Sonum Jagetia

Adrianna Milton

Lucas A Stetzik

Samantha Liu

Kavya Pai

Keiko Arakawa

Nathalie Mandairon

Daniel W Wesson
email: danielwesson@ufl.edu

Inter-and intra-mouse variability in odor preferences revealed in an olfactory multiple choice test

Animals choose between sensory stimuli, a highly complex behavior which includes detection, discrimination, preference, and memory processes. Rodents are reported to display robust preferences for some odors, for instance in the context of choosing among possible mates or food items. In contrast to the apparent robustness of responses towards these and other 'ethologically relevant' odors, little is known about the robustness of behaviors towards odors which have no overt role in the rodent ecological niche, so-called 'non-ethologically relevant' odors. We developed an apparatus for monitoring the nose-poking behavior of mice and used this apparatus to explore the prevalence and stability of choices among different odors both across mice, and within mice over successive days. Mice were tested to either a panel of ethologically relevant or non-ethologically relevant odors in an olfactory multiple choice test. Significant preferences to non-ethologically relevant odors were observed across the population of mice, with longer investigation durations to some odors more than to others. However, we found substantial intermouse variability in these responses, and that responses to these odors even varied within mice across days of testing. Tests with ethologically-relevant odors revealed that responses towards these odors were also variable across mice, but within individual mice responses were somewhat stable. This work establishes an olfactory multiple choice test for monitoring odor investigation, choice, and preference behaviors and the application of this apparatus to assess across and withinmouse odor preference choice stability. These results highlight that odor preferences, as assayed by measuring choice behaviors, are variable.

Introduction

Sensory stimuli are well known to elicit strong physiological and behavioral responses. These responses depend on the features of the stimulus itself (e.g., its intensity), the number of stimuli presented, our discrimination capacity among them, and our previous experiences. Indeed, different responses can be observed based upon whether or not the stimulus has been previously associated with a reinforcer, unconditioned, or novel. For instance, pairing a tone with a palatable reinforcer results in the tone gaining a positive valence. In other cases, stimuli may evoke unlearned, sometimes called 'innate' responses, such as the disgust elicited by drinking a highly bitter beverage.

Chemical stimuli are particularly capable of eliciting robust behavioral responses. The hedonic quality of an odor, which is highly variant across odors, is a primary feature whereby humans conceptualize olfaction [START_REF] Yeshurun | An Odor is Not Worth a Thousand Words: From Multidimensional Odors to Unidimensional Odor Object[END_REF]. Hedonics impact odorguided behavior of other animals too, including rodents. By measuring the duration of time the animal investigates an odor or is in proximity to an odor, one can operationally define attraction or 'preference' for an odor. Mice and other rodents are known to display, on average, preferences to some odors (e.g., [START_REF] Baum | Sex difference in attraction thresholds for volatile odors from male and estrous female mouse urine[END_REF][START_REF] Devore | Odor preferences shape discrimination learning in rats[END_REF][START_REF] Dibenedictis | Odor preferences of wild stock female house mice (Mus domesticus) tested at three ages using urine and other cues from conspecific males and females[END_REF][START_REF] Isles | Urinary odour preferences in mice[END_REF][START_REF] Kermen | Topographical representation of odor hedonics in the olfactory bulb[END_REF][START_REF] Kobayakawa | Innate versus learned odour processing in the mouse olfactory bulb[END_REF][START_REF] Lydell | Male rat of odor preferences for female urine as a function of sexual experience, urine age, and urine source[END_REF][START_REF] Mandairon | Humans and Mice Express Similar Olfactory Preferences[END_REF][START_REF] Sullivan | Developing a sense of safety: the neurobiology of neonatal attachment[END_REF][START_REF] Yuan | Early odor preference learning in the rat: bidirectional effects of cAMP response element-binding protein (CREB) and mutant CREB support a causal role for phosphorylated CREB[END_REF]). In the majority of studies to date, the results used to support odor 'preferences' are presented as mean durations of investigation times averaged across animals. One such well known example is the preference of a male mouse for the odor of conspecific female urine (e.g., [START_REF] Baum | Sex difference in attraction thresholds for volatile odors from male and estrous female mouse urine[END_REF]), with male mice investigating conspecific female urine longer than that of males. Another example seen at the population level is avoidance of mice for the odors of predators [START_REF] Ferrero | Detection and avoidance of a carnivore odor by prey[END_REF]. Additionally, mice display preferences at the population level to synthetic odors with little to no known ethological value [START_REF] Mandairon | Humans and Mice Express Similar Olfactory Preferences[END_REF][START_REF] Kermen | Topographical representation of odor hedonics in the olfactory bulb[END_REF]. Thus, odor hedonics inform odor driven behaviors in mice. Little literature is available, however, on how diverse these responses are among animals. Do all mice, or even most mice, show strong preferences towards the same odors? Within a mouse, does it stably choose to dominantly investigate the same odor across days? One might assume that genetically inbred lines of mice, all reared in similar contexts, would share preferences towards similar odors. In contrast, even if mice share similar preferences at the population level, it is possible that mice have unique personalities in their display of odor preferences.

In addition to investigating the basis of hedonics, studying the behaviors of animals in the presence of multiple stimuli will provide important clues needed to unravel how the brain informs preferences across different stimuli and thereby guides behavioral choices. As described above, making a choice necessitates identification of a stimulus, discriminating between one stimulus among others, as well as reflections upon whether or not either stimulus has an associated memory. If a mouse is allowed to investigate a panel of odors, one might assume it may choose a given odor, and therefore indicate its preference for that odor. Investigating odor choices of mice among panels of diverse odors would allow insights into the basis of odor choices in mice and how this may reflect preference.

In order to test whether odor preferences are 1) maintained across mice, and 2) stable within mice, in this study we developed an apparatus that allows for an animal to freely investigate, and choose among, up to 5 odors in a given behavioral session. We used this apparatus to monitor investigation behaviors of mice to panels of both non-ethologically relevant odors (those with no clear role in the rodent ecological niche) and ethologically relevant odors in an olfactory multiple choice paradigm. Our results provide a compelling example for olfactory choices and how they contribute to variability of odor preferences, both between and within mice.

Materials and Methods

Experimental Subjects

Thirty-seven adult male C57BL/6 mice (2-4 months of age) obtained from Envigo (Indianapolis, Indiana, U.S.A.) and maintained within the Case Western Reserve University animal facility were used. Mice were group housed in cohorts of 2-5 mice/cage. Mice were allowed food and water ad libitum throughout the study, and housed on a 12 h light/dark cycle with behavior testing occurring during the light phase (11:00-17:00 h). All experiments were conducted in accordance with the guidelines of the National Institutes of Health and were approved by both the University of Florida and Case Western Reserve University Institutional Animal Care and Use Committee.

5-Port Behavioral Apparatus

In order to directly monitor nose-poking towards odors, we developed a behavioral apparatus (Fig 1) with 5 odor ports wherein nose-poking into each port is monitored by 880nm infrared photobeams and digitized for later analysis. The apparatus was made of black acrylonitrile butadiene styrene (ABS) plastic, consisting of a 30.5 × 40.6cm (W × L) gray perforated floor (0.3cm perforations, ~0.5cm spaced) and a guillotine style ABS door to allow gentle insertion and removal of the mouse from the testing apparatus. The ceiling was 'open' to allow flexibility for those who may wish to monitor animals with video. Five 4 × 30.5cm thick black ABS panels formed the walls of the chamber which each housed a 3D printed odor port (Fig 1B). The odor port was T-shaped with a 2cm opening which faced into the behavioral apparatus to allow nose-entry and monitoring of infrared beam breaks. The two other ends of the T-shaped port were 2.5cm openings designed as the top and bottom of the port. The bottom of each port allowed insertion of a disposable odorless plastic cup (flanged plastic cap, MOCAP, Park Hills, MO) which allowed the user to dispense a small volume or mass of odor, and at the top of the odor port was a 'silent' 40mm exhaust fan (Scythe Mini KAZE ULTRA, SY124020L) connected by a manifold and an ~ 20cm long piece of odorless plastic pipe (Fig 1B). Due to the fan, air flows from within the behavioral arena into the port entrance thereby requiring animals to nose poke to obtain odors. The odor port design restricted direct interaction with the odor, and prevented acquisition of visual, somatosensory, or gustatory cues. The result is a semi-circle arena which allows for free choice in investigating one of five odor ports at a time (Fig 1C). The data from the photobeams were acquired using an Arduino Mega microcontroller board (ATmega 2560) running custom code written in the Arduino open source language (Arduino, https://www.arduino.cc). The microcontroller, with a 16Mhz clock speed, was programmed to monitor photobeam interruptions and their timing (with 10ms resolution) and to send that data onto an SD (secure digital) card or via a USB cable onto a computer.

