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Abstract
The endoplasmic reticulum (ER) is a key organelle of membrane biogenesis and crucial for the folding of both membrane and secretory proteins. Sensors of the unfolded protein response (UPR) monitor the unfolded protein load in the ER and convey effector functions for maintaining ER homeostasis. Aberrant compositions of the ER membrane, referred to as lipid bilayer stress, are equally potent activators of the UPR. How the distinct signals from lipid bilayer stress and unfolded proteins are processed by the conserved UPR transducer Ire1 remains unknown. Here, we have generated a functional, cysteine-less variant of Ire1 and performed systematic cysteine crosslinking experiments in native membranes to establish its transmembrane architecture in signaling-active clusters. We show that the transmembrane helices of two neighboring Ire1 molecules adopt an X-shaped configuration independent of the primary cause for ER stress. This suggests that different forms of stress converge in a common, signaling-active transmembrane architecture of Ire1.
Summary
The endoplasmic reticulum (ER) is a hotspot of lipid biosynthesis and crucial for the folding of membrane and secretory proteins. The unfolded protein response (UPR) controls the size and folding capacity of the ER. The conserved UPR transducer Ire1 senses both unfolded proteins and aberrant lipid compositions to mount adaptive responses. Using a biochemical assay to study Ire1 in signaling-active clusters, Väth et al. provide evidence that the neighboring transmembrane helices of clustered Ire1 form an ‘X’ irrespectively of the primary cause of ER stress. Hence, different forms of ER stress converge in a common, signaling-active transmembrane architecture of Ire1.
The capacity of convalescent and vaccine-elicited sera and monoclonal antibodies (mAb) to neutralize SARS-CoV-2 variants is currently of high relevance to assess the protection against infections.
We performed a cell culture-based neutralization assay focusing on authentic SARS-CoV-2 variants B.1.617.1 (Kappa), B.1.617.2 (Delta), B.1.427/B.1.429 (Epsilon), all harboring the spike substitution L452R.
We found that authentic SARS-CoV-2 variants harboring L452R had reduced susceptibility to convalescent and vaccine-elicited sera and mAbs. Compared to B.1, Kappa and Delta showed a reduced neutralization by convalescent sera by a factor of 8.00 and 5.33, respectively, which constitutes a 2-fold greater reduction when compared to Epsilon. BNT2b2 and mRNA1273 vaccine-elicited sera were less effective against Kappa, Delta, and Epsilon compared to B.1. No difference was observed between Kappa and Delta towards vaccine-elicited sera, whereas convalescent sera were 1.5-fold less effective against Delta, respectively. Both B.1.617 variants Kappa (+E484Q) and Delta (+T478K) were less susceptible to either casirivimab or imdevimab.
In conclusion, in contrast to the parallel circulating Kappa variant, the neutralization efficiency of convalescent and vaccine-elicited sera against Delta was moderately reduced. Delta was resistant to imdevimab, which however, might be circumvented by a combination therapy with casirivimab together.
The European Beech is the dominant climax tree in most regions of Central Europe and valued for its ecological versatility and hardwood timber. Even though a draft genome has been published recently, higher resolution is required for studying aspects of genome architecture and recombination. Here we present a chromosome-level assembly of the more than 300 year-old reference individual, Bhaga, from the Kellerwald-Edersee National Park (Germany). Its nuclear genome of 541 Mb was resolved into 12 chromosomes varying in length between 28 Mb and 73 Mb. Multiple nuclear insertions of parts of the chloroplast genome were observed, with one region on chromosome 11 spanning more than 2 Mb of the genome in which fragments up to 54,784 bp long and covering the whole chloroplast genome were inserted randomly. Unlike in Arabidopsis thaliana, ribosomal cistrons are present in Fagus sylvatica only in four major regions, in line with FISH studies. On most assembled chromosomes, telomeric repeats were found at both ends, while centromeric repeats were found to be scattered throughout the genome apart from their main occurrence per chromosome. The genome- wide distribution of SNPs was evaluated using a second individual from Jamy Nature Reserve (Poland). SNPs, repeat elements and duplicated genes were unevenly distributed in the genomes, with one major anomaly on chromosome 4. The genome presented here adds to the available highly resolved plant genomes and we hope it will serve as a valuable basis for future research on genome architecture and for understanding the past and future of European Beech populations in a changing climate.
DNA points accumulation for imaging in nanoscale topography (DNA-PAINT) is a super-resolution technique with relatively easy-to-implement multi-target imaging. However, image acquisition is slow as sufficient statistical data has to be generated from spatio-temporally isolated single emitters. Here, we trained the neural network (NN) DeepSTORM to predict fluorophore positions from high emitter density DNA-PAINT data. This achieves image acquisition in one minute. We demonstrate multi-color super-resolution imaging of structure-conserved semi-thin neuronal tissue and imaging of large samples. This improvement can be integrated into any single-molecule microscope and enables fast single-molecule super-resolution microscopy.
Electrocardiograms (ECG) record the heart activity and are the most common and reliable method to detect cardiac arrhythmias, such as atrial fibrillation (AFib). Lately, many commercially available devices such as smartwatches are offering ECG monitoring. Therefore, there is increasing demand for designing deep learning models with the perspective to be physically implemented on these small portable devices with limited energy supply. In this paper, a workflow for the design of small, energy-efficient recurrent convolutional neural network (RCNN) architecture for AFib detection is proposed. However, the approach can be well generalized to every type of long time series. In contrast to previous studies, that demand thousands of additional network neurons and millions of extra model parameters, the logical steps for the generation of a CNN with only 114 trainable parameters are described. The model consists of a small segmented CNN in combination with an optimal energy classifier. The architectural decisions are made by using the energy consumption as a metric in an equally important way as the accuracy. The optimisation steps are focused on the software which can be embedded afterwards on a physical chip. Finally, a comparison with some previous relevant studies suggests that the widely used huge CNNs for similar tasks are mostly redundant and unessentially computationally expensive.
Many cross-sectional findings suggest that volumes of specific hippocampal subfields increase in middle childhood and early adolescence. In contrast, a small number of available longitudinal studies observed decreased volumes in most subfields over this age range. Further, it remains unknown whether structural changes in development are associated with corresponding gains in children’s memory. Here we report cross-sectional age differences in children’s hippocampal subfield volumes together with longitudinal developmental trajectories and their relationships with memory performance. In two waves, 109 healthy participants aged 6 to 10 years (wave 1: MAge=7.25, wave 2: MAge=9.27) underwent high-resolution magnetic resonance imaging to assess hippocampal subfield volumes, and completed cognitive tasks assessing hippocampus dependent memory processes. We found that cross-sectional age-associations and longitudinal developmental trends in hippocampal subfield volumes were highly discrepant, both by subfields and in direction. Further, volumetric changes were largely unrelated to changes in memory, with the exception that increase in subiculum volume was associated with gains in spatial memory. Importantly, the observed longitudinal patterns of brain-cognition coupling could not be inferred from cross-sectional findings. We discuss potential sources of these discrepancies. This study underscores that children’s structural brain development and its relationship to cognition cannot be inferred from cross-sectional age comparisons.
