An understanding of eating behaviors is an important element of health education and treatment in clinical populations. To understand the biopsychosocial profile of eating behaviors in an ecologically valid way, ecological momentary assessment (EMA) is appropriate because its use is able to overcome the recall bias in patient-reported outcomes (PROs). As appetite is a key PRO associated with eating behaviors, this study was done to develop an EMA scale to evaluate the within-individual variation of momentary appetite and uses this scale to discuss the relationships between appetite and various psychological factors.Twenty healthy participants (age 23.6 ± 4.2 years old) wore a watch-type computer for a week. Several times a day, including just before and after meals, they recorded their momentary psychological stress, mood states, and ten items related to appetite. In addition, they recorded everything they ate and drank into a personal digital assistant (PDA)-based food diary. Multilevel factor analysis was used to investigate the factor structure of the scale, and the reliability and validity of the scale were also explored.Multilevel factor analyses found two factors at the within-individual level (hunger/fullness and cravings) and one factor at the between-individual level. Medians for the individually calculated Cronbach’s alphas were 0.89 for hunger/fullness, 0.71 for cravings, and 0.86 for total appetite . Hunger/fullness, cravings, and total appetite all decreased significantly after meals compared with those before meals, and hunger/fullness, cravings, and total appetite before meals were positively associated with energy intake. There were significant negative associations between both hunger/fullness and total appetite and anxiety and depression as well as between cravings, and depression, anxiety and stress.The within-individual reliability of the EMA scale to assess momentary appetite was confirmed in most subjects and it was also validated as a useful tool to understand eating behaviors in daily settings. Further refinement of the scale is necessary and further investigations need to be conducted, particularly on clinical populations. Understanding eating behaviors is important for the health education of healthy people as well as for treatment in diverse clinical populations, which range from obesity to eating disorders. Although psychosocial factors have been identified as having a significant effect on eating behaviors in previous studies in laboratory analyses or from recalled self-reports -3, ecoloWith regard to nutritional assessment of eating behaviors, the 24-hour recall method has been regarded as the gold standard . For assAppetite is another important PRO because it can affect eating behaviors and is thought to be affected by both physiological and psychological factors. Appetite is a subjective sensation and can vary considerably from moment to moment, similar to other PROs such as pain and mood states. Therefore, when investigating detailed changes in appetite, recall bias is often problematic; thus, the use of an EMA is beneficial to gain a more accurate picture. When incorporating appetite assessment into EMA to capture within-individual appetite changes and to investigate the temporal relationships with eating behaviors and psychosocial factors, there are two issues that need to be addressed in terms of the appetite scale. The first is the lack of any rigorous investigation on a suitable appetite scale factor structure. Appetite has been generally assessed using words related to appetite  as single-item questions with visual analog scales (VAS) -12. RespTherefore, this study developed an EMA scale to evaluate the within-individual variation in momentary appetite and discusses the psychometric properties and their relationship with psychological factors based on the EMA data collected in daily settings.In the autumn of 2008, 20 healthy undergraduate and graduate students were recruited for participation in this study. All participants were Japanese and belonged to universities in the Tokyo metropolitan area. The inclusion criterion was age ≥ 20 years, and the exclusion criteria were the presence of any physical or mental disorders, any history of an eating disorder, or being on a diet. Health status and history were based on self-reports. All participants were given a full explanation of the purposes and potential risks of the study by well-trained researchers, and written informed consent was obtained from all participants. The study was approved by the research ethics committee of the Graduate School of Education, the University of Tokyo.For seven consecutive days the participants wore a watch-type computer  ,18 and cMomentary psychosocial factors were recorded using EMA. Momentary psychological stress and nine items from the Depression and Anxiety Mood Scale (DAMS)  were shoCandidate items on the appetite scale were created in the following manner. First, every item used as a single-item question in published papers -12 was lThe EMA recordings comprised signal-contingent recordings and event-contingent recordings. Signal-contingent recordings are recordings that are cued through random alarm alerts at 30-minute intervals around the following times: 5:00, 10:00, 15:00, 20:00, and 24:00. If participants did not enter a recording when the alarm sounded, they were allowed to postpone input for up to 30 minutes. Recordings not made within 30 minutes were cancelled.Event-contingent recordings are recordings made by the participants themselves when predefined events occur. In this study, they were asked to make recordings 1) when they woke up, 2) when they went to bed, 3) just before eating or drinking, and 4) just after eating and drinking. After they made a recording when they went to bed, the computer suspended the alarms until a recording when they woke up was made.Participants were required to record everything they ate and drank using a PDA-based food diary as soon as possible after having something to eat or drink. To avoid the effect of reflecting on their food intake in terms of their psychological state, participants were asked to make an EMA recording before completing the food diary.First, the participants input information related to food intake; Starting time, place of food intake, and meal type. Place was chosen from a pull-down menu with the following choices: “Home”, “office/school”, “eating out”, and “others”. Meal types were chosen from a pull-down menu with the following choices: “Breakfast”, “lunch”, “dinner”, and “snack”. Thereafter, the participants selected each menu from the database displayed on the PDA screen. Finally, they adjusted their portion sizes according to the photos in the database and input the ratio of the actual quantity they ate to the quantity shown in the database. In this manner, the PDA-based food diary stored the time of entry, related information, menu, and quantity. The system then automatically calculated the energy, protein, fat, carbohydrate, and sodium intakes.χ2 test and the significance level was set at 0.05. The statistical analyses were conducted using the SAS Proc Mixed  and Mplus 7.1 .Because the data in this study had a nested structure due to the between-individual and within-individual levels, multilevel modeling was used for statistical analysis. First, the within- and between-individual variances for each item were estimated for descriptive purposes. Second, the appetite scale factor structure was investigated using exploratory multilevel factor analysis using a weighted least square method with a Geomin rotation. Third, the internal consistency for the scale reliability was examined by calculating the Cronbach’s α individually, rather than using multilevel modeling. Fourth, the changes in the appetite after meals and the association between the energy intake and appetite before food intake were tested to investigate the validity of the scale. Appetite scores just before meals were paired with those just after the same meal if available and the appetite differences were calculated. These differences were treated as random effects, which allowed them to vary between the participants. The variance-covariance matrix (G matrix) was modeled as unstructured. To calculate the association between energy intake and appetite, the energy intake per meal was the dependent variable and each of the three appetite scores  were the independent variable in separate models. To control for the meal type, this was also incorporated into the model. Appetite effects were modeled as random effects and the G matrix was modeled as a variance component structure. Finally, the associations between appetite and mood states before a meal were tested to determine the psychological aspects of appetite. The nominated appetite score (out of the three choices) was used as the dependent variable and a psychological factor  was used as the independent variable in separate models. The psychological factor effect was modeled as both fixed and random effects and the goodness of fit was compared between null , fixed, and random models. The G matrix was modeled as unstructured. Goodness of fit was compared using a −2 log likelihood function and This study included a total of 20 participants, and their profiles are indicated in Table The 20 participants made 1588 EMA recordings over seven days, with 138 recordings being made on awakening, 133 recordings being made at bedtime, 428 recordings being made as a result of the alarm, 455 recordings being made just before meals, and 434 recordings being made just after meals.In addition, the 20 participants made 509 food diary recordings over seven days, with 106 breakfasts, 126 lunches, 136 dinners, and 141 snacks.The within- and between-individual variances for the ten items on the appetite scale were estimated using multilevel modeling, the results for which are indicated in Table Because the primary aim of the scale was to capture a within-individual change in appetite, it first focused on the within-individual factor structure . From the eigenvalues, the scree plot, the goodness of fit and interpretability, a two-factor structure seemed appropriate at the within-individual level. An item with insufficient factor loadings <0.3) to either factor (“nausea”), items of double loading , and items with low communality  were excluded. The factor structure for the remaining five items is indicated in Table  to eitheThe medians and ranges for the individually calculated