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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.
From early to middle childhood, brain regions that underlie memory consolidation undergo profound maturational changes. However, there is little empirical investigation that directly relates age-related differences in brain structural measures to the memory consolidation processes. The present study examined system-level memory consolidations of intentionally studied object-location associations after one night of sleep (short delay) and after two weeks (long delay) in normally developing 5-to-7-year-old children (n = 50) and young adults (n = 39). Behavioural differences in memory consolidation were related to structural brain measures. Our results showed that children, in comparison to young adults, consolidate correctly learnt object-location associations less robustly over short and long delay. Moreover, using partial least squares correlation method, a unique multivariate profile comprised of specific neocortical (prefrontal, parietal, and occipital), cerebellar, and hippocampal subfield structures was found to be associated with variation in short-delay memory consolidation. A different multivariate profile comprised of a reduced set of brain structures, mainly consisting of neocortical (prefrontal, parietal, and occipital), and selective hippocampal subfield structures (CA1-2 and subiculum) was associated with variation in long-delay memory consolidation. Taken together, the results suggest that multivariate structural pattern of unique sets of brain regions are related to variations in short- and long-delay memory consolidation across children and young adults.
RESEARCH HIGHLIGHTS
* Short- and long-delay memory consolidation is less robust in children than in young adults
* Short-delay brain profile comprised of hippocampal, cerebellar, and neocortical brain regions
* Long-delay brain profile comprised of neocortical and selected hippocampal brain regions.
* Brain profiles differ between children and young adults.
From early to middle childhood, brain regions that underlie memory consolidation undergo profound maturational changes. However, there is little empirical investigation that directly relates age-related differences in brain structural measures to the memory consolidation processes. The present study examined system-level memory consolidations of intentionally studied object-location associations after one night of sleep (short delay) and after two weeks (long delay) in normally developing 5-to-7-year-old children (n = 50) and young adults (n = 39). Behavioural differences in memory consolidation were related to structural brain measures. Our results showed that children, in comparison to young adults, consolidate correctly learnt object-location associations less robustly over short and long delay. Moreover, using partial least squares correlation method, a unique multivariate profile comprised of specific neocortical (prefrontal, parietal, and occipital), cerebellar, and hippocampal subfield structures was found to be associated with variation in short-delay memory consolidation. A different multivariate profile comprised of a reduced set of brain structures, mainly consisting of neocortical (prefrontal, parietal, and occipital), and selective hippocampal subfield structures (CA1-2 and subiculum) was associated with variation in long-delay memory consolidation. Taken together, the results suggest that multivariate structural pattern of unique sets of brain regions are related to variations in short- and long-delay memory consolidation across children and young adults.
RESEARCH HIGHLIGHTS
Short- and long-delay memory consolidation is less robust in children than in young adults
* Short-delay brain profile comprised of hippocampal, cerebellar, and neocortical brain regions
* Long-delay brain profile comprised of neocortical and selected hippocampal brain regions.
* Brain profiles differ between children and young adults.
Selectivity remains a challenge for ATP-mimetic kinase inhibitors, an issue that may be overcome by targeting unique residues or binding pockets. However, to date only few strategies have been developed. Here we identify that bulky residues located N-terminal to the DFG motif (DFG-1) represent an opportunity for designing highly selective inhibitors with unexpected binding modes. We demonstrate that several diverse inhibitors exerted selective, non-canonical binding modes that exclusively target large hydrophobic DFG-1 residues present in many kinases including PIM, CK1, DAPK and CLK. Using the CLK family as a model, structural and biochemical data revealed that the DFG-1 valine controlled a non-canonical binding mode in CLK1, providing a rational for selectivity over the closely-related CLK3 which harbors a smaller DFG-1 alanine. Our data suggests that targeting the restricted back pocket in the small fraction of kinases that harbor bulky DFG-1 residues offers a versatile selectivity filter for inhibitor design.
MKK7 (MEK7) is a key regulator of the JNK stress signaling pathway and targeting MKK7 has been proposed as a chemotherapeutic strategy. Detailed understanding of the MKK7 structure and factors that impact its activity is therefore of critical importance. Here, we present a comprehensive set of MKK7 crystal structures revealing insights into catalytic domain plasticity and the role of the N-terminal regulatory helix, conserved in all MAP2Ks, mediating kinase activation. Crystal structures harboring this regulatory helix revealed typical structural features of active kinase, providing exclusively a first model of the MAP2K active state. A small molecule screening campaign yielded multiple scaffolds, including type-II irreversible inhibitors a binding mode that has not been reported previously. We also observed an unprecedented allosteric pocket located in the N-terminal lobe for the approved drug ibrutinib. Collectively, our structural and functional data expand and provide alternative targeting strategies for this important MAP2K kinase.
Recurrent cortical network dynamics plays a crucial role for sequential information processing in the brain. While the theoretical framework of reservoir computing provides a conceptual basis for the understanding of recurrent neural computation, it often requires manual adjustments of global network parameters, in particular of the spectral radius of the recurrent synaptic weight matrix. Being a mathematical and relatively complex quantity, the spectral radius is not readily accessible to biological neural networks, which generally adhere to the principle that information about the network state should either be encoded in local intrinsic dynamical quantities (e.g. membrane potentials), or transmitted via synaptic connectivity. We present two synaptic scaling rules for echo state networks that solely rely on locally accessible variables. Both rules work online, in the presence of a continuous stream of input signals. The first rule, termed flow control, is based on a local comparison between the mean squared recurrent membrane potential and the mean squared activity of the neuron itself. It is derived from a global scaling condition on the dynamic flow of neural activities and requires the separability of external and recurrent input currents. We gained further insight into the adaptation dynamics of flow control by using a mean field approximation on the variances of neural activities that allowed us to describe the interplay between network activity and adaptation as a two-dimensional dynamical system. The second rule that we considered, variance control, directly regulates the variance of neural activities by locally scaling the recurrent synaptic weights. The target set point of this homeostatic mechanism is dynamically determined as a function of the variance of the locally measured external input. This functional relation was derived from the same mean-field approach that was used to describe the approximate dynamics of flow control.
The effectiveness of the presented mechanisms was tested numerically using different external input protocols. The network performance after adaptation was evaluated by training the network to perform a time delayed XOR operation on binary sequences. As our main result, we found that flow control can reliably regulate the spectral radius under different input statistics, but precise tuning is negatively affected by interneural correlations. Furthermore, flow control showed a consistent task performance over a wide range of input strengths/variances. Variance control, on the other side, did not yield the desired spectral radii with the same precision. Moreover, task performance was less consistent across different input strengths.
