perception that inhabitants living on the two sides of the border are more different than is actually the case. This phenomenon may start a vicious circle that accentuates residential segregation over time.

Hotspots and their spreading influence are a major cause of the good or the bad reputation of urban spaces. Thus, it is crucial to better understand how the influence of hotspots interacts with borders in determining how people perceive urban spaces affectively.

Hotspots and Borders Interact in People´s Affective Perception of Their Environment

Most urban areas contain places that affectively stick out from the rest. These socalled "hotspots" [START_REF] Blaison | Affective judgment in spatial context: How places derive affective meaning from the surroundings[END_REF], such as a nice park or an unsafe housing block, affect how people perceive their surroundings. The influence of a hotspot propagates into its surroundings with declining intensity. Where the influence is intense, places are affectively assimilated, whereas farther away, they are contrasted [START_REF] Blaison | Contrast effect in spatial context: Robustness and practical significance[END_REF][START_REF] Blaison | Affective judgment in spatial context: How places derive affective meaning from the surroundings[END_REF]. The scale of this affective polarization depends on the reach of the hotspot´s influence (its "gradient of influence", see below) and on the size of the area under consideration [START_REF] Blaison | Affective judgment in spatial context: How places derive affective meaning from the surroundings[END_REF].

One intriguing finding is that a hotspot´s influence may be subjectively attenuated by a border even though that border presents no actual barrier [START_REF] Galak | Not in my backyard: The influence of arbitrary boundaries on consumer choice[END_REF][START_REF] Mishra | Border bias: The belief that state borders can protect against disasters[END_REF]. This "border effect" shows up for example when the continuing threat from the epicenter of an earthquake is felt more intensely within the state of origin than across the state´s border, all other things being equal [START_REF] Mishra | Border bias: The belief that state borders can protect against disasters[END_REF]. As a result, even though state borders clearly do not protect against earthquakes and geographic distances were the same, people subjectively preferred to live across the state border rather than in the state where the epicenter of a past earthquake had been located. In other words, the border effect emerges when people prefer places that are separated from a negative hotspot by a border, even when this border cannot protect those places against the negative influence of the hotspot.

This border effect has important implications for the affective perception of urban space. For instance, whenever an area is unattractive, a border will accentuate the attractiveness of the area across the border, even when the border is perfectly passable. Thus, a gap in the price of the real estate will result when a border is present. The border effect may also amplify residential segregation. Once an area is associated with a certain kind of inhabitants, salient borders may accentuate its perceived homogeneity and result in the more salient caused an even larger border effect, which supports the categorization hypothesis (Mishra & Mishra, 2010, Study 2). Galak and colleagues (2007) proposed an alternative account based on contagion theory (e.g., Rozin, Markwith, & McCaulay, 1994;[START_REF] Rozin | Operation of the laws of sympathetic magic in disgust and other domains[END_REF][START_REF] Rozin | Operation of the sympathetic magical law of contagion in interpersonal attitudes among Americans[END_REF]. This account states that people overgeneralize a "contagion heuristic," whereby properties (such as dirt) transfer between objects by simple contact, to situations where contagion is impossible. Participants not wanting to drink juice that was briefly in contact with a sterilized cockroach is an illustration of this heuristic [START_REF] Rozin | Operation of the laws of sympathetic magic in disgust and other domains[END_REF]. As contagion necessitates contact [START_REF] Morales | Product contagion: Changing consumer evaluations through physical contact with "disgusting" products[END_REF], Galak and colleagues (2007) reasoned that the discontinuity of contact created by the border-irrespective of whether the border is an actual barrier or not-would disturb the contagion process, resulting in a border effect.

In our opinion, these two explanations are not mutually exclusive. The thicker border in Mishra and Mishra (2010, Study 2) rendered the border more salient but it also increased the visual gap between the states. In turn, the increased perceived discontinuity of contact may have facilitated the categorization process. As the perception of some boundaries is essential to any categorization process, we will consider the perceived discontinuity of contact as the primary cause of the border effect.

A Hotspot´s Influence Modulates the Border Effect

How much discontinuity of contact is perceived-and thus, the resulting strength of the border effect-may not only depend on the visual gap caused by the border. The influence of a hotspot extending over an area that also includes a border may bridge the gap subjectively. In that sense, whereas borders divide, a hotspot´s influence unites. Even more so, the more influence reaches across the border, the weaker should be the perception of discontinuity of contact. How much influence is perceived to cross the border depends on different factors. Just as the volume of water passing at a certain point of a river will be determined both by the width of the river bed and the pressure of the current upstream, a stronger hotspot influence will be perceived to cross the border either because the influence can more easily travel across the border (wider riverbed) or because it flows more intensely (higher pressure upstream). In contrast to previous theories, we claim that the border effect depends not only on the mere presence of a border and its visual salience but also on the characteristics of the hotspot´s influence on the area. In the following, we will distinguish between the type of the influence and the intensity of the influence. Some hotspots create an influence that flows more easily across borders than others.

For instance, influence that propagates by air, like radioactivity from a nuclear power plant or bad smells from a landfill, can cross a border anywhere. In contrast, influence that propagates by surface transport, like criminal activity stemming from an unsafe housing block, flows mainly through the physical channels available to pass the border (e.g., streets or bridges). We posit that the perceived discontinuity of contact is weaker when more influence is perceived to cross the border. Consequently, perceived discontinuity of contact should be smaller for influence that propagates by air compared to influence that propagates by surface transport.

Thus, the type of influence hypothesis predicts weaker border effects for the first compared to the latter.

As for the intensity of a hotspot´s influence, what if [START_REF] Mishra | Border bias: The belief that state borders can protect against disasters[END_REF] had placed the earthquake´s epicenter at 20 instead of 200 miles from the state border? The threat at the border would have been much more intense. Would any border effect have emerged at all? Probably not. If the perceived discontinuity of contact is weaker when more influence is perceived to cross the border, the discontinuity of contact should be almost entirely masked when the influence of the hotspot is that intense, resulting in hardly any border effect at all. Thus, the influence intensity hypothesis predicts smaller border effects when the influence from the hotspot at the border is more intense.

Influence intensity at the border is also determined by the gradient of influence of a hotspot. The gradient of influence refers to people's belief about how quickly influence decreases with increasing distance to the hotspot [START_REF] Blaison | Affective judgment in spatial context: How places derive affective meaning from the surroundings[END_REF]. Some hotspots have shorter gradients of influence because their influence is believed to dissipate quickly with increasing distance (e.g., bad smells from a landfill). Shorter gradients of influence thus have steeper slopes [START_REF] Blaison | Affective judgment in spatial context: How places derive affective meaning from the surroundings[END_REF]. Other hotspots have larger gradients of influence because their influence is believed to dissipate at a slower rate (e.g., radioactivity from a nuclear power plant). Larger gradients of influence thus have slopes that are shallower. Since all other things being equal, larger gradients of influence imply a more intense influence for any given distance, weaker border effects should emerge in areas containing hotspots with larger gradients of influence because the influence is stronger when reaching the border.

To test these predictions, we conducted three experiments in which we compared the border effect for areas containing hotspotsvarying in the type or in the intensity of their influence. In Experiment 1 we tested the type of influence hypothesis by contrasting border effects elicited by a hotspots whose influence can either flow freely over the border or not.

Experiment 2 replicated Experiment 1 conceptually and in addition tested the influence intensity hypothesis. For this, we contrasted the border effects elicited by two hotspots with the same type of influence but different gradients of influence. Experiment 3 replicated the results found in Experiments 1 and 2 regarding the type of influence hypothesis and provided a better controlled setting for testing the influence intensity hypothesis

Experiment 1

Experiment 1 tested whether the type of influence (influence propagating by surface transport or by air) moderates the border effect. For this, participants were shown a satellite view of an urban area divided into two distinct neighborhoods by a railroad (see Figure 1). Two bridges crossed the railroad tracks so that the railroad was not an actual obstacle. Thus, trivial alternative explanations of the border effect in terms of perceived functional distance [START_REF] Kosslyn | Cognitive maps in children and men[END_REF]) could be safely excluded. One of the two neighborhoods contained a hotspot that was equidistant from the target locations distributed in the two neighborhoods. Participants were asked to estimate the hotspot's adverse effect on the target locations. In case of a border effect, this estimate should be lower for target locations situated across the railroad compared to those on the same side as the hotspot. Half of the participants were shown a hotspot in form of an unsafe housing block-gang criminality propagates by surface transport-whereas the other half was shown a landfill-toxic particles or bad smells propagate by air. Previously, unsafe housing blocks and landfills elicited similar aversion (i.e., participants gave the same negative evaluation) and affected areas of comparable size (i.e., same gradient of influence; Blaison, Gebauer et al., 2017). Compared to toxic particles or bad smells, criminal activity should seem to cross the border less easily because it can only flow through narrow physical channels, the two bridges. Thus, if the type of influence hypothesis is correct, then the border effect should be stronger in the unsafe housing block condition than in the landfill one.

