CXCR-4 expression by circulating endothelial progenitor cells and SDF-1 serum levels are elevated in septic patients

Background: Endothelial progenitor cell (EPC) numbers are increased in septic patients and correlate with survival. In this study, we investigated, whether surface expression of chemokine receptors and other receptors important for EPC homing is upregulated by EPC from septic patients and if this is associated with clinical outcome. Methods: Peripheral blood mononuclear cells from septic patients (n = 30), ICU control patients (n = 11) and healthy volunteers (n = 15) were isolated by Ficoll density gradient centrifugation. FACS-analysis was used to measure the expression of the CXC motif chemokine receptors (CXCR)-2 and − 4, the receptor for advanced glycation endproducts (RAGE) and the stem cell factor receptor c-Kit. Disease severity was assessed via the Simplified Acute Physiology Score (SAPS) II. The serum concentrations of vascular endothelial growth factor (VEGF), stromal cell-derived factor (SDF)-1α and angiopoietin (Ang)-2 were determined with Enzyme linked Immunosorbent Assays. Results: EPC from septic patients expressed significantly more CXCR-4, c-Kit and RAGE compared to controls and were associated with survival-probability. Significantly higher serum concentrations of VEGF, SDF-1α and Ang-2 were found in septic patients. SDF-1α showed a significant association with survival. Conclusions: Our data suggest that SDF-1α and CXCR-4 signaling could play a crucial role in EPC homing in the course of sepsis

CXCR-4 expression by circulating endothelial progenitor cells and SDF-1 serum levels are elevated in septic patients

Background: Endothelial progenitor cell (EPC) numbers are increased in septic patients and correlate with survival. In this study, we investigated, whether surface expression of chemokine receptors and other receptors important for EPC homing is upregulated by EPC from septic patients and if this is associated with clinical outcome. Methods: Peripheral blood mononuclear cells from septic patients (n = 30), ICU control patients (n = 11) and healthy volunteers (n = 15) were isolated by Ficoll density gradient centrifugation. FACS-analysis was used to measure the expression of the CXC motif chemokine receptors (CXCR)-2 and − 4, the receptor for advanced glycation endproducts (RAGE) and the stem cell factor receptor c-Kit. Disease severity was assessed via the Simplified Acute Physiology Score (SAPS) II. The serum concentrations of vascular endothelial growth factor (VEGF), stromal cell-derived factor (SDF)-1α and angiopoietin (Ang)-2 were determined with Enzyme linked Immunosorbent Assays. Results: EPC from septic patients expressed significantly more CXCR-4, c-Kit and RAGE compared to controls and were associated with survival-probability. Significantly higher serum concentrations of VEGF, SDF-1α and Ang-2 were found in septic patients. SDF-1α showed a significant association with survival. Conclusions: Our data suggest that SDF-1α and CXCR-4 signaling could play a crucial role in EPC homing in the course of sepsis

Population pharmacokinetics of mycophenolic acid in pediatric renal transplant patients using parametric and nonparametric approaches.

International audienceMycophenolic acid (MPA) is an immunosuppressive drug widely used in the prevention of acute rejection in pediatric renal transplant recipients and is characterized by a wide inter-individual variability in its pharmacokinetics. The aim of this study was to compare population pharmacokinetic modeling of MPA in pediatric renal transplant recipients given mycophenolate mofetil, the ester prodrug of MPA, using parametric and nonparametric population methods. The data from 34 pediatric renal transplants (73 full pharmacokinetic profiles obtained on day 21, months 3, 6 and 9 post-transplant) were analyzed using both the nonlinear mixed-effect modeling (NONMEM) and nonparametric adaptive grid (NPAG) approaches, based on a two-compartment model with first order lagged time absorption and first order elimination. The predictive performance of the two models was evaluated in a separate group of 32 patients. Higher mean population parameter values and ranges of individual pharmacokinetic parameters were obtained with NPAG, especially for the elimination constant ke: mean 1.16 h(-1) (0.26-4.33 h(-1)) and 0.78 h(-1) (0.66-1.15 h(-1)) with NPAG and NONMEM, respectively. With NPAG, the skewness and kurtosis values for ke (2.03 and 7.80, respectively) were far from the theoretical values expected for normal distributions. Such a non-normal distribution could explain the high value of shrinkage (35%) obtained for this parameter with the parametric NONMEM method. Bayesian forecasting of mycophenolic acid exposure using the NPAG population pharmacokinetic parameters as priors yielded a better predictive performance, with a significantly smaller bias than with the NONMEM model (-1.68% vs -9.53%, p<0.0001). In conclusion, in the present study, NPAG was found to be the most adequate population pharmacokinetic method to describe the pharmacokinetics of MPA in pediatric renal transplant recipients

