External assessment of myocardial glucose metabolism with $\sp{18}$F-2 -deoxy-2 -fluoro-D-glucose

Despite the popularity of the positron emitting glucose analog, ($\sp{18}$F) -2-deoxy-2-fluoro-D-glucose (2FDG), for the noninvasive "metabolic imaging" of organs with positron emission tomography (PET), the physiological basis for the tracer has not been tested, and the potential of 2FDG for the rapid kinetic analysis of altered glucose metabolism in the intact heart has not been fully exploited. We, therefore, developed a quantitative method to characterize metabolic changes of myocardial glucose metabolism noninvasively and with high temporal resolution.^ The first objective of the work was to provide direct evidence that the initial steps in the metabolism of 2FDG are the same as for glucose and that 2FDG is retained by the tissue in proportion to the rate of glucose utilization. The second objective was to characterize the kinetic changes in myocardial glucose transport and phosphorylation in response to changes in work load, competing substrates, acute ischemia and reperfusion, and the addition of insulin. To assess changes in myocardial glucose metabolism isolated working rat hearts were perfused with glucose and 2FDG. Tissue uptake of 2FDG and the input function were measured on-line by external detection. The steady state rate of 2FDG phosphorylation was determined by graphical analysis of 2FDG time-activity curves.^ The rate of 2FDG uptake was linear with time and the tracer was retained in its phosphorylated form. Tissue accumulation of 2FDG decreased within seconds with a reduction in work load, in the presence of competing substrates, and during reperfusion after global ischemia. Thus, most interventions known to alter glucose metabolism induced rapid parallel changes in 2FDG uptake. By contrast, insulin caused a significant increase in 2FDG accumulation only in hearts from fasted animals when perfused at a sub-physiological work load. The mechanism for this phenomenon is not known but may be related to the existence of two different glucose transporter systems and/or glycogen metabolism in the myocardial cell.^ It is concluded that (1) 2FDG traces glucose uptake and phosphorylation in the isolated working rat heart; and (2) early and transient kinetic changes in glucose metabolism can be monitored with high temporal resolution with 2FDG and a simple positron coincidence counting system. The new method has revealed transients of myocardial glucose metabolism, which would have remained unnoticed with conventional methods. These transients are not only important for the interpretation of glucose metabolic PET scans, but also provide insights into mechanisms of glucose transport and phosphorylation in heart muscle. ^

E-comparisons: A new approach to round robin tests for environmental noise assessment in transport infrastructures

The increasing importance of pollutant noise has led to the creation of many new noise testing laboratories in recent years. For this reason and due to the legal implications that noise reporting may have, it is necessary to create procedures intended to guarantee the quality of the testing and its results. For instance, the ISO/IEC standard 17025:2005 specifies general requirements for the competence of testing laboratories. In this standard, interlaboratory comparisons are one of the main measures that must be applied to guarantee the quality of laboratories when applying specific methodologies for testing. In the specific case of environmental noise, round robin tests are usually difficult to design, as it is difficult to find scenarios that can be available and controlled while the participants carry out the measurements. Monitoring and controlling the factors that can influence the measurements (source emissions, propagation, background noise…) is not usually affordable, so the most extended solution is to create very effortless scenarios, where most of the factors that can have an influence on the results are excluded (sampling, processing of results, background noise, source detection…) The new approach described in this paper only requires the organizer to make actual measurements (or prepare virtual ones). Applying and interpreting a common reference document (standard, regulation…), the participants must analyze these input data independently to provide the results, which will be compared among the participants. The measurement costs are severely reduced for the participants, there is no need to monitor the scenario conditions, and almost any relevant factor can be included in this methodology

Atmospheric Propagation at 100 and 300 GHz: Assessment of a Method to Identify Rainy Conditions during Radiosoundings