Stimuli

Three different sets of odors were used. Two sets consisted of monomolecular odorants obtained in their highest available purity (Sigma Aldrich, St. Louis, MO) diluted to 1Pa vapor pressure in mineral oil. Odor set 1 included guaiacol, l-carvone, thioglycolic acid, heptanal, and 1-decanol. Odor set 2 included (+)-limonene, 2-methylbutyraldehyde, geraniol, isopentyl acetate, and mineral oil as a semi-odorless [START_REF] Gamble | Discrimination of "Odorless" Mineral Oils Alone and as Diluents by Behaviorally Trained Mice[END_REF] comparison since the odors were diluted in mineral oil. Odorant dilutions were determined using EPI Suite Software (EPIWEB 4.0). Diluted odors were stored at room temperature under nitrogen gas until testing. 20 μL of each odorant was pipetted into a single plastic cup immediately prior to the beginning of testing. The third set of odors consisted of odors with ethological relevance to mice. These included feces collected from a gonadally intact adult male domestic cat, urine collected from adult female mice who were gonadally intact, pieces from a red delicious apple, pieces of unsalted peanut, and honey. With the exception of honey, which was stored at room temperature, all of these stimuli were aliquoted into similar masses (<1g), placed into odorless plastic storage tubes, and stored at -20°C prior to being thawed at least 15 minutes prior to behavioral testing. Urine was collected as previously described [START_REF] Wesson | Enhanced urinary odor discrimination in female aromatase knockout (ArKO) mice[END_REF]. Urine was pooled across 4-6 gonadally intact female mice per day. Since these female mice were presumably normally cycling, the samples were homogenized across multiple collection days to control for variations in timing of the estrous cycle relative to the collections. 20 μL of urine and each of the 1g samples were placed into separate new plastic cups prior to testing.

Behavioral Testing

Mice were acclimated to the 5 port behavioral apparatus by allowing them 30 minutes of free exploration for 3 consecutive days. Clean plastic cups were present in the odor ports during this acclimation phase. Next, across 5 consecutive days, mice were individually placed in the apparatus with odors available for 30 minutes (either odor set 1, odor set 2, or the ethologically relevant odor set). Mice were tested in pseudo-random order each day. Odorants were arranged in the apparatus such that no odor repeated the same port throughout the testing phase. An approximately two-week duration occurred between the last test day of the first odor set, and the first test day of the second odor set. Prior to testing with the second set of odors, mice were re-acclimated to the apparatus for 30 minutes one day before the first test day. The apparatus was thoroughly cleaned with 70% ethanol at the beginning and end of each testing session. Odorants remained in the testing apparatus until conclusion of testing for the last subject each day. The door of the apparatus was closed during all acclimation and testing sessions.

Data Analysis

Raw data (ms poking/port) was imported into Microsoft Excel and organized by port number, session number, and mouse number. To normalize the data as a means to overcome possible influences of port location (possible spatial bias) and for testing day acclimation/ habituation on analyses, we developed what we term an 'I-value' metric. I-values were calculated within mice. First, we summed the total amount of time each mouse spent nosepoking into each port. Next, within odors, the amount of time that mouse spent nose-poking for that odor on a given day was divided by the total amount of time spent nose-poking across all odors on a given port and multiplying this by 100. Finally, in order to get a mean 'I-value' for a single given odor across all testing days, the previous result was averaged across all five testing days. Statistical measures included ANOVA and repeated measures ANOVA followed with Fisher's PLSD post-hoc test where indicated.

Results

We began our investigation by monitoring the nose-poking behavior of 4 cohorts of mice (n = 18 total, cohort sizes of 4-5) in response to successive testing days with the panel of nonethologically relevant odorants. As described in detail in Materials & Methods, each mouse was acclimated to the 5-port apparatus (Fig 1) over 3 days in the absence of any odors prior to being presented with the first panel of odors over 5 successive days (30mins/day). Following, the mice were left untested for two weeks before the acclimation and testing repeated with an independent panel of non-ethologically-relevant odors.

Habituation of nose-poking behaviors and influence of port location on nose-poking

To help optimally assess the later odor-guided behavioral results, we first analyzed the effect of testing day number and port location on the nose-poking behaviors of the mice across all mice tested with non-ethologically relevant odors. Across all ports and mice, the mean duration of nose-poking decreased across repeated testing days, reflecting habituation to the apparatus and/or task (odor set 1, (F(4,68) = 17.881, p < 0.001); odor set 2 (F(3.886,66) = 11.557, p < 0.001)) (Fig 2A). Greater mean duration of nose-pokes occurred on testing day 1 than day 5 for both odor sets (odor set one: Fisher's PLSD p < 0.001), odor set two: Fisher's PLSD p < 0.001)). We used the same sessions of data as above to next analyze the impact of port spatial location on the nose-poking behavior of the mice. We found a significant effect of port location on the mean duration of nose-poking (odor set 1: (F(4,85) = 4.45, p = 0.003), odor set 2: (F(4,85) = 4.166, p = 0.004)) (Fig 2B). Pooling data across the odor sets, this effect was due to longer poking into the side ports 1 and 5 than the center ports (Fischer's PLSD port 1 vs 2 p = 0.002, port 1 vs 3 p < 0.001, port 1 vs 4 p = 0.003, port 5 vs 2 p = 0.005, port 5 vs 3 p = 0.001, port 5 vs 4 p = 0.009) (Fig 2B).

Preferences for non-ethologically relevant odors are evident, yet highly variable across and within mice

Mice displayed a variety of patterns of investigation behavior while allowed to freely explore the 5 port apparatus. Figure 3 illustrates the nose-poking activity of three mice (which are not included in other analyses) while allowed to investigate one odor set. These plots show that mice have different tendencies to poke in ports, including varieties of transitions from port to port (sometimes entering back into the previous port) and ranges of nose-poke bout durations. Across these three mice, no clear preferred stimulus is apparent.

We next sought to assess the possibility for preferences towards non-ethologically relevant odors in the five port apparatus across the entire population of mice. As shown in Figure 2, testing day and port location greatly impact investigation behaviors of mice in the 5 port apparatus. To overcome this, we calculated odor investigation behaviors using an 'I-value' metric which defines for each mouse, on each day, odor investigation as the percent of total time nose-poking in one port, independent of day or spatial location (see Materials and Methods). Across all mice and both panels of non-ethologically relevant odors, we found a significant effect of odor type on I-values (F(9,170) = 4.582, p = 0.003) (Fig 4A). Mice investigated thioglycolic acid longer than other stimuli, including guaiacol (p < 0.001), 1decanol (p < 0.001), and geraniol (p < 0.001) (Fig 4A). Interestingly, a large mean I-value was observed in response to mineral oil which, while not entirely 'odorless' [START_REF] Gamble | Discrimination of "Odorless" Mineral Oils Alone and as Diluents by Behaviorally Trained Mice[END_REF], was selected as a comparison stimulus since the odors were diluted in mineral oil.

It is notable that no single odor very clearly and robustly showed dominance in the mean Ivalues. We predicted this was due to inter-mouse variability and therefore we asked how robust preferences for thioglycolic acid and the other stimuli were across the mice. To do this, we determined the odor which contributed the greatest I-value for each mouse. As shown in Figure 4B, thioglycolic acid only contributed the highest I-value for 27.7% of mice (4/18). The majority of mice (72.3%, 14/18) displayed their highest I-value for odors other than thioglycolic acid (Fig 4B ), with l-carvone, heptanal, and mineral oil being second-most ranked. No mice displayed their highest I-values for decanol or 2-methyl butyraldehyde. Thus, there is great inter-mouse variability in preferences for non-ethologically relevant odors.