Given the better performance and simpler mathematical form of flow control, we concluded that a local control of the spectral radius via an implicit adaptation scheme is a realistic alternative to approaches using classical “set point” homeostatic feedback controls of neural firing.
Author summary How can a neural network control its recurrent synaptic strengths such that network dynamics are optimal for sequential information processing? An important quantity in this respect, the spectral radius of the recurrent synaptic weight matrix, is a non-local quantity. Therefore, a direct calculation of the spectral radius is not feasible for biological networks. However, we show that there exist a local and biologically plausible adaptation mechanism, flow control, which allows to control the recurrent weight spectral radius while the network is operating under the influence of external inputs. Flow control is based on a theorem of random matrix theory, which is applicable if inter-synaptic correlations are weak. We apply the new adaption rule to echo-state networks having the task to perform a time-delayed XOR operation on random binary input sequences. We find that flow-controlled networks can adapt to a wide range of input strengths while retaining essentially constant task performance.
Recurrent cortical network dynamics plays a crucial role for sequential information processing in the brain. While the theoretical framework of reservoir computing provides a conceptual basis for the understanding of recurrent neural computation, it often requires manual adjustments of global network parameters, in particular of the spectral radius of the recurrent synaptic weight matrix. Being a mathematical and relatively complex quantity, the spectral radius is not readily accessible to biological neural networks, which generally adhere to the principle that information about the network state should either be encoded in local intrinsic dynamical quantities (e.g. membrane potentials), or transmitted via synaptic connectivity. We present two synaptic scaling rules for echo state networks that solely rely on locally accessible variables. Both rules work online, in the presence of a continuous stream of input signals. The first rule, termed flow control, is based on a local comparison between the mean squared recurrent membrane potential and the mean squared activity of the neuron itself. It is derived from a global scaling condition on the dynamic flow of neural activities and requires the separability of external and recurrent input currents. We gained further insight into the adaptation dynamics of flow control by using a mean field approximation on the variances of neural activities that allowed us to describe the interplay between network activity and adaptation as a two-dimensional dynamical system. The second rule that we considered, variance control, directly regulates the variance of neural activities by locally scaling the recurrent synaptic weights. The target set point of this homeostatic mechanism is dynamically determined as a function of the variance of the locally measured external input. This functional relation was derived from the same mean-field approach that was used to describe the approximate dynamics of flow control.
The effectiveness of the presented mechanisms was tested numerically using different external input protocols. The network performance after adaptation was evaluated by training the network to perform a time delayed XOR operation on binary sequences. As our main result, we found that flow control can reliably regulate the spectral radius under different input statistics, but precise tuning is negatively affected by interneural correlations. Furthermore, flow control showed a consistent task performance over a wide range of input strengths/variances. Variance control, on the other side, did not yield the desired spectral radii with the same precision. Moreover, task performance was less consistent across different input strengths.
Given the better performance and simpler mathematical form of flow control, we concluded that a local control of the spectral radius via an implicit adaptation scheme is a realistic alternative to approaches using classical “set point” homeostatic feedback controls of neural firing.
Author summary How can a neural network control its recurrent synaptic strengths such that network dynamics are optimal for sequential information processing? An important quantity in this respect, the spectral radius of the recurrent synaptic weight matrix, is a non-local quantity. Therefore, a direct calculation of the spectral radius is not feasible for biological networks. However, we show that there exist a local and biologically plausible adaptation mechanism, flow control, which allows to control the recurrent weight spectral radius while the network is operating under the influence of external inputs. Flow control is based on a theorem of random matrix theory, which is applicable if inter-synaptic correlations are weak. We apply the new adaption rule to echo-state networks having the task to perform a time-delayed XOR operation on random binary input sequences. We find that flow-controlled networks can adapt to a wide range of input strengths while retaining essentially constant task performance.
The SARS-CoV-2 pandemic has challenged researchers at a global scale. The scientific community’s massive response has resulted in a flood of experiments, analyses, hypotheses, and publications, especially in the field of drug repurposing. However, many of the proposed therapeutic compounds obtained from SARS-CoV-2 specific assays are not in agreement and thus demonstrate the need for a singular source of COVID-19 related information from which a rational selection of drug repurposing candidates can be made. In this paper, we present the COVID-19 PHARMACOME, a comprehensive drug-target-mechanism graph generated from a compilation of 10 separate disease maps and sources of experimental data focused on SARS-CoV-2 / COVID-19 pathophysiology. By applying our systematic approach, we were able to predict the synergistic effect of specific drug pairs, such as Remdesivir and Thioguanosine or Nelfinavir and Raloxifene, on SARS-CoV-2 infection. Experimental validation of our results demonstrate that our graph can be used to not only explore the involved mechanistic pathways, but also to identify novel combinations of drug repurposing candidates.
The multistep PROTAC (PROteolysis TArgeting Chimeras) degradation process poses challenges for their rational development, as rate limiting steps determining PROTAC efficiency remain largely unknown. Moreover, the slow throughput of currently used endpoint assays does not allow the comprehensive analysis of larger series of PROTACs. Here we developed cell-based assays using NanoLuciferase and HaloTags, that allow measuring PROTAC induced degradation and ternary complex formation kinetics and stability in cells. Using PROTACs developed for degradation of WDR5, the characterization of the mode of action of these PROTACs in the early degradation cascade revealed a key role of ternary complex formation and stability. Comparing a series of ternary complex crystal structures highlighted the importance of an efficient E3-target interface for ternary complex stability. The developed assays outline a strategy for the rational optimization of PROTACs using a series of live cell assays monitoring key steps of the early PROTAC induced degradation pathway.
Significance The multistep PROTAC induced degradation process of a POI poses a significant challenge for the rational design of these bifunctional small molecules as critical steps that limit PROTAC efficacy cannot be easily assayed at required throughput. In addition, the cellular location of the POI may pose additional challenges as some cellular compartments, such as the nucleus, may not be easily reached by PROTAC molecules and the targeted E3 ligases may not be present in this cellular compartment. We propose therefore a comprehensive assay panel for PROTACs evaluation in cellular environments using a sensor system that allows continuous monitoring of the protein levels of the endogenous POI. We developed a cell line expressing WDR5 from its endogenous locus in fusion with a small sequence tag (HiBIT) that can be reconstituted to functional NanoLuciferase (NLuc). This system allowed continuous monitoring of endogenous WDR5 levels in cells and together with HaloTag system also the continuous monitoring of ternary complex (E3, WDR5 and PROTAC) formation. As this assay can be run at high throughput, we used this versatile system monitoring three diverse chemical series of WDR5 PROTACs that markedly differ in their degradation properties. Monitoring cell penetration, binary complex formation (PROTAC-WDR5 and PROTAC-VHL) as well as ternary complex formation we found that PROTAC efficiency highly correlated with synergy of ternary complex formation in cells. This study represents a first data set on diverse PROTACs studying this property in cellulo and it outlines a strategy for the rational optimization of PROTACs. It also provided kinetic data on ternary complex assembly and dissociation that may serve as a benchmark for future studies utilizing also kinetic properties for PROTAC development. Comparative structural studies revealed larger PROTAC mediated interaction surfaces for PROTACs that efficiently formed ternary complexes highlighting the utility of structure based optimization of PROTAC induced ternary complexes in the development process.
