A Technical Report On Grid Benchmarking using ATLAS V.O

Grids include heterogeneous resources, which are based on different hardware and software architectures or components. In correspondence with this diversity of the infrastructure, the execution time of any single job, as well as the total grid performance can both be affected substantially, which can be demonstrated by measurements. Running a simple benchmarking suite can show this heterogeneity and give us results about the differences over the grid sites.Comment: 29 pages, 35 figures, including charts and results of benchmarking over the grid, ATLAS V.

The ATLAS Simulation Infrastructure

The simulation software for the ATLAS Experiment at the Large Hadron Collider is being used for largescale production of events on the LHC Computing Grid. This simulation requires many components, from the generators that simulate particle collisions, through packages simulating the response of the various detectors and triggers. All of these components come together under the ATLAS simulation infrastructure. In this paper, that infrastructure is discussed, including that supporting the detector description, interfacing the event generation, and combining the GEANT4 simulation of the response of the individual detectors. Also described are the tools allowing the software validation, performance testing, and the validation of the simulated output against known physics processes.La lista completa de autores que integran el documento puede verse en el archivo asociado.Facultad de Ciencias Exacta

The ATLAS Simulation Infrastructure

The simulation software for the ATLAS Experiment at the Large Hadron Collider is being used for largescale production of events on the LHC Computing Grid. This simulation requires many components, from the generators that simulate particle collisions, through packages simulating the response of the various detectors and triggers. All of these components come together under the ATLAS simulation infrastructure. In this paper, that infrastructure is discussed, including that supporting the detector description, interfacing the event generation, and combining the GEANT4 simulation of the response of the individual detectors. Also described are the tools allowing the software validation, performance testing, and the validation of the simulated output against known physics processes.La lista completa de autores que integran el documento puede verse en el archivo asociado.Facultad de Ciencias Exacta

Yield gap analysis of field crops: Methods and case studies

The challenges of global agriculture have been analysed exhaustively and the need has been established for sustainable improvement in agricultural production aimed at food security in a context of increasing pressure on natural resources. Whereas the importance of R&D investment in agriculture is increasingly recognised, better allocation of limited funding is essential to improve food production. In this context, the common and often large gap between actual and attainable yield is a critical target. Realistic solutions are required to close yield gaps in both small and large scale cropping systems worldwide; to make progress in this direction, we need (1) definitions and techniques to measure and model yield at different levels (actual, attainable, potential) and different scales in space (field, farm, region, global) and time (short, long term); (2) identification of the causes of gaps between yield levels; (3) management options to reduce the gaps where feasible and (4) policies to favour adoption of gap-closing technologies. The aim of this publication is to review the methods for yield gap analysis, and to use case studies to illustrate different approaches, hence addressing the first of these four requirements. Theoretical, potential, water-limited, and actual yield are defined. Yield gap is the difference between two levels of yield in this series. Depending on the objectives of the study, different yield gaps are relevant. The exploitable yield gap accounts for both the unlikely alignment of all factors required for achievement of potential or water limited yield and the economic, management and environmental constraints that preclude, for example, the use of fertiliser rates that maximise yield, when growers’ aim is often a compromise between maximising profit and minimising risk at the whole-farm scale, rather than maximising yield of individual crops. The gap between potential and water limited yield is an indication of yield gap that can be removed with irrigation. Spatial and temporal scales for the determination of yield gaps are discussed. Spatially, yield gaps have been quantified at levels of field, region, national or mega-environment and globally. Remote sensing techniques describes the spatial variability of crop yield, even up to individual plots. Time scales can be defined in order to either remove or capture the dynamic components of the environment (soil, climate, biotic components of ecosystems) and technology. Criteria to define scales in both space and time need to be made explicit, and should be consistent with the objectives of the analysis. Satellite measurements can complement in situ measurements. The accuracy of estimating yield gaps is determined by the weakest link, which in many cases is good quality, sub-national scale data on actual yields that farmers achieve. In addition, calculation and interpretation of yield gaps requires reliable weather data, additional agronomic information and transparent assumptions. The main types of methods used in yield benchmarking and gap analysis are outlined using selected case studies. The diversity of benchmarking methods outlined in this publication reflects the diversity of spatial and temporal scales, the questions asked, and the resources available to answer them. We grouped methods in four broad approaches. Approach 1 compares actual yield with the best yield achieved in comparable environmental conditions, e.g. between neighbours with similar topography and soils. Comparisons of this type are spatially constrained by definition, and are an approximation to the gap between actual and attainable yield. With minimum input and greatest simplicity, this allows for limited but useful benchmarks; yield gaps can be primarily attributed to differences in management. This approach can be biased, however, where best management practices are not feasible; modelled yields provide more relevant benchmarks in these cases. Approach 2 is a variation of approach 1, i.e. it is based on comparisons of actual yield, but instead of a single yield benchmark, yield is expressed as a function of one or few environmental drivers in simple models. In common with Approach 1, these methods do not necessarily capture best management practices. The French and Schultz model is the archetype in this approach; this method plots actual yield against seasonal water use, fits a boundary function representing the best yield for a given water use, and calculates yield gaps as the departure between actual yields and the boundary function. A boundary model fitted to the data provides a scaled benchmark, thus partially accounting for seasonal conditions. Boundary functions can be estimated with different statistical methods but it is recommended that the shape and parameters of boundary functions are also assessed on the basis of their biophysical meaning. Variants of this approach use nitrogen uptake or soil properties instead of water. Approach 3 is based on modelling which may range from simple climatic indices to models of intermediate (e.g. AquaCrop) or high complexity (e.g. CERES-type models). More complex models are valuable agronomically because they capture some genetic features of the specific cultivar, and the critical interaction between water and nitrogen. On the other hand, more complex models have requirements of parameters and inputs that are not always available. “Best practice” approaches to model yield in gap analysis are outlined. Importantly, models to estimate potential yield require parameters that capture the physiology of unstressed crops. Approach 4 benchmarking involves a range of approaches combining actual data, remote sensing, GIS and models of varying complexity. This approach is important for benchmarking at and above the regional scale. At these large scales, particular attention needs to be paid to weather data used in modelling yield because significant bias can accrue from inappropriate data sources. Studies that have used gridded weather databases to simulate potential and water-limited yields for a grid are rarely validated against simulated yields based on actual weather station data from locations within the same grid. This should be standard practice, particularly where global scale yield gaps are used for policy decisions or investment in R&D. Alternatively, point-based simulations of potential and water-limited yields, complemented with an appropriate up-scaling method, may be more appropriate for large scale yield gap analysis. Remote sensing applied to yield gap analysis has improved over the last years, mainly through pixel-based biomass production models. Site-specific yield validation, disaggregated in biomass radiation-use-efficiency and harvest index, remains necessary and need to be carried out every 5 to 10 years