Since our paradigm involves testing each mouse to the same odor panel across 5 days, we next asked within mice, whether their 'preferred' odor is stable across days. Since I-value takes into account across-day variations, for this analysis we used the total percentage of time nose-poking towards each odor as a measure of preference. Further, since each mouse was presented with each odor over five sessions, we defined a mouse as 'preferring' a single odor if they displayed their greatest investigation time to that odor over three or more days (consecutive or otherwise). We found that only 38.8% (7/18) of mice display stable odor preferences across the five testing days (Figs 5A&B). The majority of mice (61.2%, 11/18) displayed variations on which odor they 'preferred' on a day-to-day basis. Among all 18 mice, only one (5.5%) displayed a stable odor-specific preference across all five testing days (Fig 5C ), with this mouse following thioglycolic acid across all five days regardless of port location. Of those mice which did display odor-specific preferences, 4 involved at least 2 days wherein the preferred odor was in ports 1 and 5 (those with significant spatial biases [Fig 2B ]). After eliminating those mice from the population, only 16.6% would contribute odor-specific preferences across the majority of days (Fig 5D). Thus, investigation times, and thereby choices, for non-ethologically relevant odors are not only variable across mice (Fig 4B), but as shown here, also variable within mice.

Preferences for ethologically-relevant odors are more conserved across, and stable within, mice

We next sought to test the hypothesis that mice display more robust and less variable preferences to ethologically-relevant odors, versus what we found for non-ethologically relevant odors. Therefore, we used three additional cohorts of mice (n = 16 total, cohort sizes of 4-7). We first began by analyzing task acclimation and port spatial preferences as we did for the previous cohorts of mice (Fig 2). In contrast to what we found in the context of non-ethologically relevant odors, mice did not habituate in their investigation towards ethologically relevant odors. Across all ports and mice, the mean duration of nose-poking was maintained across repeated testing days (F(4,60) = 1.228, p = 0.309) (Fig 6A). While from different cohorts of mice, animals spent greater amounts of time sampling ethologically-relevant versus non-ethologically relevant odors (t(27)=3.85, p = 0.0007) (Fig 6B). Therefore, we normalized the data within animals, among both odor sets, to the maximum duration of sampling on any of the 5 testing days to test for differences in habituation to the odors. This analysis revealed a significant interaction between testing days and the odor set type (F(1,4) = 13.288, p ≤ 0.0001).Specifically, mice persevered in investigating ethologically-relevant odors over successive testing days to similar levels (t(15)=-1.04, p = 0.316; day 1 vs. day 5) and in manners not observed towards the nonethologically relevant odor set (t(12)=9.66, p ≤ 0.0001; day 1 vs. day 5) (Fig 6C). We used the same sessions of data as above to next analyze the impact of port spatial location on the nose-poking behavior of the mice. Again in contrast to the results acquired in the non-ethologically relevant odor tests, there was no effect of port location on the mean duration of nose-poking of mice towards these ethologically relevant odors (F(4,75) = 0.833, p = 0.509) (Fig 6D). Directly comparing these data with those from the non-ethologically relevant odor sets (Fig 2B), no significant interaction effect is observed between port number and odor set type (F(1,250) = 1.121, p = 0.347). Together, the above data highlight a striking difference in behavioral responses when compared between non-ethologically relevant and ethologically relevant odors.

We next analyzed for effects of odor type on the investigation behavior of these mice. We found a significant effect of odor type on I-values (F(4,75) = 5.572, p < 0.001) (Fig 7A). As a population, the highest I-value was displayed towards peanut (Fig 7A). The lowest I-value across mice was observed for honey (Fig 7A). Thus, as with non-ethologically relevant odors, at the population level, mice display odor preferences.

We next asked how robust preferences for peanut odor and the other odors were across mice. To do this, we determined the odor which contributed the greatest I-value for each mouse. 43.7% of mice (7/16) displayed their greatest I-value to peanut (Figs 7B). No mice displayed their highest I value to honey (Fig 7C). Thus, as with non-ethologically relevant odors, there is inter-mouse variability on preferences for ethologically relevant odors (Fig 7C).

We next asked, within mice, whether preference for their 'preferred' ethologically-relevant odor is stable across days. As with the non-ethologically relevant odors, for this analysis we used the total percentage of time nose-poking in each session as a measure of preference. Again, we defined a mouse as 'preferring' a single odor if they displayed their greatest investigation percentage to that odor over three or more days (Fig 8A). The majority of mice (9/16, 56.3%) displayed an odor-specific preference (Fig 8B). Of those mice which did display odor-specific preferences, these were mostly displayed towards peanut odor (Fig 8A). In fact, among all 16 mice, 5 (31.3%) displayed a stable odor-specific preference across all five testing days to peanut odor. Thus, while variability exists, mice display somewhat stable preferences to these ethologically-relevant odors.

Discussion

In this study we used a novel behavioral apparatus to investigate the behaviors of mice to panels of both non-ethologically relevant odors (those with no clear role in the rodent ecological niche) and ethologically relevant odors. We found that 1) at the population level mice do display odor preferences in the multiple choice task, 2) that these preferences are variable at the individual mouse level, and 3) these preferences are modulated by experience. It is important to note our finding that, on average, mice display 'preferences' towards some odors more than others is consistent with previous reports [START_REF] Baum | Sex difference in attraction thresholds for volatile odors from male and estrous female mouse urine[END_REF][START_REF] Devore | Odor preferences shape discrimination learning in rats[END_REF][START_REF] Dibenedictis | Odor preferences of wild stock female house mice (Mus domesticus) tested at three ages using urine and other cues from conspecific males and females[END_REF][START_REF] Isles | Urinary odour preferences in mice[END_REF][START_REF] Kermen | Topographical representation of odor hedonics in the olfactory bulb[END_REF][START_REF] Kobayakawa | Innate versus learned odour processing in the mouse olfactory bulb[END_REF][START_REF] Lydell | Male rat of odor preferences for female urine as a function of sexual experience, urine age, and urine source[END_REF][START_REF] Mandairon | Humans and Mice Express Similar Olfactory Preferences[END_REF][START_REF] Yuan | Early odor preference learning in the rat: bidirectional effects of cAMP response element-binding protein (CREB) and mutant CREB support a causal role for phosphorylated CREB[END_REF]. This, in part, validates the 5-port apparatus for monitoring odor preferences. The main novelty of this work rests in highlighting that these preferences are not observed when inspecting individual mice within the population, nor within individual mice across days. This finding may in part be explained by the unique paradigm we designed, wherein mice must choose between multiple stimuli in the multiple choice task design, and also our measures of preferences across multiple days of experience with the odors.

Implications for understanding odor hedonics

Many studies have provided insights into odor preferences in rodents; however, most do not present data in a manner allowing for assessing individual-animal differences (e.g., [START_REF] Dibenedictis | Odor preferences of wild stock female house mice (Mus domesticus) tested at three ages using urine and other cues from conspecific males and females[END_REF][START_REF] Kermen | Topographical representation of odor hedonics in the olfactory bulb[END_REF][START_REF] Kobayakawa | Innate versus learned odour processing in the mouse olfactory bulb[END_REF][START_REF] Mandairon | Humans and Mice Express Similar Olfactory Preferences[END_REF][START_REF] Qiu | Automated Analyses of Innate Olfactory Behaviors in Rodents[END_REF]. At least one previous study documented variability in odor preferences [START_REF] Tabuchi | Rat preference for food-related odors[END_REF]. In that study, rats had to bar press to gain access to one of several food odors (cheese, black pepper odor, milk, and peppermint). The authors reported that all rats, with some variation, displayed preferences to the food odors and that these were influenced by hunger. In our study, we used multi-day testing without experimentally-induced changes in hunger, to reveal that mice spontaneously change their preferences towards odors, and in fact only in rare cases do they maintain preferences towards the same odor across multiple testing days. It will be important in future studies to determine if this across-day change in odor choices reflects that the mouse remembered the previous odor more and thus was seeking novel stimuli, or, perhaps that the odors selected among were not readily discriminated between and therefore the mouse generalized between the two.

Given that the non-ethologically relevant odors used in our task were not paired to predict rewarding or aversive outcomes, we assume all of these odors to have a neutral valence. This assumption is further supported by the low likelihood that the mice ever experienced these odors during rearing in the laboratory environment. Additionally, all of the odors were diluted to 1Pa, rendering them as ligands for the main olfactory epithelium, yet likely eliminating the trigeminal stimulation yielded by odors at high intensities. A neutral valence might translate behaviorally, as we observed herein, to a high degree of inter-and intraanimal variability in odor choices.

