The Hopf whole-brain model and its linear approximation.

Whole-brain models have proven to be useful to understand the emergence of collective activity among neural populations or brain regions. These models combine connectivity matrices, or connectomes, with local node dynamics, noise, and, eventually, transmission delays. Multiple choices for the local dynamics have been proposed. Among them, nonlinear oscillators corresponding to a supercritical Hopf bifurcation have been used to link brain connectivity and collective phase and amplitude dynamics in different brain states. Here, we studied the linear fluctuations of this model to estimate its stationary statistics, i.e., the instantaneous and lagged covariances and the power spectral densities. This linear approximation-that holds in the case of heterogeneous parameters and time-delays-allows analytical estimation of the statistics and it can be used for fast parameter explorations to study changes in brain state, changes in brain activity due to alterations in structural connectivity, and modulations of parameter due to non-equilibrium dynamics.

Perioperative Care in Cardiac Surgery: A Joint Consensus Statement by the Enhanced Recovery After Surgery (ERAS) Cardiac Society, ERAS International Society, and The Society of Thoracic Surgeons (STS).

Enhanced Recovery After Surgery (ERAS) programs have been shown to lessen surgical insult, promote recovery, and improve postoperative clinical outcomes across a number of specialty operations. A core tenet of ERAS involves the provision of protocolized evidence-based perioperative interventions. Given both the growing enthusiasm for applying ERAS principles to cardiac surgery and the broad scope of relevant interventions, an international, multidisciplinary expert panel was assembled to derive a list of potential program elements, review the literature, and provide a statement regarding clinical practice for each topic area. This article summarizes those consensus statements and their accompanying evidence. These results provide the foundation for best practice for the management of the adult patient undergoing cardiac surgery.

A view-based decision mechanism for rewards in the primate amygdala.

Primates make decisions visually by shifting their view from one object to the next, comparing values between objects, and choosing the best reward, even before acting. Here, we show that when monkeys make value-guided choices, amygdala neurons encode their decisions in an abstract, purely internal representation defined by the monkey's current view but not by specific object or reward properties. Across amygdala subdivisions, recorded activity patterns evolved gradually from an object-specific value code to a transient, object-independent code in which currently viewed and last-viewed objects competed to reflect the emerging view-based choice. Using neural-network modeling, we identified a sequence of computations by which amygdala neurons implemented view-based decision making and eventually recovered the chosen object's identity when the monkeys acted on their choice. These findings reveal a neural mechanism in the amygdala that derives object choices from abstract, view-based computations, suggesting an efficient solution for decision problems with many objects.

Critical scaling of whole-brain resting-state dynamics.

Scale invariance is a characteristic of neural activity. How this property emerges from neural interactions remains a fundamental question. Here, we studied the relation between scale-invariant brain dynamics and structural connectivity by analyzing human resting-state (rs-) fMRI signals, together with diffusion MRI (dMRI) connectivity and its approximation as an exponentially decaying function of the distance between brain regions. We analyzed the rs-fMRI dynamics using functional connectivity and a recently proposed phenomenological renormalization group (PRG) method that tracks the change of collective activity after successive coarse-graining at different scales. We found that brain dynamics display power-law correlations and power-law scaling as a function of PRG coarse-graining based on functional or structural connectivity. Moreover, we modeled the brain activity using a network of spins interacting through large-scale connectivity and presenting a phase transition between ordered and disordered phases. Within this simple model, we found that the observed scaling features were likely to emerge from critical dynamics and connections exponentially decaying with distance. In conclusion, our study tests the PRG method using large-scale brain activity and theoretical models and suggests that scaling of rs-fMRI activity relates to criticality.

One ring to rule them all: The unifying role of prefrontal cortex in steering task-related brain dynamics.