Hydrogen is a promising fuel in a carbon-neutral economy, and many efforts are currently undertaken to produce hydrogen. One of the challenges is to store and transport the highly explosive gas in a safe and easy way. One option that is intensively analyzed by chemists and biologists is the conversion of hydrogen and CO2 to formic acid, the liquid organic hydrogen carrier. Here, we demonstrate for the first time that a bio-based system, using Acetobacterium woodii as the biocatalyst, allows multiple cycles of bi-directional hydrogenation of CO2 to formic acid in one bioreactor. The process was kept running over 2 weeks producing and oxidizing 330 mM formic acid in total. Unwanted side-product formation of acetic acid was prevented through metabolic engineering of the organism. The demonstrated process design can be considered as a future “bio-battery” for the reversible storage of electrons in the form of H2 in formic acid, a versatile compound.
We propose to measure azimuthal correlations of heavy-flavor hadrons to address the status of thermalization at the partonic stage of light quarks and gluons in high-energy nuclear collisions. In particular, we show that hadronic interactions at the late stage cannot significantly disturb the initial back-to-back azimuthal correlations of DDbar pairs. Thus, a decrease or the complete absence of these initial correlations does indicate frequent interactions of heavy-flavor quarks and also light partons in the partonic stage, which are essential for the early thermalization of light partons.
In this special issue of Matrix Biology centered on proteoglycan biology we have assembled a blend of articles focused on the state-of-the-art of proteoglycanology. The field has greatly expanded in the past three decades and now encompasses all the areas of biology. This special issue is divided into five chapters describing hyaluronan metabolism, biosynthetic and catabolic pathways of proteoglycans and their roles in inflammation, cancer, repair and development. We hope that the new original work and the reviews from recognized leaders will stimulate investigations in this exciting and fertile field of research.
Gasdermin-D (GSDMD) is the ultimate effector of pyroptosis, a form of programmed cell death associated with pathogen invasion and inflammation. After proteolytic cleavage by caspases activated by the inflammasome, the GSDMD N-terminal domain (GSDMDNT) assembles on the inner leaflet of the plasma membrane and induces the formation of large membrane pores. We use atomistic molecular dynamics simulations to study GSDMDNT monomers, oligomers, and rings in an asymmetric plasma membrane mimetic. We identify distinct interaction motifs of GSDMDNT with phosphatidylinositol-4,5-bisphosphate (PI(4,5)P2) and phosphatidylserine (PS) head-groups and describe differential lipid binding between the pore and prepore conformations. Oligomers are stabilized by shared lipid binding sites between neighboring monomers acting akin to double-sided tape. We show that already small GSDMDNT oligomers form stable, water-filled and ion-conducting membrane pores bounded by curled beta-sheets. In large-scale simulations, we resolve the process of pore formation by lipid detachment from GSDMDNT arcs and lipid efflux from partial rings. We find that that high-order GSDMDNT oligomers can crack under the line tension of 86 pN created by an open membrane edge to form the slit pores or closed GSDMDNT rings seen in experiment. Our simulations provide a detailed view of key steps in GSDMDNT-induced plasma membrane pore formation, including sublytic pores that explain nonselective ion flux during early pyroptosis.
During infection the SARS-CoV-2 virus fuses its viral envelope with cellular membranes of its human host. Initial contact with the host cell and membrane fusion are both mediated by the viral spike (S) protein. Proteolytic cleavage of S at the S2′ site exposes its 40 amino acid long fusion peptide (FP). Binding of the FP to the host membrane anchors the S2 domain of S in both the viral and the host membrane. The reorganization of S2 then pulls the two membranes together. Here we use molecular dynamics (MD) simulations to study the two core functions of the SARS-CoV-2 FP: to attach quickly to cellular membranes and to form an anchor strong enough to withstand the mechanical force during membrane fusion. In eight 10 μs-long MD simulations of FP in proximity to endosomal and plasma membranes, we find that FP binds spontaneously to the membranes and that binding proceeds predominantly by insertion of two short amphipathic helices into the membrane interface. Connected via a flexible linker, the two helices can bind the membrane independently, yet binding of one promotes the binding of the other by tethering it close to the target membrane. By simulating mechanical pulling forces acting on the C-terminus of the FP we then show that the bound FP can bear forces up to 250 pN before detaching from the membrane. This detachment force is more than ten-fold higher than an estimate of the force required to pull host and viral membranes together for fusion. We identify a fully conserved disulfide bridge in the FP as a major factor for the high mechanical stability of the FP membrane anchor. We conclude, first, that the sequential binding of two short amphipathic helices allows the SARS-CoV-2 FP to insert quickly into the target membrane, before the virion is swept away after shedding the S1 domain connecting it to the host cell receptor. Second, we conclude that the double attachment and the conserved disulfide bridge establish the strong anchoring required for subsequent membrane fusion. Multiple distinct membrane-anchoring elements ensure high avidity and high mechanical strength of FP-membrane binding.
Summary We introduce fsbrain, an R package for the visualization of neuroimaging data. The package can be used to visualize vertex-wise and region-wise morphometry data, parcellations, labels and statistical results on brain surfaces in three dimensions (3D). Voxel data can be displayed in lightbox mode. The fsbrain package offers various customization options and produces publication quality plots which can be displayed interactively, saved as bitmap images, or integrated into R notebooks.
Availability and Implementation The software, source code and documentation are available under the MIT license at https://github.com/dfsp-spirit/fsbrain. Releases can be installed directly from the Comprehensive R Archive Network (CRAN).
