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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

PTDC/FER-FIL/28278/2017 UIDB/00183/2020 UIDP/00183/2020 Douglas Walton’s work is extremely vast, multifaceted, and interdisciplinary. He developed theoretical proposals that have been used in disciplines that are not traditionally related to philosophy, such as law, education, discourse analysis, artificial intelligence, or medical communication. Through his papers and books, Walton redefined the boundaries not only of argumentation theory, but also logic and philosophy. He was a philosopher in the sense that his interest was developing theoretical models that can help explain reality, and more importantly interact with it. For this reason, he proposed methods that have been used for analyzing different types of dialogical interactions, and modeling procedures for regulating them. publishersversion published

The ability to understand, process and evaluate arguments made by others and ourselves is important in many personal and professional spheres, such as political debates. Analysis typically appears in written form, but a growing number of tools support analysis through diagram-based graphical representations. These UIs might support better argument analysis because arguments have non-linear structures that are difficult to convey through linear text. However, there is little empirical evidence on the advantages or mechanisms that might make graph UIs superior to traditional textual documents. We ran and analyzed a study with twenty participants who used text and graph editors to analyze political debates. Our findings demonstrate the tradeoffs between the two approaches and explain key mechanisms that support the analysis in both media.

BACKGROUND Alexithymia is a multifaceted personality construct described as "a lack of words to express emotions" that includes difficulty identifying feelings, difficulty describing feelings and a concrete, externally oriented cognitive style. Among patients with food allergy (FA) alexithymia has been found higher than in general population and associated with dysfunctional coping strategies. Childhood traumatic experiences and/or dysfunctional parent-child relationship can contribute to alexithymia. The study aimed to explore possible associations between child alexithymia and mother alexithymia and anxiety, as well as previous anaphylaxis. METHODS Sixty-five mother-child pairs have been recruited. Children had a medical diagnosis of FA. The Toronto Alexithymia Scale-20 was used to assess alexithymia in participants. The State-Trait Anxiety Inventory was used to assess trait anxiety in mothers. Statistical analysis was performed to investigate the influence of variables sex, previous anaphylaxis, and adrenaline auto-injector prescription on child and mother's alexithymia and to explore possible association between maternal and child alexithymia. RESULTS A substantial percentage of children were alexithymic (21.5%) or borderline (33.8%), while mothers' frequencies were lower and in line with prevalence in general population. Child alexithymia was positively associated with previous anaphylaxis and mothers' trait anxiety (p

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

Background Food allergy is a common immunological disease that includes potentially fatal reactions. It impacts considerably on patients’ social life including close interpersonal relationships. Attachment theory provides a theoretical framework to evaluate the quality of close interpersonal relationships in chronic disorders. Attachment insecurity, mainly characterized by attachment avoidance, has been found in a variety of health conditions, but still needs to be investigated in food allergy. The study aimed to investigate attachment, as attitude to close interpersonal relationships, among food-allergic young patients, compared to healthy controls. Methods This is a cross-sectional study involving patients suffering from IgE-mediated food allergy sequentially recruited and matched to healthy controls for age and gender. The Attachment Style Questionnaire (ASQ) was used to assess five factors and two attachment dimensions (Anxiety-Avoidance). Associations with anaphylaxis and adrenaline prescription were explored among patients. Results 174 participants were assessed (female=45%; Mean age=17.51; SD=4.26). Food-allergic patients reported significantly higher levels of Discomfort with closeness (p

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.

Alexithymia is a multifaceted personality construct associated with several medical illnesses. However, no studies have focused on food allergy. We investigated alexithymia among food-allergic youths, compared to a matched healthy sample. A total of 220 participants aged 9–25 years were assessed using the Toronto Alexithymia Scale and the Alexithymia Questionnaire for Children. Food-allergic patients reported higher levels of alexithymia compared to a matched healthy sample. Furthermore, an association between alexithymia and a clinical history of anaphylaxis was found among patients. Some possible hypotheses have been discussed considering physiological, psychological, developmental, and cognitive/behavioral factors.

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.

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.

Maternal stress in fetal and early life has been associated with the development of respiratory allergies, but no studies exist about food allergy. Stressful events and the quality of caregiving provided, as they affect the emotional and physiologic regulation of the infant, could alter the hypothalamic-pituitary-adrenal and immune system, facilitating an increased allergic response. This study aimed to investigate the influence of perinatal stress, as perceived by mothers, on developing food allergy in childhood. A survey on pregnancy and the first three months after giving birth was submitted to 59 Italian mothers of at least one child suffering from severe food allergy and one completely healthy child, for a total of 118 children examined. The presence of stressful events and the quality of perinatal period for each child were assessed retrospectively. The food allergic children's data were compared to siblings' data through inferential statistics. The results showed a significantly higher number of stressful events occurred during patients' perinatal period, compared to siblings, in particular bereavements in pregnancy and parenting difficulties in postpartum. Mothers reported harder pregnancies and more stressful, harder, and, in general, worse postpartum when referring to their food-allergic children, in comparison with their siblings (p

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.