Cronbach’s α for the “hunger/fullness” score, “cravings” score, and “total appetite” score are indicated in Table Changes in the “hunger/fullness”, “cravings”, and “total appetite” scores were all significantly different from zero Table . Grand mThere were significant positive associations between energy intake and appetite before meals Table .Table 6Depression and anxiety were significantly negatively associated with all three appetite scores, whereas psychological stress was significantly negatively associated only with cravings Table . Models As indicated in Table Two factors were suggested at the within-individual level. Stubbs RJ et al., based on the data of children and adolescents, concluded that six items related to appetite were divided into gut-based sensations and motivation to eat . The resInternal consistency was investigated as the reliability analysis. Considering that the principal use of the scale is to capture the within-individual change of appetite, the Cronbach’s αs were calculated individually. Consequently, the median of Cronbach’s αs were generally good, with the exception of one subject who showed a low α for cravings, which may have been due to a small variance in the craving scores for that participant.The appetite scale had been previously validated in a laboratory treating eating behaviors. Because we wished to investigate the validity of the appetite scale to capture the change in usual daily settings, we used both the appetite data from the EMA and the food intake data from the PDA-based system. Although daily eating behaviors have been shown to be affected by various factors, which may act as noises in this analysis, the results showed that the appetite scores were reduced after food intake and were significantly positively associated with energy intake. Therefore, the validity for the natural eating behaviors in a daily setting was confirmed.Psychological factors have been found to affect appetite. The results of this study showed a negative association with appetite, although there was a difference in associations between hunger/fullness and cravings whereby depression and anxiety showed significantly negative associations with all three appetite scores , while stress showed a significantly negative association only with cravings. Although the relationships between psychological states and the food intake of healthy participants investigated by either laboratory studies or EMA studies have been inconsistent, suggesting that food intake is decreased with negative mood or stress in some participants while food intake is increased or unchanged in others ,5,20, thThe main limitation of this study was that all participants were healthy young people. Therefore, it will be necessary to investigate if these results can be extrapolated to other populations, such as populations of different ages and occupations, although undergraduates and graduates are thought to be fair because we can speculate that they can eat rather naturally and with less environmental constraint compared to busy working people. Investigation into clinical populations, such as obesity and eating disorders, would also be important. The between-individual factor structure might be different because some eating disorder patients are thought to have persistently intense cravings compared to hunger/fullness. The relationships between psychological states and appetite might also be different, as we discussed before. In addition, although we proposed an appetite scale with two factors of five items, assessment of as few items as possible is preferable because of the need to simultaneously assess many other psychosocial factors. Generally, composite scores derived from several items based on an appropriate factor structure are expected to be more reliable than single-item questions. However, of course it is important to confirm if it is actually more reliable and valid and particularly if its advantages outweigh the burden of responding to several questions. Therefore a careful examination is still necessary to determine if the composite scoring system used in this study is better than a scale with fewer items, especially a scale in which a score for an item is used as the variable itself. As regards the sample size, the average number of recordings per person  was 79.4 for multilevel factor analysis and internal consistency analysis and 25.4 for validity analysis and analysis of the relationship with psychological factors. They are thought to be reasonable numbers to estimate within-individual factor structure and relationships. However, the number of participants  was only 20, which is rather small to obtain a robust factor structure and may compromise the generalizability of the results.An EMA scale to assess momentary appetite was proposed. The within-individual reliability was confirmed in most of the participants, and it was also validated against eating behaviors in daily settings. It would be a useful tool when seeking to understand eating behaviors in daily settings from a biopsychosocial viewpoint.


The current article provides an introductory review of the use of LMMs for within-participant data analysis and describes a free, simple, graphical user interface (LMMgui). LMMgui uses the package  Measurements of RT are available for concurrent speech in French, German, and English, and thus language can be described as a categorical factor with three levels. RTs may have been measured from three different groups of monolingual listeners. Importantly, each measurement would be from a different listener. Such data is grouped by listener and by language, and since each listener can only belong to one language group, the grouping factors of listener and language are said to be nested. In this case, language can also be described as a “between-participants” factor, and the data may be analyzed with a standard analysis of variance (ANOVA). This method assumes that the response variable comes from a normally distributed population and shows homogeneity of variance.post-hoc tests such as Tukey LSD problematic  were collected per listener. This phenomenon, which is known as pseudoreplication, is common in neuroscience experiments and leads to the use of repeated-measures (rm) ANOVAs. rmANOVAs require two additional assumptions . An example of the latter is the sound level of the telephone in our hypothetical experiment: RTs were measured with the telephone ringing at different sound levels , while the sound level of the concurrent speech was fixed.The random-effects terms of LMMs are all the terms that include random factors; interactions between fixed and random factors are considered in the random-effects terms. For example, in the hypothetical telephone-ringing experiment, the random factor listener and its interaction with the fixed covariate sound level can be modeled using a number of different random-effects terms, which differ in their complexity (number of parameters). The simplest random-effects term, known as “random intercept only,” ignores the interaction: it only considers how RT at zero sound level (0 dB SPL) varies between listeners. This is analogous to the assumption of compound symmetry. However, RT may vary as function of sound level, for example RT could decrease with increasing sound level. The slope of this function may vary between participants, and to account for this interaction between participant and sound level, we would also need to include a “random slope” term. In the full LMM, the random-effects part would also include parameters that allow for the intercept and slope to be correlated: for example, if as shown in Figure 2, degrees of freedom and p-value are reported for the missing term. A p-value of less than 0.05 (see below) is often considered to indicate that the missing term contributed significantly to the model fit. Care should be taken in interpreting the results because the hypothesis test involves a comparison on the boundary of possible conditions for which the χ2 test can be conservative , and the test statistic χp-value, which is thought to indicate the probability that the value of the test statistic or greater would have been obtained under the null hypothesis. In contrast, exploratory analyses are based on statistical tests which are motivated by the pattern of results observed after data collection. In neuroscience, there is pressure to publish studies with p-values below 0.05, which is often considered to be “significant,” although this pressure has often been criticized  are available for inspection , SAS (“proc mixed”), S-PLUS, MLwiN, or ASReml. LMMgui, is a free, graphic user interface that uses n Figure lmer function of lme4, and an LMM could be expressed as:For the hypothetical data shown in Figures Where “RT” is the response variable and the model terms are to the right of the tilde character (“~”). The first terms are fixed-effects: “Language” and “Level.” An interaction term would include a colon “:.” The random-effects terms are those which include a bar symbol (“|”). To the right of the bar is the random factor “Listener.” The expression to the left of the bar indicates that this random term includes correlated intercepts and slopes for the fixed factors. “(Language + Level|Listener)” implicitly includes the random intercept and is equivalent to “(1 + Language + Level|Listener).” In contrast, a random-intercept only term would be “(1|Listener),” and the term for uncorrelated random intercept and slope would be “(Language + Level || Listener).” Further examples and alternative syntax for model terms are given by Bates et al. : Table 2In order to evaluate the main effect of level, the above model can be compared to a model without the term of interest, that is:Note that during evaluation of fixed-effects, it is recommended that the random-effects part of the models always includes slopes for all fixed factors because this has been shown to be important for confirmatory hypothesis testing in experimental psychology Barr, . HoweverAs with most statistical analyses, an important computational step is estimating the parameters of the LMM. Although the details of this are beyond the scope of this mini-review, the reader should be aware of standard maximum likelihood (ML) and restricted ML (REML) criteria. Although the default REML may provide a better estimate of random-effects standard deviation, it does so by averaging over some of the uncertainty in the fixed-effects parameters. For this reason, the ML criterion