Single-channel EOG sleep staging on a heterogeneous cohort of subjects with sleep disorders.

Objective.Sleep staging based on full polysomnography is the gold standard in the diagnosis of many sleep disorders. It is however costly, complex, and obtrusive due to the use of multiple electrodes. Automatic sleep staging based on single-channel electro-oculography (EOG) is a promising alternative, requiring fewer electrodes which could be self-applied below the hairline. EOG sleep staging algorithms are however yet to be validated in clinical populations with sleep disorders.Approach.We utilized the SOMNIA dataset, comprising 774 recordings from subjects with various sleep disorders, including insomnia, sleep-disordered breathing, hypersomnolence, circadian rhythm disorders, parasomnias, and movement disorders. The recordings were divided into train (574), validation (100), and test (100) groups. We trained a neural network that integrated transformers within a U-Net backbone. This design facilitated learning of arbitrary-distance temporal relationships within and between the EOG and hypnogram.Main results.For 5-class sleep staging, we achieved median accuracies of 85.0% and 85.2% and Cohen's kappas of 0.781 and 0.796 for left and right EOG, respectively. The performance using the right EOG was significantly better than using the left EOG, possibly because in the recommended AASM setup, this electrode is located closer to the scalp. The proposed model is robust to the presence of a variety of sleep disorders, displaying no significant difference in performance for subjects with a certain sleep disorder compared to those without.Significance.The results show that accurate sleep staging using single-channel EOG can be done reliably for subjects with a variety of sleep disorders.

Studying sleep: towards the identification of hypnogram features that drive expert interpretation.

STUDY OBJECTIVES: Hypnograms contain a wealth of information and play an important role in sleep medicine. However, interpretation of the hypnogram is a difficult task and requires domain knowledge and "clinical intuition." This study aimed to uncover which features of the hypnogram drive interpretation by physicians. In other words, make explicit which features physicians implicitly look for in hypnograms. METHODS: Three sleep experts evaluated up to 612 hypnograms, indicating normal or abnormal sleep structure and suspicion of disorders. ElasticNet and convolutional neural network classification models were trained to predict the collected expert evaluations using hypnogram features and stages as input. The models were evaluated using several measures, including accuracy, Cohen's kappa, Matthew's correlation coefficient, and confusion matrices. Finally, model coefficients and visual analytics techniques were used to interpret the models to associate hypnogram features with expert evaluation. RESULTS: Agreement between models and experts (Kappa between 0.47 and 0.52) is similar to agreement between experts (Kappa between 0.38 and 0.50). Sleep fragmentation, measured by transitions between sleep stages per hour, and sleep stage distribution were identified as important predictors for expert interpretation. CONCLUSIONS: By comparing hypnograms not solely on an epoch-by-epoch basis, but also on these more specific features that are relevant for the evaluation of experts, performance assessment of (automatic) sleep-staging and surrogate sleep trackers may be improved. In particular, sleep fragmentation is a feature that deserves more attention as it is often not included in the PSG report, and existing (wearable) sleep trackers have shown relatively poor performance in this aspect.

Modeling the Impact of Inter-Rater Disagreement on Sleep Statistics Using Deep Generative Learning.

Sleep staging is the process by which an overnight polysomnographic measurement is segmented into epochs of 30 seconds, each of which is annotated as belonging to one of five discrete sleep stages. The resulting scoring is graphically depicted as a hypnogram, and several overnight sleep statistics are derived, such as total sleep time and sleep onset latency. Gold standard sleep staging as performed by human technicians is time-consuming, costly, and comes with imperfect inter-scorer agreement, which also results in inter-scorer disagreement about the overnight statistics. Deep learning algorithms have shown promise in automating sleep scoring, but struggle to model inter-scorer disagreement in sleep statistics. To that end, we introduce a novel technique using conditional generative models based on Normalizing Flows that permits the modeling of the inter-rater disagreement of overnight sleep statistics, termed U-Flow. We compare U-Flow to other automatic scoring methods on a hold-out test set of 70 subjects, each scored by six independent scorers. The proposed method achieves similar sleep staging performance in terms of accuracy and Cohen's kappa on the majority-voted hypnograms. At the same time, U-Flow outperforms the other methods in terms of modeling the inter-rater disagreement of overnight sleep statistics. The consequences of inter-rater disagreement about overnight sleep statistics may be great, and the disagreement potentially carries diagnostic and scientifically relevant information about sleep structure. U-Flow is able to model this disagreement efficiently and can support further investigations into the impact inter-rater disagreement has on sleep medicine and basic sleep research.

