MADAP, a flexible clustering tool for the interpretation of one-dimensional genome annotation data.

A recurring task in the analysis of mass genome annotation data from high-throughput technologies is the identification of peaks or clusters in a noisy signal profile. Examples of such applications are the definition of promoters on the basis of transcription start site profiles, the mapping of transcription factor binding sites based on ChIP-chip data and the identification of quantitative trait loci (QTL) from whole genome SNP profiles. Input to such an analysis is a set of genome coordinates associated with counts or intensities. The output consists of a discrete number of peaks with respective volumes, extensions and center positions. We have developed for this purpose a flexible one-dimensional clustering tool, called MADAP, which we make available as a web server and as standalone program. A set of parameters enables the user to customize the procedure to a specific problem. The web server, which returns results in textual and graphical form, is useful for small to medium-scale applications, as well as for evaluation and parameter tuning in view of large-scale applications, requiring a local installation. The program written in C++ can be freely downloaded from ftp://ftp.epd.unil.ch/pub/software/unix/madap. The MADAP web server can be accessed at http://www.isrec.isb-sib.ch/madap/.

Video analysis software increases the interrater reliability of video gait assessments in children with cerebral palsy.

The aim of this study was to determine the effect of using video analysis software on the interrater reliability of visual assessments of gait videos in children with cerebral palsy. Two clinicians viewed the same random selection of 20 sagittal and frontal video recordings of 12 children with cerebral palsy routinely acquired during outpatient rehabilitation clinics. Both observers rated these videos in a random sequence for each lower limb using the Observational Gait Scale, once with standard video software and another with video analysis software (Dartfish(®)) which can perform angle and timing measurements. The video analysis software improved interrater agreement, measured by weighted Cohen's kappas, for the total score (κ 0.778→0.809) and all of the items that required angle and/or timing measurements (knee position mid-stance κ 0.344→0.591; hindfoot position mid-stance κ 0.160→0.346; foot contact mid-stance κ 0.700→0.854; timing of heel rise κ 0.769→0.835). The use of video analysis software is an efficient approach to improve the reliability of visual video assessments.

Benchmarking therapeutic drug monitoring software: A systematic evaluation of available computer tools

Introduction: Therapeutic drug monitoring (TDM) aims at optimizing treatment by individualizing dosage regimen based on measurement of blood concentrations. Maintaining concentrations within a target range requires pharmacokinetic and clinical capabilities. Bayesian calculation represents a gold standard in TDM approach but requires computing assistance. In the last decades computer programs have been developed to assist clinicians in this assignment. The aim of this benchmarking was to assess and compare computer tools designed to support TDM clinical activities.¦Method: Literature and Internet search was performed to identify software. All programs were tested on common personal computer. Each program was scored against a standardized grid covering pharmacokinetic relevance, user-friendliness, computing aspects, interfacing, and storage. A weighting factor was applied to each criterion of the grid to consider its relative importance. To assess the robustness of the software, six representative clinical vignettes were also processed through all of them.¦Results: 12 software tools were identified, tested and ranked. It represents a comprehensive review of the available software's characteristics. Numbers of drugs handled vary widely and 8 programs offer the ability to the user to add its own drug model. 10 computer programs are able to compute Bayesian dosage adaptation based on a blood concentration (a posteriori adjustment) while 9 are also able to suggest a priori dosage regimen (prior to any blood concentration measurement), based on individual patient covariates, such as age, gender, weight. Among those applying Bayesian analysis, one uses the non-parametric approach. The top 2 software emerging from this benchmark are MwPharm and TCIWorks. Other programs evaluated have also a good potential but are less sophisticated (e.g. in terms of storage or report generation) or less user-friendly.¦Conclusion: Whereas 2 integrated programs are at the top of the ranked listed, such complex tools would possibly not fit all institutions, and each software tool must be regarded with respect to individual needs of hospitals or clinicians. Interest in computing tool to support therapeutic monitoring is still growing. Although developers put efforts into it the last years, there is still room for improvement, especially in terms of institutional information system interfacing, user-friendliness, capacity of data storage and report generation.

