The ALICE Data Challenges

Since 1998, the ALICE experiment and the CERN/IT division have jointly executed several large-scale high throughput distributed computing exercises: the ALICE data challenges. The goals of these regular exercises are to test hardware and software components of the data acquisition and computing systems in realistic conditions and to execute an early integration of the overall ALICE computing infrastructure. This paper reports on the third ALICE Data Challenge (ADC III) that has been performed at CERN from January to March 2001. The data used during the ADC III are simulated physics raw data of the ALICE TPC, produced with the ALICE simulation program AliRoot. The data acquisition was based on the ALICE online framework called the ALICE Data Acquisition Test Environment (DATE) system. The data after event building were then formatted with the ROOT I/O package and a data catalogue based on MySQL was established. The Mass Storage System used during ADC III is CASTOR. Different software tools have been used to monitor the performances. DATE has demonstrated performances of more than 500 MByte/s. An aggregate data throughput of 85 MByte/s was sustained in CASTOR over several days. The total collected data amounts to 100 TBytes in 100,000 files

Vertebral osteomyelitis and native valve endocarditis due to Staphylococcus simulans: a case report

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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