In recent years, graph neural networks (GNNs) have emerged as a powerful neural architecture to learn vector representations of nodes and graphs in a supervised, end-to-end fashion. Up to now, GNNs have only been evaluated empirically-showing promising results. The following work investigates GNNs from a theoretical point of view and relates them to the 1-dimensional Weisfeiler-Leman graph isomorphism heuristic (1-WL). We show that GNNs have the same expressiveness as the 1-WL in terms of distinguishing non-isomorphic (sub-)graphs. Hence, both algorithms also have the same shortcomings. Based on this, we propose a generalization of GNNs, so-called k-dimensional GNNs (k-GNNs), which can take higher-order graph structures at multiple scales into account. These higher-order structures play an essential role in the characterization of social networks and molecule graphs. Our experimental evaluation confirms our theoretical findings as well as confirms that higher-order information is useful in the task of graph classification and regression.completing the equivalence. Since the power of the 1-WL has been completely characterized, see, e.g., (Arvind et al. 2015;Kiefer, Schweitzer, and Selman 2015), we can transfer these results to the case of GNNs, showing that both approaches have the same shortcomings.Going further, we leverage these theoretical relationships to propose a generalization of GNNs, called k-GNNs, which are neural architectures based on the k-dimensional WL algorithm (k-WL), which are strictly more powerful than GNNs. The key insight in these higher-dimensional variants is that they perform message passing directly between subgraph structures, rather than individual nodes. This higher-order form of message passing can capture structural information that is not visible at the node-level.Graph kernels based on the k-WL have been proposed in the past (Morris, Kersting, and Mutzel 2017). However, a key advantage of implementing higher-order message passing in GNNs-which we demonstrate here-is that we can design hierarchical variants of k-GNNs, which combine graph representations learned at different granularities in an end-to-end trainable framework. Concretely, in the presented hierarchical approach the initial messages in a k-GNN are based on the output of lower-dimensional k -GNN (with k < k), which allows the model to effectively capture graph structures of varying granularity. Many real-world graphs inherit a hierarchical structure-e.g., in a social network we must model both the ego-networks around individual nodes, as well as the coarse-grained relationships between entire communities, see, e.g., (Newman 2003)-and our experimental results demonstrate that these hierarchical k-GNNs are able to consistently outperform traditional GNNs on a variety of graph classification and regression tasks. Across twelve graph regression tasks from the QM9 benchmark, we find that our hierarchical model reduces the mean absolute error by 54.45% on average. For graph classification, we find that our hierarchical models...

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Glucose tolerance is lower in the evening and at night than in the morning. However, the relative contribution of the circadian system vs. the behavioral cycle (including the sleep/wake and fasting/ feeding cycles) is unclear. Furthermore, although shift work is a diabetes risk factor, the separate impact on glucose tolerance of the behavioral cycle, circadian phase, and circadian disruption (i.e., misalignment between the central circadian pacemaker and the behavioral cycle) has not been systematically studied. Here we show-by using two 8-d laboratory protocols-in healthy adults that the circadian system and circadian misalignment have distinct influences on glucose tolerance, both separate from the behavioral cycle. First, postprandial glucose was 17% higher (i.e., lower glucose tolerance) in the biological evening (8:00 PM) than morning (8:00 AM; i.e., a circadian phase effect), independent of the behavioral cycle effect. Second, circadian misalignment itself (12-h behavioral cycle inversion) increased postprandial glucose by 6%. Third, these variations in glucose tolerance appeared to be explained, at least in part, by different mechanisms: during the biological evening by decreased pancreatic β-cell function (27% lower earlyphase insulin) and during circadian misalignment presumably by decreased insulin sensitivity (elevated postprandial glucose despite 14% higher late-phase insulin) without change in early-phase insulin. We explored possible contributing factors, including changes in polysomnographic sleep and 24-h hormonal profiles. We demonstrate that the circadian system importantly contributes to the reduced glucose tolerance observed in the evening compared with the morning. Separately, circadian misalignment reduces glucose tolerance, providing a mechanism to help explain the increased diabetes risk in shift workers.circadian disruption | shift work | night work | glucose metabolism | diabetes I n healthy humans, there is a strong time-of-day variation in glucose tolerance, with a peak in the morning and a trough in the evening and night (1-6). Understanding the underlying mechanisms of the day/night variation in glucose tolerance is important for diurnally active individuals as well as shift workers, who are at increased risk for developing type 2 diabetes (7-9). The endogenous circadian system and circadian misalignment (i.e., misalignment between the endogenous circadian system and 24-h environmental/behavioral cycles) have been shown to affect glucose metabolism (4,(10)(11)(12)(13)(14). However, the relative and separate importance of the endogenous circadian system and circadian misalignment-after accounting for behavioral cycle effects (including the sleep/wake, fasting/feeding, and physical inactivity/activity cycles, etc.)-on 24-h variation in glucose tolerance is not well understood.Most species have evolved an endogenous circadian timing system that optimally times physiological variations and behaviors relative to the 24-h environmental cycle (15-17). The mammalian circadian system is comp...