Benchmarking therapeutic drug monitoring software: A systematic evaluation of available computer tools

Objectives: Therapeutic drug monitoring (TDM) aims at optimizing treatment by individualizing dosage regimen based on blood concentrations measurement. Maintaining concentrations within a target range requires pharmacokinetic (PK) and clinical capabilities. Bayesian calculation represents a gold standard in TDM approach but requires computing assistance. The aim of this benchmarking was to assess and compare computer tools designed to support TDM clinical activities.¦Methods: Literature and Internet were searched to identify software. Each program was scored against a standardized grid covering pharmacokinetic relevance, user-friendliness, computing aspects, interfacing, and storage. A weighting factor was applied to each criterion of the grid to consider its relative importance. To assess the robustness of the software, six representative clinical vignettes were also processed through all of them.¦Results: 12 software tools were identified, tested and ranked. It represents a comprehensive review of the available software characteristics. Numbers of drugs handled vary from 2 to more than 180, and integration of different population types is available for some programs. Nevertheless, 8 programs offer the ability to add new drug models based on population PK data. 10 computer tools incorporate Bayesian computation to predict dosage regimen (individual parameters are calculated based on population PK models). All of them are able to compute Bayesian a posteriori dosage adaptation based on a blood concentration while 9 are also able to suggest a priori dosage regimen, only based on individual patient covariates. Among those applying Bayesian analysis, MM-USC*PACK uses a non-parametric approach. The top 2 programs emerging from this benchmark are MwPharm and TCIWorks. Others programs evaluated have also a good potential but are less sophisticated or less user-friendly.¦Conclusions: Whereas 2 software packages are ranked at the top of the list, such complex tools would possibly not fit all institutions, and each program must be regarded with respect to individual needs of hospitals or clinicians. Programs should be easy and fast for routine activities, including for non-experienced users. Although interest in TDM tools is growing and efforts were put into it in the last years, there is still room for improvement, especially in terms of institutional information system interfacing, user-friendliness, capability of data storage and automated report generation.

Mynodbcsv: Lightweight Zero-Config Database Solution for Handling Very Large CSV Files.

Volumes of data used in science and industry are growing rapidly. When researchers face the challenge of analyzing them, their format is often the first obstacle. Lack of standardized ways of exploring different data layouts requires an effort each time to solve the problem from scratch. Possibility to access data in a rich, uniform manner, e.g. using Structured Query Language (SQL) would offer expressiveness and user-friendliness. Comma-separated values (CSV) are one of the most common data storage formats. Despite its simplicity, with growing file size handling it becomes non-trivial. Importing CSVs into existing databases is time-consuming and troublesome, or even impossible if its horizontal dimension reaches thousands of columns. Most databases are optimized for handling large number of rows rather than columns, therefore, performance for datasets with non-typical layouts is often unacceptable. Other challenges include schema creation, updates and repeated data imports. To address the above-mentioned problems, I present a system for accessing very large CSV-based datasets by means of SQL. It's characterized by: "no copy" approach - data stay mostly in the CSV files; "zero configuration" - no need to specify database schema; written in C++, with boost [1], SQLite [2] and Qt [3], doesn't require installation and has very small size; query rewriting, dynamic creation of indices for appropriate columns and static data retrieval directly from CSV files ensure efficient plan execution; effortless support for millions of columns; due to per-value typing, using mixed text/numbers data is easy; very simple network protocol provides efficient interface for MATLAB and reduces implementation time for other languages. The software is available as freeware along with educational videos on its website [4]. It doesn't need any prerequisites to run, as all of the libraries are included in the distribution package. I test it against existing database solutions using a battery of benchmarks and discuss the results.

