Spatiotemporal regulation of the BarA/UvrY two-component signaling system.

The BarA/UvrY two-component signal transduction system mediates adaptive responses of Escherichia coli to changes in growth stage. At late exponential growth phase, the BarA sensor kinase autophosphorylates and transphosphorylates UvrY, which activates transcription of the CsrB and CsrC noncoding RNAs. CsrB and CsrC, in turn, sequester and antagonize the RNA binding protein CsrA, which posttranscriptionally regulates translation and/or stability of its target mRNAs. Here, we provide evidence that during stationary phase of growth, the HflKC complex recruits BarA to the poles of the cells and silences its kinase activity. Moreover, we show that during the exponential phase of growth, CsrA inhibits hflK and hflC expression, thereby enabling BarA activation upon encountering its stimulus. Thus, in addition to temporal control of BarA activity, spatial regulation is demonstrated.

Portraits of communication in neuronal networks.

The brain is organized as a network of highly specialized networks of spiking neurons. To exploit such a modular architecture for computation, the brain has to be able to regulate the flow of spiking activity between these specialized networks. In this Opinion article, we review various prominent mechanisms that may underlie communication between neuronal networks. We show that communication between neuronal networks can be understood as trajectories in a two-dimensional state space, spanned by the properties of the input. Thus, we propose a common framework to understand neuronal communication mediated by seemingly different mechanisms. We also suggest that the nesting of slow (for example, alpha-band and theta-band) oscillations and fast (gamma-band) oscillations can serve as an important control mechanism that allows or prevents spiking signals to be routed between specific networks. We argue that slow oscillations can modulate the time required to establish network resonance or entrainment and, thereby, regulate communication between neuronal networks.

LSD-induced increase of Ising temperature and algorithmic complexity of brain dynamics.

A topic of growing interest in computational neuroscience is the discovery of fundamental principles underlying global dynamics and the self-organization of the brain. In particular, the notion that the brain operates near criticality has gained considerable support, and recent work has shown that the dynamics of different brain states may be modeled by pairwise maximum entropy Ising models at various distances from a phase transition, i.e., from criticality. Here we aim to characterize two brain states (psychedelics-induced and placebo) as captured by functional magnetic resonance imaging (fMRI), with features derived from the Ising spin model formalism (system temperature, critical point, susceptibility) and from algorithmic complexity. We hypothesized, along the lines of the entropic brain hypothesis, that psychedelics drive brain dynamics into a more disordered state at a higher Ising temperature and increased complexity. We analyze resting state blood-oxygen-level-dependent (BOLD) fMRI data collected in an earlier study from fifteen subjects in a control condition (placebo) and during ingestion of lysergic acid diethylamide (LSD). Working with the automated anatomical labeling (AAL) brain parcellation, we first create "archetype" Ising models representative of the entire dataset (global) and of the data in each condition. Remarkably, we find that such archetypes exhibit a strong correlation with an average structural connectome template obtained from dMRI (r = 0.6). We compare the archetypes from the two conditions and find that the Ising connectivity in the LSD condition is lower than in the placebo one, especially in homotopic links (interhemispheric connectivity), reflecting a significant decrease of homotopic functional connectivity in the LSD condition. The global archetype is then personalized for each individual and condition by adjusting the system temperature. The resulting temperatures are all near but above the critical point of the model in the paramagnetic (disordered) phase. The individualized Ising temperatures are higher in the LSD condition than in the placebo condition (p = 9 × 10-5). Next, we estimate the Lempel-Ziv-Welch (LZW) complexity of the binarized BOLD data and the synthetic data generated with the individualized model using the Metropolis algorithm for each participant and condition. The LZW complexity computed from experimental data reveals a weak statistical relationship with condition (p = 0.04 one-tailed Wilcoxon test) and none with Ising temperature (r(13) = 0.13, p = 0.65), presumably because of the limited length of the BOLD time series. Similarly, we explore complexity using the block decomposition method (BDM), a more advanced method for estimating algorithmic complexity. The BDM complexity of the experimental data displays a significant correlation with Ising temperature (r(13) = 0.56, p = 0.03) and a weak but significant correlation with condition (p = 0.04, one-tailed Wilcoxon test). This study suggests that the effects of LSD increase the complexity of brain dynamics by loosening interhemispheric connectivity-especially homotopic links. In agreement with earlier work using the Ising formalism with BOLD data, we find the brain state in the placebo condition is already above the critical point, with LSD resulting in a shift further away from criticality into a more disordered state.