Fractal nature in fat crystal networks

The determination of the mechanical and rheological characteristics of several plastic fats requires a detailed understanding of the microstructure of the fat crystal network aggregates. The fractal approach is useful for the characterization of this microstructure. This review begins with information on fractality and statistical self-similar structure. Estimations for fractal dimension by means of equations relating the volume fraction of solid fat to shear elastic modulus G’ in linear region are described. The influence of interesterification on fractal dimension decrease (from 2,46 to 2,15) for butterfat-canola oil blends is notable. This influence is not significant for fat blends without butterfat. The need for an increase in research concerning the relationship between fractality and rheology in plastic fats is emphasized.La determinación de las características mecánicas y reológicas de ciertas grasas plásticas requiere conocimientos detallados sobre las microestructuras de los agregados que forman la red de cristales grasos. El estudio de la naturaleza fractal de estas microestructuras resulta útil para su carac­terización. Este artículo de información se inicia con descripciones de la dimensión fractal y de la "autosimilitud estadística". A continuación se describe el cálculo de la dimensión fractal mediante ecuaciones que relacionan la fracción en volumen de grasa sólida con el módulo de recuperación (G') dentro de un comportamiento viscoelástico lineal. Se destaca la influencia que la interesterificación ejerce sobre la dimensión fractal de una mezcla de grasa láctea y aceite de canola (que pasa de 2,64 a 2,15). Esta influencia no se presenta en mezclas sin grasa láctea. Se insiste sobre la necesidad de incrementar las investi­gaciones sobre la relación entre reología y estructura fractal en grasas plásticas.Peer reviewe

Synthesis and optoelectronic properties of hexa-peri -hexabenzoborazinocoronene

The first rational synthesis of a BN-doped coronene derivative in which the central benzene ring has been replaced by a borazine core is described. This includes six C−C ring-closure steps that, through intramolecular Friedel–Crafts-type reactions, allow the stepwise planarization of the hexaarylborazine precursor. UV/Vis absorption, emission, and electrochemical investigations show that the introduction of the central BN core induces a dramatic widening of the HOMO–LUMO gap and an enhancement of the blue-shifted emissive properties with respect to its all-carbon congener

Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark

The recent Long-Range Graph Benchmark (LRGB, Dwivedi et al. 2022) introduced a set of graph learning tasks strongly dependent on long-range interaction between vertices. Empirical evidence suggests that on these tasks Graph Transformers significantly outperform Message Passing GNNs (MPGNNs). In this paper, we carefully reevaluate multiple MPGNN baselines as well as the Graph Transformer GPS (Ramp\'a\v{s}ek et al. 2022) on LRGB. Through a rigorous empirical analysis, we demonstrate that the reported performance gap is overestimated due to suboptimal hyperparameter choices. It is noteworthy that across multiple datasets the performance gap completely vanishes after basic hyperparameter optimization. In addition, we discuss the impact of lacking feature normalization for LRGB's vision datasets and highlight a spurious implementation of LRGB's link prediction metric. The principal aim of our paper is to establish a higher standard of empirical rigor within the graph machine learning community

Learning the language of QCD jets with transformers

Transformers have become the primary architecture for natural language processing. In this study, we explore their use for auto-regressive density estimation in high-energy jet physics, which involves working with a high-dimensional space. We draw an analogy between sentences and words in natural language and jets and their constituents in high-energy physics. Specifically, we investigate density estimation for light QCD jets and hadronically decaying boosted top jets. Since transformers allow easy sampling from learned densities, we exploit their generative capability to assess the quality of the density estimate. Our results indicate that the generated data samples closely resemble the original data, as evidenced by the excellent agreement of distributions such as particle multiplicity or jet mass. Furthermore, the generated samples are difficult to distinguish from the original data, even by a powerful supervised classifier. Given their exceptional data processing capabilities, transformers could potentially be trained directly on the massive LHC data sets to learn the probability densities in high-energy jet physics.Comment: Few references added; Version accepted for publication by JHE

WL meet VC

Recently, many works studied the expressive power of graph neural networks (GNNs) by linking it to the $1$-dimensional Weisfeiler--Leman algorithm ($1\text{-}\mathsf{WL}$). Here, the $1\text{-}\mathsf{WL}$ is a well-studied heuristic for the graph isomorphism problem, which iteratively colors or partitions a graph's vertex set. While this connection has led to significant advances in understanding and enhancing GNNs' expressive power, it does not provide insights into their generalization performance, i.e., their ability to make meaningful predictions beyond the training set. In this paper, we study GNNs' generalization ability through the lens of Vapnik--Chervonenkis (VC) dimension theory in two settings, focusing on graph-level predictions. First, when no upper bound on the graphs' order is known, we show that the bitlength of GNNs' weights tightly bounds their VC dimension. Further, we derive an upper bound for GNNs' VC dimension using the number of colors produced by the $1\text{-}\mathsf{WL}$. Secondly, when an upper bound on the graphs' order is known, we show a tight connection between the number of graphs distinguishable by the $1\text{-}\mathsf{WL}$ and GNNs' VC dimension. Our empirical study confirms the validity of our theoretical findings.Comment: arXiv admin note: text overlap with arXiv:2206.1116

Distinguished In Uniform: Self Attention Vs. Virtual Nodes

Graph Transformers (GTs) such as SAN and GPS are graph processing models that combine Message-Passing GNNs (MPGNNs) with global Self-Attention. They were shown to be universal function approximators, with two reservations: 1. The initial node features must be augmented with certain positional encodings. 2. The approximation is non-uniform: Graphs of different sizes may require a different approximating network. We first clarify that this form of universality is not unique to GTs: Using the same positional encodings, also pure MPGNNs and even 2-layer MLPs are non-uniform universal approximators. We then consider uniform expressivity: The target function is to be approximated by a single network for graphs of all sizes. There, we compare GTs to the more efficient MPGNN + Virtual Node architecture. The essential difference between the two model definitions is in their global computation method -- Self-Attention Vs Virtual Node. We prove that none of the models is a uniform-universal approximator, before proving our main result: Neither model's uniform expressivity subsumes the other's. We demonstrate the theory with experiments on synthetic data. We further augment our study with real-world datasets, observing mixed results which indicate no clear ranking in practice as well