Imagerie cardiaque non invasive: apport spécifique en clinique des nouvelles modalités (I). Evaluation morphologique [Cardiac imaging: specific clinical role of newly developed non invasive techniques. Part 1: Morphological evaluation].

Echocardiography is the preferred initial test to assess cardiac morphology and ventricular function. Cardiac MRI enables an optimal visualisation of heart muscle without contrast injection, and precise measurement of the ventricular volumes and systolic function. It is therefore an ideal test for patients with poor echocardiographic windows or for the specific evaluation of right heart chambers. Heart CT also remarkably images heart muscle and precisely measures ventricular systolic function after intravenous injection of iodinated contrast. Coronary CT may also, in selected cases, avoid the need for diagnostic coronary angiography. Although very accurate, these imaging modalities are expensive and may be contra-indicated for a particular patient. Their use in clinical practice has to follow the accepted guidelines.

Implementing statistical learning methods through Bayesian networks (Part 2): bayesian evaluations for results of black toner analyses in forensic document examination

This paper presents and discusses the use of Bayesian procedures - introduced through the use of Bayesian networks in Part I of this series of papers - for 'learning' probabilities from data. The discussion will relate to a set of real data on characteristics of black toners commonly used in printing and copying devices. Particular attention is drawn to the incorporation of the proposed procedures as an integral part in probabilistic inference schemes (notably in the form of Bayesian networks) that are intended to address uncertainties related to particular propositions of interest (e.g., whether or not a sample originates from a particular source). The conceptual tenets of the proposed methodologies are presented along with aspects of their practical implementation using currently available Bayesian network software.

Critical thinking and accuracy of nurses' diagnoses. Part I: risk of low accuracy diagnoses and new views of critical thinking

Interpretations of patient data are complex and diverse, contributing to a risk of low accuracy nursing diagnoses. This risk is confirmed in research findings that accuracy of nurses' diagnoses varied widely from high to low. Highly accurate diagnoses are essential, however, to guide nursing interventions for the achievement of positive health outcomes. Development of critical thinking abilities is likely to improve accuracy of nurses' diagnoses. Newer views of critical thinking serve as a basis for critical thinking in nursing. Seven cognitive skills and ten habits of mind are identified as dimensions of critical thinking for use in the diagnostic process.

Review of therapeutic drug monitoring of anticancer drugs part two - Targeted therapies.

Most of oral targeted therapies are tyrosine kinase inhibitors (TKIs). Oral administration generates a complex step in the pharmacokinetics (PK) of these drugs. Inter-individual PK variability is often large and variability observed in response is influenced not only by the genetic heterogeneity of drug targets, but also by the pharmacogenetic background of the patient (e.g. cytochome P450 and ABC transporter polymorphisms), patient characteristics such as adherence to treatment and environmental factors (drug-drug interactions). Retrospective studies have shown that targeted drug exposure, reflected in the area under the plasma concentration-time curve (AUC) correlates with treatment response (efficacy/toxicity) in various cancers. Nevertheless levels of evidence for therapeutic drug monitoring (TDM) are however heterogeneous among these agents and TDM is still uncommon for the majority of them. Evidence for imatinib currently exists, others are emerging for compounds including nilotinib, dasatinib, erlotinib, sunitinib, sorafenib and mammalian target of rapamycin (mTOR) inhibitors. Applications for TDM during oral targeted therapies may best be reserved for particular situations including lack of therapeutic response, severe or unexpected toxicities, anticipated drug-drug interactions and/or concerns over adherence treatment. Interpatient PK variability observed with monoclonal antibodies (mAbs) is comparable or slightly lower to that observed with TKIs. There are still few data with these agents in favour of TDM approaches, even if data showed encouraging results with rituximab, cetuximab and bevacizumab. At this time, TDM of mAbs is not yet supported by scientific evidence. Considerable effort should be made for targeted therapies to better define concentration-effect relationships and to perform comparative randomised trials of classic dosing versus pharmacokinetically-guided adaptive dosing.

Critical thinking and accuracy of nurses' diagnoses. Part II: application of cognitive skills and guidelines for self-development

Part I of this article, the author explained the difficulties of achieving accuracy of nurses' diagnoses, the relevance of critical thinking to the achievement of accuracy, and newer views of critical thinking. In Part II, the critical thinking dimensions identified as important for nursing practice are applied in the diagnostic process using a case study of a 16 year old girl with type 1 diabetes. Application of seven cognitive skills and ten habits of mind illustrate the importance of using critical thinking for accuracy of nurses' diagnoses. Ten strategies are proposed for self-development of critical thinking abilities.