Surviving and thriving in a complex world require intricate balancing of higher order brain functions with essential survival-related behaviours. Exactly how this is achieved is not fully understood but a large body of work has shown that different regions in the prefrontal cortex (PFC) play key roles for diverse cognitive and emotional tasks including emotion, control, response inhibition, mental set shifting and working memory. We hypothesised that the key regions are hierarchically organised and we developed a framework for discovering the driving brain regions at the top of the hierarchy, responsible for steering the brain dynamics of higher brain function. We fitted a time-dependent whole-brain model to the neuroimaging data from large-scale Human Connectome Project with over 1000 participants and computed the entropy production for rest and seven tasks (covering the main domains of cognition). This thermodynamics framework allowed us to identify the main common, unifying drivers steering the orchestration of brain dynamics during difficult tasks; located in key regions of the PFC (inferior frontal gyrus, lateral orbitofrontal cortex, rostral and caudal frontal cortex and rostral anterior cingulate cortex). Selectively lesioning these regions in the whole-brain model demonstrated their causal mechanistic importance. Overall, this shows the existence of a 'ring' of specific PFC regions ruling over the orchestration of higher brain function.

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.

Neuromorphic learning, working memory, and metaplasticity in nanowire networks.

Nanowire networks (NWNs) mimic the brain's neurosynaptic connectivity and emergent dynamics. Consequently, NWNs may also emulate the synaptic processes that enable higher-order cognitive functions such as learning and memory. A quintessential cognitive task used to measure human working memory is the n-back task. In this study, task variations inspired by the n-back task are implemented in a NWN device, and external feedback is applied to emulate brain-like supervised and reinforcement learning. NWNs are found to retain information in working memory to at least n = 7 steps back, remarkably similar to the originally proposed "seven plus or minus two" rule for human subjects. Simulations elucidate how synapse-like NWN junction plasticity depends on previous synaptic modifications, analogous to "synaptic metaplasticity" in the brain, and how memory is consolidated via strengthening and pruning of synaptic conductance pathways.

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.

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.

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

This is the first Enhanced Recovery After Surgery (ERAS®) Society guideline for primary and secondary hospitals in low­middle-income countries (LMIC's) for elective abdominal and gynecologic care. 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 43:6592­695, Nelson et al in Int J Gynecol Cancer 29(4):651­668). 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. 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. 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.

Latin American surgical outcomes study: study protocol for a multicentre international observational cohort study of patient outcomes after surgery in Latin American countries.

Background: Reported data suggest that 4.2 million deaths will occur within 30 days of surgery worldwide each year, half of which are in low- and middle-income countries. Postoperative complications are a leading cause of long-term morbidity and mortality. Patients who survive and leave the hospital after surgical complications regularly experience reductions in long-term survival and functional independence, resulting in increased costs. With a high volume of surgery performed, there is a growing perception of the substantial impact of even minor enhancements in perioperative care. The Latin American Surgical Outcomes Study (LASOS) is an international, multicentre, prospective cohort study of adults submitted to in-patient surgery in Latin America aiming to provide detailed data describing postoperative complications and surgical mortality. Methods: LASOS is a 7 day cohort study of adults undergoing surgery in Latin America. Details of preoperative risk factors, intraoperative care, and postoperative outcomes will be collected. The primary outcome will be in-hospital postoperative complications of any cause. Secondary outcomes include in-hospital all-cause mortality, duration of hospital stay after surgery, and admission to a critical care unit within 30 days after surgery during the index hospitalisation. Results: The LASOS results will be published in peer-reviewed journals, reported and presented at international meetings, and widely disseminated to patients and public in participating countries via mainstream and social media. Conclusions: The LASOS may augment our understanding of postoperative complications and surgial mortality in Latin America. Clinical trial registration: NCT05169164.

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.

Functional network antagonism and consciousness.

Spontaneous brain activity changes across states of consciousness. A particular consciousness-mediated configuration is the anticorrelations between the default mode network and other brain regions. What this antagonistic organization implies about consciousness to date remains inconclusive. In this Perspective Article, we propose that anticorrelations are the physiological expression of the concept of segregation, namely the brain's capacity to show selectivity in the way areas will be functionally connected. We postulate that this effect is mediated by the process of neural inhibition, by regulating global and local inhibitory activity. While recognizing that this effect can also result from other mechanisms, neural inhibition helps the understanding of how network metastability is affected after disrupting local and global neural balance. In combination with relevant theories of consciousness, we suggest that anticorrelations are a physiological prior that can work as a marker of preserved consciousness. We predict that if the brain is not in a state to host anticorrelations, then most likely the individual does not entertain subjective experience. We believe that this link between anticorrelations and the underlying physiology will help not only to comprehend how consciousness happens, but also conceptualize effective interventions for treating consciousness disorders in which anticorrelations seem particularly affected.