The toxicity of microplastics on Daphnia magna as a key model for freshwater zooplankton is well described. While several studies predict population-level effects based on short-term, individual-level responses, only very few have validated these predictions experimentally. Thus, we exposed D. magna populations to irregular polystyrene microplastics and diatomite as natural particle (both ≤ 63 μm) over 50 days. We used mixtures of both particle types at fixed particle concentrations (50,000 mL-1) and recorded the effects on overall population size and structure, the size of the individual animals, and resting egg production. Particle exposure adversely affected the population density and structure, and induced resting egg production. The terminal population size was 28–42% lower in exposed compared to control populations. Interestingly, mixtures containing diatomite induced stronger effects than microplastics alone, highlighting that natural particles are not per se less toxic than microplastics. Our results demonstrate that an exposure to synthetic and natural particles has negative population-level effects on zooplankton. Understanding the mixture toxicity of microplastics and natural particles is important given that aquatic organisms will experience exposure to both. Just as for chemical pollutants, better knowledge of such joint effects is essential to fully understand the environmental impacts of complex particle mixtures.
Environmental Implications While microplastics are commonly considered hazardous based on individual-level effects, there is a dearth of information on how they affect populations. Since the latter is key for understanding the environmental impacts of microplastics, we investigated how particle exposures affect the population size and structure of Daphnia magna. In addition, we used mixtures of microplastics and natural particles because neither occurs alone in nature and joint effects can be expected in an environmentally realistic scenario. We show that such mixtures adversely affect daphnid populations and highlight that population-level and mixture-toxicity designs are one important step towards more environmental realism in microplastics research.
The toxicity of microplastics on Daphnia magna as a key model for freshwater zooplankton is well described. While several studies predict population-level effects based on short-term, individual-level responses, only very few have validated these predictions experimentally. Thus, we exposed D. magna populations to irregular polystyrene microplastics and diatomite as natural particle (both ≤ 63 μm) over 50 days. We used mixtures of both particle types at fixed particle concentrations (50,000 particles mL-1) and recorded the effects on overall population size and structure, the size of the individual animals, and resting egg production. Particle exposure adversely affected the population size and structure and induced resting egg production. The terminal population size was 28–42% lower in exposed compared to control populations. Interestingly, mixtures containing diatomite induced stronger effects than microplastics alone, highlighting that natural particles are not per se less toxic than microplastics. Our results demonstrate that an exposure to synthetic and natural particles has negative population-level effects on zooplankton. Understanding the mixture toxicity of microplastics and natural particles is important given that aquatic organisms will experience exposure to both. Just as for chemical pollutants, better knowledge of such joint effects is essential to fully understand the environmental impacts of complex particle mixtures.
Environmental Implications While microplastics are commonly considered hazardous based on individual-level effects, there is a dearth of information on how they affect populations. Since the latter is key for understanding the environmental impacts of microplastics, we investigated how particle exposures affect the population size and structure of Daphnia magna. In addition, we used mixtures of microplastics and natural particles because neither occurs alone in nature and joint effects can be expected in an environmentally realistic scenario. We show that such mixtures adversely affect daphnid populations and highlight that population-level and mixture-toxicity designs are one important step towards more environmental realism in microplastics research.
Treatments for amblyopia focus on vision therapy and patching of one eye. Predicting the success of these methods remains difficult, however. Recent research has used binocular rivalry to monitor visual cortical plasticity during occlusion therapy, leading to a successful prediction of the recovery rate of the amblyopic eye. The underlying mechanisms and their relation to neural homeostatic plasticity are not known. Here we propose a spiking neural network to explain the effect of short-term monocular deprivation on binocular rivalry. The model reproduces perceptual switches as observed experimentally. When one eye is occluded, inhibitory plasticity changes the balance between the eyes and leads to longer dominance periods for the eye that has been deprived. The model suggests that homeostatic inhibitory plasticity is a critical component of the observed effects and might play an important role in the recovery from amblyopia.
Introns of human transfer RNA precursors (pre-tRNAs) are excised by the tRNA splicing endonuclease TSEN in complex with the RNA kinase CLP1. Mutations in TSEN/CLP1 occur in patients with pontocerebellar hypoplasia (PCH), however, their role in the disease is unclear. Here, we show that intron excision is catalyzed by tetrameric TSEN assembled from inactive heterodimers independently of CLP1. Splice site recognition involves the mature domain and the anticodon-intron base pair of pre-tRNAs. The 2.1-Å resolution X-ray crystal structure of a TSEN15–34 heterodimer and differential scanning fluorimetry analyses show that PCH mutations cause thermal destabilization. While endonuclease activity in recombinant mutant TSEN is unaltered, we observe assembly defects and reduced pre-tRNA cleavage activity resulting in an imbalanced pre-tRNA pool in PCH patient-derived fibroblasts. Our work defines the molecular principles of intron excision in humans and provides evidence that modulation of TSEN stability may contribute to PCH phenotypes.
The heterotetrameric human transfer RNA (tRNA) splicing endonuclease (TSEN) catalyzes the excision of intronic sequences from precursor tRNAs (pre-tRNAs)1. Mutations in TSEN and its associated RNA kinase CLP1 are linked to the neurodegenerative disease pontocerebellar hypoplasia (PCH)2–8. The three-dimensional (3D) assembly of TSEN/CLP1, the mechanism of substrate recognition, and the molecular details of PCH-associated mutations are not fully understood. Here, we present cryo-electron microscopy structures of human TSEN with intron-containing pre-tRNATyrgta and pre-tRNAArgtct. TSEN exhibits broad structural homology to archaeal endonucleases9 but has evolved additional regulatory elements that are involved in handling and positioning substrate RNA. Essential catalytic residues of subunit TSEN34 are organized for the 3’ splice site which emerges from a bulge-helix configuration. The triple-nucleotide bulge at the intron/3’-exon boundary is stabilized by an arginine tweezer motif of TSEN2 and an interaction with the proximal minor groove of the helix. TSEN34 and TSEN54 define the 3’ splice site by holding the tRNA body in place. TSEN54 adapts a bipartite fold with a flexible central region required for CLP1 binding. PCH-associated mutations are located far from pre-tRNA binding interfaces explaining their negative impact on structural integrity of TSEN without abrogating its catalytic activity in vitro10. Our work defines the molecular framework of pre-tRNA recognition and cleavage by TSEN and provides a structural basis to better understand PCH in the future.
Experiment NA49 at the Cern SPS uses a large acceptance detector for a systematic study of particle yields and correlations in nucleus-nucleus, nucleon-nucleus and nucleon-nucleon collisions. Preliminary results for Pb+Pb collisions at 40, 80 and 158 A*GeV beam energy are shown and compared to measurements at lower and higher energies.
