New Clinical Indications for (18)F/(11)C-choline, New Tracers for Positron Emission Tomography and a Promising Hybrid Device for Prostate Cancer Staging: A Systematic Review of the Literature

CONTEXT Radiolabelled choline positron emission tomography has changed the management of prostate cancer patients. However, new emerging radiopharmaceutical agents, like radiolabelled prostate specific membrane antigen, and new promising hybrid imaging will begin new challenges in the diagnostic field. OBJECTIVE The continuous evolution in nuclear medicine has led to the improvement in the detection of recurrent prostate cancer (PCa), particularly distant metastases. New horizons have been opened for radiolabelled choline positron emission tomography (PET)/computed tomography (CT) as a guide for salvage therapy or for the assessment of systemic therapies. In addition, new tracers and imaging tools have been recently tested, providing important information for the management of PCa patients. Herein we discuss: (1) the available evidence in literature on radiolabelled choline PET and their recent indications, (2) the role of alternative radiopharmaceutical agents, and (3) the advantages of a recent hybrid imaging device (PET/magnetic resonance imaging) in PCa. EVIDENCE ACQUISITION Data from recently published (2010-2015), original articles concerning the role of choline PET/CT, new emerging radiotracers, and a new imaging device are analysed. This review is reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. EVIDENCE SYNTHESIS In the restaging phase, the detection rate of choline PET varies between 4% and 97%, mainly depending on the site of recurrence and prostate-specific antigen levels. Both 68gallium (68Ga)-prostate specific membrane antigen and 18F-fluciclovine are shown to be more accurate in the detection of recurrent disease as compared with radiolabelled choline PET/CT. Particularly, Ga68-PSMA has a detection rate of 50% and 68%, respectively for prostate-specific antigen levels < 0.5ng/ml and 0.5-2ng/ml. Moreover, 68Ga- PSMA PET/magnetic resonance imaging demonstrated a particularly higher accuracy in detecting PCa than PET/CT. New tracers, such as radiolabelled bombesin or urokinase-type plasminogen activator receptor, are promising, but few data in clinical practice are available today. CONCLUSIONS Some limitations emerge from the published papers, both for radiolabelled choline PET/CT and also for new radiopharmaceutical agents. Efforts are still needed to enhance the impact of published data in the world of oncology, in particular when new radiopharmaceuticals are introduced into the clinical arena. PATIENT SUMMARY In the present review, the authors summarise the last evidences in clinical practice for the assessment of prostate cancer, by using nuclear medicine modalities, like positron emission tomography/computed tomography and positron emission tomography/magnetic resonance imaging.

ASSESSMENT OF SKELETAL MUSCLE BLOOD FLOW AND GLUCOSE METABOLISM WITH POSITRON EMITTING RADIONUCLIDES

In order to evaluate factors regulating substrate metabolism in vivo positron emitting radionuclides were used for the assessment of skeletal muscle blood flow and glucose utilization. The potassium analog, Rb-82 was used to measure skeletal muscle blood flow and the glucose analog, 18-F-2-deoxy-2-fluoro-D-glucose (FDG) was used to examine the kinetics of skeletal muscle transport and phosphorylation.^ New Zealand white rabbits' blood flow ranged from 1.0-70 ml/min/100g with the lowest flows occurring under baseline conditions and the highest flows were measured immediately after exercise. Elevated plasma glucose had no effect on increasing blood flow, whereas high physiologic to pharmacologic levels of insulin doubled flow as measured by the radiolabeled microspheres, but a proportionate increase was not detected by Rb-82. The data suggest that skeletal muscle blood flow can be measured using the positron emitting K+ analog Rb-82 under low flow and high flow conditions but not when insulin levels in the plasma are elevated. This may be due to the fact that insulin induces an increase in the Na+/K+-ATPase activity of the cell indirectly through a direct increase in the Na+/H+pump activity. This suggests that the increased cation pump activity counteracts the normal decrease in extraction seen at higher flows resulting in an underestimation of flow as measured by rubidium-82.^ Glucose uptake as measured by FDG employed a three compartment mathematical model describing the rates of transport, countertransport and phosphorylation of hexose. The absolute values for the metabolic rate of FDG were found to be an order of magnitude higher than those reported by other investigators. Changes noted in the rate constant for transport (k1) were found to disagree with the a priori information on the effects of insulin on skeletal muscle hexose transport. Glucose metabolism was however, found to increase above control levels with administration of insulin and electrical stimulation. The data indicate that valid measurements of skeletal muscle glucose transport and phosphorylation using the positron emitting glucose analog FDG requires further model application and biochemical validation. (Abstract shortened with permission of author.) ^

