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The authors acknowledge the European Commission for funding the InnoRenew CoE project (Grant Agreement 739574) under the Horizon2020 Widespread-Teaming program and the Republic of Slovenia (Investment funding of the Republic of Slovenia and European Union of the European Regional Development Fund). We also acknowledge support from the Slovenian research agency ARRS (program no. P1-0383, J1-9186, J1-1715, J5- 1796, J1-1692, and IO-0035). While virtual reality (VR) has been explored in the field of architecture, its implications on people who experience their future office space in such a way has not been extensively studied. In this explorative study, we are interested in how VR and other representation methods support users in projecting themselves into their future office space and how this might influence their willingness to relocate. In order to compare VR with other representations, we used (i) standard paper based floor plans and renders of the future building (as used by architects to present their creations to stakeholders), (ii) a highly-detailed virtual environment of the same building experienced on a computer monitor (desktop condition), and (iii) the same environment experienced on a head mounted display (VR condition). Participants were randomly assigned to conditions and were instructed to freely explore their representation method for up to 15 min without any restrictions or tasks given. The results show, that compared to other representation methods, VR significantly differed for the sense of presence, user experience and engagement, and that these measures are correlated for this condition only. In virtual environments, users were observed looking at the views through the windows, spent time on terraces between trees, explored the surroundings, and even “took a walk” to work. Nevertheless, the results show that representation method influences the exploration of the future building as users in VR spent significantly more time exploring the environment, and provided more positive comments about the building compared to users in either desktop or paper conditions. We show that VR representation used in our explorative study increased users’ capability to imagine future scenarios involving their future office spaces, better supported them in projecting themselves into these spaces, and positively affected their attitude towards relocating. Publisher PDF

Funding: This research was sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defence and was accomplished under Agreement Number W911NF-06-3-0001. In this paper we illustrate how novel AI methods can improve the performance of intelligence analysts. These analysts aim to make sense of — often conflicting or incomplete — information, weighing up competing hypotheses which serve to explain an observed situation. Analysts have access to numerous visual analytic tools which support the temporal and/or conceptual structuring of information and collection, and support the evaluation of alternative hypotheses. We believe, however, that there are currently no tools or methods which allow analysts to combine the recording and interpretation of information, and that there is little understanding about how software tools can facilitate the hypothesis formation process. Following the identification of these requirements, we developed the CISpaces (Collaborative Intelligence Spaces) decision support tool in collaboration with professional intelligence analysts. CISpaces combines multiple AI-based methods including argumentation theory, crowdsourced Bayesian analysis, and provenance recording. We show that CISpaces is able to provide support to analysts by facilitating the interpretation of different types of evidence through argumentation-based reasoning, provenance analysis and crowdsourcing. We undertook an experimental analysis with intelligence analysts which highlights three key points. (1) The novel, principled AI methods implemented in CISpaces advance performance in intelligence analysis. (2) While designed as a research prototype (at TRL 3), analysts benchmarked it against their existing software tools, and we provide results suggesting intention to adopt CISpaces in analysts’ daily activities. (3) Finally, the evaluation highlights some drawbacks in CISpaces. However, these are not due to the technologies underpinning the tool, but rather its lack of integration with existing organisational standards regarding input and output formats. Our evaluation with intelligence analysts therefore demonstrates the potential impact that an integrated tool building on state-of-the-art AI techniques can have on the process of understanding complex situations, and on how such a tool can help focus human effort on identifying more credible interpretations of evidence. Publisher PDF

Experiential learning (ExL) is the process of learning through experience or more specifically "learning through reflection on doing". In this paper, we propose a simulation of these experiences, in Augmented Reality (AR), addressing the problem of language learning. Such systems provide an excellent setting to support "adaptive guidance", in a digital form, within a real environment. Adaptive guidance allows the instructions and learning content to be customised for the individual learner, thus creating a unique learning experience. We developed an adaptive guidance AR system for language learning, we call Arigatō (Augmented Reality Instructional Guidance & Tailored Omniverse), which offers immediate assistance, resources specific to the learner's needs, manipulation of these resources, and relevant feedback. Considering guidance, we employ this prototype to investigate the effect of the amount of guidance (fixed vs. adaptive-amount) and the type of guidance (fixed vs. adaptive-associations) on the engagement and consequently the learning outcomes of language learning in an AR environment. The results for the amount of guidance show that compared to the adaptive-amount, the fixed-amount of guidance group scored better in the immediate and delayed (after 7 days) recall tests. However, this group also invested a significantly higher mental effort to complete the task. The results for the type of guidance show that the adaptive-associations group outperforms the fixed-associations group in the immediate, delayed (after 7 days) recall tests, and learning efficiency. The adaptive-associations group also showed significantly lower mental effort and spent less time to complete the task., 11 pages, 2 figures, 2 tables