The influence of atmospheric gases and tropospheric phenomena becomes more relevant at frequencies within the THz band (100 GHz to 10 THz), severely affecting the propagation conditions. The use of radiosoundings in propagation studies is a well established measurement technique in order to collect information about the vertical structure of the atmosphere, from which gaseous and cloud attenuation can be estimated with the use of propagation models. However, some of these prediction models are not suitable to be used under rainy conditions. In the present study, a method to identify the presence of rainy conditions during radiosoundings is introduced, with the aim of filtering out these events from yearly statistics of predicted atmospheric attenuation. The detection procedure is based on the analysis of a set of parameters, some of them extracted from synoptical observations of weather (SYNOP reports) and other derived from radiosonde observations (RAOBs). The performance of the method has been evaluated under different climatic conditions, corresponding to three locations in Spain, where colocated rain gauge data were available. Rain events detected by the method have been compared with those precipitations identified by the rain gauge. The pertinence Received 26 June 2012, Accepted 31 July 2012, Scheduled 15 August 2012 * Corresponding author: Gustavo Adolfo Siles Soria (gsiles@grc.ssr.upm.es). 258 Siles et al. of the method is discussed on the basis of an analysis of cumulative distributions of total attenuation at 100 and 300 GHz. This study demonstrates that the proposed method can be useful to identify events probably associated to rainy conditions. Hence, it can be considered as a suitable algorithm in order to filter out this kind of events from annual attenuation statistics.

Performance assessment of a kinetically-powered network for herd localization

Developing a herd localization system capable to operate unattended in communication-challenged areas arises from the necessity of improving current systems in terms of cost, autonomy or any other facilities that a certain target group (or overall users) may demand. A network architecture of herd localization is proposed with its corresponding hardware and a methodology to assess performance in different operating conditions. The system is designed taking into account an eventual environmental impact hence most nodes are simple, cheap and kinetically powered from animal movements-neither batteries nor sophisticated processor chips are needed. Other network elements integrating GPS and batteries operate with selectable duty cycles, thus reducing maintenance duties. Equipment has been tested on Scandinavian reindeer in Lapland and its element modeling is integrated into a simulator to analyze such localization network applicability for different use cases. Performance indicators (detection frequency, localization accuracy and delay) are fitted to assess the overall performance; system relative costs are enclosed also for a range of deployments.

Decarbonization of toll plazas: impact assessment of toll collection system management

Transport climate change impacts have become a worldwide concern. The use of Intelligent Transport Systems (ITS) could contribute to a more effective use of resources in toll road networks. Management of toll plazas is central to the reduction of greenhouse gas (GHG) emissions, as it is there that bottlenecks and congestion occur. This study focuses on management strategies aimed at reducing climate change impacts of toll plazas by managing toll collection systems. These strategies are based on the use of different collection system technologies – Electronic Toll Collection (ETC) and Open Road Tolling (ORT) – and on queue management. The carbon footprint of various toll plazas is determined by a proposed integrated methodology which estimates the carbon dioxide (CO2) emissions of the different operational stages at toll plazas (deceleration, service time, acceleration, and queuing) for the different toll collection systems. To validate the methodology, two main-line toll plazas of a Spanish toll highway were evaluated. The findings reveal that the application of new technologies to toll collection systems is an effective management strategy from an environmental point of view. The case studies revealed that ORT systems lead to savings of up to 70% of CO2 emissions at toll plazas, while ETC systems save 20% comparing to the manual ones. Furthermore, queue management can offer a 16% emissions savings when queue time is reduced by 116 seconds. The integrated methodology provides an efficient environmental management tool for toll plazas. The use of new technologies is the future of the decarbonization of toll plazas.

Mealiness assessment in apples and peaches using MRI techniques.

Since Januarv 1946 a wade EC Project entitled "Mealiness in fruits Consumers perception and means for detection is being carried out. Mealiness is a sensory attribute that cannot be defined by a single parameter but through a combination of variables (multidimensional structure) Previous studies propose the definition of mealiness as the lack of crispiness of hardness and of juiciness. A destructive instrumental procedure combined with a integration technique has been already developed enabling to identify mealy fruits by destructive instrumental means use other contributions of Barreiro and Ortiz to this Ag Eng 98. Current aims .are focused on establishing non destructive tests for mealiness assessment. Magnetic resonance Imaging (MRI) makes use of the magnetic properties that some atomic nuclei have. especially hidrogen nuclei from water molecules to obtain high quality images in the field of internal quality evaluation the MRI has been used to assess internal injury due to conservation as o treatments as chilling injury un Persimmons Clark&Forbes (1994) and water-core in apples (Wang et al. 1998. In the case of persimmons the chilling injury is described as an initial tissue breakdown and lack of cohesion between cells followed by formation of a firm gel and by a lack of juiciness without changes in the total amount ol water content. Also a browning of the flesh is indicated (Clark&Forhes 1994). This definition fits into the previous description of mealiness.

Woolliness assessment in peaches (Cv. Springcrest) by sensory and instrumental means.