BIM MEDIATES IMATINIB-INDUCED APOPTOSIS OF GASTROINTESTINAL STROMAL TUMORS: TRANSLATIONAL IMPLICATIONS

Gastrointestinal stromal tumors (GISTs) are oncogene-addicted cancers driven by activating mutations in the genes encoding receptor tyrosine kinases KIT and PDGFR-α. Imatinib mesylate, a specific inhibitor of KIT and PDGFR-α signaling, delays progression of GIST, but is incapable of achieving cure. Thus, most patients who initially respond to imatinib therapy eventually experience tumor progression, and have limited therapeutic options thereafter. To address imatinib-resistance and tumor progression, these studies sought to understand the molecular mechanisms that regulate apoptosis in GIST, and evaluate combination therapies that kill GISTs cells via complementary, but independent, mechanisms. BIM (Bcl-2 interacting mediator of apoptosis), a pro-apoptotic member of the Bcl-2 family, effects apoptosis in oncogene-addicted malignancies treated with targeted therapies, and was recently shown to mediate imatinib-induced apoptosis in GIST. This dissertation examined the molecular mechanism of BIM upregulation and its cytotoxic effect in GIST cells harboring clinically-representative KIT mutations. Additionally, imatinib-induced alterations in BIM and pro-survival Bcl-2 proteins were studied in specimens from patients with GIST, and correlated to apoptosis, FDG-PET response, and survival. Further, the intrinsic pathway of apoptosis was targeted therapeutically in GIST cells with the Bcl-2 inhibitor ABT-737. These studies show that BIM is upregulated in GIST cells and patient tumors after imatinib exposure, and correlates with induction of apoptosis, response by FDG-PET, and disease-free survival. These studies contribute to the mechanistic understanding of imatinib-induced apoptosis in clinically-relevant models of GIST, and may facilitate prediction of resistance and disease progression in patients. Further, combining inhibition of KIT and Bcl-2 induces apoptosis synergistically and overcomes imatinib-resistance in GIST cells. Given that imatinib-resistance and GIST progression may reflect inadequate BIM-mediated inhibition of pro-survival Bcl-2 proteins, the preclinical evidence presented here suggests that direct engagement of apoptosis may be an effective approach to enhance the cytotoxicity of imatinib and overcome resistance.

OntoTag - A Linguistic and Ontological Annotation Model Suitable for the Semantic Web