Method

Participants and Design. One hundred and eight participants (54 women, 1 gender unknown; all U.S. residents) with a mean age of 34.75 years (SD = 10.89) were recruited via Amazon Mechanical Turk. Participants were randomly assigned to a 2 (hotspot: unsafe housing block or landfill) x 2 (side: across the railroad, same side) x 7 (distance: 1 location in the block that contained the hotspot + 2 distractor locations + 4 locations from pairs of equidistant locations that were increasingly distant from the hotspot; see below Materials and Procedure) mixed design with the first factor as between-subjects factor and the two others as within-subjects factor. Even though the main conclusions remained unchanged by this procedure, we discarded extreme outliers with studentized-deleted residuals greater than 4 [START_REF] Judd | Data Analysis: A model comparison approach[END_REF]. Therefore, the degrees of freedom may vary slightly between analyses. 1 Materials and Procedure. We created fictitious satellite views of an urban area made up of a patchwork of satellite images (USGS Products; see Figure 1). The satellite view was in grayscale to mitigate the positive impact that vegetation might have on the assessment [START_REF] Sheets | Affect, cognition, and urban vegetation: Some effects of adding trees along city streets[END_REF][START_REF] Ulrich | Natural versus urban scenes: Some psychophysiological effects[END_REF]. The area contained a hotspot (an unsafe housing project or a landfill) and a railroad, which, as participants were informed in writing, "separates the area into distinct neighborhoods." The instructions stated also that two bridges connected the neighborhoods. To emphasize that the unsafe housing block was indeed unsafe, we added the symbols of a gun and a syringe and informed the participants in writing that these convey the notion that "gang activity and drug trafficking pervade the housing block." To emphasize the threat elicited by the landfill, we added symbols depicting a dead tree and a dead fish and informed the participants that "landfills are toxic" and that "they harm the environment" [START_REF] Blaison | Affective judgment in spatial context: How places derive affective meaning from the surroundings[END_REF]. 1 We applied the same procedure to all experiments. There were 11 target locations distributed across the two neighborhoods (see Figure 1). Four pairs of target locations were roughly equidistant from the hotspot (the locations with the dots in Figure 1).2 Participants rated the likelihood that the hotspot would adversely affect the 11 locations (see Figure 1) using a scale ranging from 1 = "0% risk that the [unsafe housing block or landfill] adversely affects the block marked by the white arrow" to 9 = "100% risk that the [unsafe housing block or landfill] adversely affects the block marked by the white arrow." As perceived distance might also explain the emergence of a border effect, participants were then asked to estimate the distance (in yards) separating the 11 target locations from the hotspot. In both phases of the experiment (risk rating and distance rating), the white arrow pointed to the target location to be rated. The target locations were rated in a different random order for each participant and for each phase. In addition, the participants rated the block that contained the hotspot as well as two distractor locations (the locations without dots in Figure 1). Only the ratings given to the equidistant pairs of locations were analyzed in Experiment 1. Finally, whether the railroad appeared to the left or to the right of the hotspot was counterbalanced across participants.

Results

Distance ratings. We first tested whether differences in perceived distance could constitute an alternative explanation for the border effect. For this, we standardized the distance ratings within participants to minimize between-subjects variability in the estimates. We further averaged the perceived distance for the two nearer equidistant pairs of distances and the two farthest ones. We then conducted a 2 (hotspot: unsafe housing block or landfill) x 2 (side: across the railroad, same side) x 2 (distance: near or far) mixed ANOVA on the standardized distance estimates. As the analysis yielded no significant effect, all Fs < 1.53, p > .23, differences in perceived distance cannot account for the border effect reported below.

Risk ratings. The risk ratings were averaged for the two nearest target locations and the two farthest ones. The results showed a significant effect of side, F(1,105) = 6.90, p = .01, η 2 p = .06, a significant side x distance interaction, F(1,105) = 231.87, p < .001, η 2 p = .69, and more importantly, a significant side x hotspot interaction, F(1,105) = 4.44, p = .04, η 2 p = .04 (see Figure 2). Simple effects analyses showed that there was less perceived risk across the railroad in the unsafe housing block condition, F(1,105) = 11.31, p = .001, η 2 p = .10, whereas similar risk was perceived on both sides of the railroad in the landfill condition, F(1,105) = 0.13, p = .72, η 2 p = .001.

In accordance with the type of influence hypothesis, the border effect was stronger for influence propagating by surface transport than for influence propagating by air. This result supports the notion that people consider criminal activity as flowing less easily across the border than bad smells or toxic particles, as expected. Somewhat surprisingly, however, no significant border effect emerged for influence travelling by air. One reason could be that the influence was perceived to bridge the gap too easily. However, [START_REF] Mishra | Border bias: The belief that state borders can protect against disasters[END_REF] obtained a border effect even though earthquakes cross state borders at least as easily. Another possibilit is that the influence was too intense as the landfill was much closer to the border here than the epicenter in Mishra and Mishra´s experiment. A different reason that is independent of how much influence is perceived to cross the border could lie in the fact that we asked participants to evaluate a risk. This type of question may have primed logical reasoning ("railroads cannot impede bad smells or toxic particles travelling by air"). Yet, a border effect may more readily occur when asking about preferences with regard to the locations on either side of the border because preferences tap into affective reactions that are less based on explicit reasoning processes. Therefore, Experiment 2 tested the type of influence hypothesis with preference instead of risk ratings.

Experiment 2

Participants were shown the same kind of urban areas and the same type of hotspots as in Experiment 1, except that they were asked how much rent they would be prepared to pay for an apartment located on either side of the border. We used rent as a proxy for preference because it has an open format and is more resistant to framing or communication biases than attitude Likert scales (e.g., Birbaum, 1999;[START_REF] Wedell | Contrasting models of assimilation and contrast[END_REF]; see also [START_REF] Blaison | Contrast effect in spatial context: Robustness and practical significance[END_REF][START_REF] Blaison | Affective judgment in spatial context: How places derive affective meaning from the surroundings[END_REF]. Moreover, money is a widely used proxy for measuring perceived utility (i.e., the value or amount of pleasure provided by a good; [START_REF] Buechel | The (relative and absolute) subjective value of money[END_REF]) and rent money is appropriate for an urban housing setting. Willingness to pay higher rents across the border than on the same side as the hotspot would indicate a border effect. If type of influence moderates the border effect, it should be weaker for the landfill than for the unsafe housing project. In addition, we tested the influence intensity hypothesis in introducing a third hotspot condition with a nuclear power plant. As radioactivity propagates by air, we expect the same type of influence as for the landfill.

However, as radioactivity travels farther than toxic particles or bad smells, it has a larger gradient of influence while being similarly aversive [START_REF] Blaison | Affective judgment in spatial context: How places derive affective meaning from the surroundings[END_REF]Blaison, Gebauer et al., 2017; see supplementary material 1). Thus, if the influence intensity hypothesis holds true, a weaker border effect should emerge for the nuclear power plant compared to the landfill. As a control condition, we introduced an area without any hotspots. Here the border should not have any effect on the rents. In sum, the border effect should be a) stronger in the hotspot conditions than in the control condition (border effect hypothesis); b) stronger in the unsafe housing project condition compared to both the landfill condition and the nuclear power plant one (type of influence hypothesis); c) and stronger in the landfill condition than in the nuclear power plant one (influence intensity hypothesis).

As a secondary aim, Experiment 2 explored the effect of the characteristics of the border. [START_REF] Mishra | Border bias: The belief that state borders can protect against disasters[END_REF] used an imaginary state line. In Experiment 1, the border was a physical object-yet one that could be easily crossed. Nonetheless, it created a large visual gap between the neighborhoods. In Experiment 2, half of the participants were shown an urban area with passable railroad tracks as in Experiment 1, whereas for the other half it was divided by a narrow avenue (see Figure 3), not unlike other roads on the map. As in [START_REF] Mishra | Border bias: The belief that state borders can protect against disasters[END_REF], we expected a stronger border effect for the railroad than for the avenue because the former was visually more salient and hence more likely to create a greater perception of discontinuity of contact.

