Cytochrome P4501-inhibiting chemicals amplify aryl hydrocarbon receptor activation and IL-22 production in T helper 17 cells

The aryl hydrocarbon receptor (AHR)controls interleukin 22 production by T helper 17 cells (Th17). IL-22contributes to intestinalhomeostasis but has also been implicated inchronic inflammatory disorders and colorectal cancer, highlighting the need for appropriate regulation of IL-22 production. Upon activation, the AHR induces expression of cytochrome P4501 (CYP1) enzymes that in turn play an important feedback role that curtails the duration of AHR signaling by metabolizingAHRligands. Recently we described how agents that inhibit CYP1 function potentiate AHR signalingby disruptingmetabolic clearance of the endogenous ligand 6-formylindolo[3,2-b]carbazole (FICZ). In the present study, we investigated the immune-modulating effects of environmental pollutants such as polycyclic aromatic hydrocarbons on Th17 differentiation and IL-22 production. Using Th17 cells deficient in CYP1 enzymes (Cyp1a1/1a2/1b1-/-)we show that these chemicals potentiate AHR activation through inhibition of CYP1 enzymes which leads to increases in intracellular AHR agonists. Our findings demonstrate that IL-22 production by Th17 cellsis profoundly enhanced by impaired CYP1-function and strongly suggest that chemicals able to modify CYP1 function or expression may disrupt AHR-mediated immune regulation by altering the levels of endogenous AHR agonist(s)

A diagrammatic treatment of neutrino oscillations

We present a covariant wave-packet approach to neutrino flavor transitions in vacuum. The approach is based on the technique of macroscopic Feynman diagrams describing the lepton number violating processes of production and absorption of virtual massive neutrinos at the macroscopically separated space-time regions ("source" and "detector"). Accordingly, the flavor transitions are a result of interference of the diagrams with neutrinos of different masses in the intermediate states. The statistically averaged probability of the process is representable as a multidimensional integral of the product of the factors which describe the differential flux density of massless neutrinos from the source, differential cross section of the neutrino interaction with the detector and a dimensionless factor responsible for the flavor transition. The conditions are analyzed under which the last factor can be treated as the flavor transition probability in the usual quantum-mechanical sense.Comment: 27 pages,7 figures, iopart class. Includes minor corrections made in proofs. References update

Corrigendum to "Higher Education Policy: Why hope for Quality when rewarding Quantity?" [Research Policy Volume 49, Issue 8(2020) /104083]

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Enabling semantic queries across federated bioinformatics databases

MOTIVATION: Data integration promises to be one of the main catalysts in enabling new insights to be drawn from the wealth of biological data available publicly. However, the heterogeneity of the different data sources, both at the syntactic and the semantic level, still poses significant challenges for achieving interoperability among biological databases. RESULTS: We introduce an ontology-based federated approach for data integration. We applied this approach to three heterogeneous data stores that span different areas of biological knowledge: (i) Bgee, a gene expression relational database; (ii) Orthologous Matrix (OMA), a Hierarchical Data Format 5 orthology DS; and (iii) UniProtKB, a Resource Description Framework (RDF) store containing protein sequence and functional information. To enable federated queries across these sources, we first defined a new semantic model for gene expression called GenEx. We then show how the relational data in Bgee can be expressed as a virtual RDF graph, instantiating GenEx, through dedicated relational-to-RDF mappings. By applying these mappings, Bgee data are now accessible through a public SPARQL endpoint. Similarly, the materialized RDF data of OMA, expressed in terms of the Orthology ontology, is made available in a public SPARQL endpoint. We identified and formally described intersection points (i.e. virtual links) among the three data sources. These allow performing joint queries across the data stores. Finally, we lay the groundwork to enable nontechnical users to benefit from the integrated data, by providing a natural language template-based search interface

Multiple query optimization using a gate-based quantum computer

Quantum computing promises to solve difficult optimization problems in chemistry, physics and mathematics more efficiently than classical computers. However, it requires fault-tolerant quantum computers with millions of qubits; a technological challenge still not mastered by engineers. To lower the barrier, hybrid algorithms combining classical and quantum computers are used, where quantum computing is only used for those parts of computation that cannot be solved efficiently otherwise. In this paper, we tackle the multiple query optimization problem (MQO), an important NP-hard problem in database research. We present an implementation based on a scheme called quantum approximate optimization algorithm to solve the MQO on a gate-based quantum computer. We perform a detailed experimental evaluation of our implementation and compare its performance against a competing approach that employs a quantum annealer – another type of quantum computer. Our implementation shows a qubit efficiency of close to 99%, which is almost a factor of 2 higher than the state-of-the-art implementation. We emphasize that the problems we can solve with current gate-based quantum technology are fairly small and might not seem practical yet compared to state-of-the-art classical query optimizers. However, our experiments on using a hybrid approach of classical and quantum computing show that our implementation scales favourably with larger problem sizes. Hence, we conclude that our approach shows promising results for near-term quantum computers and thus sets the stage for a challenging avenue of novel database research

Accelerating Network Traffic Analytics Using Query-DrivenVisualization

Realizing operational analytics solutions where large and complex data must be analyzed in a time-critical fashion entails integrating many different types of technology. This paper focuses on an interdisciplinary combination of scientific data management and visualization/analysis technologies targeted at reducing the time required for data filtering, querying, hypothesis testing and knowledge discovery in the domain of network connection data analysis. We show that use of compressed bitmap indexing can quickly answer queries in an interactive visual data analysis application, and compare its performance with two alternatives for serial and parallel filtering/querying on 2.5 billion records worth of network connection data collected over a period of 42 weeks. Our approach to visual network connection data exploration centers on two primary factors: interactive ad-hoc and multiresolution query formulation and execution over n dimensions and visual display of then-dimensional histogram results. This combination is applied in a case study to detect a distributed network scan and to then identify the set of remote hosts participating in the attack. Our approach is sufficiently general to be applied to a diverse set of data understanding problems as well as used in conjunction with a diverse set of analysis and visualization tools

Characterising the original anti-C5 function-blocking antibody, BB5.1, for species specificity, mode of action and interactions with C5

The implication of complement in multiple diseases over the last twenty years has fuelled interest in developing anti‐complement drugs. To date, the focus has been on C5; blocking cleavage of C5 prevents formation of two pro‐inflammatory activities, C5a anaphylatoxin and membrane attack complex. The concept of C5 blockade to inhibit inflammation dates back thirty years to the description of BB5.1, an anti‐C5 blocking monoclonal antibody raised in C5‐deficient mice. This antibody proved an invaluable tool to demonstrate complement involvement in mouse disease models and catalysed enthusiasm for anti‐complement drug development, culminating in the anti‐human C5 monoclonal antibody ecuizumab, the most successful anti‐complement drug to date, already in the clinic for several rare diseases. Despite its key role in providing proof‐of‐concept for C5 blockade, the mechanism of BB5.1 inhibition remains poorly understood. Here we characterised BB5.1 cross‐species inhibition, C5 binding affinity and chain specificity. BB5.1 efficiently inhibited C5 in mouse serum but not in human or other rodent sera; it prevented C5 cleavage and C5a generation. BB5.1 bound the C5 α‐chain with high affinity and slow off‐rate. BB5.1 complementarity determining regions were obtained and docking algorithms used to predict the likely binding interface on mouse C5