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.

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.

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.

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.

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.

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.

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.

Graphene as an Adsorption Template for Studying Double Bond Activation in Catalysis.

Hydrogenated graphene (H-Gr) is an extensively studied system not only because of its capabilities as a simplified model system for hydrocarbon chemistry but also because hydrogenation is a compelling method for Gr functionalization. However, knowledge of how H-Gr interacts with molecules at higher pressures and ambient conditions is lacking. Here we present experimental and theoretical evidence that room temperature O2 exposure at millibar pressures leads to preferential removal of H dimers on H-functionalized graphene, leaving H clusters on the surface. Our density functional theory (DFT) analysis shows that the removal of H dimers is the result of water or hydrogen peroxide formation. For water formation, we show that the two H atoms in the dimer motif attack one end of the physisorbed O2 molecule. Moreover, by comparing the reaction pathways in a vacuum with the ones on free-standing graphene and on the graphene/Ir(111) system, we find that the main role of graphene is to arrange the H atoms in geometrical positions, which facilitates the activation of the O=O double bond.

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.

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.

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.

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.

Real-Time Multiscale Monitoring and Tailoring of Graphene Growth on Liquid Copper.

The synthesis of large, defect-free two-dimensional materials (2DMs) such as graphene is a major challenge toward industrial applications. Chemical vapor deposition (CVD) on liquid metal catalysts (LMCats) is a recently developed process for the fast synthesis of high-quality single crystals of 2DMs. However, up to now, the lack of in situ techniques enabling direct feedback on the growth has limited our understanding of the process dynamics and primarily led to empirical growth recipes. Thus, an in situ multiscale monitoring of the 2DMs structure, coupled with a real-time control of the growth parameters, is necessary for efficient synthesis. Here we report real-time monitoring of graphene growth on liquid copper (at 1370 K under atmospheric pressure CVD conditions) via four complementary in situ methods: synchrotron X-ray diffraction and reflectivity, Raman spectroscopy, and radiation-mode optical microscopy. This has allowed us to control graphene growth parameters such as shape, dispersion, and the hexagonal supra-organization with very high accuracy. Furthermore, the switch from continuous polycrystalline film to the growth of millimeter-sized defect-free single crystals could also be accomplished. The presented results have far-reaching consequences for studying and tailoring 2D material formation processes on LMCats under CVD growth conditions. Finally, the experimental observations are supported by multiscale modeling that has thrown light into the underlying mechanisms of graphene growth.

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.

A Practical Guide to Surface Kinetic Monte Carlo Simulations.

This review article is intended as a practical guide for newcomers to the field of kinetic Monte Carlo (KMC) simulations, and specifically to lattice KMC simulations as prevalently used for surface and interface applications. We will provide worked out examples using the kmos code, where we highlight the central approximations made in implementing a KMC model as well as possible pitfalls. This includes the mapping of the problem onto a lattice and the derivation of rate constant expressions for various elementary processes. Example KMC models will be presented within the application areas surface diffusion, crystal growth and heterogeneous catalysis, covering both transient and steady-state kinetics as well as the preparation of various initial states of the system. We highlight the sensitivity of KMC models to the elementary processes included, as well as to possible errors in the rate constants. For catalysis models in particular, a recurrent challenge is the occurrence of processes at very different timescales, e.g., fast diffusion processes and slow chemical reactions. We demonstrate how to overcome this timescale disparity problem using recently developed acceleration algorithms. Finally, we will discuss how to account for lateral interactions between the species adsorbed to the lattice, which can play an important role in all application areas covered here.