Macroscopic Quantities of Collective Brain Activity during Wakefulness and Anesthesia.

The study of states of arousal is key to understand the principles of consciousness. Yet, how different brain states emerge from the collective activity of brain regions remains unknown. Here, we studied the fMRI brain activity of monkeys during wakefulness and anesthesia-induced loss of consciousness. We showed that the coupling between each brain region and the rest of the cortex provides an efficient statistic to classify the two brain states. Based on this and other statistics, we estimated maximum entropy models to derive collective, macroscopic properties that quantify the system's capabilities to produce work, to contain information, and to transmit it, which were all maximized in the awake state. The differences in these properties were consistent with a phase transition from critical dynamics in the awake state to supercritical dynamics in the anesthetized state. Moreover, information-theoretic measures identified those parameters that impacted the most the network dynamics. We found that changes in the state of consciousness primarily depended on changes in network couplings of insular, cingulate, and parietal cortices. Our findings suggest that the brain state transition underlying the loss of consciousness is predominantly driven by the uncoupling of specific brain regions from the rest of the network.

Identification of Z nucleotides as an ancient signal for two-component system activation in bacteria.

Two-component systems (TCSs) in bacteria are molecular circuits that allow the perception of and response to diverse stimuli. These signaling circuits rely on phosphoryl-group transfers between transmitter and receiver domains of sensor kinase and response regulator proteins, and regulate several cellular processes in response to internal or external cues. Phosphorylation, and thereby activation, of response regulators has been demonstrated to occur by their cognate histidine kinases but also by low molecular weight phosphodonors such as acetyl phosphate and carbamoyl phosphate. Here, we present data indicating that the intermediates of the de novo syntheses of purines and histidine, 5-aminoimidazole-4-carboxamide-1-beta-D-ribofuranosyl 5'-monophosphate (ZMP) and/or 5-aminoimidazole-4-carboxamide-1-beta-D-ribofuranosyl 5'-triphosphate (ZTP), activate the response regulator UvrY, by promoting its autophosphorylation at the conserved aspartate at position 54. Moreover, these Z nucleotides are shown to also activate the nonrelated response regulators ArcA, CpxR, RcsB, and PhoQ. We propose that ZMP and/or ZTP act as alarmones for a wide range of response regulators in vivo, providing a novel mechanism by which they could impact gene expression in response to metabolic cues.

The Escherichia coli two-component signal sensor BarA binds protonated acetate via a conserved hydrophobic-binding pocket.

The BarA/UvrY two-component signal transduction system is widely conserved in γ-proteobacteria and provides a link between the metabolic state of the cells and the Csr posttranscriptional regulatory system. In Escherichia coli, the BarA/UvrY system responds to the presence of acetate and other short-chain carboxylic acids by activating transcription of the noncoding RNAs, CsrB and CsrC, which sequester the RNA-binding protein CsrA, a global regulator of gene expression. However, the state of the carboxyl group in the acetate molecule, which serves as the BarA stimulus, and the signal reception site of BarA remain unknown. In this study, we show that the deletion or replacement of the periplasmic domain of BarA and also the substitution of certain hydroxylated and hydrophobic amino acid residues in this region, result in a sensor kinase that remains unresponsive to its physiological stimulus, demonstrating that the periplasmic region of BarA constitutes a functional detector domain. Moreover, we provide evidence that the protonated state of acetate or formate serves as the physiological stimulus of BarA. In addition, modeling of the BarA sensor domain and prediction of the signal-binding site, by blind molecular docking, revealed a calcium channels and chemotaxis receptors domain with a conserved binding pocket, which comprised uncharged polar and hydrophobic amino acid residues. Based on the comparative sequence and phylogenetic analyses, we propose that, at least, two types of BarA orthologues diverged and evolved separately to acquire distinct signal-binding properties, illustrating the wide adaptability of the bacterial sensor kinase proteins.