Method

Participants and Design. We collected a total of 472 observations online via Amazon Mechanical Turk. Of these, 10 were from participants who participated more than once. In the case of repeated participation we kept only the first. This resulted in a total of 457 participants (254 women, 2 unknown gender; all U.S. residents) with a mean age of 35.60 years (SD = 11.40). Participants were randomly assigned to a 4 (hotspot: unsafe housing block, landfill, nuclear power plant, or no hotspot control) x 2 (type of border: railroad or avenue) x 2 (side:

across the railroad, same side) x 7 (distance: 1 location in the block that contained the hotspot + 2 distractor locations + 4 locations from pairs of equidistant locations that were increasingly distant from the hotspot) mixed design with the first two factors as between-subjects factors and the two other factors as within-subjects factors.

Material and Procedure. We used slightly modified satellite views from Experiment 1 (see Figure 3). In the avenue condition, the instructions stated that "the 10 th avenue separates the area in two neighborhoods called Ingledale [or Blanton] and Blanton [or Ingledale]." The names of the neighborhoods were counterbalanced across participants. The same applied to the railroad condition except that the instructions stated that a passable railroad with bridges separated the area into two neighborhoods. In the nuclear power plant condition, we added the symbols of an electric shock and of radioactive hazard and informed the participants in writing that the nuclear power plant was "old but still functioning" [START_REF] Blaison | Affective judgment in spatial context: How places derive affective meaning from the surroundings[END_REF].

The unsafe housing block and the landfill, as well as the target locations were marked and described as in Experiment 1. For each target location participants were asked how much rent they would be willing to pay for a 750 Sq Ft apartment with 1 bedroom and 1 bathroom, and that the minimum rent was $300 in this city [START_REF] Blaison | Contrast effect in spatial context: Robustness and practical significance[END_REF] Blaison & Hess, 2016). The rest of the procedure was identical to Experiment 1. The control condition was similar except no hotspot was presented and no reference to it was made.

Results

To check whether the hotspots were identically aversive, we conducted a 4 (hotspot:

unsafe housing block, landfill, nuclear power plant, or no hotspot control) x 2 (type of border:

railroad or avenue) ANOVA on the amount of rent the participants were willing to pay in the block that contained the hotspot. The results showed a significant main effect of hotspot, F(3,445) = 71.89, p < .001, η 2 p = .33, and a significant hotspot x type of border interaction effect, F(3,445) = 5.62, p = .001, η 2 p = .04. Except that in the avenue border condition the rent was slightly lower for the unsafe housing block than for the nuclear power plant, t(110) = 2.01, p = .05, the other hotspot comparisons yielded no significant differences, all ts < 1.49, all ps > .14 (overall rent: M = 318.70, SD = 64.84). In both border conditions, the rent in the control condition was systematically higher than in the hotspot conditions, ts > 4.77, all ps < .001

(overall M = 459.38, SD = 142.98). The interaction effect emerged because the rent in the control condition was higher for the avenue than for the railway border, t(110) = 2.75, p = .007. This result suggests that railroad tracks by themselves have a negative influence on the surroundings compared to an avenue. We will come back to this result later. We then tested the target hypotheses. Similar to Experiment 1, we averaged the rent participants were willing to pay for the two nearest distances and the two farthest ones. We then conducted a 4 (hotspot: unsafe housing block, landfill, nuclear power plant, or no hotspot = control) x 2 (type of border: railroad or avenue) x 2 (side: across the railroad, same side) x 2 (distance from the hotpot: near or far) mixed ANOVA on the amounts of rent. We expected border effects only for the hotspot conditions and not for the control one. A border effect would be indexed by a significant side x hotspot interaction. However, the interaction could also indicate a significant effect of type of influence [unsafe housing block (surface transport)

versus landfill and nuclear power plant (air)] or of influence intensity [landfill (less intense)

versus nuclear power plant (more intense)]. Planned contrasts were conducted to distinguish between these effects. Finally, we predicted that if the salience of the border moderates the border effect, then a side x hotspot x type of border interaction effect should emerge as well.

The results showed a significant effect of distance, F( 1 More importantly, the predicted side x hotspot, F(3,446) = 12.52, p < .001, η 2 p = .08, as well as the side x type of border interaction emerged, F(1,446) = 7.22, p = .007, η 2 p = .02.

Both were moderated by the predicted significant side x hotspot x type of border interaction, F(3,446) = 8.13, p < .001, η 2 p = .05 (see Figure 4). To decompose the interaction effect according to our hypotheses while saving maximum statistical power, we used the following set of orthogonal contrasts: C1 = no hotspot versus unsafe housing block, landfill and nuclear power plant (border effect hypothesis); C2 = unsafe housing block versus landfill and nuclear power plant (type of influence hypothesis); C3 = landfill versus nuclear power plant (influence intensity hypothesis). Border was coded -1 for the avenue and +1 for the railroad.

This set of predictors and their interactions with type of border were entered simultaneously in a multiple regression that predicted the size of the contrast between the rent given for the locations situated on the side with the hotspot (coded -1) and the rent for the locations across the border (coded +1 Finally, there was a main effect of type of border, b = -9.60, t(446) = 2.69, p = .007, 95% CI [-16.61, 2.58], such that the rents participants were willing to pay across the border, compared to the other side, were lower in the railroad condition than in the avenue condition.

This result seems mainly driven by the railroad's negative influence, that is, the railroad itself was perceived as unattractive and spread negative influence. The remaining effects had bs < 1.75, ts < 0.35, ps > .73.

Discussion

Experiment 2 tested the border effect as a function of the type of influence and influence intensity with a dependent measure based on preference (amount of rent the participants would be willing to pay per month). In line with the type of influence hypothesis, the border effect in the unsafe housing block condition was stronger than in both the landfill and the nuclear power plant conditions. Thus, the border effect does not only depend on the visual salience of the border as previously thought, but also on the type of hotspot influence.

The influence intensity hypothesis, however, was not supported, even though we used preference instead of risk ratings. Finally, in line with previous findings, a more salient border led to more intense border effects.

As in Experiment 1, no border effect emerged for influence propagating by air (landfill and nuclear power plant conditions). It might be argued that in the avenue condition, the border was lacking in salience. Yet, similar results were found for the railroad although it is itself quite salient. Before concluding that border effects do not emerge when influence travels by air (at least at the scale of a neighborhood), a last alternative explanation must be investigated. As both the hotspot and the railroad itself have negative influence, the absence of border effect should have led to lower rents across the railroad in the hotspot conditions compared to the rents in the control condition. That the rents were not lower but in fact higher (see Figure 4) actually argues for a border effect that could not fully express itself because the railroad´s negative influence tamped down the rents across the border. To investigate this possibility in Experiment 3, we tested the border effect by controlling for the negative effect of the railroad.

Experiment 3

Participants were shown urban areas that differed in two aspects from Experiments 1 and 2. Instead of rating several target locations, participants were asked to choose between moving into one of two target locations on each side of the border (forced choice; as in Mishra & Mishra, 2010, Study 1;[START_REF] Galak | Not in my backyard: The influence of arbitrary boundaries on consumer choice[END_REF], Study 1). Importantly, the two locations were situated at equal distance from a set of railroads (see Figure 5). The latter modification ensured that the railroads' negative influence would affect both targets to the same extent. We also varied the size of the urban area under consideration. Half of the participants were shown an urban area identical to Experiments 1 and 2, whereas the other half were shown a larger area (Figure 5). As influence is more intense at closer distance, we were interested in whether the border effect would be smaller in the smaller spatial context. The border effect is indexed by a higher preference for the location across the central railroad in the hotspot conditions compared to the control condition. If the type of influence hypothesis holds true, then participants should be more likely to choose the target location across the central railroad in the unsafe housing block condition rather than in the landfill and nuclear power plant conditions. If the influence intensity hypothesis holds true, participants should be more likely to choose the location across the central railroad in the landfill condition rather than in the nuclear power plant condition.

Method

Participants and Design. We collected a total of 444 observations online via Amazon´s Mechanical Turk. Of these, two were from participants who participated more than once. In the case of repeated participation, we kept only the first. This resulted in a total of 442 participants (225 women, 2 gender unknown; all U.S. residents) with a mean age of 35.44 years (SD = 12.55). The participants were randomly assigned to a 4 (hotspot: unsafe housing block, landfill, nuclear power plant, or no hotspot control) x 2 (spatial context: small or large)

between-subjects design. Material and Procedure. We created survey views of urban areas as in the previous experiments. In the control condition, participants were shown an urban area with several railroads "separating the area into distinct neighborhoods" (see Figure 5). In contrast with the previous experiments, we added railroads on either side such that each target location was situated at equal distance from a set of two railroads (the central railroad + one of the side railroads; see Figure 5). The instructions stated that bridges are connecting the neighborhoods, and that the target locations A and B are at equal distance from the railroads. Participants were asked to "imagine as vividly as possible that [they] were contemplating the purchase of a new home." Further the instructions asked participants to select a home either at location A or at location B with a dropdown menu containing the options "A" and "B." In the hotspot conditions, the same neighborhood was used only that it contained one of three types of hotspots (an unsafe housing block, a landfill or a nuclear power plant). Similar symbols as in Experiments 1 and 2 accompanied the visual depiction of the hotspots. In the large perspective condition, the satellite view was taken from a greater altitude and the distance between the hotspot and the target locations was greater than in the small perspective one (see Figure 5). The position of the hotspot on either the left or the right side of the central railroad was counterbalanced across participants.