is used when comparing LMMs with different fixed-effects structures.A significant LRT would indicate that the missing fixed-effects term (interaction or main effect) is important. For example, the hypothetical data Figures , 2 show lme4 includes the function glmer which can be used for count data (Poisson distribution), binary/proportion data , and for data whose variance increases with the square of the mean (gamma). Introductory books are available for further reading on the use of R in general (Crawley, Although at present LMMgui is only available for continuous response variables from a normally distributed population, mixed-effects models can also be used for categorical response variables Dixon, . lme4 inCrawley, , and mixCrawley, . For theIn order to promote simplicity of use, LMMgui is not as comprehensive as using the command-line options. It is likely that there may be some criticism for a program that provides such a simple interface; Barr et al.  cite a pThe author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Micromégas, Swift’s Gulliver’s Travels, Caroll’s Alice, and Matheson’s The Shrinking man, closely examining issues relevant to architectonic embodiment such as: bodily, perceptual, cognitive, affective, and social changes related to alterations in body size relative to people, objects and architectural environments. I next provide a taxonomy of the Gulliver theme and highlight its main psychological features, and then proceed to review relevant work from cognitive science. Although fictional alterations of body-environment scaling far outreach current possibilities in experimental research, I argue that the peripetiae and morals outlined in the literary realm, as products of the human imagination, provide a unique window into the folk-psychology of body and space.Architectonic embodiment postulates a bidirectional link between bodily awareness and the architectural environment. The standard size and features of the human body, for instance, are thought to influence the structure of interiors and buildings, as well as their perception and appreciation. Whereas architectural practice and theory, the visual arts and more recently the cognitive sciences have explored this relationship of humans with their crafted environments, many fictional literary works have long experimented with alterations of body–environment scaling. This so-called Gulliver theme – popular in the science-fiction genre but also in children’s literature and philosophical satire – reveals, as a recurrent thought-experiment, our preoccupation with proportions and our fascination for the infinitely small and large. Here I provide an overview of the altered scaling theme in literature, including classics such as Voltaire’s  For I am the size of what I see not my height’s size.A Little Larger Than the Entire Universe: Selected Poems)Fernando Pessoa  famously stated that “man is the measure of all things.” Although the exact context to that statement has not reached us, it is usually interpreted as a relativistic and humanist manifesto: “reality” is merely how the world appears to each of us, and man is at the center of all things. From this general insight, it should come as no surprise that architects have long sought to elucidate the relationship between, on the one hand, the morphology and capacities of the human body and, on the other hand, how humans perceive and appreciate their built environments. This bidirectional linkage of bodily awareness and crafted environments, prominent in Renaissance architectural thinking, is currently studied under the name “architectonic embodiment” see , p. 232,imagine such alterations, as well as their consequences. This is not only what scientists and architects have to do anyway as part of their jobs, but also what writers have done routinely since the advents of the epic, the drama, the tale, and the novel. They have asked what would happen if man or the world were suddenly to dramatically change, and they have provided fictional answers, through their imaginary character’s experiences, perceptions, and thinking. By doing so, across time, their proposals have in turn sometimes made it into common knowledge, and thus entered a wider imagination beyond their own’s and their readership’s. I have argued elsewhere that literary contributions, far from mere ramblings of unconstrained imaginations, often reflect, and provide insight on, the very structure and organization of human cognition as well as its dysfunctions. This holds also, and indeed especially, for pure works of fantasy, including science-fiction and fairy-tales, where the world, characters and events depicted by the writer cannot stretch farther than what their reader’s brains can digest. In this sense, it does not matter whether fiction is “scientifically accurate” or not. Just like major inaccuracies and errors in the visual arts can provide powerful insights about the normal workings of the visual system . French writer and literary critic 1.Micromégas Complaining of his overwork as a schoolteacher and of his “children cries in too small an accommodation,” Messac lamented having to publish his impressive review in an incomplete form, but felt confident that “it does not seem that anyone would care to do better.” While listing and commenting on about a 100 authors and works relevant to the issue, he unfortunately failed to define it properly and, initiating a long lineage of scientifically minded critics, focused mostly on the physical and biological impossibility of altering the scales of the universe as well as producing giant or microscopic humans, animals, or insects. Micromégas, Swift’s Gulliver’s Travels, Carroll’s Alice as well as examples from children’s literature and the science-fiction genre. This review is not meant to be exhaustive, but suffices to provide a broad overview of principles, functions, effects, and mechanisms underlying the Gulliver theme, which will then be examined in light of cognitive science research in order to delineate the many insights literature can offer the science of architectonic embodiment.Fiction has long provided an imaginative field to explore alterations of body–environment scaling, be it as philosophical musings, satirical thought-experiments, children tales, or science-fiction stories  when they reach earth. Of course, the story allows putting into perspective the conceit of human philosophers, the insignificance of earthly quarrels and the fundamental limits of scientific knowledge. At the same time, the giants are impressed by the intelligence of these “invisible insects” and the story also serves as a philosophical warning against holding prejudices against the unknown and the dangers of judging people by their appearances or their status. Micromégas, however, probably pushed the satire too far. The characters are so ridiculously large that the imagination is excessively boggled and simply cannot cognitively digest their adventures . Perhaps this was intended, as the “Gulliver theme,” quite aside from specific meanings and lessons, often purports to convey a vertiginous sense of the infinite and a disturbing awareness of the limits of our senses , a work much better remembered perhaps, in part, because the altered body–environment scaling stays within manageable metrics. In the first two parts of Lemuel Gulliver’s memoir, the narrator discovers successively two countries where everything, including people, is scaled such as he is a giant (in Lilliput) or a dwarf (in Brobdingnag). According to Swift’s indications, the countries are, respectively, scaled 1:12 and 12:1 to Gulliver’s size, including people, animals, trees, buildings, and objects. This literary device allows for maximal satirical effects and much humorous inventions, yet it is clear that Swift attempts to convey some sense of plausibility (not least by having Gulliver repeatedly state that he aims to stay truthful to his observations) and very carefully exploits the change of perspective induced in and across both worlds. Although Gulliver’s actual body size stays constant – underscoring his status as an “average good man”  ood man” , p. 55 –ood man” . Howeverood man” . NeverthAs the book deals mainly with human politics and its failings, size is used in the first two parts as a method to investigate the nature of power and chauvinistic pride: “Swift’s attack on pride in the first two voyages is made more powerful because of his brilliant use of the chain of being. In so far as, we recognize ourselves in the Lilliputians or in Gulliver in Brobdingnag, we become aware of our pettiness, of the disproportion of our race and of the shocking difference between what we profess and what we are” , p. 60. Gulliver Travel’s is the defamiliarization effect it achieves on nearly every page. This process of “cognitive estrangement” and “de-automatization of perception” involves the twofold mechanism of naturalizing the strange and making the familiar strange: “whenever a marvelous element is introduced into a seemingly realistic world, a collision occurs between two systems of reality, producing an estranging effect. The familiar appears in new surroundings and is thereby re contextualized” , he is not in the least impressed by its size, and when he first encounters people normally scaled to his size upon returning to England, he is not surprised either by their size at all. Upon returning home from Brobdingnag, however, he is struck by the visual size of the first Englishmen he meets at sea: “I was (…) confounded at the Sight of so many Pygmies, for such I took them to be, after having so long accustomed mine Eyes to the monstrous Objects I had left” (149). We also learn that the effect existed during his stay: “while I was in [Brobdingnag], I could never endure to look in a Glass after my Eyes had been accustomed to such prodigious Objects, because the Comparison gave me so despicable a Conceit of myself.” Interestingly, looking at himself in a mirror induced one of the only self-size illusion in the book: “I really began to imagine myself dwindled many Degrees below my usual Size.” Even back at home in England, and within his family, the effect strongly persists: “observing the Littleness of the Houses, the Trees, the Cattle, and the People, I began to think myself in Lilliput”; “My wife ran out to embrace me, but I stooped lower than her knees, thinking she could otherwise never be able to reach my mouth. My daughter kneeled to ask my blessing, but I could