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.

BACKGROUND Food allergy affects up to 4-7% European schoolchildren. Studies identified important shortcomings on food allergy and anaphylaxis management in schools. In social cognitive theory, personal beliefs in own capabilities influence choices, effort levels, perseverance and performance accomplishments. This study aimed to investigate school personnel's self-efficacy in managing food allergy and anaphylaxis, providing a valid instrument to deeply understand how to support schools to effectively manage students at risk of food reactions. METHODS A total of 440 schoolteachers and caretakers from north-east Italy completed a questionnaire assessing self-efficacy in managing food allergy and anaphylaxis at school. Exploratory factor analysis was performed. Factors' internal consistency was evaluated using Cronbach's alpha. Factors' scores were estimated using Bartlett approach, and kernel density estimate of distributions was provided. Descriptive statistics explored school personnel's self-efficacy. A regression model assessed the influence of gender, school, job and previous experience. RESULTS Two factors emerged from exploratory factor analysis related to anaphylaxis management (AM) and food allergy management (FAM). The two subscales both showed good internal consistency. School personnel showed lower self-efficacy in recognizing symptoms, administering drugs and guaranteeing full participation to extra-curricular activities to food-allergic students. Participants who previously had food-allergic students showed a significantly increased self-efficacy in AM and a significantly decreased self-efficacy in FAM. CONCLUSIONS The study supports the use of self-efficacy scale to identify specific areas where teachers' confidence in their ability to care for food-allergic students is especially weak. This would empower the development of training programs specifically tailored to the needs of teachers and caregivers.

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.

Information about quantitative characteristics in local businesses and services, such as the number of people waiting in line in a cafe and the number of available fitness machines in a gym, is important for informed decision, crowd management and event detection. In this paper, we investigate the potential of leveraging crowds as sensors to report such quantitative characteristics and investigate how to recover the true quantity values from noisy crowdsourced information. Through experiments, we find that crowd sensors have both bias and variance in quantity sensing, and task difficulties impact the sensing accuracy. Based on these findings, we propose an unsupervised probabilistic model to jointly assess task difficulties, ability of crowd sensors and true quantity values. Our model differs from existing categorical truth finding models as ours is specifically designed to tackle quantitative truth. In addition to devising an efficient model inference algorithm in a batch mode, we also design an even faster online version for handling streaming data. Experimental results in various scenarios demonstrate the effectiveness of our model.

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.

In this study, four prenylated and geranylated flavonoids, cudraflavone B (1), pomiferin (2), osajin (3), and diplacone (4), were tested for their antioxidant and anti-inflammatory effects and to identify any potential relationships between chemical structure and antioxidant or anti-inflammatory properties. The selected flavonoids were examined in cell-free models to prove their ability to scavenge superoxide radicals, hydrogen peroxide, and hypochlorous acid. Further, the ability of the flavonoids to influence the formation of reactive oxygen species in the murine macrophage cell line J774.A1 was tested in the presence and absence of lipopolysaccharide (LPS). The ability of flavonoids to inhibit LPS-induced IκB-α degradation and COX-2 expression was used as a model for the inflammatory response. The present results indicated that the antioxidant activity was dependent on the chemical structure, where the catechol moiety is especially crucial for this effect. The most potent antioxidant activities in cell-free models were observed for diplacone (4), whereas cudraflavone B (1) and osajin (3) showed a pro-oxidant effect in J774.A1 cells. All flavonoids tested were able to inhibit IκB-α degradation, but only diplacone (4) also down-regulated COX-2 expression.

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.

Computational trust mechanisms aim to produce a trust rating from both direct and indirect information about agents behaviour. Josang's Subjective Logic has been widely adopted as the core of such systems via its fusion and discount operators. Recently we proposed an operator for discounting opinions based on geometrical properties, and, continuing this line of investigation, this paper describes a new geometry based fusion operator. We evaluate this fusion operator together with our geometric discount operator in the context of a trust system, and show that our operators outperform those originally described by Josang. A core advantage of our work is that these operators can be used without modifying the remainder of the trust and reputation system.

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