Operation and performance of the ATLAS semiconductor tracker

The semiconductor tracker is a silicon microstrip detector forming part of the inner tracking system of the ATLAS experiment at the LHC. The operation and performance of the semiconductor tracker during the first years of LHC running are described. More than 99% of the detector modules were operational during this period, with an average intrinsic hit efficiency of (99.74±0.04)%. The evolution of the noise occupancy is discussed, and measurements of the Lorentz angle, δ-ray production and energy loss presented. The alignment of the detector is found to be stable at the few-micron level over long periods of time. Radiation damage measurements, which include the evolution of detector leakage currents, are found to be consistent with predictions and are used in the verification of radiation background simulations

Operation and performance of the ATLAS semiconductor tracker

The semiconductor tracker is a silicon microstrip detector forming part of the inner tracking system of the ATLAS experiment at the LHC. The operation and performance of the semiconductor tracker during the first years of LHC running are described. More than 99% of the detector modules were operational during this period, with an average intrinsic hit efficiency of (99.74 +/- 0.04)%. The evolution of the noise occupancy is discussed, and measurements of the Lorentz angle, delta-ray production and energy loss presented. The alignment of the detector is found to be stable at the few-micron level over long periods of time. Radiation damage measurements, which include the evolution of detector leakage currents, are found to be consistent with predictions and are used in the verification of radiation background simulations

The ATLAS Simulation Infrastructure

The simulation software for the ATLAS Experiment at the Large Hadron Collider is being used for largescale production of events on the LHC Computing Grid. This simulation requires many components, from the generators that simulate particle collisions, through packages simulating the response of the various detectors and triggers. All of these components come together under the ATLAS simulation infrastructure. In this paper, that infrastructure is discussed, including that supporting the detector description, interfacing the event generation, and combining the GEANT4 simulation of the response of the individual detectors. Also described are the tools allowing the software validation, performance testing, and the validation of the simulated output against known physics processes