An embedded interfacial network stabilizes inorganic CsPbI3 perovskite thin films.

The black perovskite phase of CsPbI3 is promising for optoelectronic applications; however, it is unstable under ambient conditions, transforming within minutes into an optically inactive yellow phase, a fact that has so far prevented its widespread adoption. Here we use coarse photolithography to embed a PbI2-based interfacial microstructure into otherwise-unstable CsPbI3 perovskite thin films and devices. Films fitted with a tessellating microgrid are rendered resistant to moisture-triggered decay and exhibit enhanced long-term stability of the black phase (beyond 2.5 years in a dry environment), due to increasing the phase transition energy barrier and limiting the spread of potential yellow phase formation to structurally isolated domains of the grid. This stabilizing effect is readily achieved at the device level, where unencapsulated CsPbI3 perovskite photodetectors display ambient-stable operation. These findings provide insights into the nature of phase destabilization in emerging CsPbI3 perovskite devices and demonstrate an effective stabilization procedure which is entirely orthogonal to existing approaches.

Certainty about uncertainty in sleep staging: a theoretical framework.

Sleep stage classification is an important tool for the diagnosis of sleep disorders. Because sleep staging has such a high impact on clinical outcome, it is important that it is done reliably. However, it is known that uncertainty exists in both expert scorers and automated models. On average, the agreement between human scorers is only 82.6%. In this study, we provide a theoretical framework to facilitate discussion and further analyses of uncertainty in sleep staging. To this end, we introduce two variants of uncertainty, known from statistics and the machine learning community: aleatoric and epistemic uncertainty. We discuss what these types of uncertainties are, why the distinction is useful, where they arise from in sleep staging, and provide recommendations on how this framework can improve sleep staging in the future.

Representations of temporal sleep dynamics: Review and synthesis of the literature.

Sleep is characterized by an intricate variation of brain activity over time. Measuring these temporal sleep dynamics is relevant for elucidating healthy and pathological sleep mechanisms. The rapidly increasing possibilities for obtaining and processing sleep registrations have led to an abundance of data, which can be challenging to analyze and interpret. This review provides a structured overview of approaches to represent temporal sleep dynamics, categorized based on the way the source data is compressed. For each category of representations, we describe advantages and disadvantages. Standard human-defined 30-s sleep stages have the advantages of standardization and interpretability. Alternative human-defined representations are less standardized but offer a higher temporal resolution (in case of microstructural events such as sleep spindles), or reflect non-categorical information (for example spectral power analysis). Machine-learned representations offer additional possibilities: automated sleep stages are useful for handling large quantities of data, while alternative sleep stages obtained from clustering data-driven features could aid finding new patterns and new possible clinical interpretations. While newly developed sleep representations may offer relevant insights, they can be difficult to interpret in for example a clinical context. Therefore, there should always be a balance between developing these sophisticated sleep analysis techniques and maintaining clinical explainability.

Controlling the Bioreceptor Spatial Distribution at the Nanoscale for Single Molecule Counting in Microwell Arrays.

The ability to detect low concentrations of protein biomarkers is crucial for the early-stage detection of many diseases and therefore indispensable for improving diagnostic devices for healthcare. Here, we demonstrate that by integrating DNA nanotechnologies like DNA origami and aptamers, we can design innovative biosensing concepts for reproducible and sensitive detection of specific targets. DNA origami structures decorated with aptamers were studied as a novel tool to structure the biosensor surface with nanoscale precision in a digital detection bioassay, enabling control of the density, orientation, and accessibility of the bioreceptor to optimize the interaction between target and aptamer. DNA origami was used to control the spatial distribution of an in-house-generated aptamer on superparamagnetic microparticles, resulting in an origami-linked digital aptamer bioassay to detect the main peanut antigen Ara h1 with 2-fold improved signal-to-noise ratio and 15-fold improved limit of detection compared to a digital bioassay without DNA origami. Moreover, the sensitivity achieved was 4 orders of magnitude higher than commercially available and literature-reported enzyme-linked immunosorbent assay techniques. In conclusion, this novel and innovative approach to engineer biosensing interfaces will be of major interest to scientists and clinicians looking for new molecular insights and ultrasensitive detection of a broad range of targets, and, for the next generation of diagnostics.

