Realizing live sequence charts in SystemVerilog.

The design of an embedded control system starts with an investigation of properties and behaviors of the process evolving within its environment, and an analysis of the requirement for its safety performance. In early stages, system requirements are often specified as scenarios of behavior using sequence charts for different use cases. This specification must be precise, intuitive and expressive enough to capture different aspects of embedded control systems. As a rather rich and useful extension to the classical message sequence charts, live sequence charts (LSC), which provide a rich collection of constructs for specifying both possible and mandatory behaviors, are very suitable for designing an embedded control system. However, it is not a trivial task to realize a high-level design model in executable program codes effectively and correctly. This paper tackles the challenging task by providing a mapping algorithm to automatically synthesize SystemVerilog programs from given LSC specifications

The influence of consumption context on indulgent versus healthy yoghurts: exploring the relationship between the associated emotions and the actual choices

This work examines the associated emotions of consumers transmitted from extrinsic attributes (fat-related nutrition claims (full-fat, low-fat, and fat-free) and ingredient features (plain, berries, and double chocolate chunk)) labelled on yoghurt packages. It differentiates by consumption context (health versus indulgent) at the time of the survey and studies the relationship between the associated emotions (e.g., positive versus negative) attached to extrinsic attributes and the actual choices. The research was conducted in the Netherlands in 2019, with 209 regular consumers of yoghurt. Participants were divided into two treatments according to each consumption context and a control group (no context); they were instructed to imagine purchasing yoghurt to consume it as a healthy snack or as a dessert or received no instructions. After choosing their preferred option from a discrete choice experiment, participants indicated how the choice made them feel from a list of emotions. The results revealed significant differences between positive emotional profiles for choosing healthy (low-fat) yoghurts with berries and negative profiles for choosing less healthy alternatives (full-fat) with double chocolate chunk sensory features. The findings from a random parameter logit model showed that participants who continuously chose the same type of yoghurt in all choice tasks selected mostly positive rather than negative emotions. The overall findings suggest that the associated emotions affect yoghurt choices. However, the emotions were mainly affected by the consumption context. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Graph-Based Methods for Discovery Browsing with Semantic Predications

We present an extension to literature-based discovery that goes beyond making discoveries to a principled way of navigating through selected aspects of some biomedical domain. The method is a type of “discovery browsing” that guides the user through the research literature on a specified phenomenon. Poorly understood relationships may be explored through novel points of view, and potentially interesting relationships need not be known ahead of time. In a process of “cooperative reciprocity” the user iteratively focuses system output, thus controlling the large number of relationships often generated in literature-based discovery systems. The underlying technology exploits SemRep semantic predications represented as a graph of interconnected nodes (predication arguments) and edges (predicates). The system suggests paths in this graph, which represent chains of relationships. The methodology is illustrated with depressive disorder and focuses on the interaction of inflammation, circadian phenomena, and the neurotransmitter norepinephrine. Insight provided may contribute to enhanced understanding of the pathophysiology, treatment, and prevention of this disorder

