Local extracellular K+ in cortex regulates norepinephrine levels, network state, and behavioral output.

Extracellular potassium concentration ([K+]e) is known to increase as a function of arousal. [K+]e is also a potent modulator of transmitter release. Yet, it is not known whether [K+]e is involved in the neuromodulator release associated with behavioral transitions. We here show that manipulating [K+]e controls the local release of monoaminergic neuromodulators, including norepinephrine (NE), serotonin, and dopamine. Imposing a [K+]e increase is adequate to boost local NE levels, and conversely, lowering [K+]e can attenuate local NE. Electroencephalography analysis and behavioral assays revealed that manipulation of cortical [K+]e was sufficient to alter the sleep-wake cycle and behavior of mice. These observations point to the concept that NE levels in the cortex are not solely determined by subcortical release, but that local [K+]e dynamics have a strong impact on cortical NE. Thus, cortical [K+]e is an underappreciated regulator of behavioral transitions.

Treatment-related mortality among children with cancer in Denmark during 2001-2021.

BACKGROUND: Survival of children with cancer has markedly improved over recent decades, largely due to intensified treatment regimes. The intensive treatment may, however, result in fatal complications. In this retrospective cohort study, we assessed temporal variation in the incidence of treatment-related death and associated risk factors among children diagnosed with cancer in Denmark during 2001-2021. METHOD: Among all children diagnosed with first incident cancer before age 15 years recorded in the Danish Childhood Cancer Register (n = 3,255), we estimated cumulative incidence of treatment-related death (death in the absence of progressive cancer) within 5 years from diagnosis using Aalen-Johansen estimators and assessed associated risk factors using Cox regression. RESULTS: Among all 3,255 children with cancer, 93 (20% of all 459 deaths) died from treatment. Of these treatment-related deaths, 39 (42%) occurred within 3 months of diagnosis. The 5-year cumulative incidences of treatment-related death were 3.3% during 2001-2010 and 2.5% during 2011-2021 (p = 0.20). During 2011-2021, treatment-related deaths accounted for more than half of all deaths among children with haematological cancers. Risk factors varied according to cancer group and included female sex, age below 1 year at diagnosis, disease relapse, stem cell transplantation, central nervous system involvement, and metastasis at diagnosis. INTERPRETATION: Despite increasing treatment intensities, the incidence of treatment-related death has remained stable during the past 20 years in Denmark. Still, clinical attention is warranted to prevent treatment-related deaths, particularly among children with haematological cancers. Patient characteristics associated with increased treatment-related death risk support patient-specific treatment approaches to avoid these fatalities.

The Role of Cu-oxide and Cu adatoms in the Reactivity of CO2 on Cu(110).

We investigate the interaction of CO2 with metallic and oxidized Cu(110) surfaces using a combination of near-ambient pressure scanning tunneling microscopy (NAP-STM) and theory calculations. While the Cu(110) and full CuO films are inert, the interface between bare Cu(110) and the CuO film is observed to react instantly with CO2 at a 10 mbar pressure. The reaction is observed to proceed from the interfacial sites of CuO/Cu(110). During reaction with CO2, the CuO/Cu(110) interface releases Cu adatoms which combine with CO3 to produce a variety of added Cu-CO3 structures, whose stability depends on the gas pressure of CO2. A main implication for the reactivity of Cu(110) is that Cu adatoms and highly undercoordinated CuO segments are created on the Cu(110) surface through the interaction with CO2, which may act as reaction-induced active sites. In the case of CO2 hydrogenation to methanol, we used theory to assess such sites to indicate that their presence may significantly promote CH3OH formation. Our study thus implies that the CuO/Cu(110) interfacial system is highly dynamic in the presence of CO2, and it suggests a possible strong importance of reaction-induced Cu and CuO sites for the surface chemistry of Cu(110) in CO2-related catalysis.

Descriptors for binding energies at clusters: The case of nanosilicates as models of interstellar dust grains.