Graphite and Graphene Fairy Circles: A Bottom-Up Approach for the Formation of Nanocorrals.

A convenient covalent functionalization approach and nanopatterning method of graphite and graphene is developed. In contrast to expectations, electrochemically activated dediazotization of a mixture of two aryl diazonium compounds in aqueous media leads to a spatially inhomogeneous functionalization of graphitic surfaces, creating covalently modified surfaces with quasi-uniform spaced islands of pristine graphite or graphene, coined nanocorrals. Cyclic voltammetry and chronoamperometry approaches are compared. The average diameter (45-130 nm) and surface density (20-125 corrals/µm2) of these nanocorrals are tunable. These chemically modified nanostructured graphitic (CMNG) surfaces are characterized by atomic force microscopy, scanning tunneling microscopy, Raman spectroscopy and microscopy, and X-ray photoelectron spectroscopy. Mechanisms leading to the formation of these CMNG surfaces are discussed. The potential of these surfaces to investigate supramolecular self-assembly and on-surface reactions under nanoconfinement conditions is demonstrated.

Intrinsic Properties of Single Graphene Nanoribbons in Solution: Synthetic and Spectroscopic Studies.

We report a novel type of structurally defined graphene nanoribbons (GNRs) with uniform width of 1.7 nm and average length up to 58 nm. These GNRs are decorated with pending Diels-Alder cycloadducts of anthracenyl units and N- n-hexadecyl maleimide. The resultant bulky side groups on GNRs afford excellent dispersibility with concentrations of up to 5 mg mL-1 in many organic solvents such as tetrahydrofuran (THF), two orders of magnitude higher than the previously reported GNRs. Multiple spectroscopic studies confirm that dilute dispersions in THF (<0.1 mg mL-1) consist mainly of nonaggregated ribbons, exhibiting near-infrared emission with high quantum yield (9.1%) and long lifetime (8.7 ns). This unprecedented dispersibility allows resolving in real-time ultrafast excited-state dynamics of the GNRs, which displays features of small isolated molecules in solution. This study achieves a breakthrough in the dispersion of GNRs, which opens the door for unveiling obstructed GNR-based physical properties and potential applications.

Self-Assembled Polystyrene Beads for Templated Covalent Functionalization of Graphitic Substrates Using Diazonium Chemistry.

A network of self-assembled polystyrene beads was employed as a lithographic mask during covalent functionalization reactions on graphitic surfaces to create nanocorrals for confined molecular self-assembly studies. The beads were initially assembled into hexagonal arrays at the air-liquid interface and then transferred to the substrate surface. Subsequent electrochemical grafting reactions involving aryl diazonium molecules created covalently bound molecular units that were localized in the void space between the nanospheres. Removal of the bead template exposed hexagonally arranged circular nanocorrals separated by regions of chemisorbed molecules. Small molecule self-assembly was then investigated inside the resultant nanocorrals using scanning tunneling microscopy to highlight localized confinement effects. Overall, this work illustrates the utility of self-assembly principles to transcend length scale gaps in the development of hierarchically patterned molecular materials.

Thermodynamic Study of the Interaction of Bovine Serum Albumin and Amino Acids with Cellulose Nanocrystals.