Mealiness is a negative attribute of sensory texture, characterised by the lack of juiciness without variation of total water content in the tissues. In peaches, mealiness is also known as "woolliness" and "leatheriness". This internal disorder is characterised by the lack of juiciness and flavour. In peaches, it is associated with interna browning near the stone and the incapacity of ripening although there is externa ripe appearance. Woolliness is associated with inadequate cold storage and is considered as a physiological disorder that appears in stone fruits when an unbalanced pectolitic enzyme activity during storage occurs (Kailasapathy and Melton, 1992). Many attempts have been carried out to identify and measure mealiness and woolliness in fruits. The texture of a food product is composed by a wide spectrum of sensory attributes. Consumer defines the texture integrating simultaneously all the sensory attributes. However, an instrument assesses one or several parameters related to a fraction of the texture spectrum (Kramer, 1973). The complexity of sensory analysis by means of trained panels to assess the quality of some producing processes, supports the attempt to estimate texture characteristics by instrumental means. Some studies have been carried out comparing sensory and instrumental methods to assess mealiness and woolliness. The current study is centered on analysis and evaluation of woolliness in peaches and is part of the European project FAIR CT95 0302 "Mealiness in fruits: consumer perception and means for detection". The main objective of this study was to develop procedures to detect woolly peaches by sensory and by instrumental means, as well as to compare both measuring procedures.

A random utility based multiregional input-output approach to estimate the impact of a new highway on the regional distribution of jobs

This paper addresses the economic impact assessment of the construction of a new road on the regional distribution of jobs. The paper summarizes different existing model approaches considered to assess economic impacts through a literature review. Afterwards, we present the development of a comprehensive approach for analyzing the interaction of new transport infrastructure and the economic impact through an integrated model. This model has been applied to the construction of the motorway A-40 in Spain (497 Km.) which runs across three regions without passing though Madrid City. This may in turn lead to the relocation of labor and capital due to the improvement of accessibility of markets or inputs. The result suggests the existence of direct and indirect effects in other regions derived from the improvement of the transportation infrastructure, and confirms the relevance of road freight transport in some regions. We found that the changes in regional employment are substantial for some regions (increasing or decreasing jobs), but a t the same time negligible in other regions. As a result,the approach provides broad guidance to national governments and other transport-related parties about the impacts of this transport policy.

Quantity of CO2 Emissions in the Life Cycle of a Highway Infrastructure.

The complexity of climate change and its evolution during the last few years has a positive impact on new developments and approaches to reduce the emissions of CO2. Looking for a methodology to evaluate the sustainability of a roadway, a tool has been developed. Life Cycle Assessment (LCA) is being accepted by the road industry to measure and evaluate the environmental impacts of an infrastructure, as the energy consumption and carbon footprint. This paper describes the methodology to calculate the CO2 emissions associated with the energy embodied on a roadway along its life cycle, including construction, operations and demolition. It will assist to find solutions to improve the energy footprint and reduce the amount of CO2 emissions. Details are provided of both, the methodology and the data acquisition. This paper is an application of the methodology to the Spanish highways, using a local database. Two case studies and a practical example are studied to show the model as a decision support for sustainable construction in the road industry.

Preliminary results of ON/OFF detection using an integrated system for Parkinson's disease monitoring

This paper describes the experimental set up of a system composed by a set of wearable sensors devices for the recording of the motion signals and software algorithms for the signal analysis. This system is able to automatically detect and assess the severity of bradykinesia, tremor, dyskinesia and akinesia motor symptoms. Based on the assessment of the akinesia, the ON-OFF status of the patient is determined for each moment. The assessment performed through the automatic evaluation of the akinesia is compared with the status reported by the patients in their diaries. Preliminary results with a total recording period of 32 hours with two PD patients are presented, where a good correspondence (88.2 +/- 3.7 %) was observed. Best (93.7 por ciento) and worst (87 por ciento) correlation results are illustrated, together with the analysis of the automatic assessment of the akinesia symptom leading to the status determination. The results obtained are promising, and if confirmed with further data, this automatic assessment of PD motor symptoms will lead to a better adjustment of medication dosages and timing, cost savings and an improved quality of life of the patients.