Our results suggest that ethologically-relevant odors are more robust at eliciting stable odor preferences. While still not all mice displayed preferences towards the same ethologicallyrelevant odors, many mice displayed consistent preferences across repeated testing days. One limitation in this study, and in our ability to directly compare the results from the ethologically-relevant and non-ethologically relevant odors, is that we used differed cohorts of mice to test preferences towards these odor categories. That said, since we used several cohorts of mice in testing towards both types of odor sets, the comparisons between odor sets is fair. Further, while we found no mice to display odor specific preferences to odors including geraniol, limonene, heptanal, and more, it is very likely that with the considerable inter-mouse variability we report here, some mice will display strong preferences towards these odors. Additional studies will be needed to assess the basis of this difference between the two categories of odors (ethologically relevant vs non-relevant odors). For instance, it is possible that the non-ethologically relevant odors are more perceptually similar compared to the ethologically relevant odors and therefore, there may be a role for olfactory generalization in the mice choosing between one non-ethologically relevant odor and another.

Contributions of the 5-port apparatus

Various measures are used to assess preferences to odors. These can include the physical location of the animal's body in relation to the odor and the time the animal spends investigating the odor (often defined as 'snout-oriented investigation'). While it is possible that an animal being in physical proximity to an odor coincides with it sampling an odor, the measures using the physical location of the body in relation to the odor are problematic, since this certainly does not coincide with preference. For example, just because a mouse remains in a select zone of an apparatus, does not mean it is doing so in response to the hedonics of the stimulus present in that zone.

We developed the 5-port nose-poke behavioral apparatus in an effort to quantify, specifically, nose-directed investigation behaviors of mice to odors. By requiring the mice to poke their noses in the port to acquire the odor, we were allowed a direct read-out of investigation which adds precision beyond manual or video-based measures of monitoring investigation behaviors, and especially those based upon whole-body proximity. Importantly, a critical caveat to consider in comparing these results to those of previous studies, is that the different context which this behavioral apparatus provides likely alters preference behavior in a substantial manner from studies using other testing apparatuses (e.g., [START_REF] Devore | Odor preferences shape discrimination learning in rats[END_REF][START_REF] Mandairon | Humans and Mice Express Similar Olfactory Preferences[END_REF][START_REF] Tabuchi | Rat preference for food-related odors[END_REF]), or those using home cage testing [START_REF] Baum | Sex difference in attraction thresholds for volatile odors from male and estrous female mouse urine[END_REF]. The 5-port apparatus introduces a novel context in which the mice must be acclimated, and additionally, introduces the experimental variable of choice which impacts these results. That stated, we propose that additional studies using this 5-port apparatus, and the olfactory multiple choice test, will yield additional important insights into the basis of hedonics in mice and how they are shaped with experience.

Possible mechanisms underlying variable odor hedonics

What underlies these individual differences? Is it possible these results, especially those found in response to non-ethologically relevant odors, are due to individual-animal differences in odor discrimination capacity? Indeed, while the odor preference task used here did not test specifically for odor discrimination, one possible explanation for the variability in preference to the odors is that it is an effect of olfactory generalization. Olfactory generalization can be defined as the degree to which any two odorants are perceptually confused with one another [START_REF] Mandairon | Compensatory responses to age-related decline in odor quality acuity: Cholinergic neuromodulation and olfactory enrichment[END_REF] and a recent study using a go/no-go operant task linked olfactory generalization to the structural similarity of odorants [START_REF] Yoder | Characterizing olfactory perceptual similarity using carbon chain discrimination in Fischer 344 rats[END_REF]. Whether the levels of individual differences we observed in odor choices may be reflected by differences in odor tuning functions by olfactory system neurons, and therefore discrimination capacity, are both unknown. That said, this would not explain the day-to-day variations uncovered in preferences. While it is likely animals are seeking novelty from day-to-day, as rodents are well-known to do (e.g., [START_REF] Kabbaj | Neurobiological Correlates of Individual Differences in Novelty-Seeking Behavior in the Rat: Differential Expression of Stress-Related Molecules[END_REF]), an alternative possibility for this would be an interplay between the investigation duration on one day followed by memory for that odor (and even the spatial location of that odor) on a subsequent day. Additional studies utilizing long intervals between subsequent testing days (>2 weeks) will be informative in understanding the basis in variations in odor preference within mice.

Conclusion

Our finding on the diversity and variability of odor preferences in mice may be surprising to some, especially considering the common-place manner of presenting odor preference data from mice averaged as a group. However, just as humans display diverse and personal preferences towards odors, we should not be surprised to observe these same behaviors in rodents. (A) Line graph representing the mean duration of time mice spent nose-poking across all ports as a function of testing day. In this experiment, 18 mice in total (in several cohorts) were tested for their investigation towards a panel of 5 non-ethologically relevant odors (odor set 1) and then after 2 weeks of break, a different panel of non-ethologically relevant odors (odor set 2). (B) The same data from the same mice, but plotted as a function of port number, with data averaged across all 5 testing days. Nose-poking was greater in the side ports (1 and 5) for both odor sets reflecting a spatial bias. (A) Histogram illustrating the number of mice which 'preferred' the same odor across 3 or more of the 5 testing days (viz., displayed a stable odor-specific preference). No mice displayed stable odor-specific preferences for isopentyl acetate, guaiacol, heptanal, (+)limonene, or geraniol in this analysis. (B) pie chart displaying the % of all mice which displayed stable odor specific preferences (black = mice with stable preferences, gray = mice which did not). (C) An example of one mouse (the same contributing the high I-value towards thioglycolic acid in Fig 4) who across all 5 testing days spent its greatest percentage of time investigating thioglycolic acid. The data are plotted as a function of port number, to illustrate that the mouse 'followed' thioglycolic acid regardless of port location/number. (D) Pie chart displaying the % of all mice which displayed stable odor preferences (same color scale as in (B), but after removing data from mice which may have been influenced by port location (see Results). but plotted as a function of port number, with data averaged across all 5 testing days. There was no effect of port location or testing day on mean duration of investigations. (A) Histogram illustrating the number of mice which 'preferred' the same odor across 3 or more of the 5 testing days (viz., displayed a stable odor-specific preference). No mice displayed stable odor-specific preferences for honey in this analysis. (B) Pie chart displaying the % of all mice which displayed stable odor specific preferences (black = mice with stable preferences, gray = mice which did not).

Figure 1 .

 1 Figure 1. Design of the 5-port behavioral apparatus used to monitor odor investigation behaviors.(A) Top-down (left) and front view (right) of the 5-port odor investigation apparatus. This apparatus allows a mouse to freely explore and sample among 5 different odors in a single behavioral session which the timing of no-poking behavior to sample the odors is acquired by a microcontroller. See Materials and Methods for a complete description. (B) Renderings of the 3D printed odor exhaust manifold (top) and odor port (bottom). Location of the infrared (IR) photobeams is depicted on the odor port rendering. (C) Picture of the assembled 5-port apparatus which is wire connected to a project enclosure box (towards the left of the apparatus) which houses the Arduino microcontroller allowing for data acquisition onto a removable SD card or onto a computer connected by a USB cable.

Figure 2 .

 2 Figure 2. Habituation and port spatial location influence nose-poking behavior in the presence of non-ethologically relevant odors.

Figure 3 .

 3 Figure 3. Example of diversity in odor-directed nose-poking behavior between mice.Example poking strategies of 3 mice in one behavioral session. In this example, the odors were in the same spatial location for all 3 mice. Only the times the mice nose-poked are plotted as blue bars, with time refraining from poking not indicated. In these examples, one can appreciate the diversity across mice in their tendencies to sample odors. Below are pie charts representing the percentage of total time each mouse spent investigating (nosepoking) each odor. The percentage displayed indicates the maximum percentage of total time investigating a given odor.

Figure 4 .

 4 Figure 4. Population level 'preferences' for non-ethologically relevant odors are not observed at the individual mouse level. (A) Histogram of mean I-values which control for the influences of port location and testing day habituation (see Materials and Methods) illustrating that as a population, mice display preferences towards some odors more so than others. The mean I-value across all mice and stimuli (including mineral oil) was 20.0 +/-4.49 s.d. (B) Spark plot of each mouse's I-value showing that at the individual animal level, animals display their highest I-value (green bars) towards a variety of non-ethologically relevant odors. Downward arrowheads indicate which mice displayed their highest I-value towards thioglycolic acid, which according to the population-level data in (A) is the 'preferred' odor. Note that most mice did not contribute their highest I-value towards thioglycolic acid. Pie chart in (B) presents the % of mice which displayed their highest I-value towards thioglycolic acid (green) compared to those which

Figure 5 .