Dynamic summarization of bibliographic-based data

<p>Abstract</p> <p>Background</p> <p>Traditional information retrieval techniques typically return excessive output when directed at large bibliographic databases. Natural Language Processing applications strive to extract salient content from the excessive data. Semantic MEDLINE, a National Library of Medicine (NLM) natural language processing application, highlights relevant information in PubMed data. However, Semantic MEDLINE implements manually coded schemas, accommodating few information needs. Currently, there are only five such schemas, while many more would be needed to realistically accommodate all potential users. The aim of this project was to develop and evaluate a statistical algorithm that automatically identifies relevant bibliographic data; the new algorithm could be incorporated into a dynamic schema to accommodate various information needs in Semantic MEDLINE, and eliminate the need for multiple schemas.</p> <p>Methods</p> <p>We developed a flexible algorithm named Combo that combines three statistical metrics, the Kullback-Leibler Divergence (KLD), Riloff's RlogF metric (RlogF), and a new metric called PredScal, to automatically identify salient data in bibliographic text. We downloaded citations from a PubMed search query addressing the genetic etiology of bladder cancer. The citations were processed with SemRep, an NLM rule-based application that produces semantic predications. SemRep output was processed by Combo, in addition to the standard Semantic MEDLINE genetics schema and independently by the two individual KLD and RlogF metrics. We evaluated each summarization method using an existing reference standard within the task-based context of genetic database curation.</p> <p>Results</p> <p>Combo asserted 74 genetic entities implicated in bladder cancer development, whereas the traditional schema asserted 10 genetic entities; the KLD and RlogF metrics individually asserted 77 and 69 genetic entities, respectively. Combo achieved 61% recall and 81% precision, with an F-score of 0.69. The traditional schema achieved 23% recall and 100% precision, with an F-score of 0.37. The KLD metric achieved 61% recall, 70% precision, with an F-score of 0.65. The RlogF metric achieved 61% recall, 72% precision, with an F-score of 0.66.</p> <p>Conclusions</p> <p>Semantic MEDLINE summarization using the new Combo algorithm outperformed a conventional summarization schema in a genetic database curation task. It potentially could streamline information acquisition for other needs without having to hand-build multiple saliency schemas.</p

Differences in colour gamut obtained with three synthetic red food colourants compared with three natural ones: pH and heat stability

The current trend in European markets towards natural ingredients has stimulated the interest in natural colourants. When it is decided to replace a colourant, normally a synthetic colourant with a natural one, it is interesting to know how the colour would be perceived, by locating their coordinates in the a*b*-diagram. The colours of three red natural colourants and three red synthetic ones have been compared. The natural colourants would be visually perceived as less intense than the synthetic colourants; this perception could be a signal of high quality as recognised and accepted by consumers. The hue (hab) of the natural colourants was red-bluish while the synthetic ones showed a red-orange character and they were located in a wider area of hue, having values apart from the rest, thus they could not be replaceable. The synthetic colourants showed less colour differences than the natural ones when subjected to pH variations and heat treatments.Comisión Interministerial de Ciencia y Tecnología AGL2009-12785-C02-0

Clustering cliques for graph-based summarization of the biomedical research literature

BACKGROUND: Graph-based notions are increasingly used in biomedical data mining and knowledge discovery tasks. In this paper, we present a clique-clustering method to automatically summarize graphs of semantic predications produced from PubMed citations (titles and abstracts). RESULTS: SemRep is used to extract semantic predications from the citations returned by a PubMed search. Cliques were identified from frequently occurring predications with highly connected arguments filtered by degree centrality. Themes contained in the summary were identified with a hierarchical clustering algorithm based on common arguments shared among cliques. The validity of the clusters in the summaries produced was compared to the Silhouette-generated baseline for cohesion, separation and overall validity. The theme labels were also compared to a reference standard produced with major MeSH headings. CONCLUSIONS: For 11 topics in the testing data set, the overall validity of clusters from the system summary was 10% better than the baseline (43% versus 33%). While compared to the reference standard from MeSH headings, the results for recall, precision and F-score were 0.64, 0.65, and 0.65 respectively

Subtle changes in the flavour and texture of a drink enhance expectations of satiety

Background: The consumption of liquid calories has been implicated in the development of obesity and weight gain. Energy-containing drinks are often reported to have a weak satiety value: one explanation for this is that because of their fluid texture they are not expected to have much nutritional value. It is important to consider what features of these drinks can be manipulated to enhance their expected satiety value. Two studies investigated the perception of subtle changes in a drink’s viscosity, and the extent to which thick texture and creamy flavour contribute to the generation of satiety expectations. Participants in the first study rated the sensory characteristics of 16 fruit yogurt drinks of increasing viscosity. In study two, a new set of participants evaluated eight versions of the fruit yogurt drink, which varied in thick texture, creamy flavour and energy content, for sensory and hedonic characteristics and satiety expectations. Results: In study one, participants were able to perceive small changes in drink viscosity that were strongly related to the actual viscosity of the drinks. In study two, the thick versions of the drink were expected to be more filling and have a greater expected satiety value, independent of the drink’s actual energy content. A creamy flavour enhanced the extent to which the drink was expected to be filling, but did not affect its expected satiety. Conclusions: These results indicate that subtle manipulations of texture and creamy flavour can increase expectations that a fruit yogurt drink will be filling and suppress hunger, irrespective of the drink’s energy content. A thicker texture enhanced expectations of satiety to a greater extent than a creamier flavour, and may be one way to improve the anticipated satiating value of energy-containing beverages