Results

To predict participants' choice of location (side containing the hotspot or side across the border) as a function of type of hotspot (no hotspot, unsafe housing block, landfill or nuclear power plant) and spatial context (small or large), we conducted a logistic regression.

Type of hotspot was coded using similar sets of orthogonal contrasts as in Experiment 2: C1

(border effect hypothesis) = no hotspot versus unsafe housing block, landfill and nuclear power plant; C2 (type of influence hypothesis) = unsafe housing block versus landfill and nuclear power plant; and C3 (influence intensity hypothesis) = landfill versus nuclear power plant. Spatial context was coded (-1) for small and (+1) for large. This set of predictors and their interactions (C1 x spatial context, C2 x spatial context, C2 x spatial context) were entered simultaneously into the logistic regression to predict choice of location. A test of the full model against a constant only model was statistically significant, indicating that the predictors as a set reliably distinguish between the two choices of location (chi square = 44.08, p < .001 with df = 7). Prediction success overall was 66.20 % (54.10 % for the side of the central railroad containing the hotspot and 74 % across). The Wald criterion indicates that C1 (Wald = 26.08, p < .001), C2 (Wald = 7.01, p = .008), C3 (Wald = 4.05, p = .04), and spatial context (Wald = 6.08, p = .01) made a significant contribution to the prediction, whereas the interaction terms did not (all Walds < 0.10, all ps > .29) (Figure 6). As expected, the change in the odds ratio for C1 indicates that the participants in the hotspot conditions were 34% more likely to choose the location across the central railroad than they were in the control condition [Exp(B) = 1.34]. In accord with the predictions, the odds ratio for C2

indicates that the participants were 21% less likely to choose the location across the central

Discussion

Experiment 3 tested the border effect in more favorable conditions than Experiments 1 and 2. It is in effect plausible that in Experiments 1 and 2 the railroad's negative influence masked the weaker border effect in the landfill and nuclear power plant conditions. In Experiment 3, where the influence of the railroad tracks was controlled for, the border effect emerged significantly for every kind of hotspot (see supplementary material 2 for an alternative interpretation). Apart from visual salience, this finding points to the importance of another characteristic of borders for the emergence of border effects: to the degree that a border has negative influence it may mask weaker border effects.

As in Experiment 1 and 2, the results support the type of influence hypothesis: the border effect was greater for influence by surface transport (unsafe housing block) than for influence by air (landfill and nuclear power plant). The results of Experiment 3 also support the influence intensity hypothesis: weaker influence elicited a stronger border effect.

However, as the influence intensity effect emerged only in the better controlled conditions of Experiment 3, the effect seems rather weak (at least for influence by air and at the scale used here).

General Discussion

The border effect emerges when people prefer places that are separated from a negative hotspot by a border, even though this border is useless in protecting those places against the negative influence of the hotspot. The present research tested whether the influence stemming from different types of hotspots moderated the border effect. We claimed that when more influence is perceived to cross the border, a weaker perception of discontinuity of contact follows, leading to a weaker border effect. We reasoned that more influence is perceived to cross a border when influence appears to travel across the border more easily (type of influence hypothesis) or when it flows more intensely (influence intensity hypothesis). Specifically, as influence by air may cross the border everywhere whereas influence by surface transport cannot, we predicted that, compared to hotspots with surface influence, hotspots with aerial influence would lead to weaker border effects. As influences carrying across a longer distance feel more intense for any given distance, we further predicted that, compared to hotspots with a shorter reach, hotspots with a greater reach would lead to weaker border effects.

The results from three experiments support this set of predictions overall.

Specifically, a passable border elicited stronger border effects for influence by surface transport (criminal activity emanating from an unsafe housing block) than for influence by air (bad smells or radioactive particles emanating from a landfill or a nuclear power plant, respectively; see Experiments 1 to 3). This effect generalized to three different dependent variables (risk estimates, rent amount and dichotomous choice). Influence intensity was important too, even though its effect was more elusive and necessitated better controls. More intense influence by air decreased the border effect compared to milder one (nuclear power plant versus landfill, see Experiment 3). In addition, the border itself can exert a negative influence, which may mask weaker border effects. As a result, only in Experiment 3 were we able to detect a significant border effect for influence by air due to the border´s negative influence being controlled for.

Are there any alternative interpretations to the results? It might be argued that the stronger border effect in the unsafe housing block condition was due to participants applying naïve theories about residential segregation [START_REF] Schelling | Models of segregation[END_REF] more readily in that condition.

More than in the other hotspot conditions, the participants might have felt that the side containing the unsafe housing block concentrated the less privileged inhabitants and the lowest quality of life overall. According to the same residential segregation logic, however, why would a landfill or a nuclear power plant that devalue a neighborhood not also lead to the same conclusion? After all, it seems only natural to infer less privileged inhabitants and a lower quality of life overall from the presence of a landfill or a nuclear power plant in a neighborhood compared to one without. Hence, this residential segregation explanation cannot account for the differences observed between the hotspot conditions without additional assumptions. We would argue that the perception of residential segregation is an outcome rather than a cause: residential segregation should appear more intense when the border feels less passable. For instance, it may have been greater in the case of a less passable border like a railroad without bridges.

One might further argue that the causal effect of influence intensity (weaker border effect for more intense influence) is somewhat unclear because we manipulated intensity (larger gradient of influence for the nuclear power plant) and type of aerial influence (radioactive particles versus bad smell and toxic particles) at once. In the smaller spatial context of Experiment 3, however, shorter distances to the border caused more intense influence to reach the border all things being equal. In support of the influence intensity hypothesis, the border effect was weaker in the smaller spatial context than in the larger one.

In sum, the empirical evidence shows that, all other things being equal, the border effect is attenuated when variations in influence intensity are operationalized as differences in the gradient of influence or as differences in the distance of the hotspot to the border. A third operationalization to be tested in the future concerns the negative intensity of the hotspot per se. All other things being equal, a hotspot that is more intensely negative should spread more negative influence towards the border. Hence, a no-go area should attenuate the border effect compared to an area with a reputation that is only mildly negative.

We made choices that restrict the generalizability of the results. The design did not fully cross type of influence with intensity of influence. Thus, we cannot conclude whether the intensity effect found for influence by air generalizes to influence by surface transport. On one hand, intensity effects could emerge less easily when influence cannot cross the border freely. On the other hand, they could emerge more easily because the border effect is more reliable in that case. Although the present research simply aimed at providing support for the existence of the influence intensity effect, future research could investigate whether it generalizes to every type of influence. The results showed that the salient railroad elicited greater border effects (Experiment 2) but that it also carries a negative affective charge that may mask weaker border effects. What would happen with a salient border that had a positive affective charge, like a pleasant river stream? The positive influence could arguably intensify the border effect. However, since the negative influence of negative hotspots devalues positive hotspots (Blaison, Gebauer, et al., 2017), it is unclear whether a positive border like a river stream would remain positive under the influence of a negative hotspot. Do border effects emerge in combination with positive hotspot? Since positive and negative hotspots have symmetrical effects (Blaison, Gebauer, et al., 2017), positive hotspots should elicit border effects such that the area across the border appears more negative. As an example, [START_REF] Galak | Not in my backyard: The influence of arbitrary boundaries on consumer choice[END_REF] showed that attractive places, like a park, feel less pleasant across a border than on the same side as a very positive hotspot like one´s own home. However, the positive influence of an attractive border may mask weaker border effects due to positive hotspots.

One might further argue that participants' skin color could play a moderating role in the results. According to past empirical evidence, people generally associate areas with lower quality of life with black inhabitants [START_REF] Bonam | Polluting black space[END_REF][START_REF] Bonam | Racialized physical space as cultural product[END_REF]. As a result, our participants may have inferred black inhabitants in the neighborhood with the negative hotspots. [START_REF] Bonam | Polluting black space[END_REF][START_REF] Bonam | Racialized physical space as cultural product[END_REF], should further consolidate this phenomenon. Thus, physical borders may trigger a cascade of sociocognitive processes that intensify and consolidate the perceived differences between neighborhoods. These differences may also concern the perceived differences between inhabitants. Spatial context influences person perception (Corell, Park, Judd, & Wittenbrink, 2002;[START_REF] Wittenbrink | Spontaneous prejudice in context: Variability in automatically activated attitudes[END_REF]. Thus, inhabitants living across the border should be perceived as more different than they actually are, which could fuel residential segregation over time.