The role of sensory kinase proteins in two-component signal transduction.

Two-component systems (TCSs) are modular signaling circuits that regulate diverse aspects of microbial physiology in response to environmental cues. These molecular circuits comprise a sensor histidine kinase (HK) protein that contains a conserved histidine residue, and an effector response regulator (RR) protein with a conserved aspartate residue. HKs play a major role in bacterial signaling, since they perceive specific stimuli, transmit the message across the cytoplasmic membrane, and catalyze their own phosphorylation, and the trans-phosphorylation and dephosphorylation of their cognate response regulator. The molecular mechanisms by which HKs co-ordinate these functions have been extensively analyzed by genetic, biochemical, and structural approaches. Here, we describe the most common modular architectures found in bacterial HKs, and address the operation mode of the individual functional domains. Finally, we discuss the use of these signaling proteins as drug targets or as sensing devices in whole-cell biosensors with medical and biotechnological applications.

Embryologist agreement when assessing blastocyst implantation probability: is data-driven prediction the solution to embryo assessment subjectivity?

STUDY QUESTION: What is the accuracy and agreement of embryologists when assessing the implantation probability of blastocysts using time-lapse imaging (TLI), and can it be improved with a data-driven algorithm? SUMMARY ANSWER: The overall interobserver agreement of a large panel of embryologists was moderate and prediction accuracy was modest, while the purpose-built artificial intelligence model generally resulted in higher performance metrics. WHAT IS KNOWN ALREADY: Previous studies have demonstrated significant interobserver variability amongst embryologists when assessing embryo quality. However, data concerning embryologists' ability to predict implantation probability using TLI is still lacking. Emerging technologies based on data-driven tools have shown great promise for improving embryo selection and predicting clinical outcomes. STUDY DESIGN, SIZE, DURATION: TLI video files of 136 embryos with known implantation data were retrospectively collected from two clinical sites between 2018 and 2019 for the performance assessment of 36 embryologists and comparison with a deep neural network (DNN). PARTICIPANTS/MATERIALS, SETTING, METHODS: We recruited 39 embryologists from 13 different countries. All participants were blinded to clinical outcomes. A total of 136 TLI videos of embryos that reached the blastocyst stage were used for this experiment. Each embryo's likelihood of successfully implanting was assessed by 36 embryologists, providing implantation probability grades (IPGs) from 1 to 5, where 1 indicates a very low likelihood of implantation and 5 indicates a very high likelihood. Subsequently, three embryologists with over 5 years of experience provided Gardner scores. All 136 blastocysts were categorized into three quality groups based on their Gardner scores. Embryologist predictions were then converted into predictions of implantation (IPG ≥ 3) and no implantation (IPG ≤ 2). Embryologists' performance and agreement were assessed using Fleiss kappa coefficient. A 10-fold cross-validation DNN was developed to provide IPGs for TLI video files. The model's performance was compared to that of the embryologists. MAIN RESULTS AND THE ROLE OF CHANCE: Logistic regression was employed for the following confounding variables: country of residence, academic level, embryo scoring system, log years of experience and experience using TLI. None were found to have a statistically significant impact on embryologist performance at α = 0.05. The average implantation prediction accuracy for the embryologists was 51.9% for all embryos (N = 136). The average accuracy of the embryologists when assessing top quality and poor quality embryos (according to the Gardner score categorizations) was 57.5% and 57.4%, respectively, and 44.6% for fair quality embryos. Overall interobserver agreement was moderate (κ = 0.56, N = 136). The best agreement was achieved in the poor + top quality group (κ = 0.65, N = 77), while the agreement in the fair quality group was lower (κ = 0.25, N = 59). The DNN showed an overall accuracy rate of 62.5%, with accuracies of 62.2%, 61% and 65.6% for the poor, fair and top quality groups, respectively. The AUC for the DNN was higher than that of the embryologists overall (0.70 DNN vs 0.61 embryologists) as well as in all of the Gardner groups (DNN vs embryologists-Poor: 0.69 vs 0.62; Fair: 0.67 vs 0.53; Top: 0.77 vs 0.54). LIMITATIONS, REASONS FOR CAUTION: Blastocyst