Highlights
The subiculum undergoes volumetric increase between 6-10 years of age
Change across two years in CA1-2 and DG-CA3 was not observed in this age window
Change across two years did not reflect age differences spanning two years
Cross-sectional and longitudinal slopes in stark contrast for hippocampal subfields
Longitudinal brain-cognition coupling cannot be inferred from cross-sectional data
The unicellular ciliate Paramecium contains a large vegetative macronucleus with several unusual characteristics including an extremely high coding density and high polyploidy. As macronculear chromatin is devoid of heterochromatin our study characterizes the functional epigenomic organisation necessary for gene regulation and proper PolII activity. Histone marks (H3K4me3, H3K9ac, H3K27me3) revealed no narrow peaks but broad domains along gene bodies, whereas intergenic regions were devoid of nucleosomes. Our data implicates H3K4me3 levels inside ORFs to be the main factor to associate with gene expression and H3K27me3 appears to occur as a bistable domain with H3K4me3 in plastic genes. Surprisingly, silent and lowly expressed genes show low nucleosome occupancy suggesting that gene inactivation does not involve increased nucleosome occupancy and chromatin condensation. Due to a high occupancy of Pol II along highly expressed ORFs, transcriptional elongation appears to be quite different to other species. This is supported by missing heptameric repeats in the C-terminal domain of Pol II and a divergent elongation system. Our data implies that unoccupied DNA is the default state, whereas gene activation requires nucleosome recruitment together with broad domains of H3K4me3. This could represent a buffer for paused Pol II along ORFs in absence of elongation factors of higher eukaryotes.
Biomolecular condensation underlies the biogenesis of an expanding array of membraneless assemblies, including stress granules (SGs) which form under a variety of cellular stresses. Advances have been made in understanding the molecular grammar that dictates the behavior of a few key scaffold proteins that make up these phases but how the partitioning of hundreds of other SG proteins is regulated remains largely unresolved. While investigating the rules that govern the condensation of ataxin-2, a SG protein implicated in neurodegenerative disease, we unexpectedly identified a short 14aa sequence that acts as an ataxin-2 condensation switch and is conserved across the eukaryote lineage. We identify poly(A)-binding proteins as unconventional RNA-dependent chaperones that control this regulatory switch. Our results uncover a hierarchy of cis and trans interactions that fine-tune ataxin-2 condensation and reveal a new molecular function for ancient poly(A)-binding proteins as emulsifiers of biomolecular condensate proteins. These findings may inspire novel approaches to therapeutically target aberrant phases in disease.
Resting state fMRI has been employed to identify alterations in functional connectivity within or between brain regions following acute and chronic exposure to Δ9-tetrahydrocannabinol (THC), the psychoactive component in cannabis. Most studies focused a priori on a limited number of local brain areas or circuits, without considering the impact of cannabis on wholebrain network organization. The present study attempted to identify changes in the wholebrain human functional connectome as assessed with ultra-high field (7T) resting state scans of occasional (N=12) and chronic cannabis users (N=14) during placebo and following vaporization of cannabis. Two distinct data-driven methodologies, i.e. network-based statistics (NBS) and connICA, were used to identify changes in functional connectomes associated with acute cannabis intoxication and chronic cannabis use. Both methodologies revealed a broad state of hyperconnectivity within the entire range of major brain networks in chronic cannabis users compared to occasional cannabis users, which might be reflective of an adaptive network reorganization following prolonged cannabis exposure. The connICA methodology also extracted a distinct spatial connectivity pattern of hypoconnectivity involving the dorsal attention, limbic, subcortical and cerebellum networks and of hyperconnectivity between the default mode and ventral attention network, that was associated with the feeling of subjective high during THC intoxication across both user groups. Whole-brain network approaches identified spatial patterns in functional brain connectomes that distinguished acute from chronic cannabis use, and offer an important utility for probing the interplay between short and long-term alterations in functional brain dynamics when progressing from occasional to chronic use of cannabis.
Gender and attitudes toward welfare state reform: Are women really social investment promoters?
(2021)
This article contributes to the study of the demand side of welfare politics by investigating gender differences in social investment preferences systematically. Building on the different functions of social investment policies in creating, preserving, or mobilizing skills, we argue that women do not support social investment policies generally more strongly than men. Rather, women demand, in particular, policies to preserve their skills during career interruptions and help to mobilize their skills on the labour market. In a second analytical step, we examine women’s policy priorities if skill preservation and mobilization come at the expense of social compensation. We test our arguments for eight Western European countries with data from the INVEDUC survey. The confirmation of our arguments challenges a core assumption of the literatures on the social investment turn and women’s political realignment. We discuss the implication of our findings in the conclusions.
Background Stigma is one of the most significant constraints on people living with depression. There is a lack of validated scales in Portugal to measure depression stigma; therefore, validation of the Depression Stigma Scale (DSS) is an essential step to the depression stigma research in Portugal.
Methods We developed the adaptation process with the ITC Guidelines for Translation and Adapting Tests taken into consideration. We collected the sample as part of the OSPI program – Optimizing suicide prevention programs and their implementation in Europe, specifically within the application in Portugal, and included 1693 participants. Floor-ceiling effects and response ranges were analyzed, and we calculated Cronbach alphas, conducted a Principal Component Analysis and Confirmatory Analysis. Validity evidence was tested with two well-documented hypotheses, using data on gender and depression symptoms.
Results The sample was well comparable with the general Portuguese population, indicating its representativeness. We identified a three-factor structure in each subscale (personal and perceived stigma): weak-not-sick, discrimination, and dangerous/unpredictable. The Cronbach’s alphas were satisfactory, and validity was confirmed.
Conclusions This study established the validity and demonstrated good psychometric properties of the DSS in the Portuguese population. The validation of the DSS can be beneficial in exploring stigma predictors and evaluating the effectiveness of stigma reduction interventions.
