Diffusion algebras

We define the notion of "diffusion algebras". They are quadratic Poincare-Birkhoff-Witt (PBW) algebras which are useful in order to find exact expressions for the probability distributions of stationary states appearing in one-dimensional stochastic processes with exclusion. One considers processes in which one has N species, the number of particles of each species being conserved. All diffusion algebras are obtained. The known examples already used in applications are special cases in our classification. To help the reader interested in physical problems, the cases N=3 and 4 are listed separately.Comment: 29 pages; minor misprints corrected, few references adde

6′,7′‐Dihydroxybergamottin in grapefruit juice and Seville orange juice: Effects on cyclosporine disposition, enterocyte CYP3A4, and P‐glycoprotein

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109869/1/cptclpt1999363.pd

Introduction to special section: Outstanding problems in quantifying the radiative impact of mineral dust

International audienceThis paper provides an introduction to the special section of the Journal of Geophysical Research on mineral dust. We briefly review the current experimental and theoretical approaches used to quantify the dust radiative impacts, highlight the outstanding issues, and discuss possible strategies to overcome the emerging problems. We also introduce the contributing papers of this special section. Despite the recent notable advances in dust studies, we demonstrate that the radiative effects of dust remain poorly quantified due to both limited data and incomplete understanding of relative physical and chemical processes. The foremost needs are (1) to quantify the spatial and temporal variations of dust burden in the atmosphere and develop a predictive capability for the size‐ and composition‐resolved dust particle distribution; (2) to develop a quantitative description of the processes that control the spatial and temporal variabilities of dust physical and chemical properties and radiative effects; (3) to develop new instrumentation (especially to measure the dust particle size distribution in a wide range from about 0.01 μm to 100 μm, scattering phase function and light absorption by dust particles); and (4) to develop new techniques for interpreting and merging the diverse information from satellite remote sensing, in situ and ground‐based measurements, laboratory studies, and model simulations. Because dust distribution and effects are heterogeneous, both spatially and temporally, a promising strategy to advance our knowledge is to perform comprehensive studies at the targeted regions affected by mineral dust of both natural and anthropogenic origin

Time-resolved tomographic quantification of the microstructural evolution of ice cream

Ice cream is a complex multi-phase colloidal soft-solid and its three-dimensional microstructure plays a critical role in determining the oral sensory experience or mouthfeel. Using in-line phase contrast synchrotron X-ray tomography, we capture the rapid evolution of the ice cream microstructure during heat shock conditions in situ and operando, on a time scale of minutes. The further evolution of the ice cream microstructure during storage and abuse was captured using ex situ tomography on a time scale of days. The morphology of the ice crystals and unfrozen matrix during these thermal cycles was quantified as an indicator for the texture and oral sensory perception. Our results reveal that the coarsening is due to both Ostwald ripening and physical agglomeration, enhancing our understanding of the microstructural evolution of ice cream during both manufacturing and storage. The microstructural evolution of this complex material was quantified, providing new insights into the behavior of soft-solids and semi-solids, including many foodstuffs, and invaluable data to both inform and validate models of their processing

Circulating Precursor Levels of Endothelin-1 and Adrenomedullin, Two Endothelium-Derived, Counteracting Substances, in Sepsis

Plasma levels of endothelin-1 (ET-1) and adrenomedullin (ADM), two opposingly acting peptides, correlate with mortality in endotoxemia, but their measurement is cumbersome. New sandwich assays have been introduced that measure more stable precursor fragments. The objective of this study was to investigate the counterplay of their precursor peptides in septic patients and to compare them with disease severity and other biomarkers. Blood samples of an observational study in 95 consecutive critically ill patients admitted to the intensive care unit (ICU) were analyzed. CT-proET-1 and MR-proADM concentrations on admission were measured using new sandwich immunoassays. Depending on the clinical severity of the infection, both CT-proET-1 and MR-proADM levels exhibited a gradual increase from Systemic Inflammatory Response Syndrome (SIRS) to sepsis and septic shock (p < .001). Compared to the group of survivors, the group of non-survivors had higher median values of MR-proADM (5.7 nmol/L [range 0.4 to 21.0] versus 1.9 nmol/L [range 0.3 to 17.1], p < .02) and similar CT-proET-1 levels (56.0pmol/L [range 0.5 to 271.0] versus 54.1pmol/L [range 1.0 to 506.0], p = .86). Receiver operating characteristics (ROC) curve analysis showed a higher prognostic accuracy of the calculated ratio of both counteracting substances as compared to CT-proET-1 (p = 0.001) and C-reactive protein (CRP) (p = .001) and in the range of MR-proADM (p = .51), procalcitonin (p = 0.22), and the APACHE II score (p = .61). Endothelin-1 and adrenomedullin precursor peptides gradually increase with increasing severities of infection in critically ill patients. The ratio of the two counteracting peptides correlates with mortality and shows aprognostic accuracy to predict adverse outcome comparable to the APACHE II score