assessment was performed using video files acquired from time-lapse incubators, where each video contained data from a single focal plane. Clinical data regarding the underlying cause of infertility and endometrial thickness before the transfer was not available, yet may explain implantation failure and lower accuracy of IPGs. Implantation was defined as the presence of a gestational sac, whereas the detection of fetal heartbeat is a more robust marker of embryo viability. The raw data were anonymized to the extent that it was not possible to quantify the number of unique patients and cycles included in the study, potentially masking the effect of bias from a limited patient pool. Furthermore, the lack of demographic data makes it difficult to draw conclusions on how representative the dataset was of the wider population. Finally, embryologists were required to assess the implantation potential, not embryo quality. Although this is not the traditional approach to embryo evaluation, morphology/morphokinetics as a means of assessing embryo quality is believed to be strongly correlated with viability and, for some methods, implantation potential. WIDER IMPLICATIONS OF THE FINDINGS: Embryo selection is a key element in IVF success and continues to be a challenge. Improving the predictive ability could assist in optimizing implantation success rates and other clinical outcomes and could minimize the financial and emotional burden on the patient. This study demonstrates moderate agreement rates between embryologists, likely due to the subjective nature of embryo assessment. In particular, we found that average embryologist accuracy and agreement were significantly lower for fair quality embryos when compared with that for top and poor quality embryos. Using data-driven algorithms as an assistive tool may help IVF professionals increase success rates and promote much needed standardization in the IVF clinic. Our results indicate a need for further research regarding technological advancement in this field. STUDY FUNDING/COMPETING INTEREST(S): Embryonics Ltd is an Israel-based company. Funding for the study was partially provided by the Israeli Innovation Authority, grant #74556. TRIAL REGISTRATION NUMBER: N/A.

Guidelines for Perioperative Care in Elective Abdominal and Pelvic Surgery at Primary and Secondary Hospitals in Low-Middle-Income Countries (LMIC's): Enhanced Recovery After Surgery (ERAS) Society Recommendation.

BACKGROUND: This is the first Enhanced Recovery After Surgery (ERAS®) Society guideline for primary and secondary hospitals in low-middle-income countries (LMIC's) for elective abdominal and gynecologic care. METHODS: The ERAS LMIC Guidelines group was established by the ERAS® Society in collaboration with different representatives of perioperative care from LMIC's. The group consisted of seven members from the ERAS® Society and eight members from LMIC's. An updated systematic literature search and evaluation of evidence from previous ERAS® guidelines was performed by the leading authors of the Colorectal (2018) and Gynecologic (2019) surgery guidelines (Gustafsson et al in World J Surg 43:6592-695, Nelson et al in Int J Gynecol Cancer 29(4):651-668). Meta-analyses randomized controlled trials (RCTs), prospective and retrospective cohort studies from both HIC's and LMIC's were considered for each perioperative item. The members in the LMIC group then applied the current evidence and adapted the recommendations for each intervention as well as identifying possible new items relevant to LMIC's. The Grading of Recommendations, Assessment, Development and Evaluation system (GRADE) methodology was used to determine the quality of the published evidence. The strength of the recommendations was based on importance of the problem, quality of evidence, balance between desirable and undesirable effects, acceptability to key stakeholders, cost of implementation and specifically the feasibility of implementing in LMIC's and determined through discussions and consensus. RESULTS: In addition to previously described ERAS® Society interventions, the following items were included, revised or discussed: the Surgical Safety Checklist (SSC), preoperative routine human immunodeficiency virus (HIV) testing in countries with a high prevalence of HIV/AIDS (CD4 and viral load for those patients that are HIV positive), delirium screening and prevention, COVID 19 screening, VTE prophylaxis, immuno-nutrition, prehabilitation, minimally invasive surgery (MIS) and a standardized postoperative monitoring guideline. CONCLUSIONS: These guidelines are seen as a starting point to address the urgent need to improve perioperative care and to effect data-driven, evidence-based care in LMIC's.