The rapid spread and evolution of various strains of SARS-CoV-2, the virus responsible for COVID-19, continues to challenge the disease controlling measures globally. Alarming concern is, the number of second wave infections surpassed the first wave and the onset of severe symptoms manifesting rapidly. In this scenario, testing of maximum population in less time and minimum cost with existing diagnostic amenities is the only possible way to control the spread of the virus. The previously described RNA extraction-free methods using dry swab have been shown to be advantageous in these critical times by different studies. In this work, we show the temporal stability and performance of the dry swab viral detection method at two different temperatures. Contrived dry swabs holding serially diluted SARS-CoV-2 strains A2a and A3i at 25°C (room temperature; RT) and 4°C were subjected to direct RT-PCR and compared with standard VTM-RNA based method. The results clearly indicate that dry swab method of RNA detection is as efficient as VTM-RNA-based method in both strains, when checked for up to 72 hours. The lesser CT values of dry swab samples in comparison to that of the VTM-RNA samples suggest better sensitivity of the method within 48 hours of time. The results collectively suggest that dry swab samples are stable at RT for 24 hours and the detection of SARS-CoV-2 RNA by RT-PCR do not show variance from VTM-RNA. This extraction free, direct RT-PCR method holds phenomenal standing in the present life-threatening circumstances due to SARS-CoV-2.
Feeding exclusively on blood, vampire bats represent the only obligate sanguivorous lineage among mammals. To uncover genomic changes associated with adaptations to this unique dietary specialization, we generated a new haplotype-resolved reference-quality genome of the common vampire bat (Desmodus rotundus) and screened 26 bat species for genes that were specifically lost in the vampire bat lineage. We discovered previously-unknown gene losses that relate to metabolic and physiological changes, such as reduced insulin secretion (FFAR1, SLC30A8), limited glycogen stores (PPP1R3E), and a distinct gastric physiology (CTSE). Other gene losses likely reflect the biased nutrient composition (ERN2, CTRL) and distinct pathogen diversity of blood (RNASE7). Interestingly, the loss of REP15 likely helped vampire bats to adapt to high dietary iron levels by enhancing iron excretion and the loss of the 24S-hydroxycholesterol metabolizing enzyme CYP39A1 could contribute to their exceptional cognitive abilities. Finally, losses of key cone phototransduction genes (PDE6H, PDE6C) suggest that these strictly-nocturnal bats completely lack cone-based vision. These findings enhance our understanding of vampire bat biology and the genomic underpinnings of adaptations to sanguivory.
Incidence of an intracellular multiplication niche amongst Acinetobacter baumannii clinical isolates
(2021)
The spread of antibiotic resistant Acinetobacter baumannii poses a significant threat to public health worldwide. This nosocomial bacterial pathogen can be associated with life-threatening infections, particularly in intensive care units. A. baumannii is mainly described as an extracellular pathogen with restricted survival within cells. This study shows that a subset of A. baumannii clinical isolates extensively multiply within non-phagocytic immortalized and primary cells, without the induction of apoptosis, and with bacterial clusters visible up to 48 hours after infection. This phenotype was observed for the A. baumannii C4 strain associated with high mortality in a hospital outbreak, and the A. baumannii ABC141 strain which wasn’t isolated from an infection site but was found to be hyperinvasive. Intracellular multiplication of these A. baumannii strains occurred within spacious single membrane-bound vacuoles, labeled with the lysosomal associate membrane protein (LAMP1). However, these compartments excluded lysotracker, an indicator of acidic pH, suggesting that A. baumannii can divert its trafficking away from the lysosomal degradative pathway. These compartments were also devoid of autophagy features. A high-content microscopy screen of 43 additional A. baumannii clinical strains highlighted various phenotypes: (1) the majority of strains remained extracellular, (2) a significant proportion was capable of invasion and limited persistence, and (3) two strains efficiently multiplied within LAMP1-positive vacuoles, one of which was also hyperinvasive. These data identify an intracellular niche for specific A. baumannii clinical strains that enables extensive multiplication in an environment protected from host immune responses and out of reach from many antibiotics.
Importance Multidrug resistant Acinetobacter baumannii strains are associated with significant morbidity and mortality in hospitals world-wide. Understanding their pathogenicity is critical for improving therapeutics. Although A. baumannii can steadily adhere to surfaces and host cells, most bacteria remain extracellular. Recent studies have shown that a small proportion of bacteria can invade cells but present limited survival. We have found that some A. baumannii clinical isolates can establish a specialized intracellular niche that sustains extensive intracellular multiplication for a prolonged time without induction of cell death. We propose that this intracellular compartment allows A. baumannii to escape the cell’s normal degradative pathway, protecting bacteria from host immune responses and potentially hindering antibiotic accessibility. This may contribute to A. baumannii persistence, relapsing infections and enhanced mortality in susceptible patients. A high-content microscopy-based screen confirmed this pathogenicity trait is present in other clinical isolates. There is an urgent need for new antibiotics or alternative antimicrobial approaches, particularly to combat carbapenem-resistant A. baumannii. The discovery of an intracellular niche for this pathogen as well as hyperinvasive isolates may help guide the development of antimicrobial therapies and diagnostics in the future.
A key competence for open-ended learning is the formation of increasingly abstract representations useful for driving complex behavior. Abstract representations ignore specific details and facilitate generalization. Here we consider the learning of abstract representations in a multi-modal setting with two or more input modalities. We treat the problem as a lossy compression problem and show that generic lossy compression of multimodal sensory input naturally extracts abstract representations that tend to strip away modalitiy specific details and preferentially retain information that is shared across the different modalities. Furthermore, we propose an architecture to learn abstract representations by identifying and retaining only the information that is shared across multiple modalities while discarding any modality specific information.
Dysfunction of YEATS-domain-containing MLLT1, an acetyl/acyl-lysine dependent epigenetic reader domain, has been implicated in the development of aggressive cancers. Mutations in the YEATS domain have been recently reported as a cause of MLLT1 aberrant reader function. However, structural basis for the reported alterations in affinity for acetyled/acylated histone has remained elusive. Here, we report the crystal structures of both insertion and substitution present in cancer, revealing significant conformational changes of the YEATS-domain loop 8. Structural comparison demonstrates that such alteration not only altered the binding interface for acetylated/acylated histones, but the sequence alterations in the T1 loop may enable dimeric assembly consistent inducing self-association behavior. Nevertheless, we show that also the MLLT1 mutants can be targeted by developed acetyllysine mimetic inhibitors with affinities similarly to wild type. Our report provides a structural basis for the altered behaviors and potential strategy for targeting oncogenic MLLT1 mutants.