Funding: This research was supported by European Commission through the InnoRenew CoE project (Grant Agreement 739574) under the Horizon2020 Widespread-Teaming program and the Republic of Slovenia (investment funding of the Republic of Slovenia and the European Union of the European Regional Development Fund). We also acknowledge support from the Slovenian research agency ARRS (program no. BI-DE/20-21-002, P1-0383, J1-9186, J1-1715, J5-1796, and J1-1692). Learning vocabulary in a primary or secondary language is enhanced when we encounter words in context. This context can be afforded by the place or activity we are engaged with. Existing learning environments include formal learning, mnemonics, flashcards, use of a dictionary or thesaurus, all leading to practice with new words in context. In this work, we propose an enhancement to the language learning process by providing the user with words and learning tools in context, with VocabulARy. VocabulARy visually annotates objects in AR, in the user's surroundings, with the corresponding English (first language) and Japanese (second language) words to enhance the language learning process. In addition to the written and audio description of each word, we also present the user with a keyword and its visualisation to enhance memory retention. We evaluate our prototype by comparing it to an alternate AR system that does not show an additional visualisation of the keyword, and, also, we compare it to two non-AR systems on a tablet, one with and one without visualising the keyword. Our results indicate that AR outperforms the tablet system regarding immediate recall, mental effort and task-completion time. Additionally, the visualisation approach scored significantly higher than showing only the written keyword with respect to immediate and delayed recall and learning efficiency, mental effort and task-completion time. Postprint

This research was supported by NSERC (The Natural Sciences and Engineering Research Council of Canada) RGPIN-2020-04401 and EPSRC (Engineering and Physical Sciences Research Council) EP/T518062/1. Essay-writing is a complex, cognitively demanding activity. Essay-writers must synthesise source texts and original ideas into a textual essay. Previous work found that writers produce better essays when they create effective intermediate representations. Diagrams, such as concept maps and argument maps, are particularly effective. However, there is insufficient knowledge about how people use these intermediate representations in their essay-writing workflow. Understanding these processes is critical to inform the design of tools to support workflows incorporating intermediate representations. We present the findings of a study, in which 20 students planned and wrote essays. Participants used a tool that we developed, Write Reason, which combines a free-form mapping interface with an essay-writing interface. This let us observe the types of intermediate representations participants built, and crucially, the process of how they used and moved between them. The key insight is that much of the important cognitive processing did not happen within a single representation, but instead in the processes that moved between multiple representations. We label these processes `representational transformations'. Our analysis characterises key properties of these transformations: cardinality, explicitness, and change in representation type. We also discuss research questions surfaced by the focus on transformations, and implications for tool designers. Publisher PDF

Hamblin distinguished between formal and descriptive dialectic. Formal normative models of deliberation dialogue have been strongly emphasized as argumentation frameworks in computer science. But making such models of deliberation applicable to real natural language examples has reached a point where the descriptive aspect needs more interdisciplinary work. The new formal and computational models of deliberation dialogue that are being built in computer science seem to be closely related to some already existing and very well established computing technologies such as problem solving and decision making, but whether or how dialectical argumentation can be helpful to support these systems remains an open question. The aim of this paper is to examine some real examples of argumentation that seem to hover on the borderlines between deliberation, problem solving and decision making.

Oral immunotherapy (OIT) is a promising treatment for food allergy (FA) however it is a challenging process for patients and parents. Induction can generate stress and anxiety. This may in turn affect their motivation and ability to cope with OIT challenges.This study aimed to investigate psychological needs and support to patients/parents undergoing food OIT assessing participants' main characteristics, reasons for seeking psychological support, OIT phase and related psychological difficulties, type and timing of treatments and patients' perception of the effectiveness of the intervention.This is an observational, retrospective study. 50 psychological interventions required for OIT related problems were selected consecutively in a Referral Centre in North-Eastern Italy. All patients had a medical diagnosis of FA and were undergoing OIT or had just discontinued it. Data were collected from hospital records. A descriptive statistical analysis was performed.66% of patients asked for psychological support for the initial phase (e.g., oral food challenge, first maintenance doses), 20% during the up-dosing phase, 8% during maintenance and 6% after discontinuation. 70% of treatments were required mainly because of emotional problems including dysfunctional anxiety and mood disorders, increased distress and excessive worry and/or fear related to OIT; 20% because of difficulties in managing OIT; 10% because of eating difficulties; 50% of patients reported recent anaphylaxis. All patients reported improvement and felt the psychological intervention was helpful.It is recommended to evaluate the psychological needs in profiling patients and families suitable to OIT and offer specific psychological support when needed.