The biochemistry of drug metabolism--an introduction: part 5. Metabolism and bioactivity.

This review continues a general presentation of the metabolism of drugs and other xenobiotics begun in five recent issues of Chemistry & Biodiversity. The present Part is dedicated to the pharmacological and toxicological consequences of drug and xenobiotic metabolism. In other words, the key concepts here are activation vs. deactivation, toxification vs. detoxification, and their interplay. These concepts are illustrated with a number of medicinally, toxicologically, and environmentally relevant examples. But, far from being concerned only with individual cases, the review is based on broad classifications, global rationalizations, and synthetic hypotheses.

Forensic intelligence framework. Part II: study of the main generic building blocks and challenges through the examples of illicit drugs and false identity documents monitoring

The development of forensic intelligence relies on the expression of suitable models that better represent the contribution of forensic intelligence in relation to the criminal justice system, policing and security. Such models assist in comparing and evaluating methods and new technologies, provide transparency and foster the development of new applications. Interestingly, strong similarities between two separate projects focusing on specific forensic science areas were recently observed. These observations have led to the induction of a general model (Part I) that could guide the use of any forensic science case data in an intelligence perspective. The present article builds upon this general approach by focusing on decisional and organisational issues. The article investigates the comparison process and evaluation system that lay at the heart of the forensic intelligence framework, advocating scientific decision criteria and a structured but flexible and dynamic architecture. These building blocks are crucial and clearly lay within the expertise of forensic scientists. However, it is only part of the problem. Forensic intelligence includes other blocks with their respective interactions, decision points and tensions (e.g. regarding how to guide detection and how to integrate forensic information with other information). Formalising these blocks identifies many questions and potential answers. Addressing these questions is essential for the progress of the discipline. Such a process requires clarifying the role and place of the forensic scientist within the whole process and their relationship to other stakeholders.

Kernel-based methods for change detection in remote sensing images

Nowadays, the joint exploitation of images acquired daily by remote sensing instruments and of images available from archives allows a detailed monitoring of the transitions occurring at the surface of the Earth. These modifications of the land cover generate spectral discrepancies that can be detected via the analysis of remote sensing images. Independently from the origin of the images and of type of surface change, a correct processing of such data implies the adoption of flexible, robust and possibly nonlinear method, to correctly account for the complex statistical relationships characterizing the pixels of the images. This Thesis deals with the development and the application of advanced statistical methods for multi-temporal optical remote sensing image processing tasks. Three different families of machine learning models have been explored and fundamental solutions for change detection problems are provided. In the first part, change detection with user supervision has been considered. In a first application, a nonlinear classifier has been applied with the intent of precisely delineating flooded regions from a pair of images. In a second case study, the spatial context of each pixel has been injected into another nonlinear classifier to obtain a precise mapping of new urban structures. In both cases, the user provides the classifier with examples of what he believes has changed or not. In the second part, a completely automatic and unsupervised method for precise binary detection of changes has been proposed. The technique allows a very accurate mapping without any user intervention, resulting particularly useful when readiness and reaction times of the system are a crucial constraint. In the third, the problem of statistical distributions shifting between acquisitions is studied. Two approaches to transform the couple of bi-temporal images and reduce their differences unrelated to changes in land cover are studied. The methods align the distributions of the images, so that the pixel-wise comparison could be carried out with higher accuracy. Furthermore, the second method can deal with images from different sensors, no matter the dimensionality of the data nor the spectral information content. This opens the doors to possible solutions for a crucial problem in the field: detecting changes when the images have been acquired by two different sensors.

Tree-based message diffusion for managing replicated data in unreliable and resource-constrained peer-to-peer environment