not see her till she arose, having been so long used to stand with my head and eyes erect to above sixty foot.” These seemingly proportional and longstanding after-effects, Gulliver ascribes to “an Instance of the great Power of Habit and Prejudice.” Finally, Gulliver’s Travel also points to a technical scaling problem Swift had to deal with, that of the transition between different worlds. Where do Lilliput, England and Brobdingnag start and end? Some amusing (perhaps unintended) inconsistencies appear when Gulliver stands on the boundary of the discovered lands. In Lilliput’s sea, he can “wade” several 100 m before he is “forced to swim,” either making the sea a Lilliputian one, or Gulliver himself de facto a giant in a normal-sized sea. Brobdingnag’s sea seems different, as “Sea-fish are of the same Size with those in Europe, and consequently not worth catching” for the giant people. However, “a Hailstone is near eighteen hundred times as large as one in Europe.” Why such discrepancy? Gulliver merely “leave[s] the Reasons to be determined by Philosophers.” Seeing gigantic eagles from Brobdingnag fly away in the distance, normal-sized human sailors remark “nothing of their being larger than the usual Size,” which Gulliver supposes “must be imputed to the great Height they were at.” This is, surprisingly, one of the rare comments on the phenomenon of size-constancy I was able to find in the Gulliver theme literature.Perhaps the most important feature of ualized” . Swift iper se. Humans under the lens of Micromégas, and Gulliver throughout his travels, keep their usual human size. This was still the case when the Gulliver theme entered the horror and science-fiction genre, with James Fitz-O’Brian’s (1858) The Diamond Lens. In that short-story, an outcast fascinated by microscopy learns from Leeuwenhoek’s spirit2 how to devise a super microscope. Using it, he discovers in a drop of water an entirely new world, populated by a feminine creature whom he calls Animula and immediately falls in love with. Animula never realizes she is being observed, and the lover is condemned to watch her dry away. This tale highlights the paradoxical distance of neighboring worlds, when scales are dramatically different: “forgetful for an instant of everything save her presence, I withdrew my eye from the microscope eagerly, – alas! (…) my gaze fell (…) on a colorless drop of water. There (…), this beautiful being was forever imprisoned. The planet Neptune was not more distant from me than she,” “I would have gladly forfeited all claims to my human birthright, if I could only have been dwarfed to the size of an animalcule.” As Messac notes, up until the publication of The Diamond Lens, the small and the large where so to speak fixed, authors imagined no way to travel from one scale to the other. He notes that Wells’ (1904) The Food of the Gods will be the first major work to introduce the possibility of altering characters’ physical size at will , but he curiously omits a much better known book, namely Alice’s adventures in Wonderland  by Lewis Carroll (1866). “Little Alice” indeed is frequently confronted to sudden changes in her bodily size and/or in her environment . The Gulliver theme, then, reaches here its maximal expansion: not only can the environment’s scale be diminished or enlarged (as in Gulliver’s Travels), but at the same time Alice’s body can do the same. Soon after she falls “down a large rabbit-hole,” she finds herself in a room where the only escape is “a small passage, not much larger than a rat-hole.” While she wonders how she could possibly “shut up like a telescope,” she finds a bottle marked with the words “DRINK ME,” and after obliging she finds herself shrinking down to “ten inches high”: “she was now the right size for going through the little door.” After eating a piece of cake marked “EAT ME,” she then finds herself growing until “her head struck against the roof,” at “more than nine feet high.” Twelve occurrences of such changes occur during the story, either following consumption of some item or spontaneously, most often at convenient moments when there is a need for a change of scale to pursue the adventure, and sometimes offering alternative solutions: “if it makes me grow larger, I can reach the key; and if it makes me grow smaller, I can creep under the door: so either way I’ll get into the garden, and I don’t care which happens!” At moments, Alice gets confused and doesn’t remember whether she is tall, small or normal, for instance when she is swimming in the pool of her own tears and mistakes a mouse for “a walrus or hippopotamus.” In fact, along her adventures she seems to have lost track of her original size: “I must have been changed several times since then (…) being so many different sizes in a day is confusing.” Alice’s experience of body–environment scaling alterations have usually been interpreted as an embodied metaphor of children’s preoccupation with size, body, identity, and growth  facts that children are smaller than adults, that growth is a unidirectional process, and that human environments are by and large adult-scaled worlds, suffice to explain the appeal of the miniature for children. Yet the miniature and the gigantic also imbue the adult imagination and feature in a wide array of cultural facts and items  of the story thus feels empowered by Stuart Little’s engaging behavior and attitude despite his smallness, and has an empathic response to him because he or she knows what it feels like to be aware of one’s value while being ignored, derided or infantilized by the grown-ups. Furthermore, the fact that Stuart Little is a mouse in a world of humans adds to the sense of otherness and incompleteness, as well as to the bodily preoccupations of growing children achieved by the mere alteration in size . The main use of the Gulliver theme here is thus to induce a sense of wish-fulfillment toward more independence, control and power for the “little one’s.”Unsurprisingly, well before and after nd items , suggestThe Borrowers serves to illustrate the motif of the “miniature society” or “small world.” In this series, a clandestine species of small creatures cohabits secretly with humans and interact with the human-scaled world. Stories of the large and the small societies thus run in parallel and sometimes intersect, and the audience can chose to identify with the little or the normal world. Although they are despised by humans, the Borrowers can exert a great appeal to children: they live in their own secret world, they have to be smart and creative in order to hide and survive, and they frequently triumph over human adults. Perhaps there is also a strong appeal for the type of society the Borrowers have built, the epitome of the miniature as “a metaphor for the interior space and time of the bourgeois subject” (Mary Norton’s (1953) subject” , p. xii.The Shrinking of Treehorn, by Florence Parry Heide  illustrates the shrinking character theme. Although a little boy suddenly starts growing small, nobody, including his parents, takes notice. The theme here is neglect, the scourge of not being attended to or not being taken seriously. As is the case for a number of “adult” shrinking stories, the phenomenon starts with the character’s clothes suddenly becoming too large . And just like its science-fiction counterparts (see below), the theme introduces the fragility of self-worth, fantasies of destruction, disappearance and annihilation, questions about origins and goals, preoccupations with bodily appearances and experiences, and the “Idea of marginalization”  L’Homme-Fourmi; a disease induced by radioactivity coupled with an insecticide in Richard Matheson’s (1956) The Shrinking Man; chemical compounds in Wells’ (1904) The Food of the Gods, Ray Cummings’ (1923) The Girl in the golden atom, and Maurice Renard’s (1928) Un Homme chez les microbes; some “energy” produced in a human-sized glass jar in René Spitz’s (1938) L’Homme élastique  and A. Bleunard’s (1893) Toujours plus petits ; a highly sophisticated labroom in Isaac Asimov’s (1966) Fantastic Voyage ; or some unexplained curse as in Jean-Charles Rémy’s (1976) L’Arborescence and Mark Wersinger’s (1947) La chute dans le néant.Turning to fantasy and science-fiction treatments of the Gulliver theme – indeed a prominent and “traditional sf theme” , p. 386 The Food of the Gods and L’Arborescence where characters unidirectionally grow large, science-fiction usually prefers shrinking protagonists or plot devices that allow “growing” in both directions. A notable exception, however, is the famous cinematic horror-trope of the “attack” of giant creatures such as ants, reptiles, spiders, and men or women, but in these cases it is usually the hero characters that are made to look small in contrast. While these movies often reflect the nuclear and Cold War anxieties of the times, offer a comment on the power and abuses of science, technology and the military, and have been taken to allude to Freudian ideas as well as the topics of gender, race, paranoia, and invasion, the sheer entertainment value of gigantic size as also been noted (inducing awe and fear), as well as the satirical value of artificially overturning the world order by putting humans at the bottom of the food chain, and thus drawing attention to their destructive powers in the real world. These effects are all the more efficient as bugs are almost natural phobic stimuli – and thus “superoptimal” stimuli when they cover large portions of the visual field -, and they also are both extremely familiar and alien to humans, enhancing the “cognitive estrangement” effect in their gigantic form (Dominating in this genre is the micro use of the Gulliver theme. Except for tic form .The Invaders [written by Richard Matheson (1961)] from The Twilight Zone series tells the story of a woman harassed by minuscule alien visitors. After much trouble, when she finally manages to destroy their minuscule spaceship, we read on its side that it belongs to the “US Army,” its equipage just managing to warn (in English) their base to stay clear from this planet, as it is inhabited by gigantic and dangerous beings.The “reversal of perspective” trope is brought home particularly well in a cinematic example: the episode Travels in the interior, Edwin Pallander’s (1902) The Adventures of a Micro-Man, and Mark Twain’s (1905) Three Thousand Years among the Microbes are early illustrations of this literary device  see also . These tThe Girl in the Golden