SARS-CoV-2 and stroke characteristics: a report from the Multinational COVID-19 Stroke Study Group
(2020)
Background: Stroke is reported as a consequence of SARS-CoV-2 infection. However, there is a lack of regarding comprehensive stroke phenotype and characteristics
Methods: We conducted a multinational observational study on features of consecutive acute ischemic stroke (AIS), intracranial hemorrhage (ICH), and cerebral venous or sinus thrombosis (CVST) among SARS-CoV-2 infected patients. We further investigated the association of demographics, clinical data, geographical regions, and countries’ health expenditure among AIS patients with the risk of large vessel occlusion (LVO), stroke severity as measured by National Institute of Health stroke scale (NIHSS), and stroke subtype as measured by the TOAST criteria. Additionally, we applied unsupervised machine learning algorithms to uncover possible similarities among stroke patients.
Results: Among the 136 tertiary centers of 32 countries who participated in this study, 71 centers from 17 countries had at least one eligible stroke patient. Out of 432 patients included, 323(74.8%) had AIS, 91(21.1%) ICH, and 18(4.2%) CVST. Among 23 patients with subarachnoid hemorrhage, 16(69.5%) had no evidence of aneurysm. A total of 183(42.4%) patients were women, 104(24.1%) patients were younger than 55 years, and 105(24.4%) patients had no identifiable vascular risk factors. Among 380 patients who had known interval onset of the SARS-CoV-2 and stroke, 144(37.8%) presented to the hospital with chief complaints of stroke-related symptoms, with asymptomatic or undiagnosed SARS-CoV-2 infection. Among AIS patients 44.5% had LVO; 10% had small artery occlusion according to the TOAST criteria. We observed a lower median NIHSS (8[3-17], versus 11 [5-17]; p=0.02) and higher rate of mechanical thrombectomy (12.4% versus 2%; p<0.001) in countries with middle to high-health expenditure when compared to countries with lower health expenditure. The unsupervised machine learning identified 4 subgroups, with a relatively large group with no or limited comorbidities.
Conclusions: We observed a relatively high number of young, and asymptomatic SARS-CoV-2 infections among stroke patients. Traditional vascular risk factors were absent among a relatively large cohort of patients. Among hospitalized patients, the stroke severity was lower and rate of mechanical thrombectomy was higher among countries with middle to high-health expenditure.
The new variant of concern (VOC) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Omicron (B.1.1.529), is genetically very different from other VOCs. We compared Omicron with the preceding VOC Delta (B.1.617.2) and the wildtype strain (B.1) with respect to their interactions with the antiviral type I interferon (IFN-alpha/beta) response in infected cells. Our data indicate that Omicron has gained an elevated capability to suppress IFN-beta induction upon infection and to better withstand the antiviral state imposed by exogenously added IFN-alpha.
The basidiomycete smut fungi are predominantly plant parasitic, causing severe losses in some crops. Most species feature a saprotrophic haploid yeast stage, and several smut fungi are only known from this stage, with some isolated from habitats without suitable hosts, e.g. from Antarctica. Thus, these species are generally believed to be apathogenic, but recent findings that some of these might have a plant pathogenic sexual counterpart, casts doubts on the validity of this hypothesis. Here, four Pseudozyma genomes were re-annotated and compared to published smut pathogens and the well-characterised effector gene Pep1 from these species was checked for its ability to complement a Pep1 deletion strain of Ustilago maydis. It was found that 113 high-confidence putative effector proteins were conserved among smut and Pseudozyma genomes. Among these were several validated effector proteins, including Pep1. By genetic complementation we show that Pep1 homologs from the supposedly apathogenic yeasts restore virulence in Pep1-deficient mutants Ustilago maydis. Thus, it is concluded that Pseudozyma species have retained a suite of effectors. This hints at the possibility that Pseudozyma species have kept an unknown plant pathogenic stage for sexual recombination or that these effectors have positive effects when colonising plant surfaces.
The entire chemical modification repertoire of yeast ribosomal RNAs and the enzymes responsible for it have recently been identified. Nonetheless, in most cases the precise roles played by these chemical modifications in ribosome structure, function and regulation remain totally unclear. Previously, we demonstrated that yeast Rrp8 methylates m1A645 of 25S rRNA in yeast. Here, using mung bean nuclease protection assays in combination with quantitative RP-HPLC and primer extension, we report that 25S/28S rRNA of S. pombe, C. albicans and humans also contain a single m1A methylation in the helix 25.1. We characterized nucleomethylin (NML) as a human homolog of yeast Rrp8 and demonstrate that NML catalyzes the m1A1322 methylation of 28S rRNA in humans. Our in vivo structural probing of 25S rRNA, using both DMS and SHAPE, revealed that the loss of the Rrp8-catalyzed m1A modification alters the conformation of domain I of yeast 25S rRNA causing translation initiation defects detectable as halfmers formation, likely because of incompetent loading of 60S on the 43S-preinitiation complex. Quantitative proteomic analysis of the yeast Δrrp8 mutant strain using 2D-DIGE, revealed that loss of m1A645 impacts production of specific set of proteins involved in carbohydrate metabolism, translation and ribosome synthesis. In mouse, NML has been characterized as a metabolic disease-associated gene linked to obesity. Our findings in yeast also point to a role of Rrp8 in primary metabolism. In conclusion, the m1A modification is crucial for maintaining an optimal 60S conformation, which in turn is important for regulating the production of key metabolic enzymes.
While the liver, specifically hepatocytes, are widely accepted as the main source for hepatitis C virus (HCV) production, the role of the liver/hepatocytes in the clearance of circulating HCV remains largely unknown. Here we evaluated the function of the liver/hepatocytes in clearing virus from the circulation by investigating viral clearance during liver transplantation and from culture medium in vitro. Frequent HCV kinetic data during liver transplantation were recorded from 5 individuals throughout the anhepatic (AH) phase and for 4 hours after reperfusion (RP), along with recordings of fluid balances. Using mathematical modeling, the serum viral clearance rate, c, was estimated. Analogously, we monitored the clearance rate of HCV at 37°C from culture medium in vitro in the absence and presence of chronically infected Huh7 human hepatoma cells. During the AH phase, in 3 transplant cases viral levels remained at pre-AH levels, while in the other 2 cases HCV declined (half-life, t1/2~1h). Immediately post-RP, virus declined in a biphasic manner in Cases 1-4 consisting of an extremely rapid (median t1/2=5min) decline followed by a slower decline (HCV t1/2=67min). In Case 5, HCV remained at the same level post-RP as at the end of AH. Declines in virus level were not explained by adjusting for dilution from IV fluid and blood products. Consistent with what was observed in the majority of patients in the anhepatic phase, the t1/2 of HCV in cell culture was much longer in the absence of chronically HCV-infected Huh7 cells. Therefore, kinetic and modeling results from both in vivo liver transplantation cases and in vitro cell culture studies suggest that the liver plays a major role in clearing HCV from the circulation.