Code assessment and modelling for Design Basis Accident analysis of the European Sodium Fast Reactor design. Part II: Optimised core and representative transients analysis

The new reactor concepts proposed in the Generation IV International Forum require the development and validation of computational tools able to assess their safety performance. In the first part of this paper the models of the ESFR design developed by several organisations in the framework of the CP-ESFR project were presented and their reliability validated via a benchmarking exercise. This second part of the paper includes the application of those tools for the analysis of design basis accident (DBC) scenarios of the reference design. Further, this paper also introduces the main features of the core optimisation process carried out within the project with the objective to enhance the core safety performance through the reduction of the positive coolant density reactivity effect. The influence of this optimised core design on the reactor safety performance during the previously analysed transients is also discussed. The conclusion provides an overview of the work performed by the partners involved in the project towards the development and enhancement of computational tools specifically tailored to the evaluation of the safety performance of the Generation IV innovative nuclear reactor designs.

Damage Assessment of Structures with Uncertainty

Evolutionary algorithms are suitable to solve damage identification problems in a multiobjective context. However, the performance of these methods can deteriorate quickly with increasing noise intensities originating numerous uncertainties. In this work, a statistic structural damage detection method formulated in a multiobjective context is proposed, taking into account the uncertainties existing. The presented method is verified by a number of simulated damage scenarios. The effects of noise on damage detection are investigated.

Real-time incidents detection in the highways of the future

Due to ever increasing transportation of people and goods, automatic traffic surveillance is becoming a key issue for both providing safety to road users and improving traffic control in an efficient way. In this paper, we propose a new system that, exploiting the capabilities that both computer vision and machine learning offer, is able to detect and track different types of real incidents on a highway. Specifically, it is able to accurately detect not only stopped vehicles, but also drivers and passengers leaving the stopped vehicle, and other pedestrians present in the roadway. Additionally, a theoretical approach for detecting vehicles which may leave the road in an unexpected way is also presented. The system works in real-time and it has been optimized for working outdoor, being thus appropriate for its deployment in a real-world environment like a highway. First experimental results on a dataset created with videos provided by two Spanish highway operators demonstrate the effectiveness of the proposed system and its robustness against noise and low-quality videos.

Automated detection and classification of brain injury lesions on structural magnetic resonance imaging