However, when a hotspot´s influence perceived to be crossing a border is disproportionately large, this should hinder the emergence of consensual spatial categories.

For instance, when a nuclear power plant flanks an avenue, the uniting effect of the nuclear power plant should cover up any perceived discontinuity of contact created by the avenue.

Therefore, a different physical border creating discontinuity of contact further away from the hotspot may in turn facilitate the generation of a consensual spatial category. In sum, spatial categories should form along the uniting and the dividing effects of hotspots and borders.

When these uniting and dividing effects overlap, border effects-and thus borders to consensual spatial categories-should be less likely to emerge. This is especially true when a hotspot has a strong uniting effect (i.e., its influence is intense or flows easily across the border) or when the border has a weak dividing effect (i.e., it provides a weak perception of discontinuity of contact).

One of the implications of our findings is that neighborhood attractiveness measured in terms of real-estate prices should be more contrasted along salient borders like railroads, large avenues, passable rivers, etc., then somewhere else (except in cases where no border effect emerges). If we represent the real-estate price as a landscape where local minima in price indicate negative hotspots, whereas local maxima indicate the most attractive areas, then salient borders that run through the landscape should trace significant edges. One way to test this prediction with real world data is to spot a city's local minima in real-estate price and compare the price of real-estate located at a similar distance. Compared to properties located on the same side as the local minimum, properties located across a border should on average be more expensive. This hypothesis can be tested using archival data. Sometimes, specific parts of a city deteriorate, dragging real estate prices down. The rate of decrease, however, should be different on either sides of a salient border. Across the border, prices should resist more.

The results also have implications for the "not in my backyard" phenomenon.

Unpopular developments should elicit less resistance if they are located across a border, even when that border is not an actual physical barrier. In addition, unpopular developments that act as a passable border (like railroad tracks) should elicit less resistance if they subjectively shield from a negative hotspot.

Finally, we would like to take a broader perspective on the results, looking at not only urban or environmental perception but also social perception. It has been argued that distance is such a fundamental dimension that it is processed similarly in the spatial, temporal, and social domain [START_REF] Trope | Temporal construal[END_REF], 2010). Thus, results concerning spatial distance may also generalize to social distance. Imagine group members separated by varying social distances because they have different mutual relationships (e.g., love relationship, friendship, or acquaintance). One member may be a negative hotspot in the social field because she is disliked. If spatial and social distances are homologues, that member should polarize the surrounding social space. Members close by in terms of social distance should be perceived more negatively, whereas the perception of those further away in the sociogram should be biased toward the positive. Furthermore, any perceived border between subgroups (e.g., they work in different rooms) might reinforce this effect. However, a border effect is less likely for group members that are close in the sociogram.

In conclusion, the present research contributes to our understanding of environmental perception. Passable borders accentuate the affective differences between areas through the border effect. Hotspots, however, may mitigate the border effect depending on the type and the intensity of a hotspot´s influence in the area. Thus, for a full understanding of how people form affective representations of the environment, it is crucial to take into account the interaction between the effects of borders and hotspots on affective judgment.

Figure 1 .

 1 Figure 1. Example of stimuli used in Experiment 1. Top panel: landfill condition; bottom panel: unsafe housing block condition. The white arrows indicate the 11 locations rated by the participants. The locations with the white dots were included in the analyses.

Figure 2 .

 2 Figure 2. Risk ratings (Y-axis) as a function of side and type of hotspot. The risk scale ranges from 1 = "0% risk" to 9 = "100% risk." The error bars represent standard errors.

Figure 3 .

 3 Figure 3. Example of stimuli shown in Experiment 2. Top panel: nuclear power plant / railroad condition; bottom panel: nuclear power plant / avenue condition.

Figure 4 .

 4 Figure 4. Experiment 2: Amount of rent ($; Y-axis) as a function of side, type of hotspot, and type of border (left panel = avenue border; right panel = railroad border). The error bars represent standard errors.

  ,446) = 360.42, p < .001, η 2 p = .45, and significant distance x hotspot, F(3,446) = 23.19, p < .001, η 2 p = .14, distance x type of border, F(1,446) = 16.40, p < .001, η 2 p = .04, distance x hotspot x type of border, F(3,446) = 7.54, p < .001, η 2 p = .05, and distance x type of border x side interactions, F(1,446) = 4.64, p = .03, η 2 p = .01. Briefly, these effects indicate that compared to the control condition, participants in the hotspot conditions were willing to pay more rent at locations further away from the hotspot, and that the railroad had a more negative influence on the surroundings than the avenue. No other effect involving distance reached the conventional significance level.

Figure 5 .

 5 Figure 5. Example of stimuli used in Experiment 3. Top panel: nuclear power plant / small spatial context condition; bottom panel: nuclear power plant / large spatial context condition. Each location is at the same distance from a set of railways.

Figure 6 .

 6 Figure 6. Experiment 3: Percentage of choice across the railroad (Y-axis) as a function of size of spatial context (small versus large) and type of hotspot (no hotspot, unsafe housing block, landfill or nuclear power plant).

  railroad in the landfill and the nuclear power plant conditions than they were in the unsafe housing block one [Exp(B) = 0.79]. The change in odds ratio for C3 indicates that the participants were 25% less likely to choose the location across the central railroad in the nuclear power plant condition than in the landfill condition [Exp(B) = 0.75]. Finally, the change in odds ratio for spatial context indicates that the participants were 29 % more likely to choose the location across the central railroad in the large spatial context compared to the small one. Additional analyses (Bonferroni adjusted alpha level p < .017) showed that the participants in both the landfill and the unsafe housing project conditions taken in isolation were more likely to choose the location across the central railroad than the participants in the control condition: unsafe housing block versus control, Wald = 29.71, p < .001, Exp(B) = 2.30; landfill versus control, Wald = 18.38, p < .001, Exp(B) = 1.85, whereas the participants in the nuclear power plant condition were slightly more likely to do so compared to the control participants, Wald = 5.56, p = .018, Exp(B) = 1.39.

  ). If a border effect emerges, then C1 should be significant; if type of negative influence moderates the border effect, then C2 should be significant; if influence intensity moderates the border effect, then C3 should be significant. If type of border moderates these effects, then interactions involving type of border should emerge significantly.

	(type of influence hypothesis) was significant, b = -7.30, t(446) = 2.51, p = .013, 95% CI [-
	13.02, -1.58]. Simple effects analyses showed that participants were willing to pay
	significantly higher rents across the border than on the side of the hotspot in the unsafe
	housing block condition, b = 23.12, t(446) = 3.23, p = .001, 95% CI [9.06, 37.18], but not in
	the landfill condition, b = 2.99, t(446) = 0.43, p = .67, 95% CI [-10.84, 16.81], or in the
	nuclear power plant condition, b = -0.52, t(446) = 0.07, p = .94, 95% CI [-14.52, 13.48]. This
	pattern of results is in line with our type of influence hypothesis. Interestingly, the salience of
	the border had no effect on the border effect as shown by the non-significant C2 x type of
	21.62, t(446) = 7.34, p < .001, 95% CI [15.83, 27.41] but not in the avenue condition, b =
	1.68, t(446) = 0.57, p = .57. Closer inspection of Figure 4 (right panel) suggests that the
	border effect in the railroad condition was mainly driven by the results in the control
	condition; it was due to the negative effect of the railroad on the surroundings as shown

The results first showed a significant effect of C1 (border effect hypothesis), b = 11.65, t(446) = 5.60, p < .001, 95%

CI [7.56, 15.75]

, which indicates that the border effect (i.e., participants are willing to pay higher rents across the border than on the same side as the hotspot) was stronger in the hotspot conditions than in the control condition. This effect, however, was qualified by a C1 x type of border effect, b = 9.97, t(446) = 4.79, p < .001, 95%

CI [5.87, 14.06]

, which shows that the border effect was significantly stronger in the railroad than in the avenue condition. More precisely, C1 was significant in the railroad condition, b = above. When comparing the hotspot conditions, the border effect emerged more strongly in the unsafe housing block condition than in the other hotspot conditions. Specifically, the contrast C2 comparing the unsafe housing block to the landfill and nuclear power conditions border interaction, b = 3.51, t(446) = 1.21, p = .23). The effect of C3 (landfill versus nuclear power plant), was in the expected direction but did not emerge significantly, b = -1.75, t(446) = -.35, p = .72, 95% CI

[-11.59, 8.09]

. Thus, the influence intensity hypothesis was not supported.