Binding energies of radicals and molecules at dust grain surfaces are important parameters for understanding and modeling the chemical inventory of interstellar gas clouds. While first-principles methods can reliably be used to compute such binding energies, the complex structure and varying sizes and stoichiometries of realistic dust grains make a complete characterization of all adsorption sites exposed by their surfaces challenging. Here, we focus on nanoclusters composed of Mg-rich silicates as models of interstellar dust grains and two adsorbates of particular astrochemical relevance; H and CO. We employ a compressed sensing method to identify descriptors for the binding energies, which are expressed as analytical functions of intrinsic properties of the clusters, obtainable through a single first-principles calculation of the cluster. The descriptors are identified based on a diverse training dataset of binding energies at low-energy structures of nanosilicate clusters, where the latter structures were obtained using a first-principles-based global optimization method. The composition of the descriptors reveals how electronic, electrostatic, and geometric properties of the nanosilicates control the binding energies and demonstrates distinct physical origins of the bond formation for H and CO. The predictive performance of the descriptors is found to be limited by cluster reconstruction, e.g., breaking of internal metal-oxygen bonds, upon the adsorption event, and strategies to account for this phenomenon are discussed. The identified descriptors and the computed datasets of stable nanosilicate clusters along with their binding energies are expected to find use in astrochemical models of reaction networks occurring at silicate grain surfaces.

Adsorption Enthalpies for Catalysis Modeling through Machine-Learned Descriptors.

Heterogeneous catalysts are rather complex materials that come in many classes (e.g., metals, oxides, carbides) and shapes. At the same time, the interaction of the catalyst surface with even a relatively simple gas-phase environment such as syngas (CO and H2) may already produce a wide variety of reaction intermediates ranging from atoms to complex molecules. The starting point for creating predictive maps of, e.g., surface coverages or chemical activities of potential catalyst materials is the reliable prediction of adsorption enthalpies of all of these intermediates. For simple systems, direct density functional theory (DFT) calculations are currently the method of choice. However, a wider exploration of complex materials and reaction networks generally requires enthalpy predictions at lower computational cost.The use of machine learning (ML) and related techniques to make accurate and low-cost predictions of quantum-mechanical calculations has gained increasing attention lately. The employed approaches span from physically motivated models over hybrid physics-ΔML approaches to complete black-box methods such as deep neural networks. In recent works we have explored the possibilities for using a compressed sensing method (Sure Independence Screening and Sparsifying Operator, SISSO) to identify sparse (low-dimensional) descriptors for the prediction of adsorption enthalpies at various active-site motifs of metals and oxides. We start from a set of physically motivated primary features such as atomic acid/base properties, coordination numbers, or band moments and let the data and the compressed sensing method find the best algebraic combination of these features. Here we take this work as a starting point to categorize and compare recent ML-based approaches with a particular focus on model sparsity, data efficiency, and the level of physical insight that one can obtain from the model.Looking ahead, while many works to date have focused only on the mere prediction of databases of, e.g., adsorption enthalpies, there is also an emerging interest in our field to start using ML predictions to answer fundamental science questions about the functioning of heterogeneous catalysts or perhaps even to design better catalysts than we know today. This task is significantly simplified in works that make use of scaling-relation-based models (volcano curves), where the model outcome is determined by only one or two adsorption enthalpies and which consequently become the sole target for ML-based high-throughput screening or design. However, the availability of cheap ML energetics also allows going beyond scaling relations. On the basis of our own work in this direction, we will discuss the additional physical insight that can be achieved by integrating ML-based predictions with traditional catalysis modeling techniques from thermal and electrocatalysis, such as the computational hydrogen electrode and microkinetic modeling, as well as the challenges that lie ahead.

Interface between graphene and liquid Cu from molecular dynamics simulations.