 5 Figure 5. The preferred non-ethologically relevant odor varies within most mice on a day-to-day basis.

Figure 6 .

 6 Figure 6. Ethologically-relevant odor directed nose-poking behavior is not impacted by habituation or port spatial location. (A) Line graph representing the mean duration of time mice spent nose-poking across all ports as a function of testing day. In this experiment, several cohorts of mice were tested for their investigation towards a panel of 5 ethologically relevant odors. (B) Mean investigation durations for mice (earlier 4 cohorts of 18 mice) in response to non-ethologically relevant odor set 1 and from the present cohorts of mice in response to the ethologically relevant odor set. **p<0.001. (C) Normalized investigation durations of mice (same as in (B)) revealing an effect of testing day on investigation durations towards non-ethologically relevant, but not ethologically relevant odors. ***p<0.0001. (D) The same data from the same mice as in (A),

Figure 7 .

 7 Figure 7. Population level 'preferences' for ethologically relevant odors are not observed at the individual mouse level. (A) Histogram of mean I-values illustrating that as a population, mice display preferences towards some ethologically-relevant odors more so than others. The mean I-value across all mice and stimuli was 20.0 +/-7.40 s.d.. Pie chart in (B) presents the % of mice which displayed their highest I-value towards peanut (black) compared to those which did not (gray). (C) Spark plot of each mouse's I-value showing that at the individual animal level, animals display their highest I-value (green bars) towards a variety of non-ethologically relevant odors. Downward arrowheads indicate which mice displayed their highest I-value towards peanut, which according to the population-level data in (A) is the 'preferred' odor. Note that most mice did not contribute their highest I-value towards peanut.

Figure 8 .

 8 Figure 8. Most mice display stable preferences for ethologically relevant odors from day-to-day.

Behav Neurosci. Author manuscript; available in PMC 2019 May 22.

Acknowledgements:

This work was supported by NIH/NIDCD grants R01DC014443 and R01DC016519 to D.W. and CNRS and Lyon 1 University funding to N.M. S.L. and K.P. were supported by SOURCE fellowships at Case Western Reserve University.


Soumia Lilia Berrahou

Patrice Buche

Juliette Dibie-Barthelemy

Mathieu Roche

How to Extract Unit of Measure in Scientific Documents?

Keywords: Information Retrieval, Unit of Measure Extraction, Ontological and Terminological Resource, Machine Learning

A large amount of quantitative data, related to experimental results, is reported in scientific documents in a free form of text. Each quantitative result is characterized by a numerical value often followed by a unit of measure. Extracting automatically quantitative data is a painstaking process because units suffer from different ways of writing within documents. In our paper, we propose to focus on the extraction and identification of the variant units, in order to enrich iteratively the terminological part of an Ontological and Terminological Resource (OTR) and in the end to allow the extraction of quantitative data. Focusing on unit extraction involves two main steps. Since we work on unstructured documents, units are completely drowned in textual information. In the first step, our method aims at handling the crucial time-consuming process of unit location using supervised learning methods. Once the units have been located in the text, the second step of our method consists in extracting and identifying candidate units in order to enrich the OTR. The extracted candidates are compared to units already known in the OTR using a new string distance measure to validate whether or not they are relevant variants. We have made concluding experiments on our two-step method on a set of more than 35000 sentences.

INTRODUCTION

Discovering and extracting information reported in published scientific documents is a crucial stake in several scientific domains in order to be able to reuse, manage, exploit and analyze the information they contain. Nevertheless, a large amount of data in any scientific field is still published in today's web in the form of unstructured text rather than as structured semantic information. Indeed, published scientific articles use natural language combined with domainspecific terminology that is extremely tedious to extract in the free form of text.

Linguistic methods for information extraction (IE), such as the ones proposed in GATE [START_REF] Cunningham | GATE: A framework and graphical development environment for robust NLP tools and applications[END_REF], are mostly rule-based techniques, which usually use a combination of gazetteer lists and pattern-matching rules in order to define if a candidate extracted term is valid or not. Those techniques are then included in an overall architecture providing various language processing tasks as building and annotating corpora. The system involves semantic analysis at the simplest level in the process with named entities. Other systems integrate directly formal on-tologies in the natural language processing (NLP) environment, called ontology-based information extraction (OBIE), and incorporate semantic knowledge about terms in the context involving reasoning [START_REF] Maynard | Nlp techniques for term extraction and ontology population[END_REF][START_REF] Wimalasuriya | Ontology-based information extraction: An introduction and a survey of current approaches[END_REF].

Our work deals with unit recognition and extraction, widely known as a challenging issue of IE as described in Related Work section below. Indeed, most of techniques used in classical IE systems, based on linguistic methods, cannot handle quantitative data extraction issues, especially units of measure because they rely on completely different syntactic definitions. New methods and rules of information extraction must be provided and take into account those particularities such as simple or complex units, prefix of multiple or submultiple of units, dimensions, considering the presence or absence of special characters, superscript numbers and so on. Currently, none of the existing NLP methods of the state-of-the-art consider efficiently the extraction of units of measure. However, it remains a paramount importance in many quantitative research fields such as physics, chemistry, biology and food science, where the process of quantitative data extraction in experimental results is difficult to overcome.

Our work aims at leveraging knowledge in food science domain, which relies on an Ontological and Terminological Resource (OTR). The concepts related to units of measure in this OTR have been defined from the most extended ontology of units, Ontology of units of Measure and related Concepts (OM) [START_REF] Rijgersberg | How semantics can improve engineering processes: A case of units of measure and quantities[END_REF], [START_REF] Rijgersberg | Ontology of units of measure and related concepts[END_REF]. OM respects the International System of Units (SI) [START_REF] Thompson | Guide for the use of the international system of units (SI)[END_REF], which organizes quantities and units of measure. The definitions of base and derived units of SI have been translated in a formal representation in OM. Our work focuses on the unit recognition and extraction issues in order to extract typographic variants, synonyms, abbreviations and new units of measure, which are then used to populate the OTR. This is a crucial step to be completed in order to build robust add-in semantic tagging and extracting tools concerning units of measure that could be integrated in OBIE or IE systems. These tools facilitate domain expert participation in the task of annotating textual corpus and data tables for gold standard corpus construction. Moreover, the OTR such populated is used as a basis of reasoning and to make multi-criteria analysis in decision-making systems [START_REF] Hignette | Semantic annotation of Web data applied to risk in food[END_REF].

We propose a two-step approach in order to perform unit recognition and extraction, which focuses on the following contributions: (i) the first step of our work demonstrates how supervised learning methods can be successfully used to meet the challenge of reducing the time-consuming process of unit location in free form of text. (ii) the second step of our work deals with unit extraction and identification using a new string distance measure in order to overcome the scaling issue.

The paper is structured as follows. In section 2, we point out related work on unit recognition. In section 3, we describe our two-step approach, based on locating and extracting variant terms of units. In sections 4 and 5, each step is precisely detailed. In section 6 we deliver our results on real-world data and finally, discuss and conclude on our work.

RELATED WORK

Recent work [START_REF] Touhami | An ontological and terminological resource for n-ary relation annotation in web data tables[END_REF], (Jessop et al., 2011a) has revealed that most of extraction of experimental data fails because units suffer from a large variability of writing within scientific documents. Despite works to produce proper formal standard [START_REF] Thompson | Guide for the use of the international system of units (SI)[END_REF], [START_REF] Rijgersberg | Ontology of units of measure and related concepts[END_REF] in order to exchange and process quantitative information, if a unit of measure is written differently in the text, the process of identification fails even in the smallest differences (e.g. mol written mole).

Other work in related fields as chemistry, focus not only on the identification and annotation of chemical entities (Jessop et al., 2011b) but also on the relationships linking these entities to each other [START_REF] Hawizy | ChemicalTagger: a tool for semantic textmining in chemistry[END_REF] using ChemicalTagger. ChemicalTagger is the best performing state-of-the-art tool for textmining in chemistry, a phrase-based semantic natural language processing tool for the language of chemical experiments. However, in (Jessop et al., 2011a), the observation is similar when the authors note that ChemicalTagger [START_REF] Hawizy | ChemicalTagger: a tool for semantic textmining in chemistry[END_REF] fails in the process of recognizing chemical names as reagent because of the variability of writing units in the text. It perfectly matches the units of measure written in respect to the SI [START_REF] Thompson | Guide for the use of the international system of units (SI)[END_REF] but fails automatically when small changes occur in the text. The authors present this issue as a challenge to be met in future work.