Pathophysiological models are urgently needed for personalized treatments of mental disorders. However, most potential neural markers for psychopathology are limited by low interpretability, prohibiting reverse inference from brain measures to clinical symptoms and traits. Neural signatures—i.e. multivariate brain-patterns trained to be both sensitive and specific to a construct of interest—might alleviate this problem, but are rarely applied to mental disorders. We tested whether previously developed neural signatures for negative affect and discrete emotions distinguish between healthy individuals and those with mental disorders characterized by emotion dysregulation, i.e. Borderline Personality Disorder (BPD) and complex Post-traumatic Stress Disorder (cPTSD). In three different fMRI studies, a total sample of 192 women (49 BPD, 62 cPTSD, 81 healthy controls) were shown pictures of scenes with negative or neutral content. Based on pathophysiological models, we hypothesized higher negative and lower positive reactivity of neural emotion signatures in participants with emotion dysregulation. The expression of neural signatures differed strongly between neutral and negative pictures (average Cohen’s d = 1.17). Nevertheless, a mega-analysis on individual participant data showed no differences in the reactivity of neural signatures between participants with and without emotion dysregulation. Confidence intervals ruled out even small effect sizes in the hypothesized direction and were further supported by Bayes factors. Overall, these results support the validity of neural signatures for emotional states during fMRI tasks, but raise important questions concerning their link to individual differences in emotion dysregulation.
Background Coronavirus disease 2019 (COVID-19) represents a significant challenge to health care systems around the world. A well-functioning primary care system is crucial in epidemic situations as it plays an important role in the development of a system-wide response.
Methods 2,187 Austrian and German GPs answered an internet suvey on preparedness, testing, staff protection, perception of risk, self-confidence, a decrease in the number of patient contacts, and efforts to control the spread of the virus in the practice during the early phase of the COVID-pandemic (3rd to 30th April).
Results The completion rate of the questionnaire was high (90.9%). GPs gave low ratings to their preparedness for a pandemic, testing of suspected cases and efforts to protect staff. The provision of information to GPs and the perception of risk were rated as moderate. On the other hand, the participants rated their self-confidence, a decrease in patient contacts and their efforts to control the spread of the disease highly.
Conclusion Primary care is an important resource for dealing with a pandemic like COVID-19. The workforce is confident and willing to take an active role, but needs to be provided with the appropriate surrounding conditions. This will require that certain conditions are met.
Registration Trial registration at the German Clinical Trials Register: DRKS00021231
EF-P and its paralog EfpL (YeiP) differentially control translation of proline containing sequences
(2024)
Polyproline sequences (XPPX) stall ribosomes, thus being deleterious for all living organisms. In bacteria, translation elongation factor P (EF-P) plays a crucial role in overcoming such arrests. 12% of eubacteria possess an EF-P paralog – YeiP (EfpL) of unknown function. Here, we functionally and structurally characterize EfpL from Escherichia coli and demonstrate its yet unrecognized role in the translational stress response. Through ribosome profiling, we analyzed the EfpL arrest motif spectrum and discovered additional stalls beyond the canonical XPPX motifs at single-proline sequences (XPX), that both EF-P and EfpL can resolve. Notably, the two factors can also induce pauses. We further report that, contrary to the housekeeping EF-P, EfpL can sense the metabolic state of the cell, via lysine acylation. Together, our work uncovers a new player in ribosome rescue at proline-containing sequences, and provides evidence that co-occurrence of EF-P and EfpL is an evolutionary driver for higher bacterial growth rates.
Precisely estimating event timing is essential for survival, yet temporal distortions are ubiquitous in our daily sensory experience. Here, we tested whether the relative position, relative duration and relative distance in time of two sequentially-organized events —standard S, with constant duration, and comparison C, varying trial-by-trial— are causal factors in generating temporal distortions. We found that temporal distortions emerge when the first event is shorter than the second event. Importantly, a significant interaction suggests that a longer ISI helps counteracting such serial distortion effect only the constant S is in first position, but not if the unpredictable C is in first position. These results suggest the existence of a perceptual bias in perceiving ordered event durations, mechanistically contributing to distortion in time perception. We simulated our behavioral results with a Bayesian model and replicated the finding that participants disproportionately expand first-position dynamic (unpredictable) short events. Our results clarify the mechanics generating time distortions by identifying a hitherto unknown duration-dependent encoding inefficiency in human serial temporal perception, akin to a strong prior that can be overridden for highly predictable sensory events but unfolds for unpredictable ones.
Living cells constantly remodel the shape of their lipid membranes. In the endo-plasmic reticulum (ER), the reticulon homology domain (RHD) of the reticulophagy regulator 1 (RETR1/FAM134B) forms dense autophagic puncta that are associated with membrane removal by ER-phagy. In molecular dynamics (MD) simulations, we find that FAM134B-RHD spontaneously forms clusters, driven in part by curvature-mediated attraction. At a critical size, the FAM134B-RHD clusters induce the formation of membrane buds. The kinetics of budding depends sensitively on protein concentration and bilayer asymmetry. Our MD simulations shed light on the role of FAM134B-RHD in ER-phagy and show that membrane asymmetry can be used to modulate the kinetics barrier for membrane remodeling.
TriMem: a parallelized hybrid Monte Carlo software for efficient simulations of lipid membranes
(2022)
Lipid membranes are integral building blocks of living cells and perform a multitude of biological functions. Currently, molecular simulations of cellular-scale membrane structures at atomic resolution are nearly impossible, due to their size, complexity, and the large times-scales required. Instead, elastic membrane models are used to simulate membrane topologies and transitions between them, and to infer their properties and functions. Unfortunately, efficiently parallelized open-source simulation code to do so has been lacking. Here, we present TriMem, a parallel hybrid Monte Carlo simulation engine for triangulated lipid membranes. The kernels are efficiently coded in C++ and wrapped with Python for ease-of-use. The parallel implementation of the energy and gradient calculations and of Monte Carlo flip moves of edges in the triangulated membrane enable us to simulate also large and highly curved sub-cellular structures. For validation, we reproduce phase diagrams of vesicles with varying surface-to-volume ratios and area difference. The software can tackle a range of membrane remodelling processes on sub-cellular and cellular scales. Additionally, extensive documentation make the software accessible to the broad biophysics and computational cell biology communities.
