the ``near-critical'' regime that was in the focus of [EPW09] (and in which the mutation-selection ratio diverges logarithmically as N→∞). Using a Moran model, we investigate the``rule of thumb'' given in [EPW09] for the click rate of the ``classical ratchet'' by putting it into the context of new results on the long-time evolution of the size of the best class of the ratchet with (binary) tournament selection, which (other than that of the classical ratchet) follows an autonomous dynamics up to the time of its extinction. In [GSW23] it was discovered that the tournament ratchet has a hierarchy of dual processes which can be constructed on top of an Ancestral Selection graph with a Poisson decoration. For a regime in which the mutation/selection-ratio remains bounded away from 1, this was used in [GSW23] to reveal the asymptotics of the click rates as well as that of the type frequency profile between clicks. We will describe how these ideas can be extended to the near-critical regime in which the mutation-selection ratio of the tournament ratchet converges to 1 as N→∞.
Experimental data from the NA49 collaboration show an unexpectedly steep rise of the rapidity width of the ϕ meson as function of beam energy, which was suggested as possible interesting signal for novel physics. In this work we show that the Ultra-relativistic Quantum-Molecular-Dynamics (UrQMD) model is able to reproduce the shapes of the rapidity distributions of most measured hadrons and predicts a common linear increase of the width for all hadrons. Only when following the exact same analysis technique and experimental acceptance of the NA49 and NA61/SHINE collaborations, we find that the extracted value of the rapidity width of the ϕ increases drastically for the highest beam energy. We conclude that the observed steep increase of the ϕ rapidity width is a problem of limited detector acceptance and the simplified Gaussian fit approximation.
Magnetoencephalography (MEG) and Electroencephalography (EEG) provide direct electrophysiological measures at an excellent temporal resolution, but the spatial resolution of source-reconstructed current activity is limited to several millimetres. Here we show, using simulations of MEG signals and Bayesian model comparison, that non-invasive myelin estimates from high-resolution quantitative magnetic resonance imaging (MRI) can enhance MEG/EEG source reconstruction. Our approach assumes that MEG/EEG signals primarily arise from the synchronised activity of pyramidal cells, and since most of the myelin in the cortical sheet originates from these cells, myelin density can predict the strength of cortical sources measured by MEG/EEG. Leveraging recent advances in quantitative MRI, we exploit this structure-function relationship and scale the leadfields of the forward model according to the local myelin density estimates from in vivo quantitative MRI to inform MEG/EEG source reconstruction. Using Bayesian model comparison and dipole localisation errors (DLEs), we demonstrate that adapting local forward fields to reflect increased local myelination at the site of a simulated source explains the simulated data better than models without such leadfield scaling. Our model comparison framework proves sensitive to myelin changes in simulations with exact coregistration and moderate-to-high sensor-level signal-to-noise ratios (≥10 dB) for the multiple sparse priors (MSP) and empirical Bayesian beamformer (EBB) approaches. Furthermore, we sought to infer the microstructure giving rise to specific functional activation patterns by comparing the myelin-informed model which was used to generate the activation with a set of test forward models incorporating different myelination patterns. We found that the direction of myelin changes, however not their magnitude, can be inferred by Bayesian model comparison. Finally, we apply myelin-informed forward models to MEG data from a visuo-motor experiment. We demonstrate improved source reconstruction accuracy using myelin estimates from a quantitative longitudinal relaxation (R1) map and discuss the limitations of our approach.
Highlights
We use quantitative MRI to implement myelin-informed forward models for M/EEG
Local myelin density was modelled by adapting the local leadfields
Myelin-informed forward models can improve source reconstruction accuracy
We can infer the directionality of myelination patterns, but not their strength
We apply our approach to MEG data from a visuo-motor experiment
Motivated by the on-going discussion on the nature of magnetism in the quantum Ising chain CoNb2O6, we present a first-principles-based analysis of its exchange interactions by applying an \textit{ab initio} approach with additional modelling that accounts for various drawbacks of a purely density functional theory ansatz. With this method we are able to extract and understand the origin of the magnetic couplings under inclusion of all symmetry-allowed terms, and to resolve the conflicting model descriptions in CoNb2O6. We find that the twisted Kitaev chain and the transverse-field ferromagnetic Ising chain views are mutually compatible, although additional off-diagonal exchanges are necessary to provide a complete picture. We show that the dominant exchange interaction is a ligand-centered exchange process - involving the eg electrons -, which is rendered anisotropic by the low-symmetry crystal fields environments in CoNb2O6, giving rise to the dominant Ising exchange, while the smaller bond-dependent anisotropies are found to originate from d−d kinetic exchange processes involving the t2g electrons. We demonstrate the validity of our approach by comparing the predictions of the obtained low-energy model to measured THz and inelastic neutron scattering spectra.