Behavioural queueing : cellular automata and a laboratory experiment

Dans cette thèse, nous étudions les aspects comportementaux d'agents qui interagissent dans des systèmes de files d'attente à l'aide de modèles de simulation et de méthodologies expérimentales. Chaque période les clients doivent choisir un prestataire de servivce. L'objectif est d'analyser l'impact des décisions des clients et des prestataires sur la formation des files d'attente. Dans un premier cas nous considérons des clients ayant un certain degré d'aversion au risque. Sur la base de leur perception de l'attente moyenne et de la variabilité de cette attente, ils forment une estimation de la limite supérieure de l'attente chez chacun des prestataires. Chaque période, ils choisissent le prestataire pour lequel cette estimation est la plus basse. Nos résultats indiquent qu'il n'y a pas de relation monotone entre le degré d'aversion au risque et la performance globale. En effet, une population de clients ayant un degré d'aversion au risque intermédiaire encoure généralement une attente moyenne plus élevée qu'une population d'agents indifférents au risque ou très averses au risque. Ensuite, nous incorporons les décisions des prestataires en leur permettant d'ajuster leur capacité de service sur la base de leur perception de la fréquence moyenne d'arrivées. Les résultats montrent que le comportement des clients et les décisions des prestataires présentent une forte "dépendance au sentier". En outre, nous montrons que les décisions des prestataires font converger l'attente moyenne pondérée vers l'attente de référence du marché. Finalement, une expérience de laboratoire dans laquelle des sujets jouent le rôle de prestataire de service nous a permis de conclure que les délais d'installation et de démantèlement de capacité affectent de manière significative la performance et les décisions des sujets. En particulier, les décisions du prestataire, sont influencées par ses commandes en carnet, sa capacité de service actuellement disponible et les décisions d'ajustement de capacité qu'il a prises, mais pas encore implémentées. - Queuing is a fact of life that we witness daily. We all have had the experience of waiting in line for some reason and we also know that it is an annoying situation. As the adage says "time is money"; this is perhaps the best way of stating what queuing problems mean for customers. Human beings are not very tolerant, but they are even less so when having to wait in line for service. Banks, roads, post offices and restaurants are just some examples where people must wait for service. Studies of queuing phenomena have typically addressed the optimisation of performance measures (e.g. average waiting time, queue length and server utilisation rates) and the analysis of equilibrium solutions. The individual behaviour of the agents involved in queueing systems and their decision making process have received little attention. Although this work has been useful to improve the efficiency of many queueing systems, or to design new processes in social and physical systems, it has only provided us with a limited ability to explain the behaviour observed in many real queues. In this dissertation we differ from this traditional research by analysing how the agents involved in the system make decisions instead of focusing on optimising performance measures or analysing an equilibrium solution. This dissertation builds on and extends the framework proposed by van Ackere and Larsen (2004) and van Ackere et al. (2010). We focus on studying behavioural aspects in queueing systems and incorporate this still underdeveloped framework into the operations management field. In the first chapter of this thesis we provide a general introduction to the area, as well as an overview of the results. In Chapters 2 and 3, we use Cellular Automata (CA) to model service systems where captive interacting customers must decide each period which facility to join for service. They base this decision on their expectations of sojourn times. Each period, customers use new information (their most recent experience and that of their best performing neighbour) to form expectations of sojourn time at the different facilities. Customers update their expectations using an adaptive expectations process to combine their memory and their new information. We label "conservative" those customers who give more weight to their memory than to the xiv Summary new information. In contrast, when they give more weight to new information, we call them "reactive". In Chapter 2, we consider customers with different degree of risk-aversion who take into account uncertainty. They choose which facility to join based on an estimated upper-bound of the sojourn time which they compute using their perceptions of the average sojourn time and the level of uncertainty. We assume the same exogenous service capacity for all facilities, which remains constant throughout. We first analyse the collective behaviour generated by the customers' decisions. We show that the system achieves low weighted average sojourn times when the collective behaviour results in neighbourhoods of customers loyal to a facility and the customers are approximately equally split among all facilities. The lowest weighted average sojourn time is achieved when exactly the same number of customers patronises each facility, implying that they do not wish to switch facility. In this case, the system has achieved the Nash equilibrium. We show that there is a non-monotonic relationship between the degree of risk-aversion and system performance. Customers with an intermediate degree of riskaversion typically achieve higher sojourn times; in particular they rarely achieve the Nash equilibrium. Risk-neutral customers have the highest probability of achieving the Nash Equilibrium. Chapter 3 considers a service system similar to the previous one but with risk-neutral customers, and relaxes the assumption of exogenous service rates. In this sense, we model a queueing system with endogenous service rates by enabling managers to adjust the service capacity of the facilities. We assume that managers do so based on their perceptions of the arrival rates and use the same principle of adaptive expectations to model these perceptions. We consider service systems in which the managers' decisions take time to be implemented. Managers are characterised by a profile which is determined by the speed at which they update their perceptions, the speed at which they take decisions, and how coherent they are when accounting for their previous decisions still to be implemented when taking their next decision. We find that the managers' decisions exhibit a strong path-dependence: owing to the initial conditions of the model, the facilities of managers with identical profiles can evolve completely differently. In some cases the system becomes "locked-in" into a monopoly or duopoly situation. The competition between managers causes the weighted average sojourn time of the system to converge to the exogenous benchmark value which they use to estimate their desired capacity. Concerning the managers' profile, we found that the more conservative Summary xv a manager is regarding new information, the larger the market share his facility achieves. Additionally, the faster he takes decisions, the higher the probability that he achieves a monopoly position. In Chapter 4 we consider a one-server queueing system with non-captive customers. We carry out an experiment aimed at analysing the way human subjects, taking on the role of the manager, take decisions in a laboratory regarding the capacity of a service facility. We adapt the model proposed by van Ackere et al (2010). This model relaxes the assumption of a captive market and allows current customers to decide whether or not to use the facility. Additionally the facility also has potential customers who currently do not patronise it, but might consider doing so in the future. We identify three groups of subjects whose decisions cause similar behavioural patterns. These groups are labelled: gradual investors, lumpy investors, and random investor. Using an autocorrelation analysis of the subjects' decisions, we illustrate that these decisions are positively correlated to the decisions taken one period early. Subsequently we formulate a heuristic to model the decision rule considered by subjects in the laboratory. We found that this decision rule fits very well for those subjects who gradually adjust capacity, but it does not capture the behaviour of the subjects of the other two groups. In Chapter 5 we summarise the results and provide suggestions for further work. Our main contribution is the use of simulation and experimental methodologies to explain the collective behaviour generated by customers' and managers' decisions in queueing systems as well as the analysis of the individual behaviour of these agents. In this way, we differ from the typical literature related to queueing systems which focuses on optimising performance measures and the analysis of equilibrium solutions. Our work can be seen as a first step towards understanding the interaction between customer behaviour and the capacity adjustment process in queueing systems. This framework is still in its early stages and accordingly there is a large potential for further work that spans several research topics. Interesting extensions to this work include incorporating other characteristics of queueing systems which affect the customers' experience (e.g. balking, reneging and jockeying); providing customers and managers with additional information to take their decisions (e.g. service price, quality, customers' profile); analysing different decision rules and studying other characteristics which determine the profile of customers and managers.