The fMRI resting paradigm can quantify brain function by surpassing communication and sophisticated setups, hence helping to infer consciousness in individuals who are unable to communicate with their environment. A particular consciousness-mediated rsfMRI configuration is that of functional anticorrelations, that is, the antagonistic relationship between a specific set of brain regions. We suggest that anticorrelations are a key physiological prior, without which consciousness cannot be supported, because the brain cannot segregate how regions get connected. We postulate that segregation is possible thanks to neural inhibition, by regulating global and local inhibitory activity. We believe that the link between anticorrelations and the underlying physiology can help not only to comprehend how consciousness happens, but also conceptualize effective interventions for treating its disorders.

One Ring Fit to Rule Them All? An Analysis of Avatar Bodies and Customization in Exergames.

With the growing popularity of exergames, researchers have noted the importance of presenting players with customizable avatars to encourage the long-term adoption of healthy behaviors offline. However, the "idealized" avatar bodies presented in avatar customization interfaces can represent limited body types and often problematically represent gender as binary. In this paper, we present a systematic analysis of the avatar customization interfaces of six console-based exergames. Results of our analysis indicate that customization options tend to be limited in exergames, especially with regard to body type and gender. Implications for avatar self-priming, self-identification, and healthy behavior adoption are discussed.

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.

Loss of consciousness reduces the stability of brain hubs and the heterogeneity of brain dynamics.

Low-level states of consciousness are characterized by disruptions of brain activity that sustain arousal and awareness. Yet, how structural, dynamical, local and network brain properties interplay in the different levels of consciousness is unknown. Here, we study fMRI brain dynamics from patients that suffered brain injuries leading to a disorder of consciousness and from healthy subjects undergoing propofol-induced sedation. We show that pathological and pharmacological low-level states of consciousness display less recurrent, less connected and more segregated synchronization patterns than conscious state. We use whole-brain models built upon healthy and injured structural connectivity to interpret these dynamical effects. We found that low-level states of consciousness were associated with reduced network interactions, together with more homogeneous and more structurally constrained local dynamics. Notably, these changes lead the structural hub regions to lose their stability during low-level states of consciousness, thus attenuating the differences between hubs and non-hubs brain dynamics.

Nickel-Catalyzed Cyanation of Aryl Thioethers.

A nickel-catalyzed cyanation of aryl thioethers using Zn(CN)2 as a cyanide source has been developed to access functionalized aryl nitriles. The ligand dcype (1,2-bis(dicyclohexylphosphino)ethane) in combination with the base KOAc (potassium acetate) is essential for achieving this transformation efficiently. This reaction involves both a C-S bond activation and a C-C bond formation. The scalability, low catalyst and reagents loadings, and high functional group tolerance have enabled both late-stage derivatization and polymer recycling, demonstrating the reaction's utility across organic chemistry.

Circuit mechanisms for the chemical modulation of cortex-wide network interactions and behavioral variability.

Influential theories postulate distinct roles of catecholamines and acetylcholine in cognition and behavior. However, previous physiological work reported similar effects of these neuromodulators on the response properties (specifically, the gain) of individual cortical neurons. Here, we show a double dissociation between the effects of catecholamines and acetylcholine at the level of large-scale interactions between cortical areas in humans. A pharmacological boost of catecholamine levels increased cortex-wide interactions during a visual task, but not rest. An acetylcholine boost decreased interactions during rest, but not task. Cortical circuit modeling explained this dissociation by differential changes in two circuit properties: the local excitation-inhibition balance (more strongly increased by catecholamines) and intracortical transmission (more strongly reduced by acetylcholine). The inferred catecholaminergic mechanism also predicted noisier decision-making, which we confirmed for both perceptual and value-based choice behavior. Our work highlights specific circuit mechanisms for shaping cortical network interactions and behavioral variability by key neuromodulatory systems.