  This inference may have led to different consequences for black participants than for non-black participants. Black participants may have identified the inhabitants of the neighborhood with the hotspot as in-group members whereas non-black participants may have identified them as out-group members. As nonblack Mturkers greatly outnumber black Mturkers (black Mturkers circa. 8.6% f Mturkers;[START_REF] Casey | Intertemporal differences among MTurk workers: Time-based sample variations and implications for online data collection[END_REF], this difference may restrict the generality of the present results. For instance, past research showed that places linked to one's self shield against the propagation of negative influence(Blaison, Gebauer, et al., 2017).Similarly, in the eyes of black participants, the presence of in-group inhabitants could have shielded against the influence of the negative hotspots. Black participants may thus have judged the neighborhood with the hotspot less negatively than non-black participants. As we did not collect skin color information nor controlled for it, future research should investigate the effects of participants' skin color on the polarization and the border effect.The results open several other interesting research avenues. People may differ in their beliefs about how much influence crosses the border. For instance, an anti-nuclear activist may perceive the border as more porous to radioactive particles than someone keen on cheap electricity. Similarly, policemen facing delinquency on a daily basis may report a different border effect for crime than people less exposed to it. Furthermore, someone high in prevention focus (i.e., someone who seeks to avoid harm or failure chronically;[START_REF] Higgins | Promotion and prevention: Regulatory focus as a motivational principle[END_REF] may need borders that offer a clearer guarantee of protection than someone low on this trait. Thus, personal experience as well as personality traits may modulate the border effect.Collective experiences and beliefs may moderate the border effect as well. Physical borders such as rivers, parks, roads, or railroads facilitate the emergence of consensual spatial categories like neighborhoods. For example, the Harlem River in New York acts like a boundary between Manhattan and the Bronx. Categorization processes in turn amplify mild differences between areas. These spatial categories are further reinforced by people´s daily journeys and by socially constructed meanings associated with them[START_REF] Ramadier | Les usages paysagers de la ville : le paysage urbain comme régulateur des mobilités quotidiennes [The landscape uses of the city : the urban landscape as a regulator of daily mobility[END_REF]. If you spend most of your time in Manhattan, less familiar neighborhoods, such as the Bronx, may appear more subjectively distant, which triggers thinking in terms of abstract (spatial) categories(Fujita, Henderson, Eng, Trope, & Liberman,

2006;

[START_REF] Liberman | Psychological distance[END_REF]

and amplify the process. With time, developing social representations about neighborhoods, sometimes in the form of space-focused stereotypes

To compensate for the fact that borders may increase perceived distance (e.g.,[START_REF] Holding | Further evidence for the hierarchical representation of spatial information[END_REF][START_REF] Mcnamara | Mental representations of spatial relations[END_REF], the target locations across the railroad were slightly nearer to the hotspot than the ones on the other side.


Myriam Delomenie
email: myriam.delomenie@aphp.fr

Hélène Bonsang-Kitzis

Anne-Sophie Bats

Charlotte Ngo

Vincent Balaya

Huyen Thu Nguyen

Meriem Koual

Patrice Mathevet

Fabrice Lecuru

Huyen Thu Nguyen Xuan

The clinical implication of lymph nodes micrometastases and isolated tumor cells in patients with cervical cancer: A systematic review

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Cervical cancer ranks fourth for both incidence and mortality in women, contributing 6.9% of the total number of new cases diagnosed in 2018 [START_REF] Bray | Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[END_REF]. Unfortunately, the incidence and death rates are different in developing countries and particularly in Africa and in Central America. Cervical cancer remains the leading cause of cancerrelated mortality among women in these continents [START_REF] Lee | HPV status in sentinel nodes might be a prognostic factor in cervical cancer[END_REF].

Cervical cancer may spread to the pelvic or paraortic lymph nodes (LNs), as well as more distant nodes. LNs involvement is the most important prognostic factor in early stage cervical carcinoma and impacts therapeutic decisions including treatment modality and radiation fields [START_REF] Delgado | A prospective surgical pathological study of stage I squamous carcinoma of the cervix: a Gynecologic Oncology Group Study[END_REF] [START_REF] Sevin | Surgically defined prognostic parameters in patients with early cervical carcinoma. A multivariate survival tree analysis[END_REF]. Traditionally, surgery with lymphadenectomy was essential for the evaluation of LNs metastases. Currently, options for evaluating for LNs involvement include LNs dissection, imaging studies, or both. Diagnostic value of Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) warrant any use for treatment decision making with a sensitivity for MRI and CT of 56 and 58 percent in one meta-analysis, respectively [START_REF] Selman | Diagnostic accuracy of tests for lymph node status in primary cervical cancer: a systematic review and meta-analysis[END_REF]. The advantage of Positron Emission Tomography scan (PET) to other imaging modalities was illustrated in this metaanalysis of 72 studies that found a sensitivity of 75 percent and specificity of 98 percent for the detection of LNs metastases in cervical cancer with this modality [START_REF] Selman | Diagnostic accuracy of tests for lymph node status in primary cervical cancer: a systematic review and meta-analysis[END_REF].

As a result, pelvic lymphadenectomy remains the gold standard for evaluating LNs involvement in cervical cancer. Sentinel lymph node (SLN) technique is proposed for over 15 years as an alternative to lymph nodes dissection. According to the sentinel lymph node hypothesis, tumor cells migrating from a primary tumor metastasize to one or a few lymph nodes (LNs) before involving other LNs. Injection of blue dye and/or radiolabeled colloid into the uterine cervix permits identification of one or more SLNs in the majority of patients. The status of the SLNs accurately predicts the status of the remaining pelvic and paraaortic LNs [6][7].

Rob et al. in a review of 1811 patients who underwent lymphatic mapping and SLN detection as part of their cervical cancer treatment reported a sensitivity for metastatic disease of 93 percent and a sensitivity of 100 percent in tumors limited to ≤2 cm in size [START_REF] Rob | Current status of sentinel lymph node mapping in the management of cervical cancer[END_REF]. Other studies emphasized the importance of bilateral detection [START_REF] Hauspy | Sentinel lymph nodes in early stage cervical cancer[END_REF][9] [START_REF] Cormier | Establishing a sentinel lymph node mapping algorithm for the treatment of early cervical cancer[END_REF]. Forty-seven studies (4130 patients) were analyzed in the Tax's review.

Pooled data of diagnostic accuracy on ultrastaging (18 studies; 1275 patients) showed a sensitivity of 94% (95% CI 80-99%) and negative predictive values ranging between 91 and 100%. After ultrastaging, 19 false negative results remained.

Prerequisites such as early FIGO stage (IA2, IB1, IIA primary tumor size b40mm), no suspicious pre-, and per-operative lymph nodes, and bilateral negative SLNs after ultrastaging resulted in 1 remaining false negative result among 1257 patients (0.08%) [START_REF] Tax | The sentinel node procedure in early stage cervical cancer, taking the next step; a diagnostic review[END_REF].

Standard LNs assessment involves sectioning the node once along the longitudinal axis and staining it with hematoxylin and eosin (H&E) to determine if it contains metastatic tumor cells. For SLNs, enhanced pathologic assessment is performed if the initial H&E stain is negative. Ultrastaging involves additional sectioning and staining of the SLN with H&E and immunohistochemistry (IHC) in order to examine the SLN for low-volume metastatic disease. Low-volume metastatic disease, as defined in the breast cancer literature, includes micrometastases (MMs) and isolated tumor cells (ITCs) [START_REF] Singletary | Staging system for breast cancer: revisions for the 6th edition of the AJCC Cancer Staging Manual[END_REF] . Macrometastases are defined as metastatic tumor cells measuring >2 mm, MMs are defined as a focus of metastatic tumor cells measuring >0.2 mm and ≤2 mm, whereas ITCs are defined as microscopic clusters and single cells measuring ≤0.2 mm. Originally, MMs were distinguished from ITCs morphologically not only by their size, but also by contact with vessel or lymph sinus wall, extravasation, extravascular (extrasinusoidal) stromal reaction and tumor cells proliferations [START_REF] Hermanek | Classification of isolated tumor cells and micrometastasis[END_REF].

Pathology evaluation with ultrastaging is typically used for sentinel nodes, and this may result in increased identification of metastatic lymph nodes [START_REF] Frumovitz | Lymphatic mapping and sentinel lymph node detection in women with cervical cancer[END_REF].

While there is a wide consensus regarding the clinical importance and therapeutic implications of positive LNs macrometastases (MACs) in cervical cancer, the significance of MMs and ITCs needs to be assessed and discussed.