Abstract This thesis proposes a set of adaptive broadcast solutions and an adaptive data replication solution to support the deployment of P2P applications. P2P applications are an emerging type of distributed applications that are running on top of P2P networks. Typical P2P applications are video streaming, file sharing, etc. While interesting because they are fully distributed, P2P applications suffer from several deployment problems, due to the nature of the environment on which they perform. Indeed, defining an application on top of a P2P network often means defining an application where peers contribute resources in exchange for their ability to use the P2P application. For example, in P2P file sharing application, while the user is downloading some file, the P2P application is in parallel serving that file to other users. Such peers could have limited hardware resources, e.g., CPU, bandwidth and memory or the end-user could decide to limit the resources it dedicates to the P2P application a priori. In addition, a P2P network is typically emerged into an unreliable environment, where communication links and processes are subject to message losses and crashes, respectively. To support P2P applications, this thesis proposes a set of services that address some underlying constraints related to the nature of P2P networks. The proposed services include a set of adaptive broadcast solutions and an adaptive data replication solution that can be used as the basis of several P2P applications. Our data replication solution permits to increase availability and to reduce the communication overhead. The broadcast solutions aim, at providing a communication substrate encapsulating one of the key communication paradigms used by P2P applications: broadcast. Our broadcast solutions typically aim at offering reliability and scalability to some upper layer, be it an end-to-end P2P application or another system-level layer, such as a data replication layer. Our contributions are organized in a protocol stack made of three layers. In each layer, we propose a set of adaptive protocols that address specific constraints imposed by the environment. Each protocol is evaluated through a set of simulations. The adaptiveness aspect of our solutions relies on the fact that they take into account the constraints of the underlying system in a proactive manner. To model these constraints, we define an environment approximation algorithm allowing us to obtain an approximated view about the system or part of it. This approximated view includes the topology and the components reliability expressed in probabilistic terms. To adapt to the underlying system constraints, the proposed broadcast solutions route messages through tree overlays permitting to maximize the broadcast reliability. Here, the broadcast reliability is expressed as a function of the selected paths reliability and of the use of available resources. These resources are modeled in terms of quotas of messages translating the receiving and sending capacities at each node. To allow a deployment in a large-scale system, we take into account the available memory at processes by limiting the view they have to maintain about the system. Using this partial view, we propose three scalable broadcast algorithms, which are based on a propagation overlay that tends to the global tree overlay and adapts to some constraints of the underlying system. At a higher level, this thesis also proposes a data replication solution that is adaptive both in terms of replica placement and in terms of request routing. At the routing level, this solution takes the unreliability of the environment into account, in order to maximize reliable delivery of requests. At the replica placement level, the dynamically changing origin and frequency of read/write requests are analyzed, in order to define a set of replica that minimizes communication cost.

Adaption of Courses and Learning Environment to the User Context in dotLRN

The explosive growth of Internet during the last years has been reflected in the ever-increasing amount of the diversity and heterogeneity of user preferences, types and features of devices and access networks. Usually the heterogeneity in the context of the users which request Web contents is not taken into account by the servers that deliver them implying that these contents will not always suit their needs. In the particular case of e-learning platforms this issue is especially critical due to the fact that it puts at stake the knowledge acquired by their users. In the following paper we present a system that aims to provide the dotLRN e-learning platform with the capability to adapt to its users context. By integrating dotLRN with a multi-agent hypermedia system, online courses being undertaken by students as well as their learning environment are adapted in real time

Girl Connection, Girls & Sport-Part II, June 2004

Newsletter for professionals who work with girls involved in or at risk for involvement in the juvenile justice system.

Daughter of time: the postmodern midwife (Part 1)

This article presents the notion of the postmodern midwife, defining her as one who takes a relativistic stance toward bio-medicine and other knowledge systems, alternative and indigenous, moving fluidly between them to serve the women she attends. She is locally and globally aware, culturally competent, and politically engaged, working with the resources at hand to preserve midwifery in the interests of women. Her informed relativism is most accessible to professional midwives but is also beginning to characterize some savvy traditional midwives in various countries. Thus the concept of the postmodern midwife can serve as a bridge across the ethnic, racial, and status gaps that divide the professional from the traditional midwife, and as an analytical focal point for understanding how the members of each group negotiate their identities and their roles in a changing world.

Daughter of time: the postmodern midwife (Part 2)

Any effort to make sense of the complexities of contemporary midwifery must deal not only with biomedical and governmental power structures but also with the definitions such structures impose upon midwives and the ramifications of these definitions within and across national and cultural borders. The international definition of a midwife requires graduations from a government-recognized educational program. Those who have not are not considered midwives but are labeled traditional birth attendants. Since there are myriad local names for midwives in myriad languages, the impact of this naming at local levels can be hard to assess. But on the global scale, the ramifications of the distinction between midwives who meet the international definition and those who do not have been profound. Those who do are incorporated into the health care system. Those who do not remain outside of it, and suffer multiple forms of discrimination as a result.