Increased sensitivity to strong perturbations in a whole-brain model of LSD.

Lysergic acid diethylamide (LSD) is a potent psychedelic drug, which has seen a revival in clinical and pharmacological research within recent years. Human neuroimaging studies have shown fundamental changes in brain-wide functional connectivity and an expansion of dynamical brain states, thus raising the question about a mechanistic explanation of the dynamics underlying these alterations. Here, we applied a novel perturbational approach based on a whole-brain computational model, which opens up the possibility to externally perturb different brain regions in silico and investigate differences in dynamical stability of different brain states, i.e. the dynamical response of a certain brain region to an external perturbation. After adjusting the whole-brain model parameters to reflect the dynamics of functional magnetic resonance imaging (fMRI) BOLD signals recorded under the influence of LSD or placebo, perturbations of different brain areas were simulated by either promoting or disrupting synchronization in the regarding brain region. After perturbation offset, we quantified the recovery characteristics of the brain area to its basal dynamical state with the Perturbational Integration Latency Index (PILI) and used this measure to distinguish between the two brain states. We found significant changes in dynamical complexity with consistently higher PILI values after LSD intake on a global level, which indicates a shift of the brain's global working point further away from a stable equilibrium as compared to normal conditions. On a local level, we found that the largest differences were measured within the limbic network, the visual network and the default mode network. Additionally, we found a higher variability of PILI values across different brain regions after LSD intake, indicating higher response diversity under LSD after an external perturbation. Our results provide important new insights into the brain-wide dynamical changes underlying the psychedelic state - here provoked by LSD intake - and underline possible future clinical applications of psychedelic drugs in particular psychiatric disorders.

Protein dosage of the lldPRD operon is correlated with RNase E-dependent mRNA processing.

The ability of Escherichia coli to grow on L-lactate as a sole carbon source depends on the expression of the lldPRD operon. A striking feature of this operon is that the transcriptional regulator (LldR) encoding gene is located between the permease (LldP) and the dehydrogenase (LldD) encoding genes. In this study we report that dosage of the LldP, LldR, and LldD proteins is not modulated on the transcriptional level. Instead, modulation of protein dosage is primarily correlated with RNase E-dependent mRNA processing events that take place within the lldR mRNA, leading to the immediate inactivation of lldR, to differential segmental stabilities of the resulting cleavage products, and to differences in the translation efficiencies of the three cistrons. A model for the processing events controlling the molar quantities of the proteins in the lldPRD operon is presented and discussed.ImportanceAdjustment of gene expression is critical for proper cell function. For the case of polycistronic transcripts, posttranscriptional regulatory mechanisms can be used to fine-tune the expression of individual cistrons. Here, we elucidate how protein dosage of the Escherichia coli lldPRD operon, which presents the paradox of having the gene encoding a regulator protein located between genes that code for a permease and an enzyme, is regulated. Our results demonstrate that the key event in this regulatory mechanism involves the RNase E-dependent cleavage of the primary lldPRD transcript at internal site(s) located within the lldR cistron, resulting in a drastic decrease of intact lldR mRNA, to differential segmental stabilities of the resulting cleavage products, and to differences in the translation efficiencies of the three cistrons.

Routes of phosphoryl group transfer during signal transmission and signal decay in the dimeric sensor histidine kinase ArcB.