OntoTag - A Linguistic and Ontological Annotation Model Suitable for the Semantic Web 1. INTRODUCTION. LINGUISTIC TOOLS AND ANNOTATIONS: THEIR LIGHTS AND SHADOWS Computational Linguistics is already a consolidated research area. It builds upon the results of other two major ones, namely Linguistics and Computer Science and Engineering, and it aims at developing computational models of human language (or natural language, as it is termed in this area). Possibly, its most well-known applications are the different tools developed so far for processing human language, such as machine translation systems and speech recognizers or dictation programs. These tools for processing human language are commonly referred to as linguistic tools. Apart from the examples mentioned above, there are also other types of linguistic tools that perhaps are not so well-known, but on which most of the other applications of Computational Linguistics are built. These other types of linguistic tools comprise POS taggers, natural language parsers and semantic taggers, amongst others. All of them can be termed linguistic annotation tools. Linguistic annotation tools are important assets. In fact, POS and semantic taggers (and, to a lesser extent, also natural language parsers) have become critical resources for the computer applications that process natural language. Hence, any computer application that has to analyse a text automatically and ‘intelligently’ will include at least a module for POS tagging. The more an application needs to ‘understand’ the meaning of the text it processes, the more linguistic tools and/or modules it will incorporate and integrate. However, linguistic annotation tools have still some limitations, which can be summarised as follows: 1. Normally, they perform annotations only at a certain linguistic level (that is, Morphology, Syntax, Semantics, etc.). 2. They usually introduce a certain rate of errors and ambiguities when tagging. This error rate ranges from 10 percent up to 50 percent of the units annotated for unrestricted, general texts. 3. Their annotations are most frequently formulated in terms of an annotation schema designed and implemented ad hoc. A priori, it seems that the interoperation and the integration of several linguistic tools into an appropriate software architecture could most likely solve the limitations stated in (1). Besides, integrating several linguistic annotation tools and making them interoperate could also minimise the limitation stated in (2). Nevertheless, in the latter case, all these tools should produce annotations for a common level, which would have to be combined in order to correct their corresponding errors and inaccuracies. Yet, the limitation stated in (3) prevents both types of integration and interoperation from being easily achieved. In addition, most high-level annotation tools rely on other lower-level annotation tools and their outputs to generate their own ones. For example, sense-tagging tools (operating at the semantic level) often use POS taggers (operating at a lower level, i.e., the morphosyntactic) to identify the grammatical category of the word or lexical unit they are annotating. Accordingly, if a faulty or inaccurate low-level annotation tool is to be used by other higher-level one in its process, the errors and inaccuracies of the former should be minimised in advance. Otherwise, these errors and inaccuracies would be transferred to (and even magnified in) the annotations of the high-level annotation tool. Therefore, it would be quite useful to find a way to (i) correct or, at least, reduce the errors and the inaccuracies of lower-level linguistic tools; (ii) unify the annotation schemas of different linguistic annotation tools or, more generally speaking, make these tools (as well as their annotations) interoperate. Clearly, solving (i) and (ii) should ease the automatic annotation of web pages by means of linguistic tools, and their transformation into Semantic Web pages (Berners-Lee, Hendler and Lassila, 2001). Yet, as stated above, (ii) is a type of interoperability problem. There again, ontologies (Gruber, 1993; Borst, 1997) have been successfully applied thus far to solve several interoperability problems. Hence, ontologies should help solve also the problems and limitations of linguistic annotation tools aforementioned. Thus, to summarise, the main aim of the present work was to combine somehow these separated approaches, mechanisms and tools for annotation from Linguistics and Ontological Engineering (and the Semantic Web) in a sort of hybrid (linguistic and ontological) annotation model, suitable for both areas. This hybrid (semantic) annotation model should (a) benefit from the advances, models, techniques, mechanisms and tools of these two areas; (b) minimise (and even solve, when possible) some of the problems found in each of them; and (c) be suitable for the Semantic Web. The concrete goals that helped attain this aim are presented in the following section. 