We present an argumentation model to explain the optimal treatment plans recommended by a Satisfiability Modulo Theories solver for multimorbid patients. The resulting framework can be queried to obtain supporting reasons for nodes on a path following a model of argumentation schemes. The modelling approach is generic and can be used for justifying similar sequences. Postprint

SMT solvers can be used efficiently to search for optimal paths across multiple graphs when optimising for certain resources. In the medical context, these graphs can represent treatment plans for chronic conditions where the optimal paths across all plans under consideration are the ones which minimize adverse drug interactions. The SMT solvers, however, work as a black-box model and there is a need to justify the optimal plans in a human-friendly way. We aim to fulfill this need by proposing explanatory dialogue protocols based on computational argumentation to increase the understanding and trust of humans interacting with the system. The protocols provide supporting reasons for nodes in a path and also allow counter reasons for the nodes not in the graph, highlighting any potential adverse interactions during the dialogue. Postprint

Funding: This research is supported by the Brazilian National Council of Technological and Scientific Development (CNPq) and the National Research Foundation, Prime Minister’s Office, Singapore under its International Research Centres in Singapore Funding Initiative. Public, parliamentary and television debates are commonplace in modern democracies. However, developing an understanding and communicating with others is often limited to passive viewing or, at best, textual discussion on social media. To address this, we present the design and implementation of Deb8, a tool that allows collaborative analysis of video-based TV debates. The tool provides a novel UI designed to enable and capture rich synchronous collaborative discussion of videos based on argumentation graphs that link quotes of the video, opinions, questions, and external evidence. Deb8 supports the creation of rich idea structures based on argumentation theory as well as collaborative tagging of the relevance, support and trustworthiness of the different elements. We evaluated the design of the tool in a study of three groups of three people. We present the results of the study and a reflection on the challenges involved. Postprint

To enable reliable crowdsourcing applications, it is of great importance to develop algorithms that can automatically discover the truths from possibly noisy and conflicting claims provided by various information sources. In order to handle crowdsourcing applications involving big or streaming data, a desirable truth discovery algorithm should not only be effective , but also be scalable . However, with respect to quantitative crowdsourcing applications such as object counting and percentage annotation, existing truth discovery algorithms are not simultaneously effective and scalable. They either address truth discovery in categorical crowdsourcing or perform batch processing that does not scale. In this paper, we propose new parallel and streaming truth discovery algorithms for quantitative crowdsourcing applications. Through extensive experiments on real-world and synthetic datasets, we demonstrate that 1) both of them are quite effective, 2) the parallel algorithm can efficiently perform truth discovery on large datasets, and 3) the streaming algorithm processes data incrementally, and it can efficiently perform truth discovery both on large datasets and in data streams.

Truth discovery is an important technique for enabling reliable crowdsourcing applications. It aims to automatically discover the truths from possibly conflicting crowdsourced claims. Most existing truth discovery approaches focus on categorical applications, such as image classification. They use the accuracy, i.e., rate of exactly correct claims, to capture the reliability of participants. As a consequence, they are not effective for truth discovery in quantitative applications, such as percentage annotation and object counting, where similarity rather than exact matching between crowdsourced claims and latent truths should be considered. In this paper, we propose two unsupervised Quantitative Truth Finders (QTFs) for truth discovery in quantitative crowdsourcing applications. One QTF explores an additive model and the other explores a multiplicative model to capture different relationships between crowdsourced claims and latent truths in different classes of quantitative tasks. These QTFs naturally incorporate the similarity between variables. Moreover, they use the bias and the confidence instead of the accuracy to capture participants’ abilities in quantity estimation. These QTFs are thus capable of accurately discovering quantitative truths in particular domains. Through extensive experiments, we demonstrate that these QTFs outperform other state-of-the-art approaches for truth discovery in quantitative crowdsourcing applications and they are also quite efficient.