The severity of the COVID-19 pandemic, caused by the SARS-CoV-2 coronavirus, calls for the urgent development of a vaccine. The primary immunological target is the SARS-CoV-2 spike (S) protein. S is exposed on the viral surface to mediate viral entry into the host cell. To identify possible antibody binding sites not shielded by glycans, we performed multi-microsecond molecular dynamics simulations of a 4.1 million atom system containing a patch of viral membrane with four full-length, fully glycosylated and palmitoylated S proteins. By mapping steric accessibility, structural rigidity, sequence conservation and generic antibody binding signatures, we recover known epitopes on S and reveal promising epitope candidates for vaccine development. We find that the extensive and inherently flexible glycan coat shields a surface area larger than expected from static structures, highlighting the importance of structural dynamics in epitope mapping.
During animal development, it is crucial that cells can sense and adapt to mechanical forces from their environment. Ultimately, these forces are transduced through the actomyosin cortex. How the cortex can simultaneously respond to and create forces during cytokinesis is not well understood. Here we show that under mechanical stress, cortical actomyosin flow switches its polarization during cytokinesis in the C. elegans embryo. In unstressed embryos, longitudinal cortical flows contribute to contractile ring formation, while rotational cortical flow is additionally induced in uniaxially loaded embryos. Rotational cortical flow is required for the redistribution of the actomyosin cortex in loaded embryos. Rupture of longitudinally aligned cortical fibers during cortex rotation releases tension, initiates orthogonal longitudinal flow and thereby contributes to furrowing in loaded embryos. A targeted screen for factors required for rotational flow revealed that actomyosin regulators involved in RhoA regulation, cortical polarity and chirality are all required for rotational flow and become essential for cytokinesis under mechanical stress. In sum, our findings extend the current framework of mechanical stress response during cell division and show scaling of orthogonal cortical flows to the amount of mechanical stress.
A candidate gene cluster for the bioactive natural product gyrophoric acid in lichen-forming fungi
(2022)
Natural products of lichen-forming fungi are structurally diverse and have a variety of medicinal properties. Despite this, they a have limited implementation in industry, because the corresponding genes remain unknown for most of the natural products. Here we implement a long-read sequencing and bioinformatic approach to identify the biosynthetic gene cluster of the bioactive natural product gyrophoric acid (GA). Using 15 high-quality genomes representing nine GA-producing species of the lichen-forming fungal genus Umbilicaria, we identify the most likely GA cluster and investigate cluster gene organization and composition across the nine species. Our results show that GA clusters are promiscuous within Umbilicaria, with only three genes that are conserved across species, including the PKS gene. In addition, our results suggest that the same cluster codes for different but structurally similar NPs, i.e., GA, umbilicaric acid and hiascic acid, bringing new evidence that lichen metabolite diversity is also generated through regulatory mechanisms at the molecular level. Ours is the first study to identify the most likely GA cluster, and thus provides essential information to open new avenues for biotechnological approaches to producing and modifying GA and similar lichen-derived compounds. We show that bioinformatics approaches are useful in linking genes and potentially associated natural products. Genome analyses help unlocking the pharmaceutical potential of organisms such as lichens, which are biosynthetically diverse but slow growing, and difficult to cultivate due to their symbiotic nature.
Genome mining as a biotechnological tool for the discovery of novel biosynthetic genes in lichens
(2022)
The ever-increasing demand for novel drugs highlights the need for bioprospecting unexplored taxa for their biosynthetic potential. Lichen-forming fungi (LFF) are a rich source of natural products but their implementation in pharmaceutical industry is limited, mostly because the genes corresponding to a majority of their natural products is unknown. Furthermore, it is not known to what extent these genes encode structurally novel molecules. Advance in next-generation sequencing technologies has expanded the range of organisms that could be exploited for their biosynthetic potential. In this study, we mine the genomes of nine lichen-forming fungal species of the genus Umbilicaria for biosynthetic genes, and categorize the BGCs as “associated product structurally known”, and “associated product putatively novel”. We found that about 25-30% of the biosynthetic genes are divergent when compared to the global database of BGCs comprising of 1,200,000 characterized biosynthetic genes from planta, bacteria and fungi. Out of 217 total BGCs, 43 were only distantly related to known BGCs, suggesting they encode structurally and functionally unknown natural products. Clusters encoding the putatively novel metabolic diversity comprise PKSs (30), NRPSs (12) and terpenes (1). Our study emphasizes the utility of genomic data in bioprospecting microorganisms for their biosynthetic potential and in advancing the industrial application of unexplored taxa. We highlight the untapped structural metabolic diversity encoded in the lichenized fungal genomes. To the best of our knowledge, this is the first investigation identifying genes coding for NPs with potentially novel therapeutic properties in LFF.
The cortical networks that underlie behavior exhibit an orderly functional organization at local and global scales, which is readily evident in the visual cortex of carnivores and primates1-6. Here, neighboring columns of neurons represent the full range of stimulus orientations and contribute to distributed networks spanning several millimeters2,7-11. However, the principles governing functional interactions that bridge this fine-scale functional architecture and distant network elements are unclear, and the emergence of these network interactions during development remains unexplored. Here, by using in vivo wide-field and 2-photon calcium imaging of spontaneous activity patterns in mature ferret visual cortex, we find widespread and specific modular correlation patterns that accurately predict the local structure of visually-evoked orientation columns from the spontaneous activity of neurons that lie several millimeters away. The large-scale networks revealed by correlated spontaneous activity show abrupt ‘fractures’ in continuity that are in tight register with evoked orientation pinwheels. Chronic in vivo imaging demonstrates that these large-scale modular correlation patterns and fractures are already present at early stages of cortical development and predictive of the mature network structure. Silencing feed-forward drive through either retinal or thalamic blockade does not affect network structure suggesting a cortical origin for this large-scale correlated activity, despite the immaturity of long-range horizontal network connections in the early cortex. Using a circuit model containing only local connections, we demonstrate that such a circuit is sufficient to generate large-scale correlated activity, while also producing correlated networks showing strong fractures, a reduced dimensionality, and an elongated local correlation structure, all in close agreement with our empirical data. These results demonstrate the precise local and global organization of cortical networks revealed through correlated spontaneous activity and suggest that local connections in early cortical circuits may generate structured long-range network correlations that underlie the subsequent formation of visually-evoked distributed functional networks.
RuO₂: a puzzle to be solved
(2023)
Altermagnetism is a topic that has lately been gaining attention and the RuO2 compound is among one of the most studied altermagnetic candidates. However, the survey of available literature on RuO2 properties suggests that there is no consensus about the magnetism of this material. By performing density functional theory calculations, we show that the electronic properties of stoichiometric RuO2 are described in terms of a smaller Hubbard U within DFT+U than the value required to have magnetism. We further argue that Ru vacancies can actually aid the formation of a magnetic state in RuO2. This in turn suggests that a characterization of the amount of Ru vacancies in experimental samples might help the resolution of the controversy between the different experimental results.