2. GOALS OF THE PRESENT WORK As mentioned above, the main goal of this work was to specify a hybrid (that is, linguistically-motivated and ontology-based) model of annotation suitable for the Semantic Web (i.e. it had to produce a semantic annotation of web page contents). This entailed that the tags included in the annotations of the model had to (1) represent linguistic concepts (or linguistic categories, as they are termed in ISO/DCR (2008)), in order for this model to be linguistically-motivated; (2) be ontological terms (i.e., use an ontological vocabulary), in order for the model to be ontology-based; and (3) be structured (linked) as a collection of ontology-based triples, as in the usual Semantic Web languages (namely RDF(S) and OWL), in order for the model to be considered suitable for the Semantic Web. Besides, to be useful for the Semantic Web, this model should provide a way to automate the annotation of web pages. As for the present work, this requirement involved reusing the linguistic annotation tools purchased by the OEG research group (http://www.oeg-upm.net), but solving beforehand (or, at least, minimising) some of their limitations. Therefore, this model had to minimise these limitations by means of the integration of several linguistic annotation tools into a common architecture. Since this integration required the interoperation of tools and their annotations, ontologies were proposed as the main technological component to make them effectively interoperate. From the very beginning, it seemed that the formalisation of the elements and the knowledge underlying linguistic annotations within an appropriate set of ontologies would be a great step forward towards the formulation of such a model (henceforth referred to as OntoTag). Obviously, first, to combine the results of the linguistic annotation tools that operated at the same level, their annotation schemas had to be unified (or, preferably, standardised) in advance. This entailed the unification (id. standardisation) of their tags (both their representation and their meaning), and their format or syntax. Second, to merge the results of the linguistic annotation tools operating at different levels, their respective annotation schemas had to be (a) made interoperable and (b) integrated. And third, in order for the resulting annotations to suit the Semantic Web, they had to be specified by means of an ontology-based vocabulary, and structured by means of ontology-based triples, as hinted above. Therefore, a new annotation scheme had to be devised, based both on ontologies and on this type of triples, which allowed for the combination and the integration of the annotations of any set of linguistic annotation tools. This annotation scheme was considered a fundamental part of the model proposed here, and its development was, accordingly, another major objective of the present work. All these goals, aims and objectives could be re-stated more clearly as follows: Goal 1: Development of a set of ontologies for the formalisation of the linguistic knowledge relating linguistic annotation. Sub-goal 1.1: Ontological formalisation of the EAGLES (1996a; 1996b) de facto standards for morphosyntactic and syntactic annotation, in a way that helps respect the triple structure recommended for annotations in these works (which is isomorphic to the triple structures used in the context of the Semantic Web). Sub-goal 1.2: Incorporation into this preliminary ontological formalisation of other existing standards and standard proposals relating the levels mentioned above, such as those currently under development within ISO/TC 37 (the ISO Technical Committee dealing with Terminology, which deals also with linguistic resources and annotations). Sub-goal 1.3: Generalisation and extension of the recommendations in EAGLES (1996a; 1996b) and ISO/TC 37 to the semantic level, for which no ISO/TC 37 standards have been developed yet. Sub-goal 1.4: Ontological formalisation of the generalisations and/or extensions obtained in the previous sub-goal as generalisations and/or extensions of the corresponding ontology (or ontologies). Sub-goal 1.5: Ontological formalisation of the knowledge required to link, combine and unite the knowledge represented in the previously developed ontology (or ontologies). Goal 2: Development of OntoTag’s annotation scheme, a standard-based abstract scheme for the hybrid (linguistically-motivated and ontological-based) annotation of texts. Sub-goal 2.1: Development of the standard-based morphosyntactic annotation level of OntoTag’s scheme. This level should include, and possibly extend, the recommendations of EAGLES (1996a) and also the recommendations included in the ISO/MAF (2008) standard draft. Sub-goal 2.2: Development of the standard-based syntactic annotation level of the hybrid abstract scheme. This level should include, and possibly extend, the recommendations of EAGLES (1996b) and the ISO/SynAF (2010) standard draft. Sub-goal 2.3: Development of the standard-based semantic annotation level of OntoTag’s (abstract) scheme. Sub-goal 2.4: Development of the mechanisms for a convenient integration of the three