El daño cerebral adquirido (DCA) es un problema social y sanitario grave, de magnitud creciente y de una gran complejidad diagnóstica y terapéutica. Su elevada incidencia, junto con el aumento de la supervivencia de los pacientes, una vez superada la fase aguda, lo convierten también en un problema de alta prevalencia. En concreto, según la Organización Mundial de la Salud (OMS) el DCA estará entre las 10 causas más comunes de discapacidad en el año 2020. La neurorrehabilitación permite mejorar el déficit tanto cognitivo como funcional y aumentar la autonomía de las personas con DCA. Con la incorporación de nuevas soluciones tecnológicas al proceso de neurorrehabilitación se pretende alcanzar un nuevo paradigma donde se puedan diseñar tratamientos que sean intensivos, personalizados, monitorizados y basados en la evidencia. Ya que son estas cuatro características las que aseguran que los tratamientos son eficaces. A diferencia de la mayor parte de las disciplinas médicas, no existen asociaciones de síntomas y signos de la alteración cognitiva que faciliten la orientación terapéutica. Actualmente, los tratamientos de neurorrehabilitación se diseñan en base a los resultados obtenidos en una batería de evaluación neuropsicológica que evalúa el nivel de afectación de cada una de las funciones cognitivas (memoria, atención, funciones ejecutivas, etc.). La línea de investigación en la que se enmarca este trabajo de investigación pretende diseñar y desarrollar un perfil cognitivo basado no sólo en el resultado obtenido en esa batería de test, sino también en información teórica que engloba tanto estructuras anatómicas como relaciones funcionales e información anatómica obtenida de los estudios de imagen. De esta forma, el perfil cognitivo utilizado para diseñar los tratamientos integra información personalizada y basada en la evidencia. Las técnicas de neuroimagen representan una herramienta fundamental en la identificación de lesiones para la generación de estos perfiles cognitivos. La aproximación clásica utilizada en la identificación de lesiones consiste en delinear manualmente regiones anatómicas cerebrales. Esta aproximación presenta diversos problemas relacionados con inconsistencias de criterio entre distintos clínicos, reproducibilidad y tiempo. Por tanto, la automatización de este procedimiento es fundamental para asegurar una extracción objetiva de información. La delineación automática de regiones anatómicas se realiza mediante el registro tanto contra atlas como contra otros estudios de imagen de distintos sujetos. Sin embargo, los cambios patológicos asociados al DCA están siempre asociados a anormalidades de intensidad y/o cambios en la localización de las estructuras. Este hecho provoca que los algoritmos de registro tradicionales basados en intensidad no funcionen correctamente y requieran la intervención del clínico para seleccionar ciertos puntos (que en esta tesis hemos denominado puntos singulares). Además estos algoritmos tampoco permiten que se produzcan deformaciones grandes deslocalizadas. Hecho que también puede ocurrir ante la presencia de lesiones provocadas por un accidente cerebrovascular (ACV) o un traumatismo craneoencefálico (TCE). Esta tesis se centra en el diseño, desarrollo e implementación de una metodología para la detección automática de estructuras lesionadas que integra algoritmos cuyo objetivo principal es generar resultados que puedan ser reproducibles y objetivos. Esta metodología se divide en cuatro etapas: pre-procesado, identificación de puntos singulares, registro y detección de lesiones. Los trabajos y resultados alcanzados en esta tesis son los siguientes: Pre-procesado. En esta primera etapa el objetivo es homogeneizar todos los datos de entrada con el objetivo de poder extraer conclusiones válidas de los resultados obtenidos. Esta etapa, por tanto, tiene un gran impacto en los resultados finales. Se compone de tres operaciones: eliminación del cráneo, normalización en intensidad y normalización espacial. Identificación de puntos singulares. El objetivo de esta etapa es automatizar la identificación de puntos anatómicos (puntos singulares). Esta etapa equivale a la identificación manual de puntos anatómicos por parte del clínico, permitiendo: identificar un mayor número de puntos lo que se traduce en mayor información; eliminar el factor asociado a la variabilidad inter-sujeto, por tanto, los resultados son reproducibles y objetivos; y elimina el tiempo invertido en el marcado manual de puntos. Este trabajo de investigación propone un algoritmo de identificación de puntos singulares (descriptor) basado en una solución multi-detector y que contiene información multi-paramétrica: espacial y asociada a la intensidad. Este algoritmo ha sido contrastado con otros algoritmos similares encontrados en el estado del arte. Registro. En esta etapa se pretenden poner en concordancia espacial dos estudios de imagen de sujetos/pacientes distintos. El algoritmo propuesto en este trabajo de investigación está basado en descriptores y su principal objetivo es el cálculo de un campo vectorial que permita introducir deformaciones deslocalizadas en la imagen (en distintas regiones de la imagen) y tan grandes como indique el vector de deformación asociado. El algoritmo propuesto ha sido comparado con otros algoritmos de registro utilizados en aplicaciones de neuroimagen que se utilizan con estudios de sujetos control. Los resultados obtenidos son prometedores y representan un nuevo contexto para la identificación automática de estructuras. Identificación de lesiones. En esta última etapa se identifican aquellas estructuras cuyas características asociadas a la localización espacial y al área o volumen han sido modificadas con respecto a una situación de normalidad. Para ello se realiza un estudio estadístico del atlas que se vaya a utilizar y se establecen los parámetros estadísticos de normalidad asociados a la localización y al área. En función de las estructuras delineadas en el atlas, se podrán identificar más o menos estructuras anatómicas, siendo nuestra metodología independiente del atlas seleccionado. En general, esta tesis doctoral corrobora las hipótesis de investigación postuladas relativas a la identificación automática de lesiones utilizando estudios de imagen médica estructural, concretamente estudios de resonancia magnética. Basándose en estos cimientos, se han abrir nuevos campos de investigación que contribuyan a la mejora en la detección de lesiones. ABSTRACT Brain injury constitutes a serious social and health problem of increasing magnitude and of great diagnostic and therapeutic complexity. Its high incidence and survival rate, after the initial critical phases, makes it a prevalent problem that needs to be addressed. In particular, according to the World Health