Invited talk at the 7th International Conference on Strangeness in Quark Matter, SQM 2003, Atlantic Beach, North Carolina, USA, 12-17 Mar, 2003. 11 pages, 12 figures. Journal-ref: J.Phys. G30 (2004) S139-S150. We review recent developments in the field of microscopic transport model calculations for ultrarelativistic heavy ion collisions. In particular, we focus on the strangeness production, for example, the phi-meson and its role as a messenger of the early phase of the system evolution. Moreover, we discuss the important e ects of the (soft) field properties on the multiparticle system. We outline some current problems of the models as well as possible solutions to them
Invited talk at the XXXIII International Symposium on Multiparticle Dynamics, Krakow, Poland, 5-11 Sept, 2003. 5 pages, 1 figure Journal-ref: Acta Phys.Polon. B35 (2004) 23-28. We review the recent developments on microscopic transport calculations for two-particle correlations at low relative momenta in ultrarelativistic heavy ion collisions at RHIC.
Invited talk at the International Workshop XXX on Gross Properties of Nuclei and Nuclear Excitations - Ultrarelativistic Heavy-Ion Collisions, Jan. 13-19, 2002, Hirschegg, Austria. Report-no: LBNL-49674. We discuss predictions for the pion and kaon interferometry measurements in relativistic heavy ion collisions at SPS and RHIC energies. In particular, we confront relativistic transport model calculations that include explicitly a first-order phase transition from a thermalized quark-gluon plasma to a hadron gas with recent data from the RHIC experiments. We critically examine the "HBT-puzzle" both from the theoretical as well as from the experimental point of view. Alternative scenarios are briefly explained.
The centrality dependence of (multi-)strange hadron abundances is studied for Pb(158 AGeV)Pb reactions and compared to p(158 GeV)Pb collisions. The microscopic transport model UrQMD is used for this analysis. The predicted Lambda/pi-, Xi-/pi- and Omega-/pi- ratios are enhanced due to rescattering in central Pb-Pb collisions as compared to peripheral Pb-Pb or p-Pb collisions. A reduction of the constituent quark masses to the current quark masses m_s \sim 230 MeV, m_q \sim 10 MeV, as motivated by chiral symmetry restoration, enhances the hyperon yields to the experimentally observed high values. Similar results are obtained by an ad hoc overall increase of the color electric field strength (effective string tension of kappa=3 GeV/fm). The enhancement depends strongly on the kinematical cuts. The maximum enhancement is predicted around midrapidity. For Lambda's, strangeness suppression is predicted at projectile/target rapidity. For Omega's, the predicted enhancement can be as large as one order of magnitude. Comparisons of Pb-Pb data to proton induced asymmetric (p-A) collisions are hampered due to the predicted strong asymmetry in the various rapidity distributions of the different (strange) particle species. In p-Pb collisions, strangeness is locally (in rapidity) not conserved. The present comparison to the data of the WA97 and NA49 collaborations clearly supports the suggestion that conventional (free) hadronic scenarios are unable to describe the observed high (anti-)hyperon yields in central collisions. The doubling of the strangeness to nonstrange suppression factor, gamma_s \approx 0.65, might be interpreted as a signal of a phase of nearly massless particles.
Directed and elliptic flow
(1999)
We compare microscopic transport model calculations to recent data on the directed and elliptic flow of various hadrons in 2 - 10 A GeV Au+Au and Pb (158 A GeV) Pb collisions. For the Au+Au excitation function a transition from the squeeze-out to an in-plane enhanced emission is consistently described with mean field potentials corresponding to one incompressibility. For the Pb (158 A GeV) Pb system the elliptic flow prefers in-plane emission both for protons and pions, the directed flow of protons is opposite to that of the pions, which exhibit anti-flow. Strong directed transverse flow is present for protons and Lambdas in Au (6 A GeV) Au collisions as well. Both for the SPS and the AGS energies the agreement between data and calculations is remarkable.
We calculate the Gaussian radius parameters of the pion-emitting source in high energy heavy ion collisions, assuming a first order phase transition from a thermalized Quark-Gluon-Plasma (QGP) to a gas of hadrons. Such a model leads to a very long-lived dissipative hadronic rescattering phase which dominates the properties of the two-pion correlation functions. The radii are found to depend only weakly on the thermalization time tau i, the critical temperature T c (and thus the latent heat), and the specific entropy of the QGP. The dissipative hadronic stage enforces large variations of the pion emission times around the mean. Therefore, the model calculations suggest a rapid increase of R out/R side as a function of K T if a thermalized QGP were formed.
We make predictions for the kaon interferometry measurements in Au+Au collisions at the Relativistic Heavy Ion Collider (RHIC). A first order phase transition from a thermalized Quark-Gluon-Plasma (QGP) to a gas of hadrons is assumed for the transport calculations. The fraction of kaons that are directly emitted from the phase boundary is considerably enhanced at large transverse momenta K T ~ 1 GeV/c. In this kinematic region, the sensitivity of the R out/R side ratio to the QGP-properties is enlarged. Here, the results of the 1-dimensional correlation analysis are presented. The extracted interferometry radii, depending on K-Theta, are not unusually large and are strongly affected by momentum resolution effects.
We present calculations of two-pion and two-kaon correlation functions in relativistic heavy ion collisions from a relativistic transport model that includes explicitly a first-order phase transition from a thermalized quark-gluon plasma to a hadron gas. We compare the obtained correlation radii with recent data from RHIC. The predicted R_side radii agree with data while the R_out and R_long radii are overestimated. We also address the impact of in-medium modifications, for example, a broadening of the rho-meson, on the correlation radii. In particular, the longitudinal correlation radius R_long is reduced, improving the comparison to data.
The disappearance of flow
(1995)
We investigate the disappearance of collective flow in the reaction plane in heavy-ion collisions within a microscopic model (QMD). A systematic study of the impact parameter dependence is performed for the system Ca+Ca. The balance energy strongly increases with impact parameter. Momentum dependent interactions reduce the balance energies for intermediate impact parameters b ~ 4.5 fm. Dynamical negative flow is not visible in the laboratory frame but does exist in the contact frame for the heavy system Au+Au. For semi-peripheral collisions of Ca+Ca with b ~ 6.5 fm a new two-component flow is discussed. Azimuthal distributions exhibit strong collectiv flow signals, even at the balance energy.