annotation levels already mentioned. These mechanisms should take into account the recommendations included in the ISO/LAF (2009) standard draft. Goal 3: Design of OntoTag’s (abstract) annotation architecture, an abstract architecture for the hybrid (semantic) annotation of texts (i) that facilitates the integration and interoperation of different linguistic annotation tools, and (ii) whose results comply with OntoTag’s annotation scheme. Sub-goal 3.1: Specification of the decanting processes that allow for the classification and separation, according to their corresponding levels, of the results of the linguistic tools annotating at several different levels. Sub-goal 3.2: Specification of the standardisation processes that allow (a) complying with the standardisation requirements of OntoTag’s annotation scheme, as well as (b) combining the results of those linguistic tools that share some level of annotation. Sub-goal 3.3: Specification of the merging processes that allow for the combination of the output annotations and the interoperation of those linguistic tools that share some level of annotation. Sub-goal 3.4: Specification of the merge processes that allow for the integration of the results and the interoperation of those tools performing their annotations at different levels. Goal 4: Generation of OntoTagger’s schema, a concrete instance of OntoTag’s abstract scheme for a concrete set of linguistic annotations. These linguistic annotations result from the tools and the resources available in the research group, namely • Bitext’s DataLexica (http://www.bitext.com/EN/datalexica.asp), • LACELL’s (POS) tagger (http://www.um.es/grupos/grupo-lacell/quees.php), • Connexor’s FDG (http://www.connexor.eu/technology/machinese/glossary/fdg/), and • EuroWordNet (Vossen et al., 1998). This schema should help evaluate OntoTag’s underlying hypotheses, stated below. Consequently, it should implement, at least, those levels of the abstract scheme dealing with the annotations of the set of tools considered in this implementation. This includes the morphosyntactic, the syntactic and the semantic levels. Goal 5: Implementation of OntoTagger’s configuration, a concrete instance of OntoTag’s abstract architecture for this set of linguistic tools and annotations. This configuration (1) had to use the schema generated in the previous goal; and (2) should help support or refute the hypotheses of this work as well (see the next section). Sub-goal 5.1: Implementation of the decanting processes that facilitate the classification and separation of the results of those linguistic resources that provide annotations at several different levels (on the one hand, LACELL’s tagger operates at the morphosyntactic level and, minimally, also at the semantic level; on the other hand, FDG operates at the morphosyntactic and the syntactic levels and, minimally, at the semantic level as well). Sub-goal 5.2: Implementation of the standardisation processes that allow (i) specifying the results of those linguistic tools that share some level of annotation according to the requirements of OntoTagger’s schema, as well as (ii) combining these shared level results. In particular, all the tools selected perform morphosyntactic annotations and they had to be conveniently combined by means of these processes. Sub-goal 5.3: Implementation of the merging processes that allow for the combination (and possibly the improvement) of the annotations and the interoperation of the tools that share some level of annotation (in particular, those relating the morphosyntactic level, as in the previous sub-goal). Sub-goal 5.4: Implementation of the merging processes that allow for the integration of the different standardised and combined annotations aforementioned, relating all the levels considered. Sub-goal 5.5: Improvement of the semantic level of this configuration by adding a named entity recognition, (sub-)classification and annotation subsystem, which also uses the named entities annotated to populate a domain ontology, in order to provide a concrete application of the present work in the two areas involved (the Semantic Web and Corpus Linguistics). 3. MAIN RESULTS: ASSESSMENT OF ONTOTAG’S UNDERLYING HYPOTHESES The model developed in the present thesis tries to shed some light on (i) whether linguistic annotation tools can effectively interoperate; (ii) whether their results can be combined and integrated; and, if they can, (iii) how they can, respectively, interoperate and be combined and integrated. Accordingly, several hypotheses had to be supported (or rejected) by the development of the OntoTag model and OntoTagger (its implementation). The hypotheses underlying OntoTag are surveyed below. Only one of the hypotheses (H.6) was rejected; the other five could be confirmed. H.1 The annotations of different levels (or layers) can be integrated into a sort of overall, comprehensive, multilayer and multilevel annotation, so that their elements can complement and refer to each other. • CONFIRMED by the development of: o OntoTag’s annotation scheme, o OntoTag’s