Controllable synthesis of defect-free graphene is crucial for applications since the properties of graphene are highly sensitive to any deviations from the crystalline lattice. We focus here on the emerging use of liquid Cu catalysts, which have high potential for fast and efficient industrial-scale production of high-quality graphene. The interface between graphene and liquid Cu is studied using force field and ab initio molecular dynamics, revealing a complete or partial embedding of finite-sized flakes. By analyzing flakes of different sizes, we find that the size-dependence of the embedding can be rationalized based on the energy cost of embedding vs bending the graphene flake. The embedding itself is driven by the formation of covalent bonds between the under-coordinated edge C atoms and the liquid Cu surface, which is accompanied by a significant charge transfer. In contrast, the central flake atoms are located around or slightly above 3 Å from the liquid Cu surface and exhibit weak van der Waals-bonding and much lower charge transfer. The structural and electronic properties of the embedded state revealed in our work provide the atomic-scale information needed to develop effective models to explain the special growth observed in experiments where various interesting phenomena such as flake self-assembly and rotational alignment, high growth speeds, and low defect densities in the final graphene product have been observed.

Chemically-resolved determination of hydrogenated graphene-substrate interaction.

Functionalization of graphene on Ir(111) is a promising route to modify graphene by chemical means in a controlled fashion at the nanoscale. Yet, the nature of such functionalized sp3 nanodots remains unknown. Density functional theory (DFT) calculations alone cannot differentiate between two plausible structures, namely true graphane and substrate stabilized graphane-like nanodots. These two structures, however, interact dramatically differently with the underlying substrate. Discriminating which type of nanodots forms on the surface is thus of paramount importance for the applications of such prepared nanostructures. By comparing X-ray standing wave measurements against theoretical model structures obtained by DFT calculations we are able to exclude the formation of true graphane nanodots and clearly show the formation graphane-like nanodots.

Does Global Astrocytic Calcium Signaling Participate in Awake Brain State Transitions and Neuronal Circuit Function?

We continuously need to adapt to changing conditions within our surrounding environment, and our brain needs to quickly shift between resting and working activity states in order to allow appropriate behaviors. These global state shifts are intimately linked to the brain-wide release of the neuromodulators, noradrenaline and acetylcholine. Astrocytes have emerged as a new player participating in the regulation of brain activity, and have recently been implicated in brain state shifts. Astrocytes display global Ca2+ signaling in response to activation of the noradrenergic system, but whether astrocytic Ca2+ signaling is causative or correlative for shifts in brain state and neural activity patterns is not known. Here we review the current available literature on astrocytic Ca2+ signaling in awake animals in order to explore the role of astrocytic signaling in brain state shifts. Furthermore, we look at the development and availability of innovative new methodological tools that are opening up for new ways of visualizing and perturbing astrocyte activity in awake behaving animals. With these new tools at hand, the field of astrocyte research will likely be able to elucidate the causal and mechanistic roles of astrocytes in complex behaviors within a very near future.

Assessment of mean-field microkinetic models for CO methanation on stepped metal surfaces using accelerated kinetic Monte Carlo.

First-principles screening studies aimed at predicting the catalytic activity of transition metal (TM) catalysts have traditionally been based on mean-field (MF) microkinetic models, which neglect the effect of spatial correlations in the adsorbate layer. Here we critically assess the accuracy of such models for the specific case of CO methanation over stepped metals by comparing to spatially resolved kinetic Monte Carlo (kMC) simulations. We find that the typical low diffusion barriers offered by metal surfaces can be significantly increased at step sites, which results in persisting correlations in the adsorbate layer. As a consequence, MF models may overestimate the catalytic activity of TM catalysts by several orders of magnitude. The potential higher accuracy of kMC models comes at a higher computational cost, which can be especially challenging for surface reactions on metals due to a large disparity in the time scales of different processes. In order to overcome this issue, we implement and test a recently developed algorithm for achieving temporal acceleration of kMC simulations. While the algorithm overall performs quite well, we identify some challenging cases which may lead to a breakdown of acceleration algorithms and discuss possible directions for future algorithm development.