Mosquito species belonging to the genus Aedes have attracted the interest of scientists and public health officers for their invasive species traits and efficient capacity of transmitting viruses affecting humans. Some of these species were brought outside their native range by human activities such as trade and tourism, and colonised new regions thanks to a unique combination of eco-physiological traits.
Considering mosquito physiological and behavioural traits to understand and predict the spatial and temporal population dynamics is thus a crucial step to develop strategies to mitigate the local densities of invasive Aedes populations.
Here, we synthesised the life cycle of four invasive Aedes species (Ae. aegypti, Ae. albopictus, Ae. japonicus and Ae. koreicus) in a single multi-scale stochastic modelling framework which we coded in the R package dynamAedes. We designed a stage-based and time-discrete stochastic model driven by temperature, photo-period and inter-specific larval competition that can be applied to three different spatial scales: punctual, local and regional. These spatial scales consider different degrees of spatial complexity and data availability, by accounting for both active and passive dispersal of mosquito species as well as for the heterogeneity of the input temperature data.
Our overarching aim was to provide a flexible, open-source and user-friendly tool rooted in the most updated knowledge on species biology which could be applied to the management of invasive Aedes populations as well as for more theoretical ecological inquiries.
Vocal communication is essential to coordinate social interactions in mammals and it requires a fine discrimination of communication sounds. Auditory neurons can exhibit selectivity for specific calls, but how it is affected by preceding sounds is still debated. We tackled this using ethologically relevant vocalizations in a highly vocal mammalian species: Seba’s short-tailed bat. We show that cortical neurons present several degrees of selectivity for echolocation and distress calls. Embedding vocalizations within natural acoustic streams leads to stimulus-specific suppression of neuronal responses that changes sound selectivity in disparate manners: increases in neurons with poor discriminability in silence and decreases in neurons selective in silent settings. A computational model indicates that the observed effects arise from two forms of adaptation: presynaptic frequency specific adaptation acting in cortical inputs and stimulus unspecific postsynaptic adaptation. These results shed light into how acoustic context modulates natural sound discriminability in the mammalian cortex.
The development of super-resolution microscopy (SRM) has widened our understanding of biomolecular structure and function in biological materials. Imaging multiple targets within a single area would elucidate their spatial localization relative to the cell matrix and neighboring biomolecules, revealing multi-protein macromolecular structures and their functional co-dependencies. SRM methods are, however, limited to the number of suitable fluorophores that can be imaged during a single acquisition as well as the loss of antigens during antibody washing and restaining for organic dye multiplexing. We report the visualization of multiple protein targets within the pre- and postsynapse in 350-400 nm thick neuronal tissue sections using DNA-assisted single-molecule localization microscopy. Using antibodies labeled with short DNA oligonucleotides, multiple targets are visualized successively by sequential exchange of fluorophore-labeled complementary oligonucleotides present in the imaging buffer. The structural integrity of the tissue is maintained owing to only a single labelling step during sample preparation. Multiple targets are imaged using a single laser wavelength, minimizing chromatic aberration. This method proved robust for multi-target imaging in semi-thin tissue sections, paving the way towards structural cell biology with single-molecule super-resolution microscopy.
From loss to recovery: how to effectively assess chemosensory impairments during COVID-19 pandemic
(2021)
Chemosensory impairments have been established as a specific indicator of COVID-19. They affect most patients and may persist long past the resolution of respiratory symptoms, representing an unprecedented medical challenge. Since the SARS-CoV-2 pandemic started, we now know much more about smell, taste, and chemesthesis loss associated with COVID-19. However, the temporal dynamics and characteristics of recovery are still unknown. Here, capitalizing on data from the Global Consortium for Chemosensory Research (GCCR) crowdsourced survey, we assessed chemosensory abilities after the resolution of respiratory symptoms in participants diagnosed with COVID-19 during the first wave of the pandemic in Italy. This analysis led to the identification of two patterns of chemosensory recovery, limited (partial) and substantial, which were found to be associated with differential age, degrees of chemosensory loss, and regional patterns. Uncovering the self-reported phenomenology of recovery from smell, taste, and chemesthetic disorders is the first, yet essential step, to provide healthcare professionals with the tools to take purposeful and targeted action to address chemosensory disorders and its severe discomfort.
Mathematical modeling of the molecular switch of TNFR1-mediated signaling pathways using Petri nets
(2021)
The paper describes a mathematical model of the molecular switch of cell survival, apoptosis, and necroptosis in cellular signaling pathways initiated by tumor necrosis factor 1. Based on experimental findings in the current literature, we constructed a Petri net model in terms of detailed molecular reactions for the molecular players, protein complexes, post-translational modifications, and cross talk. The model comprises 118 biochemical entities, 130 reactions, and 299 connecting edges. Applying Petri net analysis techniques, we found 279 pathways describing complete signal flows from receptor activation to cellular response, representing the combinatorial diversity of functional pathways.120 pathways steered the cell to survival, whereas 58 and 35 pathways led to apoptosis and necroptosis, respectively. For 65 pathways, the triggered response was not deterministic, leading to multiple possible outcomes. Based on the Petri net, we investigated the detailed in silico knockout behavior and identified important checkpoints of the TNFR1 signaling pathway in terms of ubiquitination within complex I and the gene expression dependent on NF-κB, which controls the caspase activity in complex II and apoptosis induction.
Under natural conditions, the visual system often sees a given input repeatedly. This provides an opportunity to optimize processing of the repeated stimuli. Stimulus repetition has been shown to strongly modulate neuronal-gamma band synchronization, yet crucial questions remained open. Here we used magnetoencephalography in 30 human subjects and find that gamma decreases across ~10 repetitions and then increases across further repetitions, revealing plastic changes of the activated neuronal circuits. Crucially, changes induced by one stimulus did not affect responses to other stimuli, demonstrating stimulus specificity. Changes partially persisted when the inducing stimulus was repeated after 25 minutes of intervening stimuli. They were strongest in early visual cortex and increased interareal feedforward influences. Our results suggest that early visual cortex gamma synchronization enables adaptive neuronal processing of recurring stimuli. These and previously reported changes might be due to an interaction of oscillatory dynamics with established synaptic plasticity mechanisms.