annotation architecture, o OntoTagger’s (XML, RDF, OWL) annotation schemas, o OntoTagger’s configuration. H.2 Tool-dependent annotations can be mapped onto a sort of tool-independent annotations and, thus, can be standardised. • CONFIRMED by means of the standardisation phase incorporated into OntoTag and OntoTagger for the annotations yielded by the tools. H.3 Standardisation should ease: H.3.1: The interoperation of linguistic tools. H.3.2: The comparison, combination (at the same level and layer) and integration (at different levels or layers) of annotations. • H.3 was CONFIRMED by means of the development of OntoTagger’s ontology-based configuration: o Interoperation, comparison, combination and integration of the annotations of three different linguistic tools (Connexor’s FDG, Bitext’s DataLexica and LACELL’s tagger); o Integration of EuroWordNet-based, domain-ontology-based and named entity annotations at the semantic level. o Integration of morphosyntactic, syntactic and semantic annotations. H.4 Ontologies and Semantic Web technologies (can) play a crucial role in the standardisation of linguistic annotations, by providing consensual vocabularies and standardised formats for annotation (e.g., RDF triples). • CONFIRMED by means of the development of OntoTagger’s RDF-triple-based annotation schemas. H.5 The rate of errors introduced by a linguistic tool at a given level, when annotating, can be reduced automatically by contrasting and combining its results with the ones coming from other tools, operating at the same level. However, these other tools might be built following a different technological (stochastic vs. rule-based, for example) or theoretical (dependency vs. HPS-grammar-based, for instance) approach. • CONFIRMED by the results yielded by the evaluation of OntoTagger. H.6 Each linguistic level can be managed and annotated independently. • REJECTED: OntoTagger’s experiments and the dependencies observed among the morphosyntactic annotations, and between them and the syntactic annotations. In fact, Hypothesis H.6 was already rejected when OntoTag’s ontologies were developed. We observed then that several linguistic units stand on an interface between levels, belonging thereby to both of them (such as morphosyntactic units, which belong to both the morphological level and the syntactic level). Therefore, the annotations of these levels overlap and cannot be handled independently when merged into a unique multileveled annotation. 4. OTHER MAIN RESULTS AND CONTRIBUTIONS First, interoperability is a hot topic for both the linguistic annotation community and the whole Computer Science field. The specification (and implementation) of OntoTag’s architecture for the combination and integration of linguistic (annotation) tools and annotations by means of ontologies shows a way to make these different linguistic annotation tools and annotations interoperate in practice. Second, as mentioned above, the elements involved in linguistic annotation were formalised in a set (or network) of ontologies (OntoTag’s linguistic ontologies). • On the one hand, OntoTag’s network of ontologies consists of − The Linguistic Unit Ontology (LUO), which includes a mostly hierarchical formalisation of the different types of linguistic elements (i.e., units) identifiable in a written text; − The Linguistic Attribute Ontology (LAO), which includes also a mostly hierarchical formalisation of the different types of features that characterise the linguistic units included in the LUO; − The Linguistic Value Ontology (LVO), which includes the corresponding formalisation of the different values that the attributes in the LAO can take; − The OIO (OntoTag’s Integration Ontology), which  Includes the knowledge required to link, combine and unite the knowledge represented in the LUO, the LAO and the LVO;  Can be viewed as a knowledge representation ontology that describes the most elementary vocabulary used in the area of annotation. • On the other hand, OntoTag’s ontologies incorporate the knowledge included in the different standards and recommendations for linguistic annotation released so far, such as those developed within the EAGLES and the SIMPLE European projects or by the ISO/TC 37 committee: − As far as morphosyntactic annotations are concerned, OntoTag’s ontologies formalise the terms in the EAGLES (1996a) recommendations and their corresponding terms within the ISO Morphosyntactic Annotation Framework (ISO/MAF, 2008) standard; − As for syntactic annotations, OntoTag’s ontologies incorporate the terms in the EAGLES (1996b) recommendations and their corresponding terms within the ISO Syntactic Annotation Framework (ISO/SynAF, 2010) standard draft; − Regarding semantic annotations, OntoTag’s ontologies generalise and extend the recommendations in EAGLES (1996a; 1996b) and, since no stable standards or standard drafts have been released for semantic annotation by ISO/TC 37 yet, they incorporate the terms in SIMPLE (2000) instead; − The terms coming from all these recommendations and standards were supplemented by those within