The Arc (anoxic redox control) two-component system of Escherichia coli, comprising ArcA as the response regulator and ArcB as the sensor histidine kinase, modulates the expression of numerous genes in response to respiratory growth conditions. Under reducing growth conditions, ArcB autophosphorylates at the expense of ATP, and transphosphorylates ArcA via a His292 → Asp576 → His717 → Asp54 phosphorelay, whereas under oxidizing growth conditions, ArcB catalyzes the dephosphorylation of ArcA-P by a reverse Asp54 → His717 → Asp576 → Pi phosphorelay. However, the exact phosphoryl group transfer routes and the molecular mechanisms determining their directions are unclear. Here, we show that, during signal propagation, the His292 → Asp576 and Asp576 → His717 phosphoryl group transfers within ArcB dimers occur intra- and intermolecularly, respectively. Moreover, we report that, during signal decay, the phosphoryl group transfer from His717 to Asp576 takes place intramolecularly. In conclusion, we present a mechanism that dictates the direction of the phosphoryl group transfer within ArcB dimers and that enables the discrimination of the kinase and phosphatase activities of ArcB.

Distinct modes of functional connectivity induced by movie-watching.

A fundamental question in systems neuroscience is how endogenous neuronal activity self-organizes during particular brain states. Recent neuroimaging studies have demonstrated systematic relationships between resting-state and task-induced functional connectivity (FC). In particular, continuous task studies, such as movie watching, speak to alterations in coupling among cortical regions and enhanced fluctuations in FC compared to the resting-state. This suggests that FC may reflect systematic and large-scale reorganization of functionally integrated responses while subjects are watching movies. In this study, we characterized fluctuations in FC during resting-state and movie-watching conditions. We found that the FC patterns induced systematically by movie-watching can be explained with a single principal component. These condition-specific FC fluctuations overlapped with inter-subject synchronization patterns in occipital and temporal brain regions. However, unlike inter-subject synchronization, condition-specific FC patterns were characterized by increased correlations within frontal brain regions and reduced correlations between frontal-parietal brain regions. We investigated these condition-specific functional variations as a shorter time scale, using time-resolved FC. The time-resolved FC showed condition-specificity over time; notably when subjects watched both the same and different movies. To explain self-organisation of global FC through the alterations in local dynamics, we used a large-scale computational model. We found that condition-specific reorganization of FC could be explained by local changes that engendered changes in FC among higher-order association regions, mainly in frontal and parietal cortices.

Importance of point defect reactions for the atomic-scale roughness of III-V nanowire sidewalls.

The surface morphology of III-V semiconductor nanowires (NWs) protected by an arsenic cap and subsequently evaporated in ultrahigh vacuum is investigated with scanning tunneling microscopy and scanning transmission electron microscopy. We show that the changes of the surface morphology as a function of the NW composition and the nature of the seed particles are intimately related to the formation and reaction of surface point defects. Langmuir evaporation close to the congruent evaporation temperature causes the formation of vacancies which nucleate and form vacancy islands on {110} sidewalls of self-catalyzed InAs NWs. However, for annealing temperatures much smaller than the congruent temperature, a new phenomenon occurs: group III vacancies form and are filled by excess As atoms, leading to surface AsGa antisites. The resulting Ga adatoms nucleate with excess As atoms at the NW edges, producing monoatomic-step islands on the {110} sidewalls of GaAs NWs. Finally, when gold atoms diffuse from the seed particle onto the {110} sidewalls during evaporation of the protective As cap, Langmuir evaporation does not take place, leaving the sidewalls of InAsSb NWs atomically flat.

Linking Entropy at Rest with the Underlying Structural Connectivity in the Healthy and Lesioned Brain.