In human interactions, trust is regularly updated during a discussion. For example, if someone is caught lying, any further utterances they make will be discounted, until trust is regained. This paper seeks to model such behaviour by introducing a dialogue game which operates over several iterations, with trust updates occurring at the end of each iteration. In turn, trust changes are computed based on intuitive properties, captured through three rules. By representing agent knowledge within a preference-based argumentation framework, we demonstrate how trust can change over the course of a dialogue. Postprint

The conclusions drawn from a dialogue depend both on the content of the arguments, and the level of trust placed in the arguments and the entity advancing them. In this paper, we describe a framework for dialogue where such trust forms the basis for expressing preferences between arguments, and in turn, for computing conclusions of the dialogue. Our framework contains object and meta-level arguments, and uses ASPIC+ to represent arguments, while argument schemes capture meta-level arguments about trust and preferences.

This paper seeks to better understand the links between human reasoning and preferred extensions as found within formal argumentation, especially in the context of uncertainty. The degree of believability of a conclusion may be associated with the number of preferred extensions in which the conclusion is credulously accepted. We are interested in whether people agree with this evaluation. A set of experiments with human participants is presented to investigate the validity of such an association. Our results show that people tend to agree with the outcome of a version of Thimm’s probabilistic semantics in purely qualitative domains as well as in domains in which conclusions express event likelihood. Furthermore, we are able to characterise this behaviour: the heuristics employed by people in understanding preferred extensions are similar to those employed in understanding probabilities. Postprint

Collaborative design is an iterative process of selecting and evaluating solutions in the presence of several potentially conflicting requirements dictated by different professional expertise and preferences, a concept central to Building Information Modelling (BIM) implementation. In previous research, we showed that the core decision process can be better understood using argumentation-based models of practical reasoning. This allows the identification of the rationale and the relationships between conflicting issues, thus providing clarity and transparency for decisions achieved. This paper aims to take one step further in modelling this process, and suggests a novel methodological approach that translates collaborative, conceptual and perceptual activities related to design processes undertaken by design and construction professionals to correspondent argumentative dialogue types such as information seeking, deliberation and negotiation. Computational models of dialogue identify protocols for collaboration establishing reasons and evidence of claims made and joint identification of goals and solutions. Therefore, the proposed argumentative dialogue permits the formalisation of collaboration analysis, design decision steps can be monitored and the dependency relationships between professionals involved and decisions taken can be traced. Future research would develop a computational model to be employed by intelligent software agents to assist professionals in analysing complex collaborative decisions.

Aim/hypothesis: Monocytes/macrophages play important roles in adipose and vascular tissues and can be polarised as inflammatory M1 or anti inflammatory M2. We sought to analyse monocyte polarisation status in type 2 diabetes which is characterised by chronic inflammation. Methods: We enrolled 60 individuals without diabetes and 53 patients with type 2 diabetes. We quantified standard monocyte subsets defined by cluster of differentiation (CD)14 and CD16. In addition based on the phenotype of polarised macrophages in vitro we characterised and quantified more definite M1 (CD68+CCR2+) and M2 (CX3CR1+CD206+/CD163+) monocytes. We also analysed bone marrow (BM) samples and the effects of granulocyte colony stimulating factor (G CSF) stimulation in diabetic and control individuals. Results: We found no alterations in standard monocyte subsets (classical intermediate and non classical) when comparing groups. For validation of M1 and M2 phenotypes we observed that M2 were enriched in non classical monocytes and had lower TNF a content higher LDL scavenging and lower transendothelial migratory capacity than M1. Diabetic patients displayed an imbalanced M1/M2 ratio compared with the control group attributable to a reduction in M2. The M1/M2 ratio was directly correlated with waist circumference and HbA 1c and among diabetic patients M2 reduction and M1/M2 increase were associated with microangiopathy. A decrease in M2 was also found in the BM from diabetic patients with a relative M2 excess compared with the bloodstream. BM stimulation with G CSF mobilised M2 macrophages in diabetic but not in healthy individuals. Conclusions/interpretation: We show that type 2 diabetes markedly reduces anti inflammatory M2 monocytes through a dysregulation in bone marrow function. This defect may have a negative impact on microangiopathy. © 2013 Springer Verlag Berlin Heidelberg.