the ISO Data Category Registry (ISO/DCR, 2008) and also of the ISO Linguistic Annotation Framework (ISO/LAF, 2009) standard draft when developing OntoTag’s ontologies. Third, we showed that the combination of the results of tools annotating at the same level can yield better results (both in precision and in recall) than each tool separately. In particular, 1. OntoTagger clearly outperformed two of the tools integrated into its configuration, namely DataLexica and FDG in all the combination sub-phases in which they overlapped (i.e. POS tagging, lemma annotation and morphological feature annotation). As far as the remaining tool is concerned, i.e. LACELL’s tagger, it was also outperformed by OntoTagger in POS tagging and lemma annotation, and it did not behave better than OntoTagger in the morphological feature annotation layer. 2. As an immediate result, this implies that a) This type of combination architecture configurations can be applied in order to improve significantly the accuracy of linguistic annotations; and b) Concerning the morphosyntactic level, this could be regarded as a way of constructing more robust and more accurate POS tagging systems. Fourth, Semantic Web annotations are usually performed by humans or else by machine learning systems. Both of them leave much to be desired: the former, with respect to their annotation rate; the latter, with respect to their (average) precision and recall. In this work, we showed how linguistic tools can be wrapped in order to annotate automatically Semantic Web pages using ontologies. This entails their fast, robust and accurate semantic annotation. As a way of example, as mentioned in Sub-goal 5.5, we developed a particular OntoTagger module for the recognition, classification and labelling of named entities, according to the MUC and ACE tagsets (Chinchor, 1997; Doddington et al., 2004). These tagsets were further specified by means of a domain ontology, namely the Cinema Named Entities Ontology (CNEO). This module was applied to the automatic annotation of ten different web pages containing cinema reviews (that is, around 5000 words). In addition, the named entities annotated with this module were also labelled as instances (or individuals) of the classes included in the CNEO and, then, were used to populate this domain ontology. • The statistical results obtained from the evaluation of this particular module of OntoTagger can be summarised as follows. On the one hand, as far as recall (R) is concerned, (R.1) the lowest value was 76,40% (for file 7); (R.2) the highest value was 97, 50% (for file 3); and (R.3) the average value was 88,73%. On the other hand, as far as the precision rate (P) is concerned, (P.1) its minimum was 93,75% (for file 4); (R.2) its maximum was 100% (for files 1, 5, 7, 8, 9, and 10); and (R.3) its average value was 98,99%. • These results, which apply to the tasks of named entity annotation and ontology population, are extraordinary good for both of them. They can be explained on the basis of the high accuracy of the annotations provided by OntoTagger at the lower levels (mainly at the morphosyntactic level). However, they should be conveniently qualified, since they might be too domain- and/or language-dependent. It should be further experimented how our approach works in a different domain or a different language, such as French, English, or German. • In any case, the results of this application of Human Language Technologies to Ontology Population (and, accordingly, to Ontological Engineering) seem very promising and encouraging in order for these two areas to collaborate and complement each other in the area of semantic annotation. Fifth, as shown in the State of the Art of this work, there are different approaches and models for the semantic annotation of texts, but all of them focus on a particular view of the semantic level. Clearly, all these approaches and models should be integrated in order to bear a coherent and joint semantic annotation level. OntoTag shows how (i) these semantic annotation layers could be integrated together; and (ii) they could be integrated with the annotations associated to other annotation levels. Sixth, we identified some recommendations, best practices and lessons learned for annotation standardisation, interoperation and merge. They show how standardisation (via ontologies, in this case) enables the combination, integration and interoperation of different linguistic tools and their annotations into a multilayered (or multileveled) linguistic annotation, which is one of the hot topics in the area of Linguistic Annotation. And last but not least, OntoTag’s annotation scheme and OntoTagger’s annotation schemas show a way to formalise and annotate coherently and uniformly the different units and features associated to the different levels and layers of linguistic annotation. This is a great scientific step ahead towards the global standardisation of this area, which is the aim of ISO/TC 37 (in particular, Subcommittee 4, dealing with the standardisation of linguistic annotations and resources).