Incidence

In patients with FIGO stages IA2-IB2 cervical cancer (FIGO 2009), although with a lack of consensual definition, MMs were detected between 9% [START_REF] Hafner | HPV16-E6 mRNA is superior to cytokeratin 19 mRNA as a molecular marker for the detection of disseminated tumour cells in sentinel lymph nodes of patients with cervical cancer by quantitative reverse-transcription PCR[END_REF] and 15% (19 of 132 patients; 95% confidence interval _CI_ 9%, 22%) [START_REF] Lentz | Identification of micrometastases in histologically negative lymph nodes of early-stage cervical cancer patients[END_REF], while MACs were detected in 14.7% of patient in a large retrospective multicenter cohort of 645 patients [START_REF] Cibula | Prognostic significance of low volume sentinel lymph node disease in early-stage cervical cancer[END_REF]. This study showed over 30% LNs involvement in early stage cervical cancer, when all types of spread are considered. In the SENTICOL study, 139 patients were included. All macrometastases were identified in standard analysis.

After ultrastaging, 8 SLN were positive for isolated tumor cells [START_REF] Guani | Recurrence risk after sentinel lymph node biopsy in patients with early cervical cancer: Follow-up results after 3 years of a prospective study (SENTICOL)[END_REF]. The incidence rate of SLNs with MMs/ITCs depends on the pathological technique that is being used. Pathological ultrastaging, as described above, may result in increased identification of metastatic lymph nodes [START_REF] Frumovitz | Lymphatic mapping and sentinel lymph node detection in women with cervical cancer[END_REF]. Pathological techniques, which include serial sections, IHC and H&E reveal MMs in 5.1-8.1% of nodes considered normal by conventional techniques [START_REF] Silva | Sentinel node detection in cervical cancer with (99m)Tc-phytate[END_REF] [START_REF] Juretzka | Detection of pelvic lymph node micrometastasis in stage IA2-IB2 cervical cancer by immunohistochemical analysis[END_REF]. In the older studies we don't have information for ITCs. The incidence of MMs/ITCs differs according to the technique used for their identification: H&E staining on a few slides, the number of sections and their thickness, IHC with or without an anti-cytokeratin antibody cocktail, reversetranscriptase PCR (RT-PCR) detection of cytokeratin 19 (CK 19) mRNA, or HPV-16 and -18 DNA detection by PCR. The rates of MMs in apparently negative nodes in several studies using molecular biology methods are listed in table 1. Unfortunately, except from the study of Van Trappen et al. [START_REF] Van Trappen | Molecular quantification and mapping of lymph-node micrometastases in cervical cancer[END_REF] a description of the histological slides thickness could not be found in any of the other studies mentioned in table 1 [21] [START_REF] Czeglédy | Detection of human papilloma virus DNA in lymph nodes extirpated at radical surgery for cervical cancer is not predictive of recurrence[END_REF][23] [START_REF] Lee | HPV status in sentinel nodes might be a prognostic factor in cervical cancer[END_REF], neither data on the median size of LNs metastases diagnosed histologically. Several studies, using various molecular biology methods for LNs evaluation have reported positive MMs in up to 44% of the apparently negative LNs for MMs by conventional histology as demonstrated by Van Trappen et al. using RT-PCR for CK 19 in 32 patients [START_REF] Czeglédy | Detection of human papilloma virus DNA in lymph nodes extirpated at radical surgery for cervical cancer is not predictive of recurrence[END_REF]. Consequently, CK19 is probably not a suitable marker due to its low specificity and relative higher baseline expression in normal nodes [START_REF] Van De Lande | SCC-Ag, lymph node metastases and sentinel node procedure in early stage squamous cell cervical cancer[END_REF]. Recently, Okomoto et al. used one-step nucleic acid amplification (OSNA) method to detect sentinel lymph node metastases in cervical cancer by assessment of KRT19 mRNA in 32 patients [START_REF] Okamoto | Detection of sentinel lymph node metastases in cervical cancer: assessment of KRT19 mRNA in the one-step nucleic acid amplification (OSNA) method[END_REF]. The concordance between the twomm interval histopathological examination and the OSNA assay was 96.2%, with sensitivity and specificity of 50% and 98.4%, respectively [START_REF] Okamoto | Detection of sentinel lymph node metastases in cervical cancer: assessment of KRT19 mRNA in the one-step nucleic acid amplification (OSNA) method[END_REF]. Pappa et al. compared the detection rates of LNs MMs in cervical, endometrial and vulvar cancers using histology versus CK 19 and carbonic anhydrase 9 (CA9) levels and qRT-PCR for CK19 and CA9 expression [START_REF] Pappa | Comparative assessment of lymph node micrometastasis in cervical, endometrial and vulvar cancer: insights on the real time qRT-PCR approach versus immunohistochemistry, employing dual molecular markers[END_REF]. In this study, 11 positive and 71 negative histologically nodes were examined by IHC and genetically. There weren't negative histologically nodes that were positive by IHC or qRT-PCR. The author suggested that qRT-PCR exhibits a better diagnostic accuracy compared to IHC, while CK19 displays a consistent pattern of detection compared to CA9 [START_REF] Pappa | Comparative assessment of lymph node micrometastasis in cervical, endometrial and vulvar cancer: insights on the real time qRT-PCR approach versus immunohistochemistry, employing dual molecular markers[END_REF].

Approximately 80% of cervical cancer cases are squamous cell (SCC) carcinomas. SCC-Ag is the serum tumor marker most commonly used for clinical monitoring of SCC cervical cancer [START_REF] Kato | Tumor-antigen TA-4 in the detection of recurrence in cervical squamous cell carcinoma[END_REF][28] and elevated pre-treatment SCC-Ag levels correlate with nodal involvement [START_REF] Gaarenstroom | Can initial serum cyfra 21-1, SCC antigen, and TPA levels in squamous cell cervical cancer predict lymph node metastases or prognosis?[END_REF] Using polymerase chain reaction (PCR) found positive HPV DNA lymph nodes in 91% (10/11) of patients with histologically detectable metastasis at surgery and in 60% (12/20) of patients without metastasis [START_REF] Czeglédy | Detection of human papilloma virus DNA in lymph nodes extirpated at radical surgery for cervical cancer is not predictive of recurrence[END_REF]. HPV 16 DNA was detected in extirpated lymph nodes in 75% (6/8) of patients with recurrence and in 70% (16/23) of recurrence-free patients. The author concluded that the presence of HPV DNA in extirpated lymph nodes at cervical cancer operation does not appear to be predictive of tumor recurrence.

In another study HPV DNA was detected in 69.8% of the LNs dissected intraoperatively and the presence of HPV DNA in LNs was independent parameter correlating with survival and mortality risk [START_REF] Lukaszuk | Predictive value of HPV DNA in lymph nodes in surgically treated cervical carcinoma patients--a prospective study[END_REF]. In a study that investigated the correlation between the SLNs HPV status and pelvic lymph node metastasis in patients with cervical cancer with a 3 years follow up, the combination of SLNs histology and HPV typing showed a negative predictive value of 100% in predicting non-metastasis of LNs and no recurrence of disease [START_REF] Lee | HPV status in sentinel nodes might be a prognostic factor in cervical cancer[END_REF] .

Dürst in a prospective, multi-center prognostic study, analyzed 189 patients free of lymph node metastases by conventional histopathology All patients underwent complete lymphadenectomy. Of each sentinel node (SLN) a biopsy was taken for the detection of HPV-E6-E7-mRNA.

HPV-mRNA could be detected in SLN of 52 patients (27.5%). Recurrence was observed in 22 patients. Recurrence-free-survival was significantly longer for patients with HPV-negative SLN (log rank p = 0.002). By Cox regression analysis the hazard ratio (95%CI) for disease-recurrence was 3.8 (1.5 -9.3, p = 0.004) for HPV-mRNApositive compared to HPV-mRNA-negative patients [START_REF] Dürst | Prognostic value of HPV-mRNA in sentinel lymph nodes of cervical cancer patients with pN0-status[END_REF].

Topographic distribution of LNs MMs

There was no significant difference between the distribution of the lymph node metastases depending from their size (micro-versus macrometastases) in a study aimed to investigate the topographic distribution and prognostic impact of nodal MMs in patients with cervical carcinoma. The most frequent sites of pelvic lymph node involvement were the obturator and internal iliac nodes, without any differences within the right and the left sides [START_REF] Horn | Detection of micrometastases in pelvic lymph nodes in patients with carcinoma of the cervix uteri using step sectioning: Frequency, topographic distribution and prognostic impact[END_REF]. This study is consistent with the results of other studies dealing with the mapping of sentinel lymph nodes in cervical cancer [START_REF] Plante | Laparoscopic sentinel node mapping in early-stage cervical cancer[END_REF][33] [START_REF] Levenback | Lymphatic mapping and sentinel node identification in patients with cervix cancer undergoing radical hysterectomy and pelvic lymphadenectomy[END_REF].