In [START_REF] Willems | Identifying and extracting quantitative data in annotated text[END_REF], in the same trend as we aim at working, the authors present a method for annotating and extracting quantities and units. Their approach relies on the use of semantic markups available in Latex files. Our approach must address the full-text unstructured documents.

In [START_REF] Van Assem | Converting and annotating quantitative data tables[END_REF], the authors propose to overcome unit identification issue in data tables by using a string distance metric, called Jaro-Winkler-TFIDF [START_REF] Cohen | A comparison of string distance metrics for namematching tasks[END_REF]. We have used a new string distance measure, we adapted from Damerau-Levenshtein distance [START_REF] Damerau | A technique for computer detection and correction of spelling errors[END_REF], in order to tackle unit identification issue when units are drowned in textual information. Moreover, unlike our work, they do not need to consider unit location in order to overcome scalability issue since they work on structured data tables.

The current state-of-the-art methods of related domains is not suitable enough to handle efficiently the identification of variant terms of unit issue. Our approach aims at addressing this concern using a novel method to extract unit information in a two-step extraction process.

GLOBAL APPROACH

In this section, we present our two-step approach to extract units in order to enrich a domain OTR. We assume that the more we extract units from text, the more we will succeed in the experimental data identification process. Indeed, variant terms of units encountered in scientific documents, due to the variabil-ity of writing, are seen as potential synonyms in the terminological part of the OTR. Once referenced in the OTR, they will increase the positive results in the identification and extraction process of experimental data. Thus, our work on unit identification task is seen as a sub-step of an overall process of experimental result identification and extraction but remains a crucial stake.

Before presenting our global approach, let us briefly present the unit concepts of the considered OTR. As shown in Figure 1, units are specifically organized around the concept Unit concept and divided into 4 classes of units (units being considered as instances):

• Singular Unit such as day or meter;

• Unit Exponentiation such as Square Meter;

• Unit Division Or Multiplication such as Milliliter Per Square Meter Per Day;

• Unit Multiple Or Submultiple such as Micrometer. Each unit has several possible labels, called terms, which are defined in the terminological part of the OTR and denote unit instances, called units in the following, defined in the conceptual part of the OTR.

In Figure 2, we give an example in order to clearly illustrate the main problem of unit recognition. The terms amol.m -1 .s -1 .Pa -1 and amol/m/s/Pa referenced in the terminological part of the OTR denote the unit AttoMole Per Meter Per Second Per Pascal, instance of the Unit Division Or Multiplication concept. Those terms are not sufficient enough to recognize variant terms of units such as suggested in Figure 2. Our method aims at locating those variant terms and extracting them to enrich the OTR with new terms (e.g. amol x m -1 x s -1 x Pa -1 where a multiplication sign has been added).

The method must face several challenges: (i) the question of locating units within the text, do we need to skim all the text or can we predict the best information location without any assumption. The question is important to be asked because the major limit of classical methods of string distance metrics concerns the scalability issue. Currently, these metrics can not be applied on a huge corpus. The first step is a major concern in order to reduce significantly the search space to the relevant sentences where we will be able to apply string distance metrics (ii) the question of extracting variant terms of units, once the best location has been identified, how do we extract variant terms of units from that part of text; and (iii) once extracted, the question of semantically identifying these variant terms of units and find which unit instances they can denote in the OTR. Our approach tackles these key points and intends at discovering more variant terms of units in order to enrich the terminological part of OTR as an iterative process.

The objective of the first step is to reduce significantly the size of search space and overcome the painstaking process of unit location. As a matter of fact, it is often assumed that experimental data are located in specific paragraphs of the text, e.g. Experiments or Results and discussion in [START_REF] Hawizy | ChemicalTagger: a tool for semantic textmining in chemistry[END_REF]. We aim at locating it without any of these assumptions. However, we are aware that going through all documents involves a time-consuming process. The method to reduce search space involves text mining and supervised machine learning approaches. Our aim in this first step is obtaining the most efficient model from machine learning algorithms that could be then applied in any corpus of a domain where extracting units is a major concern. In section 4, we give more details on different experiments that have been led with the purpose of achieving this best model.

The second step, detailed in section 5, works on textual fragments obtained from the learning model and focuses on terms of unit extraction. As soon as the optimal location for discovering variant terms of units is defined, we first highlight in this location the ones to be extracted. After extracting potential candidates, we compute a new string distance measure in order to distinguish which of the known units in the OTR can match these variant terms and so are potential synonyms to enrich the OTR.

In the first step of the approach, our objective is to focus on locating relevant data concerning units of measure. In the work of [START_REF] Hawizy | ChemicalTagger: a tool for semantic textmining in chemistry[END_REF], the authors use a Regex-tagger based on regular expressions in order to capture the sentences where quantitative data, chemical entities and units appear. However, when testing the tagger in (Jessop et al., 2011a), the process of recognition fails because of typographic variations of units in the text. This observation has motivated our choices towards supervised learning methods instead of using regular expressions. Hence, our process consists of using supervised classifier to determine whether a part of text is classified as containing "units" or "non-units". In order to obtain the best model to be applied on the corpus, we work on several training sets, each one corresponds to a specific textual context. Thus, as soon as the process recognizes a unit annotation in the sentence of the text, we observe the context around it. We consider different case studies to build these training sets for supervised learning methods:

• the first case study takes into account only the sentence where at least one unit appears and is recognized according to the units defined in the OTR;

• the second and third case study take respectively into account one sentence before and one sentence after the one where at least one unit appears;

• the fourth and fifth case study take respectively into account two sentences before and two sentences after the one where at least one unit appears.

The following is a description of the transformation process tasks and explains our choices in order to obtain the model.

Preparing Data

Data preparation involves text processing and text transformation tasks. Text processing sub-step consists of:

• text segmentation in order to generate a set of sentences, which deals with our issues, e.g. not going to new sentence when units with point separators are encountered;

• text cleanup, which removes punctuation and special characters from text, except those involved in units;

• text tokenization, which splits up a string of characters into a set of tokens;

• text reducing, which prunes away tokens containing junk words according to a list of stop words; • text tagging, which annotates the units in the text.

Text tagging relies on comparing the terms denoting the referenced units in the OTR with tokens of each sentence. As soon as the process recognizes a unit or a part of unit, this one is tagged. Our main purpose is obtaining a tagged data used to test our different case studies under learning algorithms.

Transforming Data

As soon as we have obtained our training data, we can proceed with text transformation, which consists of representing a text according to the words it contains and their occurrences in the sentences. The representative words (features) form the bag-of-words according to a relevant threshold we can pre-select (e.g. words appearing more than just once), and we compute their occurrences in each sentence according to three word weighting measures:

• Term Frequency (TF), which considers that the word is more important if it appears several times in sentences; • Term Frequency-Inverse Document Frequency (TF.IDF [START_REF] Hiemstra | A probabilistic justification for using tf x idf term weighting in information retrieval[END_REF]), which considers that the word is more important if it appears in less sentences; • Okapi BM25 [START_REF] Jones | A probabilistic model of information retrieval: development and comparative experiments -part 1[END_REF], which takes also into consideration the sentences length where the word appears to define its relevancy. Sentences become vectors that constitute our training corpus in order to define, with supervised learning methods, whether we are in a "unit" or "nonunit" context.

Learning Model

Our five case studies performed under those three weight-based measures are run under several learning algorithms. Our aim is to obtain exhaustive experiments and results in order to conclude about the best model in our issue. Various learning algorithms have been tested in our experiments:

• Naive Bayes classifier and the Discriminative Multinominal Naive Bayes (DMNB) classifier; • J48 decision tree classifier ;

• Sequential Minimal Optimization (SMO), which is a Support Vector Machines classifier (SVM). The motivation of our choices relies on comparing the behavior of those widely known learning algorithms on a corpus containing many quantitative data.

Naive Bayes [START_REF] John | Estimating continuous distributions in bayesian classifiers[END_REF] is competitive for computational efficiency. Decision tree [START_REF] Kohavi | Data mining tasks and methods: Classification: decision-tree discovery[END_REF] classifiers are known to obtain good classification results but are less competitive in speed of execution. SMO [START_REF] Platt | Fast training of support vector machines using sequential minimal optimization[END_REF]) is a discriminative classifier known to efficiently behave on text classification and, DMNB is a new text classification algorithm [START_REF] Su | Discriminative parameter learning for bayesian networks[END_REF] which performs competitively with discriminative classifiers in terms of accuracy, without losing the advantages of computational efficiency of Naive Bayes classifiers.