Constructing a semantic predication gold standard from the biomedical literature

<p>Abstract</p> <p>Background</p> <p>Semantic relations increasingly underpin biomedical text mining and knowledge discovery applications. The success of such practical applications crucially depends on the quality of extracted relations, which can be assessed against a gold standard reference. Most such references in biomedical text mining focus on narrow subdomains and adopt different semantic representations, rendering them difficult to use for benchmarking independently developed relation extraction systems. In this article, we present a multi-phase gold standard annotation study, in which we annotated 500 sentences randomly selected from MEDLINE abstracts on a wide range of biomedical topics with 1371 semantic predications. The UMLS Metathesaurus served as the main source for conceptual information and the UMLS Semantic Network for relational information. We measured interannotator agreement and analyzed the annotations closely to identify some of the challenges in annotating biomedical text with relations based on an ontology or a terminology.</p> <p>Results</p> <p>We obtain fair to moderate interannotator agreement in the practice phase (0.378-0.475). With improved guidelines and additional semantic equivalence criteria, the agreement increases by 12% (0.415 to 0.536) in the main annotation phase. In addition, we find that agreement increases to 0.688 when the agreement calculation is limited to those predications that are based only on the explicitly provided UMLS concepts and relations.</p> <p>Conclusions</p> <p>While interannotator agreement in the practice phase confirms that conceptual annotation is a challenging task, the increasing agreement in the main annotation phase points out that an acceptable level of agreement can be achieved in multiple iterations, by setting stricter guidelines and establishing semantic equivalence criteria. Mapping text to ontological concepts emerges as the main challenge in conceptual annotation. Annotating predications involving biomolecular entities and processes is particularly challenging. While the resulting gold standard is mainly intended to serve as a test collection for our semantic interpreter, we believe that the lessons learned are applicable generally.</p

Automation of a problem list using natural language processing

BACKGROUND: The medical problem list is an important part of the electronic medical record in development in our institution. To serve the functions it is designed for, the problem list has to be as accurate and timely as possible. However, the current problem list is usually incomplete and inaccurate, and is often totally unused. To alleviate this issue, we are building an environment where the problem list can be easily and effectively maintained. METHODS: For this project, 80 medical problems were selected for their frequency of use in our future clinical field of evaluation (cardiovascular). We have developed an Automated Problem List system composed of two main components: a background and a foreground application. The background application uses Natural Language Processing (NLP) to harvest potential problem list entries from the list of 80 targeted problems detected in the multiple free-text electronic documents available in our electronic medical record. These proposed medical problems drive the foreground application designed for management of the problem list. Within this application, the extracted problems are proposed to the physicians for addition to the official problem list. RESULTS: The set of 80 targeted medical problems selected for this project covered about 5% of all possible diagnoses coded in ICD-9-CM in our study population (cardiovascular adult inpatients), but about 64% of all instances of these coded diagnoses. The system contains algorithms to detect first document sections, then sentences within these sections, and finally potential problems within the sentences. The initial evaluation of the section and sentence detection algorithms demonstrated a sensitivity and positive predictive value of 100% when detecting sections, and a sensitivity of 89% and a positive predictive value of 94% when detecting sentences. CONCLUSION: The global aim of our project is to automate the process of creating and maintaining a problem list for hospitalized patients and thereby help to guarantee the timeliness, accuracy and completeness of this information