Atom, Un Homme chez les Microbes and Fantastic Voyage). However, science-fiction is also concerned about the interconnections of scientific concepts and social concerns. In that respect, Richard Matheson’s (1956) The Shrinking Man  probably remains the most complete and successful treatment of the Gulliver theme. Staying in the same environment – the suburban American home – the ineluctable shrinking process of Scott Carey, the main protagonist, allows for many dramatic effects and powerful metaphors. These include, through an astutely constructed narrative , careful explorations of bodily and perceptual alterations, feelings of estrangement toward a progressively growing spatial environment , commentaries on the consumer culture, importance of social status, as well as gender roles of the 1950s, changes in attitude and character of the protagonist , the need for constantly adapting in an increasingly unpredictable, dangerous, and unfamiliar world, alterations of the experienced passage of time, and of course metaphysical questions about the infinite and the unknown, as well as the inevitability of self-annihilation , science-fiction’s usage of the Gulliver theme adds, as expected, many elements derived from the sciences, such as considerations on the interaction of gravitational and surface tension forces, the physics of the shrinking process , the issue of the relationship between sense data and altered sense organs , and, more interestingly for the field of architectonic embodiment, the altered experience of time during body–environment scaling alterations .Any alteration of body–environment scaling mechanically produces a twofold process: the small induces feelings of bigness, the large induces feelings of smallness. Which is to say that as the human body, as experienced from the first-person (egocentric) perspective, shrinks, the entire environment is concomitantly perceived as larger than normal, and vice-versa. This taxonomy centers on the subjective first-person perspective of body–environment altered scaling, usually that shared by a main character and its empathetic reader, and does not distinguish the direction of the change . Six plot devices, I think, suffice to encompass the whole range of the Gulliver theme, and many works involve more than one of them  metric of corresponding or other familiar items. Authors also like to play with measure units: these are either from the normal-sized world , or are scaled to the current altered bodily size . At times the scales can intermix, leading to much confusing, striking, or amusing situations or descriptions. Characters frequently pass through a process of adaptation or habituation to the new environment, which involves confusions about scales and size after-effects as the body–environment scaling alterations progress or reverse. Authors have also exploited other modalities: sounds, smells, and tactile sensations are minimized, magnified, or transformed altogether as a character’s size, or that of the environment, change scales. For instance, a giant has to lay low and pay attention to hear Lilliputian people, a giant’s perfume is offensive to a normal-sized nose, an even ground becomes a rocky and perilous area to a microscopic character. Alterations of body–environment scaling thus lead to numerous multisensory effects. Less explored are the effects on the experience of time, which would necessarily accompany any dramatic changes in space and distances.Umwelt that is afforded to agents varying in size, most notably to human children, is a frequent topic explored by the Gulliver theme, with special emphasis on the universal biological fact of growth. Fictional alterations of body–environment scaling provide a unique means to develop, think about and gain insight into this issue.Like Gulliver, Alice’s experience of body–environment scaling alterations brings to the fore the psychological notion of “affordances.” Following More prominently than the previous features, the Gulliver theme has put much focus on the affective and behavioral consequences of body–environment alterations, thereby anticipating current interest for the notion of “embodiment” in cognitive science e.g., . Here thmodus operandi of fantastic and horror fiction  techniques to create the sense of embodying differently scaled bodies, or more sophisticated virtual reality equipments. Thus, Other modalities  have yet to be explored with such paradigms. An older study, however, provided evidence that phenomenological time is associated with body–environment scaling. Subjects were asked to imagine themselves as a scale figure in a reduced model-environment. During successful immersion, participants estimated the felt passage of time to be proportional to the scale they were immersed in. In a 1/12 model (similar to that experienced by Gulliver in Lilliput), participants estimated a duration of 30 min  to be elapsed after about 2.5 to 2.9 min in the reduced environment, a timing strikingly close to the 1/12 spatial ratio .Some clinical conditions also provide insight into the Gulliver theme. Neurologists have even dubbed some peculiar symptoms of migraine or epilepsy “Alice in Wonderland syndrome,” which refers to distortions of body–environment scaling following disturbances of the somatosensory cortices or the posterior parietal cortex. In these cases, patients experience body parts or their entire body to be disproportionately large or small, which also affects the perception of the size of objects, people and the environment. Such sensations are sometimes also called micro- and macrosomatognosia when they affect the body, and micro- and macropsia when they affect the visual system, mostly after disturbances of the occipital lobes. Some hallucinations, foremost observed as part of Charles Bonnet syndrome, are called “Lilliputian,” and refer to the illusory perception of small characters , has also been shown to alter emotional and social experience. Introducing the so-called “Proteus effect,” More profound effects of size and height disparities relative to the human body have been suggested, pointing to possible relationships of architectonic embodiment with belief systems. In a theoretical paper, Philosophical Enquiry into the Origin of Our Ideas of the Sublime and Beautiful. Burke stated that it “is too evident, and the observation too common” that “Greatness of dimension is a powerful cause of the sublime” . To explain this phenomenon, he suggested that large stimuli are composed of “a vast number of distinct points, every one of which, or the ray from every one, makes an impression on the retina. So that, though the image of one point should cause but a small tension of this membrane, another and another, and another stroke, must in their progress cause a very great one, until it arrives at last to the highest degree; and the whole capacity of the eye, vibrating in all its parts, must approach near to the nature of what causes pain, and consequently must produce an idea of the sublime” . Of course, notwithstanding its dubious merits, such a proposed visual mechanism would not account for the metaphorical uses of vastness in other modalities or in writing (viz. Darwin’s closing statements in The Origin of species: “There is grandeur in this view of life”), and for the literary examples reviewed in this paper [see also Although speculative, such an account illustrates the far ranging ramifications of body–environment scaling for an architectonic embodiment research program. Notably, the experience of awe provides a promising locus for interfacing perceptual systems and higher-level value construals. A similar idea has permeated philosophical thinking since Aristotle’s concept of “aesthetic size”  and receGulliver’s Travels and other literary works reviewed herein, values might also be altered by consideration of our relative size. Other tentative links between size cognition and belief systems, values and spirituality accrue, for example, from reports of large unidentified marine animals sightings which, despite inadequate evidence, have been widespread since the 18th century and are not explained by sighting distance . Such prThis article has attempted to remediate The current paper also contributes to the growing field of cognitive literary studies e.g., . I suggeSD initiated, researched, and wrote the article.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The proliferation of the scientific literature in the field of biomedicine makes it difficult to keep abreast of current knowledge, even for domain experts. While general Web search engines and specialized information retrieval (IR) systems have made important strides in recent decades, the problem of accurate knowledge extraction from the biomedical literature is far from solved. Classical IR systems usually return a list of documents that have to be read by the user to extract relevant information. This tedious and time-consuming work can be lessened with automatic Question Answering (QA) systems, which aim to provide users with direct and precise answers to their questions. In this work we propose a novel methodology for QA based on semantic relations extracted from the biomedical literature.http://sembt.mf.uni-lj.si). We conducted an extensive evaluation of the proposed methodology in order to estimate the accuracy of extracting a particular semantic relation from a particular sentence. Evaluation was performed by 80 domain experts. In total 7,510 semantic relation instances belonging to 2,675 distinct relations were evaluated 12,083 times. The instances were evaluated as correct 8,228 times (68%).We extracted semantic relations with the SemRep natural language processing system from 122,421,765 sentences, which came from 21,014,382 MEDLINE citations . A total of 58,879,300 semantic relation instances were extracted and organized in a relational database. The QA process is implemented as a search in this database, which is accessed through a Web-based application, called SemBT  contains supplementary material, which is available to authorized users. The large size of the life sciences literature makes it difficult even for experts to absorb all the relevant knowledge in their field of interest. Sophisticated technologies are needed, and automatic text mining techniques are increasingly used to help access and exploit online textual resources. The most widely used are information retrieval systems such as PubMed, which searches the MEDLINE biomedical bibliographic