Computational mapping of regulatory domains of human genes

Das menschliche Genom enthält Millionen von regulatorischen Elementen - Enhancern -, die die Genexpression quantitativ regulieren. Trotz des enormen Fortschritts beim Verständnis, wie Enhancer die Genexpression steuern, fehlt es in diesem Bereich immer noch an einem systematischen, integrativen und zugänglichen Ansatz zur Entdeckung und Dokumentation von cis-regulatorischen Beziehungen im gesamten Genom. Wir haben eine neuartige Methode - reg2gene - entwickelt, die Genexpression~Enhancer-Aktivität modelliert und integriert. reg2gene besteht aus drei Hauptschritten: 1) Datenquantifizierung, 2) Datenmodellierung und Signifikanzbewertung und 3) Datenintegration, die in dem R-Paket reg2gene zusammengefasst sind. Als Ergebnis haben wir zwei Sätze von Enhancer-Gen-Assoziationen (EGAs) identifiziert: den flexiblen Satz von ~230K EGAs (flexibleC) und den stringenten Satz von ~60K EGAs (stringentC). Wir haben große Unterschiede zwischen den bisher veröffentlichten Berechnungsmodellen für Enhancer-Gene-Assoziationen festgestellt, vor allem in Bezug auf die Lage, die Anzahl und die Eigenschaften der definierten Enhancer-Regionen und EGAs. Wir führten ein detailliertes Benchmarking von sieben Sets von rechnerisch modellierten EGAs durch, zeigten jedoch, dass keiner der derzeit verfügbaren Benchmark-Datensätze als "goldener Standard" verwendet werden kann. Wir definierten einen zusätzlichen Benchmark-Datensatz mit positiven und negativen EGAs, mit dem wir zeigten, dass das stringentC-Modell den höchsten positiven Vorhersagewert (PPV) hatte. Wir haben das Potenzial von EGAs zur Identifizierung von Genzielen von nicht-kodierenden SNP-Gene-Assoziationen nachgewiesen. Schließlich führten wir eine funktionelle Analyse durch, um neue Genziele, Enhancer-Pleiotropie und Mechanismen der Enhancer-Aktivität zu ermitteln. Insgesamt bringt diese Arbeit unser Verständnis der durch Enhancer vermittelten Regulierung der Genexpression in Gesundheit und Krankheit voran.Human genome contains millions of regulatory elements - enhancers - that quantitatively regulate gene expression. Multiple experimental and computational approaches were developed to associate enhancers with their gene targets. Despite the tremendous progress in understanding how enhancers tune gene expression, the field still lacks an approach that is systematic, integrative and accessible for discovering and documenting cis-regulatory relationships across the genome. We developed a novel computational approach - reg2gene- that models and integrates gene expression ~ enhancer activity. reg2gene consists of three main steps: 1) data quantification, 2) data modelling and significance assessment, and 3) data integration gathered in the reg2gene R package. As a result we identified two sets of enhancer-gene associations (EGAs): the flexible set of ~230K EGAs (flexibleC), and the stringent set of ~60K EGAs (stringentC). We identified major differences across previously published computational models of enhancer-gene associations; mostly in the location, number and properties of defined enhancer regions and EGAs. We performed detailed benchmarking of seven sets of computationally modelled EGAs, but showed that none of the currently available benchmark datasets could be used as a “golden-standard” benchmark dataset. To account for that observation, we defined an additional benchmark set of positive and negative EGAs with which we showed that the stringentC model had the highest positive predictive value (PPV) across all analyzed computational models. We reviewed the influence of EGA sets on the functional analysis of risk SNPs and demonstrated the potential of EGAs to identify gene targets of non-coding SNP-gene associations. Lastly, we performed a functional analysis to detect novel gene targets, enhancer pleiotropy, and mechanisms of enhancer activity. Altogether, this work advances our understanding of enhancer-mediated gene expression regulation in health and disease.Ljudski genom sadrži milijune regulatornih elemenata - enhancera - koji kvantitativno reguliraju ekspresiju gena. Unatoč ogromnom napretku u razumijevanju načina na koji enhanceri reguliraju ekspresiju gena, području još uvijek nedostaje pristup koji je sustavan, integrativan i dostupan za otkrivanje i dokumentiranje cis-regulatornih odnosa u cijelom genomu. Razvili smo novu računalnu metodu - reg2gene - koja modelira i integrira aktivnost enhancera~ekspresije gena. reg2gene sastoji se od tri glavna koraka: 1) kvantifikacija podataka, 2) modeliranje podataka i procjena značaja, i 3) integracija podataka prikupljenih u reg2gene R paketu. Kao rezultat toga, identificirali smo dva skupa enhancer-gen interakcija (EGA): fleksibilni skup od ~ 230K EGA (flexibleC) i strogi skup od ~ 60K EGA (stringentC). Utvrdili smo velike razlike u prethodno objavljenim računalnim modelima enhancer-gen interakcija; uglavnom u lokaciji, broju i svojstvima definiranih enhancera i EGA. Izveli smo detaljno mjerenje performansi sedam skupova računalno modeliranih EGA-a, ali smo pokazali da se niti jedan od trenutno dostupnih skupova referentnih podataka ne može koristiti kao referentni skup podataka "zlatnI standard". Definirali smo dodatni referentni skup pozitivnih i negativnih EGA -a pomoću kojih smo pokazali da stringentC ima najveću pozitivnu prediktivnu vrijednost (PPV). Pokazali smo potencijal EGA-a za identifikaciju genskih meta nekodirajucih SNP-ova. Proveli smo funkcionalnu analizu kako bismo otkrili nove genske mete, pleiotropiju enhancera i mehanizme aktivnosti enhancera. Ovaj rad poboljšava naše razumijevanje regulacije ekspresije gena posredovane enhancerima