Explainable AI using expressive Boolean formulas

We propose and implement an interpretable machine learning classification model for Explainable AI (XAI) based on expressive Boolean formulas. Potential applications include credit scoring and diagnosis of medical conditions. The Boolean formula defines a rule with tunable complexity (or interpretability), according to which input data are classified. Such a formula can include any operator that can be applied to one or more Boolean variables, thus providing higher expressivity compared to more rigid rule-based and tree-based approaches. The classifier is trained using native local optimization techniques, efficiently searching the space of feasible formulas. Shallow rules can be determined by fast Integer Linear Programming (ILP) or Quadratic Unconstrained Binary Optimization (QUBO) solvers, potentially powered by special purpose hardware or quantum devices. We combine the expressivity and efficiency of the native local optimizer with the fast operation of these devices by executing non-local moves that optimize over subtrees of the full Boolean formula. We provide extensive numerical benchmarking results featuring several baselines on well-known public datasets. Based on the results, we find that the native local rule classifier is generally competitive with the other classifiers. The addition of non-local moves achieves similar results with fewer iterations, and therefore using specialized or quantum hardware could lead to a speedup by fast proposal of non-local moves.Comment: 28 pages, 16 figures, 4 table

Procalcitonin for diagnosis of infection and guide to antibiotic decisions: past, present and future

There are a number of limitations to using conventional diagnostic markers for patients with clinical suspicion of infection. As a consequence, unnecessary and prolonged exposure to antimicrobial agents adversely affect patient outcomes, while inappropriate antibiotic therapy increases antibiotic resistance. A growing body of evidence supports the use of procalcitonin (PCT) to improve diagnosis of bacterial infections and to guide antibiotic therapy. For patients with upper and lower respiratory tract infection, post-operative infections and for severe sepsis patients in the intensive care unit, randomized-controlled trials have shown a benefit of using PCT algorithms to guide decisions about initiation and/or discontinuation of antibiotic therapy. For some other types of infections, observational studies have shown promising first results, but further intervention studies are needed before use of PCT in clinical routine can be recommended. The aim of this review is to summarize the current evidence for PCT in different infections and clinical settings, and discuss the reliability of this marker when used with validated diagnostic algorithms

Signatures of arithmetic simplicity in metabolic network architecture

Metabolic networks perform some of the most fundamental functions in living cells, including energy transduction and building block biosynthesis. While these are the best characterized networks in living systems, understanding their evolutionary history and complex wiring constitutes one of the most fascinating open questions in biology, intimately related to the enigma of life's origin itself. Is the evolution of metabolism subject to general principles, beyond the unpredictable accumulation of multiple historical accidents? Here we search for such principles by applying to an artificial chemical universe some of the methodologies developed for the study of genome scale models of cellular metabolism. In particular, we use metabolic flux constraint-based models to exhaustively search for artificial chemistry pathways that can optimally perform an array of elementary metabolic functions. Despite the simplicity of the model employed, we find that the ensuing pathways display a surprisingly rich set of properties, including the existence of autocatalytic cycles and hierarchical modules, the appearance of universally preferable metabolites and reactions, and a logarithmic trend of pathway length as a function of input/output molecule size. Some of these properties can be derived analytically, borrowing methods previously used in cryptography. In addition, by mapping biochemical networks onto a simplified carbon atom reaction backbone, we find that several of the properties predicted by the artificial chemistry model hold for real metabolic networks. These findings suggest that optimality principles and arithmetic simplicity might lie beneath some aspects of biochemical complexity