Machine learning for spatial environmental data. Theory, applications and software

The book presents the state of the art in machine learning algorithms (artificial neural networks of different architectures, support vector machines, etc.) as applied to the classification and mapping of spatially distributed environmental data. Basic geostatistical algorithms are presented as well. New trends in machine learning and their application to spatial data are given, and real case studies based on environmental and pollution data are carried out. The book provides a CD-ROM with the Machine Learning Office software, including sample sets of data, that will allow both students and researchers to put the concepts rapidly to practice.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Quantification of Myocardial Perfusion Using 13N-ammonia PET: a Cross-Comparison Study on Analysis Software Packages - Carimas, PMOD and FlowQuant

Aim. Several software packages (SWP) and models have been released for quantification of myocardial perfusion (MP). Although they all are validated against something, the question remains how well their values agree. The present analysis focused on cross-comparison of three SWP for MP quantification of 13N-ammonia PET studies. Materials & Methods. 48 rest and stress MP 13N-ammonia PET studies of hypertrophic cardiomyopathy (HCM) patients (Sciagrà et al., 2009) were analysed with three SW packages - Carimas, PMOD, and FlowQuant - by three observers blinded to the results of each other. All SWP implement the one-tissue-compartment model (1TCM, DeGrado et al. 1996), and first two - the two-tissue-compartment model (2TCM, Hutchins et al. 1990) as well. Linear mixed model for the repeated measures was fitted to the data. Where appropriate we used Bland-Altman plots as well. The reproducibility was assessed on global, regional and segmental levels. Intraclass correlation coefficients (ICC), differences between the SWPs and between models were obtained. ICC≥0.75 indicated excellent reproducibility, 0.4≤ICC<0.75 indicated fair to good reproducibility, ICC<0.4 - poor reproducibility (Rosner, 2010). Results. When 1TCM MP values were compared, the SW agreement on global and regional levels was excellent, except for Carimas vs. PMOD at RCA: ICC=0.715 and for PMOD vs. FlowQuant at LCX:ICC=0.745 which were good. In segmental analysis in five segments: 7,12,13, 16, and 17 the agreement between all SWP was excellent; in the remaining 12 segments the agreement varied between the compared SWP. Carimas showed excellent agreement with FlowQuant in 13 segments and good in four - 1, 5, 6, 11: 0.687≤ICCs≤0.73; Carimas had excellent agreement with PMOD in 11 segments, good in five_4, 9, 10, 14, 15: 0.682≤ICCs≤0.737, and poor in segment 3: ICC=0.341. PMOD had excellent agreement with FlowQuant in eight segments and substantial-to-good in nine_1, 2, 3, 5, 6,8-11: 0.585≤ICCs≤0.738. Agreement between Carimas and PMOD for 2TCM was good at a global level: ICC=0.745, excellent at LCX (0.780) and RCA (0.774), good at LAD (0.662); agreement was excellent for ten segments, fair-to-substantial for segments 2, 3, 8, 14, 15 (0.431≤ICCs≤0.681), poor for segments 4 (0.384) and 17 (0.278). Conclusions. The three SWP used by different operators to analyse 13N-ammonia PET MP studies provide results that agree well at a global level, regional levels, and mostly well even at a segmental level. Agreement is better for 1TCM. Poor agreement at segments 4 and 17 for 2TCM needs further clarification.