The brain is a network that mediates information processing through a wide range of states. The extent of state diversity is a reflection of the entropy of the network. Here we measured the entropy of brain regions (nodes) in empirical and modeled functional networks reconstructed from resting state fMRI to address the connection of entropy at rest with the underlying structure measured through diffusion spectrum imaging. Using 18 empirical and 18 modeled stroke networks, we also investigated the effect that focal lesions have on node entropy and information diffusion. Overall, positive correlations between node entropy and structure were observed, especially between node entropy and node strength in both empirical and modeled data. Although lesions were restricted to one hemisphere in all stroke patients, entropy reduction was not only present in nodes from the damaged hemisphere, but also in nodes from the contralesioned hemisphere, an effect replicated in modeled stroke networks. Globally, information diffusion was also affected in empirical and modeled strokes compared with healthy controls. This is the first study showing that artificial lesions affect local and global network aspects in very similar ways compared with empirical strokes, shedding new light into the functional nature of stroke.

Stereotypical modulations in dynamic functional connectivity explained by changes in BOLD variance.

Spontaneous activity measured in human subject under the absence of any task exhibits complex patterns of correlation that largely correspond to large-scale functional topographies obtained with a wide variety of cognitive and perceptual tasks. These "resting state networks" (RSNs) fluctuate over time, forming and dissolving on the scale of seconds to minutes. While these fluctuations, most prominently those of the default mode network, have been linked to cognitive function, it remains unclear whether they result from random noise or whether they index a nonstationary process which could be described as state switching. In this study, we use a sliding windows-approach to relate temporal dynamics of RSNs to global modulations in correlation and BOLD variance. We compare empirical data, phase-randomized surrogate data, and data simulated with a stationary model. We find that RSN time courses exhibit a large amount of coactivation in all three cases, and that the modulations in their activity are closely linked to global dynamics of the underlying BOLD signal. We find that many properties of the observed fluctuations in FC and BOLD, including their ranges and their correlations amongst each other, are explained by fluctuations around the average FC structure. However, we also report some interesting characteristics that clearly support nonstationary features in the data. In particular, we find that the brain spends more time in the troughs of modulations than can be expected from stationary dynamics.

Spontaneous cortical activity is transiently poised close to criticality.

Brain activity displays a large repertoire of dynamics across the sleep-wake cycle and even during anesthesia. It was suggested that criticality could serve as a unifying principle underlying the diversity of dynamics. This view has been supported by the observation of spontaneous bursts of cortical activity with scale-invariant sizes and durations, known as neuronal avalanches, in recordings of mesoscopic cortical signals. However, the existence of neuronal avalanches in spiking activity has been equivocal with studies reporting both its presence and absence. Here, we show that signs of criticality in spiking activity can change between synchronized and desynchronized cortical states. We analyzed the spontaneous activity in the primary visual cortex of the anesthetized cat and the awake monkey, and found that neuronal avalanches and thermodynamic indicators of criticality strongly depend on collective synchrony among neurons, LFP fluctuations, and behavioral state. We found that synchronized states are associated to criticality, large dynamical repertoire and prolonged epochs of eye closure, while desynchronized states are associated to sub-criticality, reduced dynamical repertoire, and eyes open conditions. Our results show that criticality in cortical dynamics is not stationary, but fluctuates during anesthesia and between different vigilance states.

Resting state networks in empirical and simulated dynamic functional connectivity.