The family of cubic noncentrosymmetric 3-4-3 compounds has become a fertile ground for the discovery of novel correlated metallic and insulating phases. Here, we report the synthesis of a new heavy fermion compound, Ce3Bi4Ni3. It is an isoelectronic analog of the prototypical Kondo insulator Ce3Bi4Pt3 and of the recently discovered Weyl-Kondo semimetal Ce3Bi4Pd3. In contrast to the volume-preserving Pt-Pd substitution, structural and chemical analyses reveal a positive chemical pressure effect in Ce3Bi4Ni3 relative to its heavier counterparts. Based on the results of electrical resistivity, Hall effect, magnetic susceptibility, and specific heat measurements, we identify an energy gap of 65-70 meV, about eight times larger than that in Ce3Bi4Pt3 and about 45 times larger than that of the Kondo-insulating background hosting the Weyl nodes in Ce3Bi4Pd3. We show that this gap as well as other physical properties do not evolve monotonically with increasing atomic number, i.e., in the sequence Ce3Bi4Ni3-Ce3Bi4Pd3-Ce3Bi4Pt3, but instead with increasing partial electronic density of states of the d orbitals at the Fermi energy. To understand under which condition topological states form in these materials is a topic for future studies.
Heterostructures of graphene in proximity to magnetic insulators open the possibility to investigate exotic states emerging from the interplay of magnetism, strain and charge transfer between the layers. Recent reports on the growth of self-integrated atomic wires of β-RuCl3 on graphite suggest these materials as versatile candidates to investigate these effects. Here we present detailed first principles calculations on the charge transfer and electronic structure of β-RuCl3/heterostructures and provide a comparison with the work function analysis of the related honeycomb family members α-RuX3 (X = Cl,Br,I). We find that proximity of the two layers leads to a hole-doped graphene and electron-doped RuX3 in all cases, which is sensitively dependent on the distance between the two layers. Furthermore, strain effects due to lattice mismatch control the magnetization which itself has a strong effect on the charge transfer. Charge accumulation in β-RuCl3 strongly drops away from the chain making such heterostructures suitable candidates for sharp interfacial junctions in graphene-based devices.
canning tunneling microscopy (STM) is perhaps the most promising way to detect the superconducting gap size and structure in the canonical unconventional superconductor Sr2RuO4 directly. However, in many cases, researchers have reported being unable to detect the gap at all in simple STM conductance measurements. Recently, an investigation of this issue on various local topographic structures on a Sr-terminated surface found that superconducting spectra appeared only in the region of small nanoscale canyons, corresponding to the removal of one RuO surface layer. Here, we analyze the electronic structure of various possible surface structures using first principles methods, and argue that bulk conditions favorable for superconductivity can be achieved when removal of the RuO layer suppresses the RuO4 octahedral rotation locally. We further propose alternative terminations to the most frequently reported Sr termination where superconductivity surfaces should be observed.
On the potential for GWAS with phenotypic population means and allele-frequency data (popGWAS)
(2024)
This study explores the potential of a novel genome-wide association study (GWAS) approach for identifying loci underlying quantitative polygenic traits in natural populations. Extensive population genetic forward simulations demonstrate that the approach is generally effective for oligogenic and moderately polygenic traits and relatively insensitive to low heritability, but applicability is limited for highly polygenic architectures and pronounced population structure. The required sample size is moderate with very good results being obtained already for a few dozen populations scored. The method performs well in predicting population means even with a moderate false positive rate. When combined with machine learning for feature selection, this rate can be further reduced. The data efficiency of the method, particularly when using pooled sequencing, makes GWAS studies more accessible for research in biodiversity genomics. Overall, this study highlights the promise of this popGWAS approach for dissecting the genetic basis of complex traits in natural populations.