The conclusion on the best model is taken after paying a particular attention to several key points:

• Training time and best classification balance;

• Precision, Recall and F-measure results, we focus on the recall, which means we want to maximize the fraction of relevant sentences that are retrieved, without losing too much precision in the results ;

• Confusion matrix, which compares the results of the classifier under test with trusted external judgements.

As we want to estimate how accurately the model of each classifier will perform in practice, we use a 10-fold cross-validation: the original sample is randomly partitioned into 10 equal size subsamples. One subsample is used as the validation data for testing the model while the other subsamples are used as training data. This process is repeated 10 times with each of the subsample used once as the validation data. The averaged result produces the estimation of the model. Using cross-validation is a crucial task in order to avoid "overfitting" effect of the model.

EXTRACTING UNITS

In the second step of the approach, we work on textual fragments obtained from the first step. We have significantly reduced the search space and need now to detect, extract and identify variant terms of units or new units. It involves working on textual fragments as follows:

• detecting terms of units inside textual fragments: the tags used in the annotation process of the first step are our entry point;

• extracting terms (or fragment of terms of units) from the entry point: both sides of the tag are scanned and the process stops when it encounters a common word of the dictionary;

• identifying variant terms of units or new units using the new string distance measure.

The identifying process relies on a new string distance measure, adapted from Damerau-Levenshtein (DL) distance [START_REF] Damerau | A technique for computer detection and correction of spelling errors[END_REF]. The DL distance between two strings is defined as the minimum number of edits needed to transform one string into another, with the edit operations being insertion, deletion, or substitution of a single character. The distance can then be normalized by using [START_REF] Maedche | Measuring similarity between ontologies[END_REF] approach:

SM DL (u1, u2) = max[0; min(|u1|, |u2|) -DL(u1, u2) min(|u1|, |u2|) ] ∈ [0; 1]
The similarity measure is computed and the higher it is, the closer the variant terms of units is to the units of the OTR.

Example 1. Let us consider the similarity between amol/(m.s.Pa) and amol/m.sec.Pa according to the classical DL distance. 2 characters are removed ( "(" and ")"), 2 characters are inserted ("e" and "c"). The D c (the distance between those units) is 4 and the similarity distance normalized according to D c is then:

SM Dc (amol/(m.s.Pa), amol/m.sec.Pa) = max[0; 13 -4 13 ] SM Dc = 0.69
We adapted this distance by considering each block of characters forming the terms of units rather than the simple characters. Thus, in our previous example, the process considers each block as amol, m, s, Pa and compares each of them with each block of characters from the units referenced in the OTR. The new string distance measure is more accurate and more relevant to address units of measure similarity.

EXPERIMENTS AND RESULTS

Data Description

Experiments have been led on real-world dataset composed of 115 scientific documents in food science domain, which represents a set of more than 35 000 sentences. The positive corpus, where at least one unit appears, represents more than 5 000 sentences. We use a list of 211 terms denoting units of the OTR in order to conduct unit identification. During experiments, we can set the number of instances that will constitute our training data. The results delivered in our paper rely on a training set of 2 000 instances randomly chosen from the corpus, and size balanced between the positive and negative instances. According to the different case studies, the bag-of-words varies from 3 000 to 4 800 features.

Learning the Best Location

Learning under Various Supervised Algorithms

We have tested our five case studies. Only the 3 more relevant cases are reported in Table 1. Each case is represented by a symbol in order to make a readable table:

• XP1: only the sentence where at least one unit appears;

• XP2: two sentences after the one where at least one unit appears;

• XP3: two sentences before the one where at least one unit appears.

Table 1 restitutes the results with all weight-based measures included and according to the case studies. This first table helps us to determine case is the most relevant context. We take into consideration several parameters in order to conclude about the most relevant context: recall, overall classification, F-measure, stability of the various results in the weight-based measures that are presented in section 6.2.2 and confusion matrix results. We are particularly interested in recall since our aim is obtaining the most relevant instances that are retrieved considering "unit" class but, without losing too much precision in the results, which is described by the F-measure. Firstly, we can say that Naive Bayes returns F-measure rates ranging from 0.85 to 0.88. Decision tree (i.e. J48) returns better rates from 0.93 to 0.96. DMNB and SMO return better values (0.95 to 0.99).

Secondly, we can observe that a larger context (i.e. composed of two sentences -XP2 and XP3) does not improve the results. We can conclude that considering the smallest context based on one sentence (i.e. XP1) is enough for unit extraction. This allows us to significantly reduce the search space while being in an optimal context of discovery.

Learning under Various Weight-based Measure

Table 2 restitutes the results on the XP1 context, previously underlined, according to the three weightbased measures and the boolean matrix. This second table allows us to conclude whether the learning algorithm is constant on the weight-based measures.

As the previous analysis, we take into consideration recall, overall classification and F-measure. Firstly, we can say that, all weight-based measures included, Naive Bayes returns rates declining from 86.9% (boolean matrix) to 73.4% (okapi measure), decision tree J48 overall classification falls a bit around 92 -93%. SMO loses almost 20% with a rate declining from 99.6% to 80.9%. DMNB stays constant with 95.5% regardless of weight-based measures. The DMNB model stays constant on precision, recall and F-measure and achieves a very good recall at 0.96.

Discussion

In this first step, we have studied how supervised learning methods can be successfully used in order to optimize the process of unit location drowned in unstructured scientific documents. For this, we have compared several algorithms and weight-based measures before concluding that the best context for discovering variant terms of units and even new units (cf. next sub-section) is the sentence where at least one unit appears. This result is constantly confirmed regardless weight-based measures under DMNB algorithm with a recall at 0.96 and a precision at 0.95. Thus, we have successfully overcome the timeconsuming process of location issue. Besides that, we have performed some extra experiments in order to know what kind of features are involved in the learning methods. The question asked is: are the terms of units referenced in the OTR the best features used in learning step? To answer this question, we have removed from the bag-of-words "unit" features and we have relaunched the learning algorithm DMNB in the same context. The recall and the precision drop slightly but remains quite honorable with respectively 0.81 and 0.76. This result shows that other features are involved in the learning process. We have then conducted a feature classification according to their different weight-based measures. The top k classification with k=10 shows that there are not only units but features like nouns, verbs, or adverbs describing a context where unit may appear (e.g. water, temperature, RH for relative humidity, thickness, modified, solutions, samples, concentration, min, permeability, respectively). We can say that units and other types of terms are closely related in the overall expression of an experimental data.

Extracting Variant Terms of Units and Discovering New Ones

From the results obtained in step 1, we conclude that the best location to work in, is the sentence where at least one unit appears. We apply the process according to different thresholds of the new similarity measure, adapted to the block and compare those experiments to the results obtained with the classical string distance measure as described in section 5.

In our experiments, we assume that a similarity measure between 0.2 and 0.4 is favourable for discov-ering new terms of units. Indeed, these values represent the case where at least one part of the blocks, a subset of units is recognized using the new string distance measure. Let us consider the following derived unit g.m -1 .d -1 .Pa -1 . This unit is referenced in the OTR and used in the process of recognition. In our approach, we consider 4 blocks g, m -1 , d -1 and Pa -1 . In the process of recognition, the base unit g, found in the text and not referenced in the OTR yet, is recognized compared to the derived unit with a similarity measure equals 0.2. The g block is identified. It is then relevant to propose this new base unit to be introduced in the OTR. Considering this assumption, we obtained 59 new possible units for the four classes of the Unit concept, organized as described in Table 3.

The similarity measure between 0.5 and 1 is favourable for identifying variant terms of units. Indeed, the more the similarity measure is close to 1, the more terms of units are the same. We obtained 17 variant terms of units, we have 8 relevant variant terms that can be introduced in the OTR. The relevant variant terms of units are described in Table 4.

In this second step, we have successfully detected and extracted either relevant variant terms of units or new units. We can potentially enrich the OTR of 38 terms of units or 18% of enrichment (the actual OTR references 211 units). We have adapted a new string distance measure in order to compare variant and new terms of units to the ones referenced in the OTR. We compare blocks of units rather than characters, which proves to be more efficient in our issue. We have noticed that this process is quite relevant to identify new terms of units but is under our expectations concerning the identification of variant terms of units.