Analyzing non-invasive recordings of electroencephalography (EEG) and magnetoencephalography (MEG) directly in sensor space, using the signal from individual sensors, is a convenient and standard way of working with this type of data. However, volume conduction introduces considerable challenges for sensor space analysis. While the general idea of signal mixing due to volume conduction in EEG/MEG is recognized, the implications have not yet been clearly exemplified. Here, we illustrate how different types of activity overlap on the level of individual sensors. We show spatial mixing in the context of alpha rhythms, which are known to have generators in different areas of the brain. Using simulations with a realistic 3D head model and lead field and data analysis of a large resting-state EEG dataset, we show that electrode signals can be differentially affected by spatial mixing by computing a sensor complexity measure. While prominent occipital alpha rhythms result in less heterogeneous spatial mixing on posterior electrodes, central electrodes show a diversity of rhythms present. This makes the individual contributions, such as the sensorimotor mu-rhythm and temporal alpha rhythms, hard to disentangle from the dominant occipital alpha. Additionally, we show how strong occipital rhythms rhythms can contribute the majority of activity to frontal channels, potentially compromising analyses that are solely conducted in sensor space. We also outline specific consequences of signal mixing for frequently used assessment of power, power ratios and connectivity profiles in basic research and for neurofeedback application. With this work, we hope to illustrate the effects of volume conduction in a concrete way, such that the provided practical illustrations may be of use to EEG researchers to in order to evaluate whether sensor space is an appropriate choice for their topic of investigation.
In the course of global climate change, central Europe is experiencing more frequent and prolonged periods of drought. The drought years 2018 and 2019 affected European beeches (Fagus sylvatica L.) differently: even in the same stand, drought damaged trees neighboured healthy trees, suggesting that the genotype rather than the environment was responsible for this conspicuous pattern. We used this natural experiment to study the genomic basis of drought resistance with Pool-GWAS. Contrasting the extreme phenotypes identified 106 significantly associated SNPs throughout the genome. Most annotated genes with associated SNPs (>70%) were previously implicated in the drought reaction of plants. Non-synonymous substitutions led either to a functional amino acid exchange or premature termination. A SNP-assay with 70 loci allowed predicting drought phenotype in 98.6% of a validation sample of 92 trees. Drought resistance in European beech is a moderately polygenic trait that should respond well to natural selection, selective management, and breeding.
Recently, significant advances have been made by identifying the levels of synchronicity of the underlying dynamics of a given brain state. This research has demonstrated that unconscious dynamics tend to be more synchronous than those found in conscious states, which are more asynchronous. Here we go beyond this dichotomy to demonstrate that the different brain states are always underpinned by spatiotemporal chaos but with dissociable turbulent dynamics. We investigated human neuroimaging data from different brain states (resting state, meditation, deep sleep, and disorders of consciousness after coma) and were able to distinguish between them using complementary model-free and model-based measures of turbulent information transmission. Our model-free approach used recent advances describing a measure of information cascade across spatial scales using tools from turbulence theory. Complementarily, our model-based approach used exhaustive in silico perturbations of whole-brain models fitted to the empirical neuroimaging data, which allowed us to study the information encoding capabilities of the brain states. Overall, the current framework demonstrates that different levels of turbulent dynamics are fundamental for describing and differentiating between brain states.
Mitochondrial NADH:ubiquinone oxidoreductase (complex I) is a 1 MDa membrane protein complex with a central role in energy metabolism. Redox-driven proton translocation by complex I contributes substantially to the proton motive force that drives ATP synthase. Several structures of complex I from bacteria and mitochondria have been determined but its catalytic mechanism has remained controversial. We here present the cryo-EM structure of complex I from Yarrowia lipolytica at 2.1 Å resolution, which reveals the positions of more than 1600 protein-bound water molecules, of which ∼100 are located in putative proton translocation pathways. Another structure of the same complex under steady-state activity conditions at 3.4 Å resolution indicates conformational transitions that we associate with proton injection into the central hydrophilic axis. By combining high-resolution structural data with site-directed mutagenesis and large-scale molecular dynamics simulations, we define details of the proton translocation pathways, and offer new insights into the redox-coupled proton pumping mechanism of complex I.
Substantia nigra dopamine (SN DA) neurons are progressively lost in Parkinson disease (PD). While the molecular and cellular mechanisms of their differential vulnerability and degeneration have been extensively studied, we still know very little about potential functional adaptations of those SN DA neurons that – at least for some time – manage to survive during earlier stages of PD. We utilized a partial lesion 6-OHDA mouse model to characterize initial electrophysiological impairments and chronic adaptations of surviving identified SN DA neurons, both in vivo and in vitro. Early after lesion (3 weeks), we detected a selective loss of in vivo burst firing in surviving SN DA neurons, which was accompanied by in vitro pacemaker instability. In contrast, late after lesion (>2 months), in vivo firing properties of surviving SN DA neurons had recovered in the presence of 2-fold accelerated pacemaking in vitro. Finally, we show that this chronic cell-autonomous adaptation in surviving SN DA neurons was mediated by Kv4.3 channel downregulation. Our study demonstrates substantial homeostatic plasticity of surviving SN DA neurons after a single-hit non-progressive lesion, which might contribute to the phenotype of initially surviving SN DA neurons in PD.
Background Transposable elements (TEs) are an important source of genome plasticity across the tree of life. Accumulating evidence suggests that TEs may not be randomly distributed in the genome. Drift and natural selection are important forces shaping TE distribution and accumulation, acting directly on the TE element or indirectly on the host species. Fungi, with their multifaceted phenotypic diversity and relatively small genome size, are ideal models to study the role of TEs in genome evolution and their impact on the host’s ecological and life history traits. Here we present an account of all TEs found in a high-quality reference genome of the lichen-forming fungus Umbilicaria pustulata, a macrolichen species comprising two climatic ecotypes: Mediterranean and cold-temperate. We trace the occurrence of the newly identified TEs in populations along three replicated elevation gradients using a Pool-Seq approach, to identify TE insertions of potential adaptive significance.
Results We found that TEs cover 21.26 % of the 32.9 Mbp genome, with LTR Gypsy and Copia clades being the most common TEs. Out of a total of 182 TE copies we identified 28 insertions displaying consistent insertion frequency differences between the two host ecotypes across the elevation gradients. Most of the highly differentiated insertions were located near genes, indicating a putative function.
Conclusions This pioneering study into the content and climate niche-specific distribution of TEs in a lichen-forming fungus contributes to understanding the roles of TEs in fungal evolution. Particularly, it may serve as a foundation for assessing the impact of TE dynamics on fungal adaptation to the abiotic environment, and the impact of TE activity on the evolution and maintenance of a symbiotic lifestyle.