T-Coffee: a web server for the multiple sequence alignment of protein and RNA sequences using structural information and homology extension.

This article introduces a new interface for T-Coffee, a consistency-based multiple sequence alignment program. This interface provides an easy and intuitive access to the most popular functionality of the package. These include the default T-Coffee mode for protein and nucleic acid sequences, the M-Coffee mode that allows combining the output of any other aligners, and template-based modes of T-Coffee that deliver high accuracy alignments while using structural or homology derived templates. These three available template modes are Expresso for the alignment of protein with a known 3D-Structure, R-Coffee to align RNA sequences with conserved secondary structures and PSI-Coffee to accurately align distantly related sequences using homology extension. The new server benefits from recent improvements of the T-Coffee algorithm and can align up to 150 sequences as long as 10,000 residues and is available from both http://www.tcoffee.org and its main mirror http://tcoffee.crg.cat.

Monitoring of noninvasive ventilation by built-in software of home bilevel ventilators: a bench study.

BACKGROUND: Current bilevel positive-pressure ventilators for home noninvasive ventilation (NIV) provide physicians with software that records items important for patient monitoring, such as compliance, tidal volume (Vt), and leaks. However, to our knowledge, the validity of this information has not yet been independently assessed. METHODS: Testing was done for seven home ventilators on a bench model adapted to simulate NIV and generate unintentional leaks (ie, other than of the mask exhalation valve). Five levels of leaks were simulated using a computer-driven solenoid valve (0-60 L/min) at different levels of inspiratory pressure (15 and 25 cm H(2)O) and at a fixed expiratory pressure (5 cm H(2)O), for a total of 10 conditions. Bench data were compared with results retrieved from ventilator software for leaks and Vt. RESULTS: For assessing leaks, three of the devices tested were highly reliable, with a small bias (0.3-0.9 L/min), narrow limits of agreement (LA), and high correlations (R(2), 0.993-0.997) when comparing ventilator software and bench results; conversely, for four ventilators, bias ranged from -6.0 L/min to -25.9 L/min, exceeding -10 L/min for two devices, with wide LA and lower correlations (R(2), 0.70-0.98). Bias for leaks increased markedly with the importance of leaks in three devices. Vt was underestimated by all devices, and bias (range, 66-236 mL) increased with higher insufflation pressures. Only two devices had a bias < 100 mL, with all testing conditions considered. CONCLUSIONS: Physicians monitoring patients who use home ventilation must be aware of differences in the estimation of leaks and Vt by ventilator software. Also, leaks are reported in different ways according to the device used.

Improving globally distributed software development and support processes - A workflow view

We propose a new approach and related indicators for globally distributed software support and development based on a 3-year process improvement project in a globally distributed engineering company. The company develops, delivers and supports a complex software system with tailored hardware components and unique end-customer installations. By applying the domain knowledge from operations management on lead time reduction and its multiple benefits to process performance, the workflows of globally distributed software development and multitier support processes were measured and monitored throughout the company. The results show that the global end-to-end process visibility and centrally managed reporting at all levels of the organization catalyzed a change process toward significantly better performance. Due to the new performance indicators based on lead times and their variation with fixed control procedures, the case company was able to report faster bug-fixing cycle times, improved response times and generally better customer satisfaction in its global operations. In all, lead times to implement new features and to respond to customer issues and requests were reduced by 50%.