This research was partially supported by Social Sciences and Humanities Research Council of Canada Insight Grant 435-2012-0104. This research was also partially supported by the award made by the RCUK Digital Economy program to the dot.rural Digital Economy Hub at the University of Aberdeen; award ref. : EP/G066051/1. Further refinements of this work were supported by the SICSA PECE scheme. Models of deliberative dialogue are fundamental for developing autonomous systems that support human practical reasoning. The question discussed in this paper is whether existing models are able to capture the complexity and richness of natural deliberation. In real-world contexts, circumstances relevant to the decision can change rapidly. We reflect on today's leading model of deliberation dialogue and we propose an extension to capture how newly exchanged information about changing circumstances may shape the dialogue.Moreover, in natural deliberation, a dialogue may be successful even if a decision on what to do has not been made. A set of criteria is proposed to address the problem of when to close off the practical reasoning phase of dialogue. We discuss some measures for evaluating the success of a dialogue after closure and we present some initial efforts to introduce the new deliberation features within an existing model of agent dialogue. We believe that our extended model of dialogue may contribute to representing that richness of natural deliberative dialogue that is yet to be addressed in existing models of agent deliberation. Publisher PDF

Background Food allergy is major public health concern affecting nearly 15 million Americans and 80 million Europeans. Risk of anaphylaxis and implications for social activities affect patients’ quality of life and psychological wellbeing. We previously found that young patients reported higher levels of alexithymia (difficulty in recognizing and expressing emotions) compared to healthy peers, and may influence affect, management style and clinical outcomes. This study aimed to explore links between coping strategies, alexithymia and anxiety among food-allergic adolescents and young adults. Methods 92 patients with IgE-mediated food allergy (Mean age 18.6 years) completed Coping Orientation to Problems Experienced Inventory, Toronto Alexithymia Scale and Trait Anxiety subscale of State-Trait Anxiety Inventory. Multivariate Analyses of Variance assessed differences and associations between subgroups on the scales. Results Significant differences found between alexithymia levels in coping style were explained by Avoidance strategies. “Avoidance” had the highest contribution in explaining alexithymia, followed by trait-anxiety, age, anaphylaxis, and social support. Respondents with higher alexithymia use avoidance as coping strategy over and above other coping strategies such as problem solving and positive thinking, are younger, will have experienced anaphylaxis, and will have lower social support. Conclusions Recognizing the specific role of affect regulation in health behaviours may constitute an important step in supporting patients to explore more adaptive strategies. This article is protected by copyright. All rights reserved.

Background Inappropriate dietary eliminations may impair quality of life, affect children's growth and unnecessarily impact on healthcare costs. Previous retrospective studies reported that around 25% of children continue a food-avoidance diet despite a negative oral food challenge (OFC). A definite pattern has not been found yet for patients not reintroducing the food. This study aimed to examine the role of child's nutritional attitudes and maternal anxiety in reintroducing food after a negative OFC. Methods A prospective study was conducted involving 81 mothers of children with IgE-mediated food allergy. They completed a survey on nutritional behaviour and attitudes and the State-Trait Anxiety Inventory on the day of OFC and 6 months later. Results In total, 11.1% of children never or rarely ate the food after a negative OFC. Consumption of the reintroduced food is positively correlated to child's interest in tasting new foods before and after OFC and to changes in child's nutritional habits after OFC. It is negatively correlated to monotony of the diet after OFC. No correlations were found with other participants’ characteristics or maternal anxiety. State anxiety significantly decreased after the OFC. A correlation was found between trait and state anxiety and the degree of change in nutritional habits after OFC. Conclusions Evaluating child's approach towards food before the OFC is a promising approach to identify patients at risk of food reintroduction failure. Furthermore, it underlined the importance of reassessing food consumption in all patients after a negative OFC and supporting patients in the reintroduction of food.

The early design stages are the most vital for the development of the design of a building project and the decisions taken during these steps are significant for the further progress of the project, regarding aspects like cost, performance, reliability and sustainability. Being able to trace backwards the thoughts that led to these decisions at any point of the design process provides clarity and transparency during the decision making process. Additionally, monitoring the design steps and decisions can lead to further observations on the effects of the design decisions during the early design stage, thus leading to a more sustainable and holistic design approach. This paper claims that efficient monitoring of the decision steps can be achieved employing a computational model of argumentation. Argumentation-based reasoning helps identify the rationale for a decision (i.e. arguments) and the relationships (i.e. attacks and preferences) between conflicting issues involved in making decisions. Our approach provides a method to rigorously trace the resolution of conflicts by extracting the set of acceptable arguments that led to a decision, and may eventually assist designers in analysing complex collaborative decisions within a pre-Building Information Modelling context. This paper was presented at the International Conference on Building Information Modelling (BIM) in Design, Construction and Operations, held in Bristol, 9-11 September 2015.