Untangling the cell immune response dynamic for severe and critical cases of SARS-CoV-2 infection
(2021)
COVID-19 is a global pandemic leading high death tolls worldwide day by day. Clinical evidence suggests that COVID-19 patients can be classified as non-severe, severe and critical cases. In particular, studies have highlighted the relationship between the lymphopenia and the severity of the illness, where CD8+ T cells have the lowest levels in critical cases. In this work, we aim to elucidate the key parameters that define the course of the disease deviating from severe to critical case. To this end, several mathematical models are proposed to represent the dynamic of the immune response in patients with SARS-CoV-2 infection. The best model had a good fit to reported experimental data, and in accordance with values found in the literature. Our results suggest that a rapid proliferation of CD8+ T cells is decisive in the severity of the disease.
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.
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.
Probing the association between resting state brain network dynamics and psychological resilience
(2021)
Abstract
This study aimed at replicating a previously reported negative correlation between node flexibility and psychological resilience, i.e., the ability to retain mental health in the face of stress and adversity. To this end, we used multiband resting-state BOLD fMRI (TR = .675 sec) from 52 participants who had filled out three psychological questionnaires assessing resilience. Time-resolved functional connectivity was calculated by performing a sliding window approach on averaged time series parcellated according to different established atlases. Multilayer modularity detection was performed to track network reconfigurations over time and node flexibility was calculated as the number of times a node changes community assignment. In addition, node promiscuity (the fraction of communities a node participates in) and node degree (as proxy for time-varying connectivity) were calculated to extend previous work. We found no substantial correlations between resilience and node flexibility. We observed a small number of correlations between the two other brain measures and resilience scores, that were however very inconsistently distributed across brain measures, differences in temporal sampling, and parcellation schemes. This heterogeneity calls into question the existence of previously postulated associations between resilience and brain network flexibility and highlights how results may be influenced by specific analysis choices.
Author Summary We tested the replicability and generalizability of a previously proposed negative association between dynamic brain network reconfigurations derived from multilayer modularity detection (node flexibility) and psychological resilience. Using multiband resting-state BOLD fMRI data and exploring several parcellation schemes, sliding window approaches, and temporal resolutions of the data, we could not replicate previously reported findings regarding the association between node flexibility and resilience. By extending this work to other measures of brain dynamics (node promiscuity, degree) we observe a rather inconsistent pattern of correlations with resilience, that strongly varies across analysis choices. We conclude that further research is needed to understand the network neuroscience basis of mental health and discuss several reasons that may account for the variability in results.
To a crucial extent, the efficiency of reading results from the fact that visual word recognition is faster in predictive contexts. Predictive coding models suggest that this facilitation results from pre-activation of predictable stimulus features across multiple representational levels before stimulus onset. Still, it is not sufficiently understood which aspects of the rich set of linguistic representations that are activated during reading – visual, orthographic, phonological, and/or lexical-semantic – contribute to context-dependent facilitation. To investigate in detail which linguistic representations are pre-activated in a predictive context and how they affect subsequent stimulus processing, we combined a well-controlled repetition priming paradigm, including words and pseudowords (i.e., pronounceable nonwords), with behavioral and magnetoencephalography measurements. For statistical analysis, we used linear mixed modeling, which we found had a higher statistical power compared to conventional multivariate pattern decoding analysis. Behavioral data from 49 participants indicate that word predictability (i.e., context present vs. absent) facilitated orthographic and lexical-semantic, but not visual or phonological processes. Magnetoencephalography data from 38 participants show sustained activation of orthographic and lexical-semantic representations in the interval before processing the predicted stimulus, suggesting selective pre-activation at multiple levels of linguistic representation as proposed by predictive coding. However, we found more robust lexical-semantic representations when processing predictable in contrast to unpredictable letter strings, and pre-activation effects mainly resembled brain responses elicited when processing the expected letter string. This finding suggests that pre-activation did not result in ‘explaining away’ predictable stimulus features, but rather in a ‘sharpening’ of brain responses involved in word processing.
Across languages, the speech signal is characterized by a predominant modulation of the amplitude spectrum between about 4.3-5.5Hz, reflecting the production and processing of linguistic information chunks (syllables, words) every ∼200ms. Interestingly, ∼200ms is also the typical duration of eye fixations during reading. Prompted by this observation, we demonstrate that German readers sample written text at ∼5Hz. A subsequent meta-analysis with 142 studies from 14 languages replicates this result, but also shows that sampling frequencies vary across languages between 3.9Hz and 5.2Hz, and that this variation systematically depends on the complexity of the writing systems (character-based vs. alphabetic systems, orthographic transparency). Finally, we demonstrate empirically a positive correlation between speech spectrum and eye-movement sampling in low-skilled readers. Based on this convergent evidence, we propose that during reading, our brain’s linguistic processing systems imprint a preferred processing rate, i.e., the rate of spoken language production and perception, onto the oculomotor system.
Music, like language, is characterized by hierarchically organized structure that unfolds over time. Music listening therefore requires not only the tracking of notes and beats but also internally constructing high-level musical structures or phrases and anticipating incoming contents. Unlike for language, mechanistic evidence for online musical segmentation and prediction at a structural level is sparse. We recorded neurophysiological data from participants listening to music in its original forms as well as in manipulated versions with locally or globally reversed harmonic structures. We discovered a low-frequency neural component that modulated the neural rhythms of beat tracking and reliably parsed musical phrases. We next identified phrasal phase precession, suggesting that listeners established structural predictions from ongoing listening experience to track phrasal boundaries. The data point to brain mechanisms that listeners use to segment continuous music at the phrasal level and to predict abstract structural features of music.
In an earlier paper we proposed a recursive model for epidemics; in the present paper we generalize this model to include the asymptomatic or unrecorded symptomatic people, which we call dark people (dark sector). We call this the SEPARd-model. A delay differential equation version of the model is added; it allows a better comparison to other models. We carry this out by a comparison with the classical SIR model and indicate why we believe that the SEPARd model may work better for Covid-19 than other approaches.
In the second part of the paper we explain how to deal with the data provided by the JHU, in particular we explain how to derive central model parameters from the data. Other parameters, like the size of the dark sector, are less accessible and have to be estimated more roughly, at best by results of representative serological studies which are accessible, however, only for a few countries. We start our country studies with Switzerland where such data are available. Then we apply the model to a collection of other countries, three European ones (Germany, France, Sweden), the three most stricken countries from three other continents (USA, Brazil, India). Finally we show that even the aggregated world data can be well represented by our approach.
At the end of the paper we discuss the use of the model. Perhaps the most striking application is that it allows a quantitative analysis of the influence of the time until people are sent to quarantine or hospital. This suggests that imposing means to shorten this time is a powerful tool to flatten the curves.