database. These systems are very efficient and robust. However, in response to a user’s query they do not provide answers (or facts), but a set of documents (citations) that the user has to read in order to extract the required answers. For example, if a user wants an overview of the Parkinson’s disease literature, a PubMed search will return tens of thousands of documents. If the user is interested in treatments for a disease, with some skill it is possible to specify an effective query, but the result will be a set of documents that have to be read.Question answering (QA) systems, on the other hand, aim at providing answers (known facts). For the above example about the treatment of a disease, a QA system would provide as answers particular drugs that are used to treat that disease or short text passages that contain the answers. The overall goal of QA systems is to allow users to quickly get precise answers with the least amount of reading required.Evidence-based medicine  is an imClinical medicine is not the only field that needs efficient access to the literature for answers to questions. Genomic research is another example of such a field. With high-throughput technologies, such as genomic microarrays, it is now possible to measure the expression levels of essentially all genes within an entire genome scale simultaneously in a single experiment and to provide information on gene functions and transcriptional networks . HoweverIn this paper we present a QA tool, SemBT, that we have developed. It is able to answer a wide range of biomedical questions, not only for clinical medicine, but also for medical research in general, including pharmacogenomics and microarray experiment result interpretation. The tool returns answers in a top-down fashion, first very precise answers in the form of semantic relations, and then, on demand, more detailed answers. The tool is very fast and publicly available.QA can be open-domain ,8 or cloSeveral statistical techniques have been used to manipulate features in MEDLINE citations on microarray experiments. Shatkay et al. , for exaSemRep  is a symSemRep identifies many semantic predications representing various aspects of biomedicine. The core relations addressed refer to clinical actions  and organism characteristics . SemRep has recently been enhanced to address pharmacogenomics text . RelatioEach semantic relation extracted by SemRep is based on an ontological predication contained in a modified version of the UMLS Semantic Network. The arguments in these predications are UMLS semantic types, such as “Human” or “Anatomical Structure”, which can, for example, appear in the predication “Anatomical Structure PART_OF Human.” All predications extracted from text by SemRep must conform to an ontological predication.Extracorporeal shock wave lithotripsy in the treatment of renal pelvicalyceal stones produces the structure below.Semantic interpretation is based on the underspecified parse structure, in which simple noun phrases are enhanced with corresponding Metathesaurus concepts by MetaMap . For exa, )], prep(of), mod, head, metaconc]]Extracorporeal shock wave lithotripsy has been mapped to the concept “Extracorporeal Shockwave Lithotripsy” with semantic type “Therapeutic or Preventive Procedure” (topp). The parse structure enhanced with Metathesaurus concepts underpins the construction of a semantic predication. SemRep first applies “indicator” rules which map syntactic elements  to the predicate of an ontological predication. Argument identification rules  then find syntactically allowable noun phrases (enhanced with Metathesaurus concepts) to serve as arguments of indicators. The semantic types of the Metathesaurus concepts for the noun phrases must match the semantic types serving as arguments of the indicated ontological predication. In the example above, treatment is an indicator for TREATS, which has the corresponding ontological predication “Therapeutic or Preventive Procedure TREATS Pathologic Function.” The concepts corresponding to the noun phrases Extracorporeal shock wave lithotripsy and renal pelvicalyceal stones can serve as arguments of TREATS because their semantic types (“Therapeutic or Preventive Procedure” (topp) and “Pathologic Function” (patf)) match those in the ontological predication. In the final interpretation, ”Extracorporeal ShockWave Lithotripsy TREATS Kidney Calculi,” the Metathesaurus concepts from the noun phrases are substituted for the semantic types in the ontological predication.The noun phrase According to Hirschman and Gaizauskas , the proThe processing involved in our approach differs from the general approach described above. We start with an extensive preprocessing step during which we first extract semantic relations from MEDLINE with SemRep . For us,During preprocessing, we first extract semantic relations from MEDLINE with SemRep . The semantic types provide broad classification of the UMLS concepts serving as arguments of these relations. For example, “Levodopa” has semantic type “Pharmacologic Substance” (abbreviated as phsu), “Parkinson Disease” has semantic type “Disease or Syndrome” (abbreviated as dsyn) and “alpha-Synuclein” has type “Amino Acid, Peptide or Protein” (abbreviated as aapp). During the question specifying phase, the abbreviations of the semantic types can be used to pose more precise questions and to limit the range of possible answers.We store the large set of extracted semantic relations in a MySQL database. The database design takes into consideration the peculiarities of the semantic relations, the fact that there can be more than one concept as a subject or object, and that one concept can have more than one semantic type. The data is spread across several relational tables. For the concepts, in addition to the preferred name, we also store the UMLS CUI (Concept Unique Identifier) as well as the Entrez Gene ID (supplied by SemRep) for the concepts that are genes. The concept ID field serves as a link to other relevant information. For each processed MEDLINE citation we store the PMID (PubMed ID), the publication date and some other information. We use the PMID when we want to link to the PubMed record for additional information. We also store information about each sentence processed: the PubMed record from which it was extracted and whether it is from the title or the abstract. The most important part of the database is that containing the semantic relations. For each semantic relation we store the arguments of the relations as well as all the semantic relation instances. We refer to semantic relation instance when a semantic relation is extracted from a particular sentence. For example, the semantic relation “Levodopa-TREATS-Parkinson Disease” is extracted many times from MEDLINE and an example of an instance of that relation is from the sentence “Since the introduction of levodopa to treat Parkinson's disease (PD), several new therapies have been directed at improving symptom control, which can decline after a few years of levodopa therapy.” (PMID 10641989).At the semantic relation level we also store the total number of semantic relation instances. And at the semantic relation instance level, we store information indicating: from which sentence the instance was extracted, the location in the sentence of the text of the arguments and the relation (this is useful for highlighting purposes), the extraction score of the arguments (tells us how confident we are in identification of the correct argument) and how far the arguments are from the relation indicator word (this is used for filtering and ranking). We also wanted to make our approach useful for the interpretation of the results of microarray experiments. Therefore, it is possible to store in the database information, such as an experiment name, description and Gene Expression Omnibus ID. For each experiment, it is possible to store lists of up-regulated and down-regulated genes, together with appropriate Entrez gene IDs and statistical measures showing by how much and in which direction the genes are differentially expressed. We are aware that semantic relation extraction is not a perfect process and therefore we provide mechanisms for evaluation of extraction accuracy. In regard to evaluation, we store information about the users conducting the evaluation as well as the evaluation outcome. The evaluation is done at the semantic relation instance level; in other words, a user can evaluate the correctness of a semantic relation extracted from a particular sentence.The database of semantic relations stored in MySQL, with its many tables, is well suited for structured data storage and some analytical processing. However, it is not so well suited for fast searching, which, inevitably in our usage scenarios, involves joining several tables. Consequently, and especially because many of these searches are text searches, we have built separate indexes for text searching with Apache Lucene, an open source tool specialized for information retrieval and text searching. In Lucene, our major indexing unit is a semantic relation with all of its subject and object concepts, including their names and semantic type abbreviations and all the numeric measures at semantic relation level. Our overall approach is to use Lucene indexes first, for fast searching, and get the rest of the data from the MySQL database afterwards.The QA tool presented in this paper has as a major goal answering what is currently known based on the biomedical literature. The tool is web-based and is one of the several tools available at the SemBT website. The other tools’ major focus is on literature-based discovery, or discovering new knowledge from the literature. The front-end (user interface) for all SemBT tools was developed with the Ruby on Rails application development framework. Figure In contrast to the usual QA methodology, in this phase we do not create a query for document retrieval, but rather we create a query for searching in the database of extracted semantic relations described in the previous section. In the interface shown in Figure In the examples shown above we did not refer to the subject, relation and/or object explicitly, but rather implicitly. A query such as “donepezil treats down syndrome” searches all the words in all the fields of the relations. Most of the time, such a query will be satisfactory; however, it is possible to construct more precise queries by referring explicitly to particular search fields. Subject