Perspective: On the active site model in computational catalyst screening.

First-principles screening approaches exploiting energy trends in surface adsorption represent an unparalleled success story in recent computational catalysis research. Here we argue that our still limited understanding of the structure of active sites is one of the major bottlenecks towards an ever extended and reliable use of such computational screening for catalyst discovery. For low-index transition metal surfaces, the prevalently chosen high-symmetry (terrace and step) sites offered by the nominal bulk-truncated crystal lattice might be justified. For more complex surfaces and composite catalyst materials, computational screening studies will need to actively embrace a considerable uncertainty with respect to what truly are the active sites. By systematically exploring the space of possible active site motifs, such studies might eventually contribute towards a targeted design of optimized sites in future catalysts.

The influence of coronene super-hydrogenation on the coronene-graphite interaction.

The changes in the strength of the interaction between the polycyclic aromatic hydrocarbon, coronene, and graphite as a function of the degree of super-hydrogenation of the coronene molecule are investigated using temperature programmed desorption. A decrease in binding energy is observed for increasing degrees of super-hydrogenation, from 1.78 eV with no additional hydrogenation to 1.43 eV for the fully super-hydrogenated molecule. Density functional theory calculations using the optB88-vdW functional suggest that the decrease in binding energy is mostly due to an increased buckling of the molecule rather than the associated decrease in the number of π-electrons.

Analyzing the Case for Bifunctional Catalysis.

Bifunctional coupling of two different catalytic site types has often been invoked to explain experimentally observed enhanced catalytic activities. We scrutinize such claims with generic scaling-relation-based microkinetic models that allow exploration of the theoretical limits for such a bifunctional gain for several model reactions. For sites at transition-metal surfaces, the universality of the scaling relations between adsorption energies largely prevents any improvements through bifunctionality. Only the consideration of systems that involve the combination of different materials, such as metal particles on oxide supports, offers hope for significant bifunctional gains.

Exclusive Breastfeeding Duration and Risk of Childhood Cancers.

Importance: Breastfeeding has been suggested to protect against childhood cancers, particularly acute lymphoblastic leukemia (ALL). However, the evidence stems from case-control studies alone. Objective: To investigate whether longer duration of exclusive breastfeeding is associated with decreased risk of childhood ALL and other childhood cancers. Design, Setting, and Participants: This population-based cohort study used administrative data on exclusive breastfeeding duration from the Danish National Child Health Register. All children born in Denmark between January 2005 and December 2018 with available information on duration of exclusive breastfeeding were included. Children were followed up from age 1 year until childhood cancer diagnosis, loss to follow-up or emigration, death, age 15 years, or December 31, 2020. Data were analyzed from March to October 2023. Exposure: Duration of exclusive breastfeeding in infancy. Main Outcomes and Measures: Associations between duration of exclusive breastfeeding and risk of childhood cancer overall and by subtypes were estimated as adjusted hazard ratios (AHRs) with 95% CIs using stratified Cox proportional hazards regression models. Results: A total of 309 473 children were included (51.3% boys). During 1 679 635 person-years of follow-up, 332 children (0.1%) were diagnosed with cancer at ages 1 to 14 years (mean [SD] age at diagnosis, 4.24 [2.67] years; 194 boys [58.4%]). Of these, 124 (37.3%) were diagnosed with hematologic cancers (81 [65.3%] were ALL, 74 [91.4%] of which were B-cell precursor [BCP] ALL), 44 (13.3%) with central nervous system tumors, 80 (24.1%) with solid tumors, and 84 (25.3%) with other and unspecified malignant neoplasms. Compared with exclusive breastfeeding duration of less than 3 months, exclusive breastfeeding for 3 months or longer was associated with a decreased risk of hematologic cancers (AHR, 0.66; 95% CI, 0.46-0.95), which was largely attributable to decreased risk of BCP-ALL (AHR, 0.62; 95% CI, 0.39-0.99), but not with risk of central nervous system tumors (AHR, 0.96; 95% CI, 0.51-1.88) or solid tumors (AHR, 0.87; 95% CI, 0.55-1.41). Conclusions and Relevance: In this cohort study, longer duration of exclusive breastfeeding was associated with reduced risk of childhood BCP-ALL, corroborating results of previous case-control investigations in this field. To inform future preemptive interventions, continued research should focus on the potential biologic mechanisms underlying the observed association.