The interaction of bovine serum albumin (BSA) with sulfated, carboxylated, and pyridinium-grafted cellulose nanocrystals (CNCs) was studied as a function of the degree of substitution by determining the adsorption isotherm and by directly measuring the thermodynamics of interaction. The adsorption of BSA onto positively charged pyridinium-grafted cellulose nanocrystals followed Langmuirian adsorption with the maximum amount of adsorbed protein increasing linearly with increasing degree of substitution. The binding mechanism between the positively charged pyridinum-grafted cellulose nanocrystals and BSA was found to be endothermic and based on charge neutralization. A positive entropy of adsorption associated with an increase of the degree of disorder upon addition of BSA compensated for the unfavorable endothermic enthalpy and enabled formation of pyridinium-g-CNC-BSA complexes. The endothermic enthalpy of adsorption was further found to decrease as a function of increasing degree of substitution. Negatively charged cellulose nanocrystals bearing sulfate and/or carboxylic functionalities were found to not interact significantly with the BSA protein. To investigate in more detail the role of single amino acids in the adsorption of proteins onto cellulose nanocrystals, we also studied the interaction of different types of amino acids with CNCs, i.e., charged (lysine, aspartic acid), aromatic (tryptophan, tyrosine), and polar (serine) amino acids. We found that none of the single amino acids bound with CNCs irrespective of surface charge and that therefore the binding of proteins with CNCs appears to require larger amino acid sequences that induce a greater entropic contribution to stabilize binding. Single amino acids are thus not adsorbed onto cellulose nanocrystals.

Area-selective passivation of sp2 carbon surfaces by supramolecular self-assembly.

Altering the chemical reactivity of graphene can offer new opportunities for various applications. Here, we report that monolayers of densely packed n-pentacontane significantly reduce the covalent grafting of aryl radicals to graphitic surfaces. The effect is highly local in nature and on fully covered substrates grafting can occur only at monolayer imperfections such as interdomain borders and vacancy defects. Grafting partially covered substrates primarily results in the covalent modification of uncoated areas.

Poly(ethylene oxide) Functionalized Graphene Nanoribbons with Excellent Solution Processability.

Structurally well-defined graphene nanoribbons (GNRs) have attracted great interest as next-generation semiconductor materials. The functionalization of GNRs with polymeric side chains, which can widely broaden GNR-related studies on physiochemical properties and potential applications, has remained unexplored. Here, we demonstrate the bottom-up solution synthesis of defect-free GNRs grafted with flexible poly(ethylene oxide) (PEO) chains. The GNR backbones possess an armchair edge structure with a width of 1.0-1.7 nm and mean lengths of 15-60 nm, enabling near-infrared absorption and a low bandgap of 1.3 eV. Remarkably, the PEO grafting renders the GNRs superb dispersibility in common organic solvents, with a record concentration of ∼1 mg mL(-1) (for GNR backbone) that is much higher than that (<0.01 mg mL(-1)) of reported GNRs. Moreover, the PEO-functionalized GNRs can be readily dispersed in water, accompanying with supramolecular helical nanowire formation. Scanning probe microscopy reveals raft-like self-assembled monolayers of uniform GNRs on graphite substrates. Thin-film-based field-effect transistors (FETs) of the GNRs exhibit a high carrier mobility of ∼0.3 cm(2) V(-1) s(-1), manifesting promising application of the polymer-functionalized GNRs in electronic devices.

Covalent modification of graphene and graphite using diazonium chemistry: tunable grafting and nanomanipulation.

We shine light on the covalent modification of graphite and graphene substrates using diazonium chemistry under ambient conditions. We report on the nature of the chemical modification of these graphitic substrates, the relation between molecular structure and film morphology, and the impact of the covalent modification on the properties of the substrates, as revealed by local microscopy and spectroscopy techniques and electrochemistry. By careful selection of the reagents and optimizing reaction conditions, a high density of covalently grafted molecules is obtained, a result that is demonstrated in an unprecedented way by scanning tunneling microscopy (STM) under ambient conditions. With nanomanipulation, i.e., nanoshaving using STM, surface structuring and functionalization at the nanoscale is achieved. This manipulation leads to the removal of the covalently anchored molecules, regenerating pristine sp(2) hybridized graphene or graphite patches, as proven by space-resolved Raman microscopy and molecular self-assembly studies.

On the stability of surface-confined nanoporous molecular networks.

Self-assembly of molecular building blocks into two-dimensional nanoporous networks has been a topic of broad interest for many years. However, various factors govern the specific outcome of the self-assembly process, and understanding and controlling these are key to successful creation. In this work, the self-assembly of two alkylated dehydrobenzo[12]annulene building blocks was compared at the liquid-solid interface. It turned out that only a small chemical modification within the building blocks resulted in enhanced domain sizes and stability of the porous packing relative to the dense linear packing. Applying a thermodynamic model for phase transition revealed some key aspects for network formation.