related search fields are: “sub_name” meaning subject name, and “sub_semtype” meaning subject semantic type abbreviation. Object related search fields are: “obj_name” meaning object name, and “obj_semtype” meaning object semantic type abbreviation. If we do not want to distinguish between the subject and the object, we can use: “arg_name” meaning the name of the subject or the object, and “arg_semtype” meaning the semantic type abbreviation of the subject or the object. And finally, there is one semantic relation related field – “relation” meaning the name of the relation. The query above with explicit search fields would look like “sub_name:donepezil relation:treats obj_name:down syndrome”.Another implicit aspect of the queries shown so far is the logical connection or operator between the question terms. If there is no explicit logical operator present then AND is assumed. For example, the last query above really means and is equivalent to “sub_name:donepezil AND relation:treats AND obj_name:down syndrome”. In all the examples so far, the actual question is the text between the quotation marks, without the quotation marks themselves. In other words, when constructing questions, it is possible to use the standard Boolean operators AND, OR and NOT and group the search terms with parenthesis. In contrast to the search terms themselves, the logical operators must be capitalized to be properly understood by the tool. The question “What are the genes or proteins known to be etiologically related to Alzheimer?” can be specified with explicit Boolean operators as “sub_semtype:(aapp OR gngm) AND relation:(CAUSES OR PREDISPOSES OR ASSOCIATED_WITH) AND obj_name:Alzheimer” where “aapp” stands for “Amino acid, peptide or protein” and “gngm” for “Gene or genome”. An example of the NOT operator might be the question “What has been used to treat Alzheimer that is not a pharmacological substance?” which could be minimally specified as “NOT phsu treats Alzheimer” and in full form as “NOT sub_semtype:phsu AND relation:treats AND obj_name:Alzheimer”. As practical advice, we recommend that users of our tool first try specifying questions without explicit field reference and Boolean operators. In any case, knowing the names of the available semantic relations and the semantic type abbreviations is essential.Automatic argument expansion is another useful feature of our QA tool. If requested, it expands the question arguments with semantically narrower concepts (hyponyms). For example, if we issue the query “arg_name:antipsychotic treats” we will get only relations where antipsychotic agents appears. However, if we use argument expansion by selecting from the “Expand” set of options before the query is submitted, the semantic relation ISA (meaning “is a”) is used behind the scenes to search for narrower concepts, and the original query is expanded with them. The results will then also contain particular antipsychotic agents, such as clozapine, olanzapine, risperidone, haloperidol and so on. As another example, we can deal with a whole class of disorders in a question such as “What are the most common treatments for neurodegenerative disorders?” This question can be answered by using expansion in the query “treats arg_name:neurodegenerative”. Here, “neurodegenerative” is expanded with the particular neurodegenerative disorders, such as Alzheimer’s disease, Parkinson disease and so on. A similar question might be “What are the most common treatments for various neoplasms?” Here again we require expansion and use the query “treats arg_name:neoplasms”. Currently, there are some limitations in the argument expansion facility: explicit field reference must be used ; if there are many narrower concepts, only the first one hundred are used; and finally, only a single word can be used to specify the concepts to be expanded (that’s why we used “antipsychotic” and “neurodegenerative” above). The last limitation means that when using expansion, the single word entered (e.g. “antipsychotic”) is used to search for all the concepts containing that word , and, finally, all the concepts found are expanded. Therefore, although a single word is entered, it is possible to expand on multiple word concepts. These limitations are due to technical issues faced when parsing and modifying the original query, and we plan to remove them in the future.N subjects, relations and objects of a query, and to use these for further query refinement or result filtering. Faceting results are shown in the left column of the user interface . Then we load these data into SemBT, after which the experiment becomes available in the microarray filter. At this time, there are a few microarray experiments available, but we are working on allowing users to upload their own experiments into the tool directly without our intervention. The next option is selecting which argument of the semantic relations to be limited to the differentially expressed genes. For example, selecting subject here means that only those semantic relations are eligible in which the subject is one of the differentially expressed genes. The next few options allow the selection of up-regulated or down-regulated genes based on several parameters. To illustrate the microarray filter, we describe how the question mentioned earlier  can be specified. In the query field we can use “relation: (ASSOCIATED_WITH OR PREDISPOSES OR CAUSES) AND obj_semtype:dsyn”. This query alone would search for semantic relations in which something (not yet specified) is etiologically related (i.e. causes) some disease or syndrome (semantic type “dsyn”). Now we can use the microarray filter to limit the list of semantic relations to only those where the subject is one of the up-regulated genes from one of the experiments. For example, we can first select an experiment (e.g. “Parkinson Meta …”) and then in the field “Limit arguments” we select “Subject” and then “Upregulated”. When the query is submitted, before it is executed, it is modified so that only the up-regulated genes from the selected microarray experiment can appear as subject. The resulting list of semantic relations shows the specific genes and the specific diseases that they cause.In the question processing phase the question entered by the user is interpreted depending on user-selected options; then it is executed. Answers are presented in a top-down fashion, semantic relations first, then, on demand, semantic relation instances, and finally, MEDLINE citations. In Figures The frequency field is a hyperlink and if followed, a new browser window shows the relation instances and a list of sentences from which each relation was extracted. In the sentences, whenever possible, the subject, relation and object are highlighted in different colors to make it easier to identify the relation and its context. Figure It is important to notice that the highlighted terms are not always the same as the official names used for the subject, relation or object. For example, in some sentences the abbreviation AD appears, but SemRep correctly recognizes this as Alzheimer’s disease. Also, the words “in” and “for” are used several times in the text to indicate treatment, which is quite frequent in medical text. This is even more common when gene symbols are mentioned in the text. Many genes have more than one symbol to denote them. And often, different genes might have the same symbol. To make things even more difficult, some gene symbols can also have another, often more common, meaning. For example, CT and MR are gene symbols, but more often mean Computed Tomography and Magnetic Resonance, respectively. This problem is known as gene symbol ambiguity and SemRep attempts to address it as described in the We would like to stress that this view Figure  also allFacets, if exploited, can also be considered as part of the answers. Facets convey an aggregated view of the set of answers. The type of information facets contain and their usage were described in the previous subsection and shown in Figure In this section we first describe the size of the processing involved. Then aggregated counts for the most important semantic relations and semantic types are presented, and finally, the results of the extraction correctness evaluation are shown.In the preprocessing phase we extracted semantic relations with SemRep from 122,421,765 sentences. These sentences come from 21,014,382 MEDLINE citations (the whole MEDLINE database up to the end of 2012). 13,099,644 semantic relations were extracted having a total of 58,879,300 semantic relation instances.Table In Table The quality of the answers provided in our approach largely depends on the quality of the semantic relation extraction process. Our questions must be in the form Subject-Relation-Object, and thus evaluating matching semantic relation extraction is a good  indicator of question-answering performance. We currently deal with a subset of all possible questions, as illustrated by the example, “Find all the drugs that inhibit the up-regulated genes from a particular microarray.” For this type of question, evaluating information extraction is very close to evaluating question answering.Since the evaluation results shown in this paper were done for questions of the type noted above, we conducted an evaluation to estimate the correctness of the information extraction. Technically, the evaluation was done using the same QA tool used for browsing the answers, and the evaluation outcome was immediately stored in the database. The evaluation was conducted at a semantic relation instance level. In other words, the goal was to determine whether a particular semantic relation was correctly extracted from a particular sentence. The evaluators could select as outcome “correct”, “not correct” or “undecided”. Eighty subjects, students in the final year of medical school, conducted the evaluation. They were divided into four groups of twenty persons each. Each group spent three hours on an evaluation session. The subjects were organized in such a way that three of them independently evaluated the same semantic relation instance. They were not allowed to consult with each other about the outcome, and this was strictly enforced by their instructor. The idea was that each semantic relation instance included in the evaluation was to be assessed by three subjects so that voting could determine disagreement in the outcome. But in reality, since the subjects had some freedom whether to skip a relation to be evaluated and which one to evaluate from the set of assigned relations, it turned out that some instances were really evaluated by three subjects, but some were evaluated by two and some by only one person. The subjects were also instructed that the quality of the evaluation was more important than the quantity. This is probably another reason that some subjects evaluated more and some fewer relations.N = 3,089), we found that 1,866 (60%) instances were evaluated more times as correct than as wrong, and 775 (25%) instances were evaluated more times as wrong than as correct, which means that the remaining 15% were evaluated as many times as correct as wrong. However, if we consider only the relation instances being evaluated by exactly three evaluators , then 781 (68%) relation instances were evaluated more times as correct than as wrong, and 365 (32%) instances were evaluated more times as wrong than as correct. The relations with most instances evaluated by exactly three evaluators were INHIBITS (N = 865), STIMULATES (N = 219) and ASSOCIATED_WITH (N = 46) with respective extraction precisions 71%, 54% and 85%.In total 7,510 semantic relation instances belonging to 2,675 distinct relations were evaluated 12,083 times. The instances were evaluated as correct 8,228 times (68%) and as wrong 3,855 times (32%). 