Using Fiber Photometry in Mice to Estimate Fluorescent Biosensor Levels During Sleep.

Sleep is not homogenous but contains a highly diverse microstructural composition influenced by neuromodulators. Prior methods used to measure neuromodulator levels in vivo have been limited by low time resolution or technical difficulties in achieving recordings in a freely moving setting, which is essential for natural sleep. In this protocol, we demonstrate the combination of electroencephalographic (EEG)/electromyographic (EMG) recordings with fiber photometric measurements of fluorescent biosensors for neuromodulators in freely moving mice. This allows for real-time assessment of extracellular neuromodulator levels during distinct phases of sleep with a high temporal resolution.

The association between sleep duration and detailed measures of obesity: A cross sectional analysis in the ADDITION-PRO study.

Background: Sleep duration is associated with BMI and waist circumference. However, less is known about whether sleep duration affects different measurements of obesity differently. Objective: To investigate the association between sleep duration and different measures of obesity. Methods: In this cross-sectional analysis 1309, Danish, older adults (55% men) completed at least 3 days of wearing a combined accelerometer and heart rate-monitor for assessing sleep duration (hours/night) within self-reported usual bedtime. Participants underwent anthropometry and ultrasonography to assess BMI, waist circumference, visceral fat, subcutaneous fat, and fat percentage. Linear regression analyses examined the associations between sleep duration and obesity-related outcomes. Results: Sleep duration was inversely associated with all obesity-related outcomes, except visceral-/subcutaneous-fat-ratio. After multivariate adjustment the magnitude of associations became stronger and statistically significant for all outcomes except visceral-/subcutaneous-fat-ratio, and subcutaneous fat in women. The associations with BMI and waist circumference demonstrated the strongest associations, when comparing standardized regression coefficients. Conclusions: Shorter sleep duration were associated with higher obesity across all outcomes except visceral-/subcutaneous-fat-ratio. No specifically salient associations with local or central obesity were observed. Results suggest that poor sleep duration and obesity correlate, however, further research is needed to conclude on beneficial effects of sleep duration regarding health and weight loss.

Astrocytic chloride is brain state dependent and modulates inhibitory neurotransmission in mice.

Information transfer within neuronal circuits depends on the balance and recurrent activity of excitatory and inhibitory neurotransmission. Chloride (Cl-) is the major central nervous system (CNS) anion mediating inhibitory neurotransmission. Astrocytes are key homoeostatic glial cells populating the CNS, although the role of these cells in regulating excitatory-inhibitory balance remains unexplored. Here we show that astrocytes act as a dynamic Cl- reservoir regulating Cl- homoeostasis in the CNS. We found that intracellular chloride concentration ([Cl-]i) in astrocytes is high and stable during sleep. In awake mice astrocytic [Cl-]i is lower and exhibits large fluctuation in response to both sensory input and motor activity. Optogenetic manipulation of astrocytic [Cl-]i directly modulates neuronal activity during locomotion or whisker stimulation. Astrocytes thus serve as a dynamic source of extracellular Cl- available for GABAergic transmission in awake mice, which represents a mechanism for modulation of the inhibitory tone during sustained neuronal activity.

Predicting binding motifs of complex adsorbates using machine learning with a physics-inspired graph representation.