It is well-established that patterns of functional connectivity (FC) - measures of correlated activity between pairs of voxels or regions observed in the human brain using neuroimaging - are robustly expressed in spontaneous activity during rest. These patterns are not static, but exhibit complex spatio-temporal dynamics. Over the last years, a multitude of methods have been proposed to reveal these dynamics on the level of the whole brain. One finding is that the brain transitions through different FC configurations over time, and substantial effort has been put into characterizing these configurations. However, the dynamics governing these transitions are more elusive, specifically, the contribution of stationary vs. non-stationary dynamics is an active field of inquiry. In this study, we use a whole-brain approach, considering FC dynamics between 66 ROIs covering the entire cortex. We combine an innovative dimensionality reduction technique, tensor decomposition, with a mean field model which possesses stationary dynamics. It has been shown to explain resting state FC averaged over time and multiple subjects, however, this average FC summarizes the spatial distribution of correlations while hiding their temporal dynamics. First, we apply tensor decomposition to resting state scans from 24 healthy controls in order to characterize spatio-temporal dynamics present in the data. We simultaneously utilize temporal and spatial information by creating tensors that are subsequently decomposed into sets of brain regions ("communities") that share similar temporal dynamics, and their associated time courses. The tensors contain pairwise FC computed inside of overlapping sliding windows. Communities are discovered by clustering features pooled from all subjects, thereby ensuring that they generalize. We find that, on the group level, the data give rise to four distinct communities that resemble known resting state networks (RSNs): default mode network, visual network, control networks, and somatomotor network. Second, we simulate data with our stationary mean field model whose nodes are connected according to results from DTI and fiber tracking. In this model, all spatio-temporal structure is due to noisy fluctuations around the average FC. We analyze the simulated data in the same way as the empirical data in order to determine whether stationary dynamics can explain the emergence of distinct FC patterns (RSNs) which have their own time courses. We find that this is the case for all four networks using the spatio-temporal information revealed by tensor decomposition if nodes in the simulation are connected according to model-based effective connectivity. Furthermore, we find that these results require only a small part of the FC values, namely the highest values that occur across time and ROI pair. Our findings show that stationary dynamics can account for the emergence of RSNs. We provide an innovative method that does not make strong assumptions about the underlying data and is generally applicable to resting state or task data from different subject populations.

Isolation of detergent-resistant membranes (DRMs) from Escherichia coli.

Lipid rafts or membrane microdomains have been proposed to compartmentalize cellular processes by spatially organizing diverse molecules/proteins in eukaryotic cells. Such membrane microdomains were recently reported to also exist in a few bacterial species. In this work, we report the development of a procedure for membrane microdomain isolation from Escherichia coli plasma membranes as well as a method to purify the latter. The method here reported could easily be adapted to other gram-negative bacteria, wherein the isolation of this kind of sub-membrane preparation imposes special difficulties. The analysis of isolated membrane microdomains might provide important information on the nature and function of these bacterial structures and permit their comparison with the ones of eukaryotic cells.

Estimation of Directed Effective Connectivity from fMRI Functional Connectivity Hints at Asymmetries of Cortical Connectome.

The brain exhibits complex spatio-temporal patterns of activity. This phenomenon is governed by an interplay between the internal neural dynamics of cortical areas and their connectivity. Uncovering this complex relationship has raised much interest, both for theory and the interpretation of experimental data (e.g., fMRI recordings) using dynamical models. Here we focus on the so-called inverse problem: the inference of network parameters in a cortical model to reproduce empirically observed activity. Although it has received a lot of interest, recovering directed connectivity for large networks has been rather unsuccessful so far. The present study specifically addresses this point for a noise-diffusion network model. We develop a Lyapunov optimization that iteratively tunes the network connectivity in order to reproduce second-order moments of the node activity, or functional connectivity. We show theoretically and numerically that the use of covariances with both zero and non-zero time shifts is the key to infer directed connectivity. The first main theoretical finding is that an accurate estimation of the underlying network connectivity requires that the time shift for covariances is matched with the time constant of the dynamical system. In addition to the network connectivity, we also adjust the intrinsic noise received by each network node. The framework is applied to experimental fMRI data recorded for subjects at rest. Diffusion-weighted MRI data provide an estimate of anatomical connections, which is incorporated to constrain the cortical model. The empirical covariance structure is reproduced faithfully, especially its temporal component (i.e., time-shifted covariances) in addition to the spatial component that is usually the focus of studies. We find that the cortical interactions, referred to as effective connectivity, in the tuned model are not reciprocal. In particular, hubs are either receptors or feeders: they do not exhibit both strong incoming and outgoing connections. Our results sets a quantitative ground to explore the propagation of activity in the cortex.