Transport of lipids across membranes is fundamental for diverse biological pathways in cells. Multiple ion-coupled transporters participate in lipid translocation, but their mechanisms remain largely unknown. Major facilitator superfamily (MFS) lipid transporters play central roles in cell wall synthesis, brain development and function, lipids recycling, and cell signaling. Recent structures of MFS lipid transporters revealed overlapping architectural features pointing towards a common mechanism. Here we used cysteine disulfide trapping, molecular dynamics simulations, mutagenesis analysis, and transport assays in vitro and in vivo, to investigate the mechanism of LtaA, a proton-dependent MFS lipid transporter essential for lipoteichoic acids synthesis in the pathogen Staphylococcus aureus. We reveal that LtaA displays asymmetric lateral openings with distinct functional relevance and that cycling through outward- and inward-facing conformations is essential for transport activity. We demonstrate that while the entire amphipathic central cavity of LtaA contributes to lipid binding, its hydrophilic pocket dictates substrate specificity. We propose that LtaA catalyzes lipid translocation by a ‘trap-and-flip’ mechanism that might be shared among MFS lipid transporters.
The human visual cortex enables visual perception through a cascade of hierarchical computations in cortical regions with distinct functionalities. Here, we introduce an AI-driven approach to discover the functional mapping of the visual cortex. We related human brain responses to scene images measured with functional MRI (fMRI) systematically to a diverse set of deep neural networks (DNNs) optimized to perform different scene perception tasks. We found a structured mapping between DNN tasks and brain regions along the ventral and dorsal visual streams. Low-level visual tasks mapped onto early brain regions, 3-dimensional scene perception tasks mapped onto the dorsal stream, and semantic tasks mapped onto the ventral stream. This mapping was of high fidelity, with more than 60% of the explainable variance in nine key regions being explained. Together, our results provide a novel functional mapping of the human visual cortex and demonstrate the power of the computational approach.
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.
Nuclear pore complexes (NPCs) mediate nucleocytoplasmic transport. Their intricate 120 MDa architecture remains incompletely understood. Here, we report a near-complete structural model of the human NPC scaffold with explicit membrane and in multiple conformational states. We combined AI-based structure prediction with in situ and in cellulo cryo-electron tomography and integrative modeling. We show that linker Nups spatially organize the scaffold within and across subcomplexes to establish the higher-order structure. Microsecond-long molecular dynamics simulations suggest that the scaffold is not required to stabilize the inner and outer nuclear membrane fusion, but rather widens the central pore. Our work exemplifies how AI-based modeling can be integrated with in situ structural biology to understand subcellular architecture across spatial organization levels.
Precise estimates of genome sizes are important parameters for both theoretical and practical biodiversity genomics. We present here a fast, easy-to-implement and precise method to estimate genome size from the number of bases sequenced and the mean sequence coverage. To estimate the latter, we take advantage of the fact that a precise estimation of the Poisson distribution parameter lambda is possible from truncated data, restricted to the part of the coverage distribution representing the true underlying distribution. With simulations we could show that reasonable genome size estimates can be gained even from low-coverage (10X), highly discontinuous genome drafts. Comparison of estimates from a wide range of taxa and sequencing strategies with flow-cytometry estimates of the same individuals showed a very good fit and suggested that both methods yield comparable, interchangeable results.
The brain adapts to the sensory environment. For example, simple sensory exposure can modify the response properties of early sensory neurons. How these changes affect the overall encoding and maintenance of stimulus information across neuronal populations remains unclear. We perform parallel recordings in the primary visual cortex of anesthetized cats and find that brief, repetitive exposure to structured visual stimuli enhances stimulus encoding by decreasing the selectivity and increasing the range of the neuronal responses that persist after stimulus presentation. Low-dimensional projection methods and simple classifiers demonstrate that visual exposure increases the segregation of persistent neuronal population responses into stimulus-specific clusters. These observed refinements preserve the representational details required for stimulus reconstruction and are detectable in post-exposure spontaneous activity. Assuming response facilitation and recurrent network interactions as the core mechanisms underlying stimulus persistence, we show that the exposure-driven segregation of stimulus responses can arise through strictly local plasticity mechanisms, also in the absence of firing rate changes. Our findings provide evidence for the existence of an automatic, unguided optimization process that enhances the encoding power of neuronal populations in early visual cortex, thus potentially benefiting simple readouts at higher stages of visual processing.
Epilepsy can have many different causes and its development (epileptogenesis) involves a bewildering complexity of interacting processes. Here, we present a first-of-its-kind computational model to better understand the role of neuroimmune interactions in the development of acquired epilepsy. Our model describes the interactions between neuroinflammation, blood-brain barrier disruption, neuronal loss, circuit remodeling, and seizures. Formulated as a system of nonlinear differential equations, the model is validated using data from animal models that mimic human epileptogenesis caused by infection, status epilepticus, and blood-brain barrier disruption. The mathematical model successfully explains characteristic features of epileptogenesis such as its paradoxically long timescales (up to decades) despite short and transient injuries, or its dependence on the intensity of an injury. Furthermore, stochasticity in the model captures the variability of epileptogenesis outcomes in individuals exposed to identical injury. Notably, in line with the concept of degeneracy, our simulations reveal multiple routes towards epileptogenesis with neuronal loss as a sufficient but non-necessary component. We show that our framework allows for in silico predictions of therapeutic strategies, providing information on injury-specific therapeutic targets and optimal time windows for intervention.
The measurement of protein dynamics by proteomics to study cell remodeling has seen increased attention over the last years. This development is largely driven by a number of technological advances in proteomics methods. Pulsed stable isotope labeling in cell culture (SILAC) combined with tandem mass tag (TMT) labeling has evolved as a gold standard for profiling protein synthesis and degradation. While the experimental setup is similar to typical proteomics experiments, the data analysis proves more difficult: After peptide identification through search engines, data extraction requires either custom scripted pipelines or tedious manual table manipulations to extract the TMT-labeled heavy and light peaks of interest. To overcome this limitation, which deters researchers from using protein dynamic proteomics, we developed a user-friendly, browser-based application that allows easy and reproducible data analysis without the need for scripting experience. In addition, we provide a python package that can be implemented in established data analysis pipelines. We anticipate that this tool will ease data analysis and spark further research aimed at monitoring protein translation and degradation by proteomics.