Shift work is a risk factor for hypertension, inflammation, and cardiovascular disease. This increased risk cannot be fully explained by classic risk factors. One of the key features of shift workers is that their behavioral and environmental cycles are typically misaligned relative to their endogenous circadian system. However, there is little information on the impact of acute circadian misalignment on cardiovascular disease risk in humans. Here we show-by using two 8-d laboratory protocols-that short-term circadian misalignment (12-h inverted behavioral and environmental cycles for three days) adversely affects cardiovascular risk factors in healthy adults. Circadian misalignment increased 24-h systolic blood pressure (SBP) and diastolic blood pressure (DBP) by 3.0 mmHg and 1.5 mmHg, respectively. These results were primarily explained by an increase in blood pressure during sleep opportunities (SBP, +5.6 mmHg; DBP, +1.9 mmHg) and, to a lesser extent, by raised blood pressure during wake periods (SBP, +1.6 mmHg; DBP, +1.4 mmHg). Circadian misalignment decreased wake cardiac vagal modulation by 8-15%, as determined by heart rate variability analysis, and decreased 24-h urinary epinephrine excretion rate by 7%, without a significant effect on 24-h urinary norepinephrine excretion rate. Circadian misalignment increased 24-h serum interleukin-6, C-reactive protein, resistin, and tumor necrosis factor-α levels by 3-29%. We demonstrate that circadian misalignment per se increases blood pressure and inflammatory markers. Our findings may help explain why shift work increases hypertension, inflammation, and cardiovascular disease risk.circadian misalignment | hypertension | inflammatory markers | night work | shift work

Levels of numerous hormones vary across the day and night. Such fluctuations are not only attributable to changes in sleep/wakefulness and other behaviors but also to a biological timing system governed by the suprachiasmatic nucleus of the hypothalamus. Sleep has a strong effect on levels of some hormones such as growth hormone but little effect on others which are more strongly regulated by the biological timing system (e.g., melatonin). Whereas the exact mechanisms through which sleep affects circulating hormonal levels are poorly understood, more is known about how the biological timing system influences the secretion of hormones. The suprachiasmatic nucleus exerts its influence on hormones via neuronal and humoral signals but it is also now apparent that peripheral cells can rhythmically secrete hormones independent of signals from the suprachiasmatic nucleus. Under normal circumstances, behaviors and the biological timing system are synchronized and consequently hormonal systems are exquisitely regulated. However, many individuals (e.g., shift-workers) frequently undergo circadian misalignment by desynchronizing their sleep/wake cycle from the biological timing system. Recent experiments indicate that circadian misalignment has an adverse effect on metabolic and hormonal factors such as glucose and insulin. Further research is needed to determine the underlying mechanisms that cause the negative effects induced by circadian misalignment. Such research could aid the development of countermeasures for circadian misalignment.

Graph kernels have become an established and widely-used technique for solving classification tasks on graphs. This survey gives a comprehensive overview of techniques for kernel-based graph classification developed in the past 15 years. We describe and categorize graph kernels based on properties inherent to their design, such as the nature of their extracted graph features, their method of computation and their applicability to problems in practice.In an extensive experimental evaluation, we study the classification accuracy of a large suite of graph kernels on established benchmarks as well as new datasets. We compare the performance of popular kernels with several baseline methods and study the effect of applying a Gaussian RBF kernel to the metric induced by a graph kernel. In doing so, we find that simple baselines become competitive after this transformation on some datasets. Moreover, we study the extent to which existing graph kernels agree in their predictions (and prediction errors) and obtain a data-driven categorization of kernels as result. Finally, based on our experimental results, we derive a practitioner's guide to kernel-based graph classification. 1

Objective. The transcription factor–κB (NF‐κB) has been implicated in the inflammatory response and is known to be activated by a process involving reactive oxygen intermediates. The purpose of the present study was to demonstrate the presence and distribution of activated NF‐κB in synovium samples from patients with rheumatoid arthritis (RA) and osteoarthritis (OA) and from autopsy subjects with no known history of arthritis. Methods. Immunohistochemical staining was performed using both polyclonal and monoclonal “activity‐specific” antibodies to the Rel‐A (p65) subunit of NF‐κB (anti‐Rel‐A nuclear location sequences). Histologic features of inflammation were also scored. Results. Both antibodies demonstrated positive staining of synovial tissue, with a cellular distribution that was nuclear. The staining was associated with specific cell types within the tissue, in particular, type A synoviocytes and vascular endothelium. Notably, lymphoid aggregates were unstained. Using the monoclonal antibody, a further study was carried out to investigate the distribution of staining in tissues from patients with different disease activities and clinical diagnoses, as well as in normal control tissue obtained at autopsy. Patients with acute RA more commonly showed vessel staining (P = 0.05) and, conversely, showed less frequent staining of the synovial lining (P < 0.005) compared with OA patients. Synovial tissue from controls exhibited either no staining or only weak staining in the synovial lining. Conclusion. The activation of NF‐κB in vascular endothelium and type A synovial lining cells is a feature of synovial tissue from both RA and OA patients. The distribution of this staining appears to be related to the clinical diagnosis.

The synthesis of biocompatible, thermo-responsive ABA triblock copolymers in which the outer A blocks comprise poly(N-isopropylacrylamide) and the central B block is poly(2-methacryloyloxyethyl phosphorylcholine) is achieved using atom transfer radical polymerization with a commercially available bifunctional initiator. These novel triblock copolymers are water-soluble in dilute aqueous solution at 20 degrees C and pH 7.4 but form free-standing physical gels at 37 degrees C due to hydrophobic interactions between the poly(N-isopropylacrylamide) blocks. This gelation is reversible, and the gels are believed to contain nanosized micellar domains; this suggests possible applications in drug delivery and tissue engineering.