Organization (WHO), brain injury will be among the 10 most common causes of disability by 2020. Neurorehabilitation improves both cognitive and functional deficits and increases the autonomy of brain injury patients. The incorporation of new technologies to the neurorehabilitation tries to reach a new paradigm focused on designing intensive, personalized, monitored and evidence-based treatments. Since these four characteristics ensure the effectivity of treatments. Contrary to most medical disciplines, it is not possible to link symptoms and cognitive disorder syndromes, to assist the therapist. Currently, neurorehabilitation treatments are planned considering the results obtained from a neuropsychological assessment battery, which evaluates the functional impairment of each cognitive function (memory, attention, executive functions, etc.). The research line, on which this PhD falls under, aims to design and develop a cognitive profile based not only on the results obtained in the assessment battery, but also on theoretical information that includes both anatomical structures and functional relationships and anatomical information obtained from medical imaging studies, such as magnetic resonance. Therefore, the cognitive profile used to design these treatments integrates information personalized and evidence-based. Neuroimaging techniques represent an essential tool to identify lesions and generate this type of cognitive dysfunctional profiles. Manual delineation of brain anatomical regions is the classical approach to identify brain anatomical regions. Manual approaches present several problems related to inconsistencies across different clinicians, time and repeatability. Automated delineation is done by registering brains to one another or to a template. However, when imaging studies contain lesions, there are several intensity abnormalities and location alterations that reduce the performance of most of the registration algorithms based on intensity parameters. Thus, specialists may have to manually interact with imaging studies to select landmarks (called singular points in this PhD) or identify regions of interest. These two solutions have the same inconvenient than manual approaches, mentioned before. Moreover, these registration algorithms do not allow large and distributed deformations. This type of deformations may also appear when a stroke or a traumatic brain injury (TBI) occur. This PhD is focused on the design, development and implementation of a new methodology to automatically identify lesions in anatomical structures. This methodology integrates algorithms whose main objective is to generate objective and reproducible results. It is divided into four stages: pre-processing, singular points identification, registration and lesion detection. Pre-processing stage. In this first stage, the aim is to standardize all input data in order to be able to draw valid conclusions from the results. Therefore, this stage has a direct impact on the final results. It consists of three steps: skull-stripping, spatial and intensity normalization. Singular points identification. This stage aims to automatize the identification of anatomical points (singular points). It involves the manual identification of anatomical points by the clinician. This automatic identification allows to identify a greater number of points which results in more information; to remove the factor associated to inter-subject variability and thus, the results are reproducible and objective; and to eliminate the time spent on manual marking. This PhD proposed an algorithm to automatically identify singular points (descriptor) based on a multi-detector approach. This algorithm contains multi-parametric (spatial and intensity) information. This algorithm has been compared with other similar algorithms found on the state of the art. Registration. The goal of this stage is to put in spatial correspondence two imaging studies of different subjects/patients. The algorithm proposed in this PhD is based on descriptors. Its main objective is to compute a vector field to introduce distributed deformations (changes in different imaging regions), as large as the deformation vector indicates. The proposed algorithm has been compared with other registration algorithms used on different neuroimaging applications which are used with control subjects. The obtained results are promising and they represent a new context for the automatic identification of anatomical structures. Lesion identification. This final stage aims to identify those anatomical structures whose characteristics associated to spatial location and area or volume has been modified with respect to a normal state. A statistical study of the atlas to be used is performed to establish which are the statistical parameters associated to the normal state. The anatomical structures that may be identified depend on the selected anatomical structures identified on the atlas. The proposed methodology is independent from the selected atlas. Overall, this PhD corroborates the investigated research hypotheses regarding the automatic identification of lesions based on structural medical imaging studies (resonance magnetic studies). Based on these foundations, new research fields to improve the automatic identification of lesions in brain injury can be proposed.

In vivo detection of gene expression in liver by 31P nuclear magnetic resonance spectroscopy employing creatine kinase as a marker gene

In vivo assessment of gene expression is desirable to obtain information on the extent and duration of transduction of tissue after gene delivery. We have developed an in vivo, potentially noninvasive, method for detecting virally mediated gene transfer to the liver. The method employs an adenoviral vector carrying the gene for the brain isozyme of murine creatine kinase (CK-B), an ATP-buffering enzyme expressed mainly in muscle and brain but absent from liver, kidney, and pancreas. Gene expression was monitored by 31P magnetic resonance spectroscopy (MRS) using the product of the CK enzymatic reaction, phosphocreatine, as an indicator of transfection. The vector was administered into nude mice by tail vein injection, and exogenous creatine was administered in the drinking water and by i.p. injection of 2% creatine solution before 31P MRS examination, which was performed on surgically exposed livers. A phosphocreatine resonance was detected in livers of mice injected with the vector and was absent from livers of control animals. CK expression was confirmed in the injected animals by Western blot analysis, enzymatic assays, and immunofluorescence measurements. We conclude that the syngeneic enzyme CK can be used as a marker gene for in vivo monitoring of gene expression after virally mediated gene transfer to the liver.