SAMHD1 is discussed as a tumour suppressor protein, but its potential role in cancer has only been investigated in very few cancer types. Here, we performed a systematic analysis of the TCGA (adult cancer) and TARGET (paediatric cancer) databases, the results of which did not suggest that SAMHD1 should be regarded as a bona fide tumour suppressor. SAMHD1 mutations that interfere with SAMHD1 function were not associated with poor outcome, which would be expected for a tumour suppressor. High SAMHD1 tumour levels were associated with increased survival in some cancer entities and reduced survival in others. Moreover, the data suggested differences in the role of SAMHD1 between males and females and between different races. Often, there was no significant relationship between SAMHD1 levels and cancer outcome. Taken together, our results indicate that SAMHD1 may exert pro- or anti-tumourigenic effects and that SAMHD1 is involved in the oncogenic process in a minority of cancer cases. These findings seem to be in disaccord with a perception and narrative forming in the field suggesting that SAMHD1 is a tumour suppressor. A systematic literature review confirmed that most of the available scientific articles focus on a potential role of SAMHD1 as a tumour suppressor. The reasons for this remain unclear but may include confirmation bias and publication bias. Our findings emphasise that hypotheses, perceptions, and assumptions need to be continuously challenged by using all available data and evidence.
The hippocampal formation is linked to spatial navigation, but there is little corroboration from freely-moving primates with concurrent monitoring of three-dimensional head and gaze stances. We recorded neurons and local field potentials across hippocampal regions in rhesus macaques during free foraging in an open environment while tracking their head and eye. Theta band activity was intermittently present at movement onset and modulated by saccades. Many cells were phase-locked to theta, with few showing theta phase precession. Most hippocampal neurons encoded a mixture of spatial variables beyond place fields and a negligible number showed prominent grid tuning. Spatial representations were dominated by facing location and allocentric direction, mostly in head, rather than gaze, coordinates. Importantly, eye movements strongly modulated neural activity in all regions. These findings reveal that the macaque hippocampal formation represents three-dimensional space using a multiplexed code, with head orientation and eye movement properties dominating over simple place and grid coding during free exploration.
Path integration is a sensorimotor computation that can be used to infer latent dynamical states by integrating self-motion cues. We studied the influence of sensory observation (visual/vestibular) and latent control dynamics (velocity/acceleration) on human path integration using a novel motion-cueing algorithm. Sensory modality and control dynamics were both varied randomly across trials, as participants controlled a joystick to steer to a memorized target location in virtual reality. Visual and vestibular steering cues allowed comparable accuracies only when participants controlled their acceleration, suggesting that vestibular signals, on their own, fail to support accurate path integration in the absence of sustained acceleration. Nevertheless, performance in all conditions reflected a failure to fully adapt to changes in the underlying control dynamics, a result that was well explained by a bias in the dynamics estimation. This work demonstrates how an incorrect internal model of control dynamics affects navigation in volatile environments in spite of continuous sensory feedback.
Olivo-cerebellar loops, where anatomical patches of the cerebellar cortex and inferior olive project one onto the other, form an anatomical unit of cerebellar computation. Here, we investigated how successive computational steps map onto olivo-cerebellar loops. Lobules IX-X of the cerebellar vermis, i.e. the nodulus and uvula, implement an internal model of the inner ear’s graviceptor, the otolith organs. We have previously identified two populations of Purkinje cells that participate in this computation: Tilt-selective cells transform egocentric rotation signals into allocentric tilt velocity signals, to track head motion relative to gravity, and translation-selective cells encode otolith prediction error. Here we show that, despite very distinct simple spike response properties, both types of Purkinje cells emit complex spikes that are proportional to sensory prediction error. This indicates that both cell populations comprise a single olivo-cerebellar loop, in which only translation-selective cells project to the inferior olive. We propose a neural network model where sensory prediction errors computed by translation-selective cells are used as a teaching signal for both populations, and demonstrate that this network can learn to implement an internal model of the otoliths.
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.
The production of prompt Λ+c baryons at midrapidity (|y|<0.5) was measured in central (0-10%) and mid-central (30-50%) Pb-Pb collisions at the center-of-mass energy per nucleon-nucleon pair sNN−−−√=5.02 TeV with the ALICE detector. The Λ+c production yield, the Λ+c/D0 production ratio, and the Λ+c nuclear modification factor RAA are reported. The results are more precise and more differential in transverse momentum (pT) and centrality with respect to previous measurements. The Λ+c/D0 ratio, which is enhanced with respect to the pp measurement for 4<pT<8 GeV/c, is described by theoretical calculations that model the charm-quark transport in the quark-gluon plasma and include hadronization via both coalescence and fragmentation mechanisms.
The present article proposes a re-reading of what "inclusion" into the sphere of the historical actually means in modern European historical discourse. It argues that this re-reading permits challenging a powerful, but problematic norm of ontological homogeneity as something to be achieved in and by historical discourse. At least some of the more conceptually profound challenges that accounts of "deep history" - of very distant pasts - pose to historical discourse have to do with pursuits of this norm. Historical theory has the potential of responding to some of these challenges and actually reverting them back at the practice of accounting for deep times in historical writing. The argument proceeds, in a first step, by analyzing the ties between modern European mortuary cultures and historical writing. In a second step, the history of humanitarian moralities is brought to bear on the analysis, in order to make visible, thirdly, the fractured presences of deep time in modern-era and contemporary historical writing. The fractures in question emerge, the article argues, from the ontological heterogeneity of historical knowledge. So in the end, a position beyond ontological homogeneity is adumbrated.
Release of neuropeptides from dense core vesicles (DCVs) is essential for neuromodulation. Compared to the release of small neurotransmitters, much less is known about the mechanisms and proteins contributing to neuropeptide release. By optogenetics, behavioral analysis, electrophysiology, electron microscopy, and live imaging, we show that synapsin SNN-1 is required for cAMP-dependent neuropeptide release in Caenorhabditis elegans hermaphrodite cholinergic motor neurons. In synapsin mutants, behaviors induced by the photoactivated adenylyl cyclase bPAC, which we previously showed to depend on acetylcholine and neuropeptides (Steuer Costa et al., 2017), are altered like in animals with reduced cAMP. Synapsin mutants have slight alterations in synaptic vesicle (SV) distribution, however, a defect in SV mobilization was apparent after channelrhodopsin-based photostimulation. DCVs were largely affected in snn-1 mutants: DCVs were ∼30% reduced in synaptic terminals, and not released following bPAC stimulation. Imaging axonal DCV trafficking, also in genome-engineered mutants in the serine-9 protein kinase A phosphorylation site, showed that synapsin captures DCVs at synapses, making them available for release. SNN-1 co-localized with immobile, captured DCVs. In synapsin deletion mutants, DCVs were more mobile and less likely to be caught at release sites, and in non-phosphorylatable SNN-1B(S9A) mutants, DCVs traffic less and accumulate, likely by enhanced SNN-1 dependent tethering. Our work establishes synapsin as a key mediator of neuropeptide release.