Computational trust mechanisms aim to produce trust ratings from both direct and indirect information about agents' behaviour. Subjective Logic (SL) has been widely adopted as the core of such systems via its fusion and discount operators. In recent research we revisited the semantics of these operators to explore an alternative, geometric interpretation. In this paper we present principled desiderata for discounting and fusion operators in SL. Building upon this we present operators that satisfy these desirable properties, including a family of discount operators. We then show, through a rigorous empirical study, that specific, geometrically interpreted, operators significantly outperform standard SL operators in estimating ground truth. These novel operators offer real advantages for computational models of trust and reputation, in which they may be employed without modifying other aspects of an existing system.

Specific reactive oxygen species (ROS) from different sources, might lead to different and even opposite, cellular effects. We studied the production of specific ROS resulting from the exposure of human umbilical veins endothelial cells (HUVEC) to H2O2 derived from the natural antioxidant epigallocathechin gallate (EGCG) or from the exposure to IL-1β using a fluorogenic probe and flow cytometry, and evaluated by western blot analysis and immunocytochemistry the associated expression of transcription factors sensitive to both inflammatory and oxidative stress, such as NF-κB and Nrf2, and some downstream activated genes such as cyclooxygenase-2 (COX-2) and hemeoxygenase 1 (HO-1). The results obtained showed that exogenously-generated H2O2 induce anti-inflammatory and antioxidant effects in HUVECs counteracting the pro-inflammatory and pro-oxidant effect of IL-1β related to the production of superoxide anions. The underlying mechanisms resulting from the extracellular production of H2O2, include (1) Nrf2 nuclear translocation and the enhanced expression of antioxidant enzymes such as HO-1, and (2) the previously unreported inhibition of NF-κB and COX-2 expression. Overall, these findings provide evidence that the production of specific reactive oxygen species finely tunes endothelial cell function and might be relevant for the reappraisal of the effects of exogenous antioxidants in the context of cardiovascular diseases.

The ubiquity of smartphones has led to the emergence of mobile crowdsourcing tasks such as the detection of spatial events when smartphone users move around in their daily lives. However, the credibility of those detected events can be negatively impacted by unreliable participants with low-quality data. Consequently, a major challenge in mobile crowdsourcing is truth discovery, i.e., to discover true events from diverse and noisy participants’ reports. This problem is uniquely distinct from its online counterpart in that it involves uncertainties in both participants’ mobility and reliability . Decoupling these two types of uncertainties through location tracking will raise severe privacy and energy issues, whereas simply ignoring missing reports or treating them as negative reports will significantly degrade the accuracy of truth discovery. In this paper, we propose two new unsupervised models, i.e., Truth finder for Spatial Events (TSE) and Personalized Truth finder for Spatial Events (PTSE), to tackle this problem. In TSE, we model location popularity, location visit indicators, truths of events, and three-way participant reliability in a unified framework. In PTSE, we further model personal location visit tendencies. These proposed models are capable of effectively handling various types of uncertainties and automatically discovering truths without any supervision or location tracking. Experimental results on both real-world and synthetic datasets demonstrate that our proposed models outperform existing state-of-the-art truth discovery approaches in the mobile crowdsourcing environment.

During their reproductive years, women are generally protected from cardiovascular disease events by their estrogen-replete status. Our starting hypothesis was that lower estrogen levels after menopause are associated with macrophage activation profiles skewed toward proinflammatory phenotypes. Research Design and Setting: This was an in vitro and ex vivo study in human blood-derived macrophages.We obtained blood from 12 healthy male donors for the in vitro study and from 5 premenopausal and 8 postmenopausal women for the ex vivo study.We measured macrophage immunophenotypes in the resting state and after activation with M1-associated (lipopolysaccharide [LPS]/interferon-γ [IFN-γ]) or M2-associated (IL-4/IL-13) stimuli and expression of estrogen receptors (ERs) and other transcription factors.Unpolarized macrophages expressed both ERα and ERβ, and ERα but not ERβ levels were decreased by M1 stimuli. LPS/IFN-γ also induced down-regulation of CD163 and CD206, markers of alternative activation, and increased cell-bound TNF-α and IL-10. These effects were prevented by 17β-estradiol treatment through impaired nuclear factor-κB liberation. In agreement with a role for 17β-estradiol in attenuating the inflammatory response, M1/M2 subpopulations in monocytes and unstimulated macrophages from premenopausal and postmenopausal donors were similar. In contrast, M2 activation appeared to be blunted in macrophages from postmenopausal women, leading to an increased M1/M2 response ratio.Estrogen treatment prevented LPS/IFN-γ action on human M2 macrophage markers and cytokine production, whereas menopausal estrogen loss was associated with an impaired response to alternative activation, suggesting that these mechanisms affect the cardiovascular risk profile in relation to menopausal status.