In our process of identifying blocks, we currently ignore all separators between those blocks, which are very specific in the case of units (e.g. /, ., (, ), x). Those characters have semantic meanings that need to be added in the concern of extraction and identification, in order to improve the results of the similarity measure. We intend to carry out this work given these promising results. Another future work is completing the process with a new step in order to identify what kind of terms of units are discovered (e.g. a temperature unit, a relative humidity unit, a permeability unit) and allow an automatic integration of variant or new units directly in the OTR.

CONCLUSIONS AND FUTURE WORK

The paper presents our overall two-step approach in order to identify variant terms of units or new units in unstructured scientific documents. We have successfully demonstrated how supervised learning methods can help reduce the search space of terms of units drowned in textual information. We have achieved almost 86% of reducing. For this purpose, we have used many algorithms and weight-based measures to prove the relevancy of our approach. We have also presented our first results to detect and extract variant terms and new units, using a new string distance measure adapted to our unit identification issue. As presented in the paper, this second step needs to be deepened. Our approach, presented in this paper, addresses the entire issue: location, extraction and identification of units. Further work on semantical identification of variant terms and new units should be addressed in order to automatically populate the OTR.

Figure 1 :

 1 Figure 1: Excerpt of the OTR: Unit concept.

Figure 2 :

 2 Figure 2: Relevant variant terms of units.

  The edit distance adapted to the block becomes D b = 1 since only the block sec varies and SM Db (amol/(m.s.Pa), amol/m.sec.Pa) = max[0;

Table 1 :

 1 Results of "Unit" instances: Precision (P), Recall (R), F-measure (F) are given for each case study. Best recall in bold considering P and F.

	Dec. Tree J48 Naive Bayes		DMNB			SMO
	P	R	F	P	R	F	P	R	F	P	R	F
	XP1 0.99 0.87 0.93 0.83 0.93 0.88 0.95 0.96 0.95 0.99 0.99 0.99
	XP2 0.99 0.92 0.96 0.95 0.77 0.85 0.93 0.96 0.95 0.99 0.97 0.99
	XP3 0.99 0.92 0.95 0.77 0.98 0.86 0.94 0.96 0.95 0.99 0.97 0.98

Table 2 :

 2 Results of "Unit" instances: Precision (P), Recall (R), F-measure (F) are given for each weight-based measures and boolean matrix. Best results in bold.

	Algo.	Dec. Tree J48 Naive Bayes		DMNB			SMO
		P	R	F	P	R	F	P	R	F	P	R	F
	Boolean 0.99 0.87 0.93 0.83 0.93 0.88 0.95 0.96 0.95 0.99 0.99 0.99
	TF	0.99 0.86 0.92 0.69 0.85 0.76 0.95 0.96 0.95 0.84 0.90 0.87
	TF.IDF 0.99 0.86 0.92 0.69 0.85 0.76 0.95 0.96 0.95 0.84 0.90 0.87
	Okapi 0.99 0.86 0.92 0.69 0.86 0.76 0.95 0.96 0.95 0.77 0.88 0.82

Table 4 :

 4 Identifying variant terms of units.

	Variant term	Reference	SMDc	SMDb
	10e10 (cm3.m-1.sec-1.Pa-1)	10e10.cm3.m-1.sec-1.Pa-1	0.87	1
	10e-14(cm3/m.s.Pa)	10e-14.cm3/(m.s.Pa)	0.89	1
	10e-16cm3.cm/cm.cm2.s.Pa	(10e-16cm3.cm)/(cm2.s.Pa)	0.76	0.8
	10e18 (mol.m/Pa.sec.m2)	10e18.mol.m/(Pa.sec.m2)	0.87	1
	amol.m-1.s-1.Pa-1	amol.s-1.m-1.Pa-1	0.88	0.75
	amol/m.s.Pa	amol/(m.s.Pa)	0.84	1
	amol/m.sec.Pa	amol/(m.s.Pa)	0.69	0.75
	cm3.um/m2.d.kPa	cm3.µm/(m2.d.kPa)	0.77	0.8

Table 3 :

 3 Discovering new terms of units.

	Class	Nb	Relevancy	Terms of units
	Singular Unit	9	55.5%	mol, s, m, g, d, etc.
	Unit Exponentiation	7	85.7%	m -2 , m -1 , d -1 ,
				cm 3 , s -1 , m 2 , etc.
	Unit Division or Multi.	39	43.6%	mol.L -1 ,	mol/L,
				mL/kg/h, etc.
	Unit Multiple or Submulti.	4	50.0%	amol, mL, etc.
	Total	59	50.8%	All previous units


Lilia Soumia

Patrice Berrahou

Juliette Buche

Mathieu Dibie-Barthelemy

Roche

Units of measure identification in unstructured scientific documents in microbial risk in food

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Units of measure identification in unstructured scientific documents in microbial risk in food

Soumia Lilia Berrahou 1,2 , Patrice Buche 1,2 , Juliette Dibie-Barthélemy 3 and Mathieu Roche 1 1. LIRMM-CNRS -Univ. Montpellier

OBJECTIVE(S)

A preliminary step in microbial risk assessment in food is to gather and capitalize experimental data. Data capitalization is a crucial stake in an overall decision support system which consists of predicting microbial behavior [START_REF] Leporq | The ``Sym'Previus'' software, a tool to support decisions to the foodstuff safety[END_REF]. In the framework of the French ANR project MAP'OPT (Equilibrium Gas Composition in Modified Atmosphere Packaging and Food Quality), the predictive modeling platform Sym'Previus (www.symprevius.org) should be able to propose a global approach to establish a scientifically sound method for choosing an appropriate modified atmosphere and associated packaging solution.

Our work is part of this overall system and aims at extracting semi-automatically experimental data from unstructured scientific documents. Indeed, these documents use natural language combined with domainspecific terminology that is extremely time-consuming and tedious to extract in the free form of text and therefore to gather and capitalize. Our work relies on the MAP'OPT-Onto ontology [START_REF] Damerau | A technique for computer detection and correction of spelling errors[END_REF], which has been built as an extension of the ontology used in Sym'Previus by adding concepts about food packaging, quantity concepts and concepts managing units of measures.

Experimental data are often expressed with concepts (e.g packaging, permeability) or a numerical value often followed with its unit of measure (e.g. 258 amol m-1 s-1 Pa-1). In this paper, our work deals with unit recognition, known as a scientific challenge.

METHOD(S)

Extracting automatically quantitative data is a painstaking process because units suffer from different ways of writing within documents. We can encounter same units written in different manners such as amol m -1 s -1 Pa -1 written as amol.m -1 .s -1 .Pa -1 or as amol/m/s/Pa. We aim at focusing on the extraction and identification of these variant units seen as synonyms, in order to enrich iteratively an ontology, which represents a predefined vocabulary used to annotate, capitalize and query experimental data extracted from texts [START_REF] Buche | Flexible Querying Of Web Data To Simulate Bacterial Growth In Food[END_REF]. Our work addresses unit extraction and identification issues from texts to enrich an ontology in a two-step approach. First, we use text-mining methods and supervised learning approaches in order to predict relevant parts of the text where synonyms of units or new units are. The second step of our method consists in extracting specific strings representing units in the segments of texts found in the previous step. The extracted candidates are compared to units already present in the ontology using a new edit measure based on Damerau-Levenshtein [START_REF] Buche | Fuzzy Web Data Tables Integration Guided by an Ontological and Terminological Resource[END_REF].

RESULTS

We have made experiments on 115 scientific documents (i.e. around 35 000 sentences) on food packaging. Each unit is recognized from a list of 211 units already defined in the MAP'OPT-Onto. Our learning algorithms predict that almost 5 000 sentences contain units. This prediction is correct for 95,5% of cases. In the second step, we have successfully extracted 38 terms as either synonyms or new units from sentences selected in the first step. So, we can propose 18% of enrichment of the pre-existing MAP'OPT-Onto.

  2, F-34095 Montpellier Cedex 5, France 2. INRA -UMR IATE, 2, place Pierre Viala, F-34060 Montpellier Cedex 2 France 3. INRA -Mét@risk & AgroParisTech, 16 rue Claude Bernard, F-75231 Paris Cedex 5, France

We propose a two-step approach to enrich an ontology with unit synonyms. Our approach addresses both issues: location and extraction of units. Future work should be defined in order to automatically populate the ontology with new concepts (e.g. food product or packaging names) and link the new units discovered.