Abstract
Seed harvesting from wild plant populations is key for ecological restoration, but may threaten the persistence of source populations. Consequently, several countries have set guidelines limiting the proportions of harvestable seeds. However, these guidelines are so far inconsistent, and they lack a solid empirical basis. Here, we use high-resolution data from 298 plant species to model the demographic consequences of seed harvesting. We find that the current guidelines do not protect populations of annuals and short-lived perennials, while they are overly restrictive for long-lived plants. We show that the maximum possible fraction of seed production – what can be harvested without compromising the long-term persistence of populations – is strongly related to the generation time of the target species. When harvesting every year, this safe seed fraction ranges from 80% in long-lived species to 2% in most annuals. Less frequent seed harvesting substantially increases the safe seed fraction: In the most vulnerable annual species, it is safe to harvest 5%, 10% or 30% of population seed production when harvesting every two, five or ten years, respectively. Our results provide a quantitative basis for seed harvesting legislations worldwide, based on species’ generation time and harvesting regime.
Significance The UN Decade on Ecosystem Restoration, 2021-2030, foresees upscaling restoration, and the demand for native seed is skyrocketing. Seeds for restoring native vegetation are often harvested in wild, but too intensive harvest can threaten the donor populations. Existing guidelines that set limits to wild seed harvest are mostly based on expert opinions, yet they commonly lack empirical basis and vary among regions in one order of magnitude. We show that the current guidelines urgently need to be reformulated, because they are overly restrictive in long-lived species, while they do not protect annual plants from extinction. Using matrix population models of nearly 300 plant species, we provide a quantitative basis for a new seed harvesting legislation world-wide.
Dynamic imaging of landmark organelles, such as nuclei, cell membrane, nuclear envelope, and lipid droplets enables image-based phenotyping of functional states of cells. Multispectral fluorescent imaging of landmark organelles requires labor-intensive labeling, limits throughput, and compromises cell health. Virtual staining of label-free images with deep neural networks is an emerging solution for this problem. Multiplexed imaging of cellular landmarks from scattered light and subsequent demultiplexing with virtual staining saves the light spectrum for imaging additional molecular reporters, photomanipulation, or other tasks. Published approaches for virtual staining of landmark organelles are fragile in the presence of nuisance variations in imaging, culture conditions, and cell types. This paper reports model training protocols for virtual staining of nuclei and membranes robust to label-free imaging parameters, cell states, and cell types. We developed a flexible and scalable convolutional architecture, named UNeXt2, for supervised training and self-supervised pre-training. The strategies we report here enable robust virtual staining of nuclei and cell membranes in multiple cell types, including neuromasts of zebrafish, across a range of imaging conditions. We assess the models by comparing the intensity, segmentations, and application-specific measurements obtained from virtually stained and experimentally stained nuclei and membranes. The models rescue the missing label, non-uniform expression of labels, and photobleaching. We share three pre-trained models, named VSCyto3D, VSCyto2D, and VSNeuromast, as well as VisCy, a PyTorch-based pipeline for training, inference, and deployment that leverages the modern OME-Zarr format.
Motivated by recently reported magnetic-field induced topological phases in ultracold atoms and correlated Moiré materials, we investigate topological phase transitions in a minimal model consisting of interacting spinless fermions described by the Hofstadter model on a square lattice. For interacting lattice Hamiltonians in the presence of a commensurate magnetic flux it has been demonstrated that the quantized Hall conductivity is constrained by a Lieb-Schultz-Mattis (LSM)-type theorem due to magnetic translation symmetry. In this work, we revisit the validity of the theorem for such models and establish that a topological phase transition from a topological to a trivial insulating phase can be realized but must be accompanied by spontaneous magnetic translation symmetry breaking caused by charge ordering of the spinless fermions. To support our findings, the topological phase diagram for varying interaction strength is mapped out numerically with exact diagonalization for different flux quantum ratios and band fillings using symmetry indicators. We discuss our results in the context of the LSM-type theorem.