Methods Among patients (pts) who attended the Food Allergy Centre in Padua in the last 16 months, 44 cow’s milk (CM) and 60 egg (E) allergic pts were recruited. Median age was 9 yrs for CM and 8 yrs for E pts. All pts had reported severe allergic reactions (anaphylaxis), were on a restricted diet for CM or E, and had been prescribed self-injectable epinephrine. All pts underwent an oral food challenge (OFC) for milk/egg before entering the study, to define the threshold of tolerated protein dose. Median CM protein dose was 0,031 g (range 0,00042-1,65); median E protein dose was 0,49 g (range 0,015-3). Up-dosing was scheduled at a 4-6 week intervals and performed in the hospital, under medical supervision. After discharge, pts were requested to daily consume the new tolerated dose for at least one month before the next up-dosing step. Any adverse event or symptom appearing at home could be reported through a 24-hour on-call service. Specific IgE were screened at recruitment: mean value 7.7 kU/L for casein, 3.8kU/L for egg white and 2,3kU/L for ovomucoid. Another blood sample has been scheduled after 12 months to evaluate a change in specific IgE levels. Specific IgG4 to milk/egg proteins will also be screened in both samples.

Methods We observed 54 children (mean age 8,2±4,5) with CMA which had at least one dietetic evaluation and received dietary support to adequately integrate their diet, and 54 children (mean age 8,4±4,7) with no dietary restrictions and no dietary intervention. The assessment of nutrient intakes (energy, proteins, lipids, carbohydrates, fibre, calcium, iron) was based on a 7-day food diary. All values were compared to the Italian Recommended Daily Allowance (RDA). Children were divided by age groups: 1-5years (1), 6-10years (2), 11-18years (3). Nutritional status was assessed by measuring weight and height related to the standard for age and sex.

Background Cow’s milk allergy affects about 2-3 % of general population; the prevalence of egg allergy is 1-2% among childhood. According to the literature the development of tolerance for cow’s milk and for egg is reached in early childhood, in the majority of case. In addiction it is reported that over six years of age there is a significant decrease in the resolution rate both for cow’s milk and egg allergy.

Background Avoidance of food allergens requires adapting dietetic habits, changing nutritional approach. A restriction of food choice can result in a monotonous diet and impact social life. This study investigated the impact of food allergy on nutritional behavior and attitudes of patients and their families. Methods A survey involving mothers of food allergic children aged 0–16 years was carried out. We primarily studied the variables related to the child (age, gender, clinical history, food and social events attitudes). In addition, Spielberg Trait-Anxiety Inventory (STAI-T) test was applied to the mothers. We assessed separately the associations between characteristics of child-mother pairs and diet monotony, and attendance to social events, by means of proportional odds regression models. Results Nearly 10% of the 124 participants completely banned allergenic foods at home and 15.3% consumed their meals separately. More than one fourth attended parties rarely or never. Most of the participants reported a “monotonous diet”. Model results suggested significant associations between child age (p = 0.05), mother age (p = 0.05), number of excluded foods (p = 0.003) and monotony of the diet. The attendance of social events was inversely associated with the number of excluded foods (p = 0.04) and the mother’s STAI-T T-score (p = 0.04). Conclusions The results highlighted the impact of food allergy in reducing interest about food and influencing patients’ approach to social life. It is important to support families in managing allergens avoidance.

Background The incidence of food allergy is such that most schools will be attended by at least one food allergic child, obliging school personnel to cope with cases at risk of severe allergic reactions. Schools need to know about food allergy and anaphylaxis management to ensure the personal safety of an increasing number of students. The aim of this study was to investigate Italian school teachers and principals’ knowledge, perceptions and feelings concerning food allergy and anaphylaxis, to deeply understand how to effectively support schools to manage a severely allergic child. In addition a further assessment of the impact of multidisciplinary courses on participants was undertaken. Methods 1184 school teachers and principals attended courses on food allergy and anaphylaxis management at school were questioned before and after their course. Descriptive and inferential statistics were used to analyze the resulting data. Results Participants tended to overestimate the prevalence of food allergy; 79.3% were able to identify the foods most likely involved and 90.8% knew the most frequent symptoms. 81.9% were familiar with the typical symptoms of anaphylaxis but, while the majority (65.4%) knew that “adrenaline” is the best medication for anaphylaxis, only 34.5% knew indications of using adrenaline in children. 48.5% thoroughly understood dietary exclusion. School personnel considered that food allergic students could have social difficulties (10.2%) and/or emotional consequences (37.2%) because of their condition. “Concern” was the emotion that most respondents (66.9%) associated with food allergy. At the end of the course, the number of correct answers to the test increased significantly. Conclusions Having adequately trained and cooperative school personnel is crucial to significantly reduce emergencies and fatal reactions. The results emphasize the need for specific educational interventions and improvements in school health policies to support schools to deal with allergic students ensuring their safety and psychological well-being.

