ROOT - A C++ Framework for Petabyte Data Storage, Statistical Analysis and Visualization

ROOT is an object-oriented C++ framework conceived in the high-energy physics (HEP) community, designed for storing and analyzing petabytes of data in an efficient way. Any instance of a C++ class can be stored into a ROOT file in a machine-independent compressed binary format. In ROOT the TTree object container is optimized for statistical data analysis over very large data sets by using vertical data storage techniques. These containers can span a large number of files on local disks, the web, or a number of different shared file systems. In order to analyze this data, the user can chose out of a wide set of mathematical and statistical functions, including linear algebra classes, numerical algorithms such as integration and minimization, and various methods for performing regression analysis (fitting). In particular, ROOT offers packages for complex data modeling and fitting, as well as multivariate classification based on machine learning techniques. A central piece in these analysis tools are the histogram classes which provide binning of one- and multi-dimensional data. Results can be saved in high-quality graphical formats like Postscript and PDF or in bitmap formats like JPG or GIF. The result can also be stored into ROOT macros that allow a full recreation and rework of the graphics. Users typically create their analysis macros step by step, making use of the interactive C++ interpreter CINT, while running over small data samples. Once the development is finished, they can run these macros at full compiled speed over large data sets, using on-the-fly compilation, or by creating a stand-alone batch program. Finally, if processing farms are available, the user can reduce the execution time of intrinsically parallel tasks - e.g. data mining in HEP - by using PROOF, which will take care of optimally distributing the work over the available resources in a transparent way

ROOT - A C++ Framework for Petabyte Data Storage, Statistical Analysis and Visualization

ROOT is an object-oriented C++ framework conceived in the high-energy physics (HEP) community, designed for storing and analyzing petabytes of data in an efficient way. Any instance of a C++ class can be stored into a ROOT file in a machine-independent compressed binary format. In ROOT the TTree object container is optimized for statistical data analysis over very large data sets by using vertical data storage techniques. These containers can span a large number of files on local disks, the web, or a number of different shared file systems. In order to analyze this data, the user can chose out of a wide set of mathematical and statistical functions, including linear algebra classes, numerical algorithms such as integration and minimization, and various methods for performing regression analysis (fitting). In particular, the RooFit package allows the user to perform complex data modeling and fitting while the RooStats library provides abstractions and implementations for advanced statistical tools. Multivariate classification methods based on machine learning techniques are available via the TMVA package. A central piece in these analysis tools are the histogram classes which provide binning of one- and multi-dimensional data. Results can be saved in high-quality graphical formats like Postscript and PDF or in bitmap formats like JPG or GIF. The result can also be stored into ROOT macros that allow a full recreation and rework of the graphics. Users typically create their analysis macros step by step, making use of the interactive C++ interpreter CINT, while running over small data samples. Once the development is finished, they can run these macros at full compiled speed over large data sets, using on-the-fly compilation, or by creating a stand-alone batch program. Finally, if processing farms are available, the user can reduce the execution time of intrinsically parallel tasks -- e.g.\ data mining in HEP -- by using PROOF, which will take care of optimally distributing the work over the available resources in a transparent way

Selective iNOS inhibition for the treatment of sepsis-induced acute kidney injury.

Contains fulltext : 81592.pdf (publisher's version ) (Closed access)The incidence and mortality of sepsis and the associated development of acute kidney injury (AKI) remain high, despite intense research into potential treatments. Targeting the inflammatory response and/or sepsis-induced alterations in the (micro)circulation are two therapeutic strategies. Another approach could involve modulating the downstream mechanisms that are responsible for organ system dysfunction. Activation of inducible nitric oxide (NO) synthase (iNOS) during sepsis leads to elevated NO levels that influence renal hemodynamics and cause peroxynitrite-related tubular injury through the local generation of reactive nitrogen species. In many organs iNOS is not constitutively expressed; however, it is constitutively expressed in the kidney and, in humans, a relationship between the upregulation of renal iNOS and proximal tubular injury during systemic inflammation has been demonstrated. For these reasons, the selective inhibition of renal iNOS might have important implications for the treatment of sepsis-induced AKI. Various animal studies have demonstrated that selective iNOS inhibition-in contrast to nonselective NOS inhibition-attenuates sepsis-induced renal dysfunction and improves survival, a finding that warrants investigation in clinical trials. In this Review, the selective inhibition of iNOS as a potential novel treatment for sepsis-induced AKI is discussed