Cyclin CLB2 mRNA localization and protein synthesis link cell cycle progression to bud growth
(2024)
Clb2 is a conserved mitotic B-type cyclin, the levels of which are finely controlled to drive progression through the cell cycle. While it is known that CLB2 transcription and Clb2 protein degradation are important for precise control of its expression, it remains unclear whether the synthesis of Clb2 is also regulated. To address whether and how Clb2 expression levels respond to cell growth changes and adapt cell cycle progression, we combined single-cell and single-molecule imaging methods to measure CLB2 mRNA and protein expression throughout the Saccharomyces cerevisiae cell cycle. We found that the CLB2 mRNA was efficiently localized to the yeast bud as soon as this compartment was formed, but strikingly the Clb2 protein accumulated in the mother nucleus. The CLB2 mRNA localization in the yeast bud by the She2-3 complex did not control protein localization but rather promoted CLB2 translation. Moreover, CLB2 mRNA bud localization and protein synthesis were coupled and dependent on a single secondary structure -a ZIP code-located in the coding sequence. In a CLB2 ZIP code mutant, mRNA localization was impaired and Clb2 protein synthesis decreased, resulting in changes in cell cycle distribution and increased size of daughter cells at birth. Finally, while in WT cells the Clb2 protein concentration followed bud growth, this relationship was impaired in the ZIP code mutant. We propose that S. cerevisiae couples the control of CLB2 mRNA bud localization and protein synthesis to coordinate cell growth and cell cycle progression. This mechanism extends our knowledge of CLB2 expression regulation, and constitutes a novel function for mRNA localization.
Memory consolidation tends to be less robust in childhood than adulthood. However, little is known about the corresponding functional differences in the developing brain that may underlie age-related differences in retention of memories over time. This study examined system-level memory consolidation of object-scene associations after learning (immediate delay), one night of sleep (short delay), as well as two weeks (long delay) in 5-to-7-year-old children (n = 49) and in young adults (n = 39), as a reference group with mature consolidation systems. Particularly, we characterized how functional neural activation and reinstatement of neural patterns change over time, assessed by functional magnetic resonance imaging combined with representational similarity analysis (RSA). Our results showed that memory consolidation in children was less robust and strong (i.e., more forgetting) compared to young adults. Contrasting correctly retained remote versus recent memories across time delay, children showed less upregulation in posterior parahippocampal gyrus, lateral occipital cortex, and cerebellum than adults. In addition, both children and adults showed decrease in scene-specific neural reinstatement over time, indicating time-related decay of detailed differentiated memories. At the same time, we observed more generic gist-like neural reinstatement in medial-temporal and prefrontal brain regions uniquely in children, indicating qualitative difference in memory trace in children. Taken together, 5-to-7-year-old children, compared to young adults, show less robust memory consolidation, possibly due to difficulties in engaging in differentiated neural reinstatement in neocortical mnemonic regions during retrieval of remote memories, coupled with relying more on gist-like generic neural reinstatement.
The production of K∗(892)± meson resonance is measured at midrapidity (|y|<0.5) in Pb-Pb collisions at sNN−−−√=5.02 TeV using the ALICE detector at the LHC. The resonance is reconstructed via its hadronic decay channel K∗(892)±→K0Sπ±. The transverse momentum distributions are obtained for various centrality intervals in the pT range of 0.4-16 GeV/c. The reported measurements of integrated yields, mean transverse momenta, and particle yield ratios are consistent with previous ALICE measurements for K∗(892)0. The pT-integrated yield ratio 2K∗(892)±/(K++K−) in central Pb-Pb collisions shows a significant suppression (9.3σ) relative to pp collisions. Thermal model calculations overpredict the particle yield ratio. Although both simulations consider the hadronic phase, only HRG-PCE accurately represents the measurements, whereas MUSIC+SMASH tends to overpredict them. These observations, along with the kinetic freeze-out temperatures extracted from the yields of light-flavored hadrons using the HRG-PCE model, indicate a finite hadronic phase lifetime, which increases towards central collisions. The pT-differential yield ratios 2K∗(892)±/(K++K−) and 2K∗(892)±/(π++π−) are suppressed by up to a factor of five at pT<2 GeV/c in central Pb-Pb collisions compared to pp collisions at s√= 5.02 TeV. Both particle ratios and are qualitatively consistent with expectations for rescattering effects in the hadronic phase. The nuclear modification factor shows a smooth evolution with centrality and is below unity at pT>8 GeV/c, consistent with measurements for other light-flavored hadrons. The smallest values are observed in most central collisions, indicating larger energy loss of partons traversing the dense medium.