In this paper we provide a first analysis of the research questions that arise when dealing with the problem of communicating pieces of formal argumentation through natural language interfaces. It is a generally held opinion that formal models of argumentation naturally capture human argument, and some preliminary studies have focused on justifying this view. Unfortunately, the results are not only inconclusive, but seem to suggest that explaining formal argumentation to humans is a rather articulated task. Graphical models for expressing argumentation-based reasoning are appealing, but often humans require significant training to use these tools effectively. We claim that natural language interfaces to formal argumentation systems offer a real alternative, and may be the way forward for systems that capture human argument., Comment: 17 pages, 4 figures, technical report

The aim of intelligence analysis is to make sense of information that is often conflicting or incomplete, and to weigh competing hypotheses that may explain a situation. This imposes a high cognitive load on analysts, and there are few automated tools to aid them in their task. In this paper, we present an agent-based tool to help analysts in acquiring, evaluating and interpreting information in collaboration with others. Agents assist analysts in reasoning with different types of evidence to identify what happened and why, what is credible, and how to obtain further evidence. Argumentation schemes lie at the heart of the tool, and sense-making agents assist analysts in structuring evidence and identifying plausible hypotheses. A crowdsourcing agent is used to reason about structured information explicitly obtained from groups of contributors, and provenance is used to assess the credibility of hypotheses based on the origins of the supporting information.

We address the collaborative planning problem among agents where they have different objectives and norms. In this context, agreeing on the best course of action to adopt represents a significant challenge. Concurrent actions and causal plan-constraints may lead to conflicts of opinion on what to do. Moreover, individual norms can constrain agent behaviour. We propose an argumentation-based model for deliberative dialogues based on argumentation schemes. This model facilitates agreements about joint plans by enriching the quality of the dialogue through the exchange of relevant information about plan commitments and norms.

Dialogue has long been used in human society to explain seemingly opaque concepts. In this paper we focus on how to better explain training models for neural networks, to entertain as well as inform. We present a multi-agent argumentation-based dialogue system to generate human understandable dialogue to explain backpropagation. The system incorporates a model of agent personality and introduces social elements between agents to produce characterful discussion. Natural language templates are used to render utterances in English. Publisher PDF

Models of deliberative dialogue are fundamental for developing autonomous systems that support human practical reasoning. However, we must consider whether these existing models are able to capture the complexity and richness of natural deliberation for developing real applications. In real contexts, circumstances relevant to the decision can change rapidly. In this paper, we introduce an extension to today's leading model of deliberation dialogue to capture dynamic changes of circumstances during dialogue. Moreover, in natural deliberation, a dialogue may be successful even if a decision on what to do has not been made. A set of criteria is proposed to address the problem of when to close o the practical reasoning phase of dialogue. We then discuss some initial efforts to introduce those characteristics within an existing model of deliberation for agent collaboration. We believe that our extended model of dialogue may represent that richness of natural deliberative dialogue that is yet to be addressed in existing models of agent deliberation

Background A 6 year old boy came to our attention reporting two recent episodes of diffuse urticaria and eyelid and lip swelling, accompanied by a burning feeling of the oral mucosa and tingling of the throat. In the first episode symptoms appeared after ingestion of a dessert homemade by his grandma: a layer of dough (wheat flour, water and lard) filled with soft cheese and lemon juice, fried in peanut oil and covered by honey. In the second episode, again at his grandma’s, symptoms had a more rapid onset, after the very first bite of a compound made of cheese and parsley. From the age of 6 months the boy had had mild atopic dermatitis; at the age of 3 years he had undergone SPT which were positive for egg white, peanut and grass pollen. Since then he had been on a diet free of egg, peanut and tree nuts; he had recently started to eat baked products containing egg with no symptoms. The boy was commonly eating lemon juice, honey, parsley as well as cow’s milk and its derivates.

The results showed school-aged children are significantly less interested in tasting new foods (p