5,519 distinct instances were evaluated as correct (73%) at least once and 2,818 distinct instances were evaluated as wrong (37%) at least once. 4,692 distinct instances were always evaluated as correct (62%) and 1,991 distinct instances were always evaluated as wrong (26%). If we did not take into consideration the number of persons who evaluated a particular relation instance, we found that 4,905 (65%) distinct instances were evaluated more frequently as correct than as wrong: 2,157 (29%) instances were evaluated more often as wrong than as correct, and 448 (6%) relation instances were evaluated as correct exactly as many times as they were evaluated as wrong. If we consider only the relation instances that have been evaluated by two or more evaluators  divided by the total number of cases evaluated by the same evaluators. The inter-evaluator agreement for instances evaluated by two evaluators was 80% and for instances evaluated by three evaluators was 76%.We analyzed 100 system errors selected at random from predications deemed as false positives by three judges. From this analysis it became clear that incorrect answers returned by the system were largely due to erroneous predications identified by SemRep in the source documents (MEDLINE citations). Efforts are being made to address the SemRep errors noted in this research.cyclin-dependent CDK system was missed, and CDK alone was consequently interpreted as an argument of a false positive predication.Slightly fewer than a quarter of the errors (21) were wrong because of concept misidentification. One subtype of this class involves failure to analyze a larger structure containing a misidentified component concept. For example, the larger structure inhibits is colonic aberrant crypt foci formation, but this concept does not have semantic type ‘Physiologic Function’ and was not allowed to be the object of this predication.Another type of error involving concept identification is due to missing information in the Metathesaurus, specifically not having the semantic type ‘Physiologic Function’, which is one of the semantic types allowed for objects of predications with predicate STIMULATES or INHIBITS. For example, in the following sentence, the correct object of Beta-escin inhibits colonic aberrant crypt foci formation in rats and regulates the cell cycle growth by inducing p21(waf1/cip1) in colon cancer cells.SemRep then wrongly moved to the next concept with a semantic type allowable as an object of INHIBITS  and produced the false positive “beta-escin INHIBITS CDK1A”Four errors were due to misinterpretation of a predicate. For example, SemRep extracted the predication “Meclofenamic Acid STIMULATES ATPase” from the following sentence based on the highlighted items.At low glibenclamide concentrations,MFA inducedadditional inhibition of the K(ATP) current.MFA and ATP were correctly mapped to the concepts “Meclofenamic Acid” and “ATPase,” respectively. However, the correct relation between these concepts in this sentence is INHIBITS, not STIMULATES. The complex predicate induced … inhibition was not interpreted correctly (as INHIBITS), but rather induced was interpreted as STIMULATES by SemRep.and and the so-called subordinating conjunctions, such as while and when. All of these are ambiguous in that they can conjoin structures other than clauses. And is notorious for signaling a variety of coordination types, including, noun phrases and verb phrases, in addition to clauses. When SemRep fails to mark a clause boundary, a false positive may result. For example, SemRep extracted the predication “CDK2 INHIBITS CDKA1A” from the sentence below.The rest of the errors (75) were due to various deficits in syntactic processing. We note a few of the more prominent error types in this class. Incorrect interpretation of clausal boundaries accounted for fifteen errors. Semantic predications are not allowed to cross clausal boundaries, so correct identification of this phenomenon is crucial. Intrasentential clause boundaries are commonly marked by The levels of CDK2, CDC2, Cyclin A and Cyclin B proteins decreased,] pyrazol-6(2H)-one , as well as by antioxidants.MAPK, inhibited, and bis. MAPK was correctly mapped to the Metathesaurus concept “Mitogen-Activated Protein Kinases,” and the verb inhibited is the correct indicator for the semantic relation INHIBITS. However, the text bis is a part of the complex chemical name in which it appears and should not have been mapped to the concept “BAG3.” Further, with respect to syntax, MAPK is not allowed to be an argument of inhibited in this sentence. In addition this verb is in passive voice (immediately preceded by was). Any argument to its left must be its (semantic) object, rather than subject as in the false positive being discussed.This predication was based on the three highlighted components: Our approach to biomedical QA is, first, to extract semantic relations from MEDLINE with SemRep and build a database of these relations during a preprocessing step. Then, QA is implemented as a search in the semantic relation database. Due to the preprocessing and the appropriate database design, our approach is very fast. A typical question is answered within a few seconds, and if faceting is used or the microarray filter is switched on, the processing time takes a few additional seconds. One of the limitations of our approach is that it is currently not possible to provide contextual information when specifying the questions. For example, it is not possible to ask “What is used to treat patients with a certain disease who also have another disease or situation at the same time?” We can answer the first part of the question separately: “What is used to treat patients with a certain disease”, and the second part of the question also, but not the full question at once. One of the first items in our further work list is to allow more complex questions, which will involve combining several semantic relations in order to answer them.We also plan to allow asking questions in natural language format, which will be useful for processing more complex questions like the one mentioned above. For less complex questions, the current approach is quite satisfactory, provided that the user spends some time learning how to formulate a question. For example, the natural language question “What drugs can be used to treat diabetes?” can be asked in our tool as “phsu treats diabetes” where “phsu” stands for “pharmacological substance” and “treats” is the name of the semantic relation. Because a lot of issues are involved with processing natural language queries, we also plan to explore another direction. We plan to develop a sophisticated user interface, based on question templates, which will allow formulating complex questions in a more structured way – a way that will be easier to process with a computer.Since important information is present in the full text of the articles, we also plan to process a considerable number of electronically available full text articles. And since in addition to the biomedical articles there are also important websites with useful information, we also plan to process the most important such sites.We were aware before, and the evaluation conducted for this paper has confirmed, that the semantic relation extraction process is not perfect. Therefore, improving the extraction performance is a permanent ongoing process. We believe that a very useful feature of our tool is the ability to evaluate the answers within the tool itself. In other words, whenever the user sees an answer (semantic relation) that she thinks is not correct, she can record her opinion and it is stored in our database. Right now, only the users we know and trust can evaluate, but we would like to allow any user to register and evaluate the relations. The major goal of collecting evaluation-related information is that it allows us to find the weak points and improve them. There are a few more things that can be done once we have enough evaluation data. For instance, the answers can be sorted by how many positive evaluations they have received, or they can be automatically filtered out if the users believe they are not correct. The value of this kind of evaluation is that by helping us, the users of our tool will also help themselves. This is similar to a crowdsourcing approach in which we would like to involve the biomedical community.In the future, we also plan to conduct an evaluation of the user interface of SemBT to see how easy or difficult it is to use, and whether it is intuitive or not. Based on the results of that evaluation, we will improve the user interface accordingly.http://sembt.mf.uni-lj.si and is a useful complement to existing information retrieval systems.We propose a methodology and describe a tool, SemBT, for biomedical QA. The system is able to provide answers to a wide array of questions, from clinical medicine through pharmacogenomics to microarray results interpretation. SemBT is based on semantic relations extracted from the biomedical literature and is able to quickly provide precise answers to user questions. More details are provided only on demand. SemBT is publicly available at