Tree-related microhabitats (TReMs) have been proposed as important indicators of biodiversity to guide forest management. However, their application has been limited mostly to temperate ecosystems, and it is largely unknown how the diversity of TReMs varies along environmental gradients. In this study, we assessed the diversity of TReMs on 180 individual trees and 46 plots alongside a large environmental gradient on Kilimanjaro, Tanzania. We used a typology adjusted to tropical ecosystems and a tree-climbing protocol to obtain quantitative information on TreMs on large trees and dense canopies. We computed the diversity of TReMs for each individual tree and plot and tested how TReM diversity was associated with properties of individual trees and environmental conditions in terms of climate and human impact. We further used non-metric multidimensional scaling (NMDS) to investigate the composition of TReM assemblages alongside the environmental gradients. We found that diameter at breast height (DBH) and height of the first branch were the most important determinants of TReM diversity on individual trees, with higher DBH and lower first branch height promoting TReM diversity. At the plot level, we found that TReM diversity increased with mean annual temperature and decreased with human impact. The composition of TReMs showed high turnover across ecosystem types, with a stark difference between forest and non-forest ecosystems. Climate and the intensity of human impact were associated with TReM composition. Our study is a first test of how TReM diversity and composition vary along environmental gradients in tropical ecosystems. The importance of tree size and architecture in fostering microhabitat diversity underlines the importance of large veteran trees in tropical ecosystems. Because diversity and composition of TReMs are sensitive to climate and land-use effects, our study suggests that TReMs can be used to efficiently monitor consequences of global change for tropical biodiversity.
Tree-related microhabitats (TReMs) have been proposed as important indicators of biodiversity to guide forest management. However, their application has been limited mostly to temperate ecosystems, and it is largely unknown how the diversity of TReMs varies along environmental gradients. In this study, we assessed the diversity of TReMs on 180 individual trees and 44 plots alongside a large environmental gradient on Kilimanjaro, Tanzania. We used a typology adjusted to tropical ecosystems and a tree-climbing protocol to obtain quantitative information on TreMs on large trees and dense canopies. We computed the diversity of TReMs for each individual tree and plot and tested how TReM diversity was associated with properties of individual trees and environmental conditions in terms of climate and human impact. We further used non-metric multidimensional scaling (NMDS) to investigate the composition of TReM assemblages alongside the environmental gradients. We found that diameter at breast height (DBH) and height of the first branch were the most important determinants of TReM diversity on individual trees, with higher DBH and lower first branch height promoting TReM diversity. At the plot level, we found that TReM diversity increased with mean annual temperature and decreased with human impact. The composition of TReMs showed high turnover across ecosystem types, with a stark difference between forest and non-forest ecosystems. Climate and the intensity of human impact were associated with TReM composition. Our study is a first test of how TReM diversity and composition vary along environmental gradients in tropical ecosystems. The importance of tree size and architecture in fostering microhabitat diversity underlines the importance of large veteran trees in tropical ecosystems. Because diversity and composition of TReMs are sensitive to climate and land-use effects, our study suggests that TReMs can be used to efficiently monitor consequences of global change for tropical biodiversity.
Dynamic imaging of landmark organelles, such as nuclei, cell membrane, nuclear envelope, and lipid droplets enables image-based phenotyping of functional states of cells. Multispectral fluorescent imaging of landmark organelles requires labor-intensive labeling, limits throughput, and compromises cell health. Virtual staining of label-free images with deep neural networks is an emerging solution for this problem. Multiplexed imaging of cellular landmarks from scattered light and subsequent demultiplexing with virtual staining saves the light spectrum for imaging additional molecular reporters, photomanipulation, or other tasks. Published approaches for virtual staining of landmark organelles are fragile in the presence of nuisance variations in imaging, culture conditions, and cell types. This paper reports model training protocols for virtual staining of nuclei and membranes robust to cell types, cell states, and imaging parameters. We developed a flexible and scalable convolutional architecture, named UNeXt2, for supervised training and self-supervised pre-training. The strategies we report here enable robust virtual staining of nuclei and cell membranes in multiple cell types, including neuromasts of zebrafish, across a range of imaging conditions. We assess the models by comparing the intensity, segmentations, and application-specific measurements obtained from virtually stained and experimentally stained nuclei and membranes. The models rescue the missing label, non-uniform expression of labels, and photobleaching. We share three pre-trained models, named VSCyto3D, VSCyto2D, and VSNeuromast, as well as VisCy, a PyTorch-based pipeline for training, inference, and deployment that leverages the modern OME-Zarr format.