Imaging adenoviral-directed reporter gene expression in living animals with positron emission tomography

We are developing quantitative assays to repeatedly and noninvasively image expression of reporter genes in living animals, using positron emission tomography (PET). We synthesized positron-emitting 8-[18F]fluoroganciclovir (FGCV) and demonstrated that this compound is a substrate for the herpes simplex virus 1 thymidine kinase enzyme (HSV1-TK). Using positron-emitting FGCV as a PET reporter probe, we imaged adenovirus-directed hepatic expression of the HSV1-tk reporter gene in living mice. There is a significant positive correlation between the percent injected dose of FGCV retained per gram of liver and the levels of hepatic HSV1-tk reporter gene expression (r2 > 0.80). Over a similar range of HSV1-tk expression in vivo, the percent injected dose retained per gram of liver was 0–23% for ganciclovir and 0–3% for FGCV. Repeated, noninvasive, and quantitative imaging of PET reporter gene expression should be a valuable tool for studies of human gene therapy, of organ/cell transplantation, and of both environmental and behavioral modulation of gene expression in transgenic mice.

The feasibility of establishing highway safety manpower development and research centers at university-level institutions. Volume I: study report. Final report.

National Highway Safety Bureau, Washington, D.C.

The feasibility of establishing highway safety manpower development and research centers at university-level institutions. Volume II: appendixes. Final report.

National Highway Safety Bureau, Washington, D.C.

Survey of specialized technical information centers. Final report.

National Highway Safety Bureau, Washington, D.C.

Man-key to accident control.

Mode of access: Internet.

Facility requirements study for the National Traffic Safety Research Center.

National Highway Safety Bureau, Washington, D.C.

The organization and program structure of the National Highway Safety Bureau.

Mode of access: Internet.

Summary of predictions and comparison of observed effects of Gnome on public safety : final report /

"U.S. Atomic Energy Commission Plowshare Program"--Cover.

Multi-centre Phase II trial of the polyamine synthesis inhibitor SAM486A (CGP48664) in patients with metastatic melanoma

Purpose: To determine the activity and tolerability of SAM496A, an inhibitor of S-adenosylmethionine decarboxylase (SAMDC), in patients with metastatic melanoma who had not received prior chemotherapy. Selected patients were offered participation in two sub-studies examining early changes in tumor metabolism with FDG-PET and changes in tumor polyamine content. Patients and methods: Fifteen patients with measurable metastatic melanoma, normal cardiac function, and no known CNS metastases were eligible and received SAM486A by 1-hour IV infusion daily for 5 days every 3 weeks. Response was assessed by SWOG criteria. Results: No patient had a confirmed partial response. Fatigue/lethargy, myalgia and neutropenia were the main toxicities but no febrile neutropenia or grade 4 non-hematological toxicity occurred. Five patients had PET scans pre-treatment and on days 8-12 of cycle 1. No patient had reduction of tumor metabolism. Serial biopsy in one patient showed alterations in polyamines consistent with SAMDC inhibition. Conclusions: Using the present dose and schedule of administration, SAM486A does not have significant therapeutic potential in patients with metastatic melanoma.

Aluminium hydroxide stabilised MnFe2O4 and Fe3O4 nanoparticles as dual-modality contrasts agent for MRI and PET imaging

Magnetic nanoparticles (NPs) MnFe2O4 and Fe3O4 were stabilised by depositing an Al(OH)3 layer via a hydrolysis process. The particles displayed excellent colloidal stability in water and a high affinity to [18F]-fluoride and bisphosphonate groups. A high radiolabeling efficiency, 97% for 18F-fluoride and 100% for 64Cu-bisphosphonate conjugate, was achieved by simply incubating NPs with radioactivity solution at room temperature for 5min. The properties of particles were strongly dependant on the thickness and hardness of the Al(OH)3 layer which could in turn be controlled by the hydrolysis method. The application of these Al(OH)3 coated magnetic NPs in molecular imaging has been further explored. The results demonstrated that these NPs are potential candidates as dual modal probes for MR and PET. In vivo PET imaging showed a slow release of 18F from NPs, but no sign of efflux of 64Cu. © 2014 The Authors.

18-Fluorodeoxyglucose positron emission tomography/computed tomography in the management of aggressive non-Hodgkin's B-cell lymphoma

18-Fluorodeoxyglucose (FDG-PET/CT) is an established imaging modality that has been proven to be of benefit in the management of aggressive B-cell non-Hodgkin's lymphoma, such as diffuse large B-cell lymphoma and advanced stage follicular lymphoma. The combination of anatomic and functional imaging afforded by FDG-PET/CT has led to superior sensitivity and specificity in the primary staging, restaging, and assessment of response to treatment of hematological malignancies when compared to FDG-PET and CT alone. The use of FDG-PET/CT for post treatment surveillance imaging remains controversial, and further study is needed to ascertain whether this modality is cost effective and appropriate for use in this setting.