Computational screening in heterogeneous catalysis relies increasingly on machine learning models for predicting key input parameters due to the high cost of computing these directly using first-principles methods. This becomes especially relevant when considering complex materials spaces such as alloys, or complex reaction mechanisms with adsorbates that may exhibit bi- or higher-dentate adsorption motifs. Here we present a data-efficient approach to the prediction of binding motifs and associated adsorption enthalpies of complex adsorbates at transition metals and their alloys based on a customized Wasserstein Weisfeiler-Lehman graph kernel and Gaussian process regression. The model shows good predictive performance, not only for the elemental transition metals on which it was trained, but also for an alloy based on these transition metals. Furthermore, incorporation of minimal new training data allows for predicting an out-of-domain transition metal. We believe the model may be useful in active learning approaches, for which we present an ensemble uncertainty estimation approach.

Water Chemistry beneath Graphene: Condensation of a Dense OH-H2O Phase under Graphene.

Room temperature oxygen hydrogenation below graphene flakes supported by Ir(111) is investigated through a combination of X-ray photoelectron spectroscopy, scanning tunneling microscopy, and density functional theory calculations using an evolutionary search algorithm. We demonstrate how the graphene cover and its doping level can be used to trap and characterize dense mixed O-OH-H2O phases that otherwise would not exist. Our study of these graphene-stabilized phases and their response to oxygen or hydrogen exposure reveals that additional oxygen can be dissolved into them at room temperature creating mixed O-OH-H2O phases with an increased areal coverage underneath graphene. In contrast, additional hydrogen exposure converts the mixed O-OH-H2O phases back to pure OH-H2O with a reduced areal coverage underneath graphene.

Memory-enhancing properties of sleep depend on the oscillatory amplitude of norepinephrine.

Sleep has a complex micro-architecture, encompassing micro-arousals, sleep spindles and transitions between sleep stages. Fragmented sleep impairs memory consolidation, whereas spindle-rich and delta-rich non-rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep promote it. However, the relationship between micro-arousals and memory-promoting aspects of sleep remains unclear. In this study, we used fiber photometry in mice to examine how release of the arousal mediator norepinephrine (NE) shapes sleep micro-architecture. Here we show that micro-arousals are generated in a periodic pattern during NREM sleep, riding on the peak of locus-coeruleus-generated infraslow oscillations of extracellular NE, whereas descending phases of NE oscillations drive spindles. The amplitude of NE oscillations is crucial for shaping sleep micro-architecture related to memory performance: prolonged descent of NE promotes spindle-enriched intermediate state and REM sleep but also associates with awakenings, whereas shorter NE descents uphold NREM sleep and micro-arousals. Thus, the NE oscillatory amplitude may be a target for improving sleep in sleep disorders.

Graphene at Liquid Copper Catalysts: Atomic-Scale Agreement of Experimental and First-Principles Adsorption Height.

Liquid metal catalysts have recently attracted attention for synthesizing high-quality 2D materials facilitated via the catalysts' perfectly smooth surface. However, the microscopic catalytic processes occurring at the surface are still largely unclear because liquid metals escape the accessibility of traditional experimental and computational surface science approaches. Hence, numerous controversies are found regarding different applications, with graphene (Gr) growth on liquid copper (Cu) as a prominent prototype. In this work, novel in situ and in silico techniques are employed to achieve an atomic-level characterization of the graphene adsorption height above liquid Cu, reaching quantitative agreement within 0.1 Å between experiment and theory. The results are obtained via in situ synchrotron X-ray reflectivity (XRR) measurements over wide-range q-vectors and large-scale molecular dynamics simulations based on efficient machine-learning (ML) potentials trained to first-principles density functional theory (DFT) data. The computational insight is demonstrated to be robust against inherent DFT errors and reveals the nature of graphene binding to be highly comparable at liquid Cu and solid Cu(111). Transporting the predictive first-principles quality via ML potentials to the scales required for liquid metal catalysis thus provides a powerful approach to reach microscopic understanding, analogous to the established computational approaches for catalysis at solid surfaces.