Role of Frozen section in detection LNs MMs

The diagnosis of lymph node metastases in women undergoing surgical management for cervical cancer is an indication to abort the surgery. Hence, frozen section evaluation of sentinel lymph nodes has been used for these patients.

However, several studies have found significantly reduced sensitivity of intraoperative frozen section in detection of MMs and small MACs in cervical cancer [START_REF] Bats | Diagnostic value of intraoperative examination of sentinel lymph node in early cervical cancer: a prospective, multicenter study[END_REF] [START_REF] Slama | High false negative rate of frozen section examination of sentinel lymph nodes in patients with cervical cancer[END_REF]. In our multi-center study of 139 patients with stage IA1 and lymphovascular space involvement to IB1 cervical cancer, we noted the overall sensitivity of frozen section per node was only 20.7%. In this study we failed in the intraoperative diagnosis of the all nodes that finally were diagnosed with MMs and ICTs: 4 nodes with MMs and 9 nodes with ICTs [START_REF] Slama | High false negative rate of frozen section examination of sentinel lymph nodes in patients with cervical cancer[END_REF].

In another study of 225 patients, Slama et al. noted the overall sensitivity of frozen section to be 56% for detecting LNs metastases and only 8% for the detection of MMs [START_REF] Slama | High false negative rate of frozen section examination of sentinel lymph nodes in patients with cervical cancer[END_REF] .

Prognostic value and treatment options for women diagnosed with LNs MMs

-Prognostic value

The prognostic value of LNs ITCs and MMs is controversial and most of the studies are limited due to their small sample size, short follow up period and different methodology detecting MMs. Some data are taken from sentinel node series, in which patients with positive LNs for MMs/ITCs treated as with MACs. In the largest retrospective series to date which included 645 patients, Cibula and his colleagues observed the same prognosis in patients with positive LNs for MMs and MACs in terms of overall survival. In this study the overall survival was significantly reduced in patients with MACs and MMs; the hazard ratio for overall survival reached 6.85 (95% CI, 2.59-18.05) and 6.86 (95% CI, 2.09-22.61) respectively [START_REF] Cibula | Prognostic significance of low volume sentinel lymph node disease in early-stage cervical cancer[END_REF][START_REF] Cibula | Risk of micrometastases in nonsentinel pelvic lymph nodes in cervical cancer[END_REF].

This result was supported by other series [START_REF] Cormier | Establishing a Sentinel Lymph Node Mapping Algorithm for the Treatment of Early Cervical Cancer[END_REF], especially for IB and IIA FIGO stages; lymph node involvement was illustrated as the most important prognostic factor and LNs MMs was an independent prognostic factor in multivariate analysis of 894 patients [START_REF] Horn | Detection of micrometastases in pelvic lymph nodes in patients with carcinoma of the cervix uteri using step sectioning: Frequency, topographic distribution and prognostic impact[END_REF]. Other authors reported an increased risk of recurrence in cases of LNs MMs, whether detected by pathological or molecular biology techniques such as quantitative reverse transcription-PCR assay for detecting SCC-Ag in LNs [START_REF] Yuan | Molecular diagnosis of sentinel lymph node metastases in cervical cancer using squamous cell carcinoma antigen[END_REF][START_REF] Fregnani | Assessment of pelvic lymph node micrometastatic disease in stages IB and IIA of carcinoma of the uterine cervix[END_REF] and IIA cervical cancer who were surgically treated (radical hysterectomy) with no lymph nodes metastases after standard pathological examination, without adjuvant treatment and with a minimum of 5 years of follow-up [START_REF] Colturato | Lymph node micrometastases in initial stage cervical cancer and tumoral recurrence[END_REF]. Every tumor sections were reviewed and lymph nodes were analyzed with pathological ultrastaging. 6 patients/ 83 (7%) were found to have pelvic lymph node micrometastases. Multivariate regression analysis results showed significant associations between recurrence and the presence of lymph node micrometastases: patients with micrometastases had an 11.73-times higher risk of recurrence than did those without micrometastase [START_REF] Colturato | Lymph node micrometastases in initial stage cervical cancer and tumoral recurrence[END_REF] (Table 2 ). Contrarily, 2 studies found no impact of micrometastasis in sentinel lymph nodes on disease-free survival.

The Stany et al. retrospective review of 129 patients with early-stage cervical cancer treated with radical hysterectomy and pelvic lymphadenectomy, found retrospectively LNs MMs in 26 patients (20%) diagnosed with negative nodes before [START_REF] Stany | Lymph Node Micrometastases in Early-Stage Cervical Cancer are Not Predictive of Survival[END_REF]. LNs MMs were not associated with any other high-risk clinical or pathologic variables and survival data analysis did not demonstrate an association between the presence of MMs and recurrence or overall survival in an average follow-up of 70 months [START_REF] Stany | Lymph Node Micrometastases in Early-Stage Cervical Cancer are Not Predictive of Survival[END_REF].

The author did not explain why patients with MMs were more likely to have received adjuvant radiation and chemotherapy.

Secondly, the recent follow-up results after 3 years of the prospective study SENTICOL (Ganglion Sentinelle dans le Cancer du Col) reported 13 recurrences.

The study included patients with cervical carcinoma FIGO (International Federation of Gynecology and Obstetrics) stage IA1 with lymphovascular space invasion, stage IA2 and IB1, who participated in the prospective SENTICOL study. Among patients with sentinel lymph nodes positive for micrometastasis, only one had a recurrence [START_REF] Guani | Recurrence risk after sentinel lymph node biopsy in patients with early cervical cancer: Follow-up results after 3 years of a prospective study (SENTICOL)[END_REF].

Two recurrences in patients with positive sentinel lymph nodes (one macrometastasis and one micrometastasis) and 11 recurrences in patients with negative lymph nodes (sentinel lymph nodes and non-sentinel lymph nodes) were found. There was no statistically significant difference in disease-free survival in terms of prognostic factors in the cohort, except patient age: a significant decrease in disease-free survival was found in patients >50 years old (p=0.01). In the study the discovery of micrometastasis did not change oncological management, therefore these patients did not receive additional adjuvant therapy. These data were obtained after ultrastaging of the sentinel lymph nodes and non-sentinel lymph nodes several months after the surgery, when the adjuvant therapy had been defined.

Unlike previous retrospective studies, this prospective study of 139 patients showed no impact of micrometastasis in sentinel lymph nodes on disease-free survival.

However, due to the relatively short follow-up, the authors concluded that some late recurrences may have been missed [START_REF] Guani | Recurrence risk after sentinel lymph node biopsy in patients with early cervical cancer: Follow-up results after 3 years of a prospective study (SENTICOL)[END_REF].

The precise prognosis of ITCs and MMs need further evaluation. The main limitation of must studies is the small size of the cohorts. Well conducted prospective series with long term follow-up are necessary. This is mandatory to indicate the appropriate adjuvant treatment in case of low volume nodal disease if these lesions have an impact. This specific issue will be studied in the ongoing SENTICOL III trial [START_REF] Guani | Recurrence risk after sentinel lymph node biopsy in patients with early cervical cancer: Follow-up results after 3 years of a prospective study (SENTICOL)[END_REF]. On the other hand, very small deposits could have no prognosis impact, making their diagnosis unnecessary.

-

Treatment options

LNs involvement with MACs is considered a major prognostic factor and a decision criterion for adjuvant therapy. This approach is based primarily on a trial in which 243 patients that were diagnosed with cervical cancer, FIGO stage Ia2-IIa, after radical hysterectomy and lymphadenectomy with positive pelvic lymph nodes and/or positive margins and/or microscopic involvement of the parametrium, were randomly divided into 2 group: 127 patients who received radiotherapy (RT) and chemotherapy (CT) (Cisplatin based) and 116 patients who received RT only. The progression-free survival at 4 years was 63% with RT and 80% with RT/CT. The overall survival rate at 4 years was 71% with RT and 81% with RT/CT. The author concluded that addition of cisplatin-based CT to RT significantly improves progression-free and overall survival for high-risk, early-stage patients [START_REF] Peters | Concurrent chemotherapy and pelvic radiation therapy compared with pelvic radiation therapy alone as adjuvant therapy after radical surgery in high-risk early-stage cancer of the cervix[END_REF]. The current different treatment protocols in the presence of MMs or ITCs in women with cervical cancer are based mainly on retrospective data. The incidence of MMs observed in previous studies (table 1 andtable 2) and the presented incidence of recurrence in apparent negative lymph node patients might argue in favor of aggressive treatment of these patients.

In 2005, the prognostic value of MMs was evaluated in a retrospective case control study including 52 patients (26 with and 26 without recurrence) [START_REF] Zaal | Pelvic lymphadenectomy improves survival in patients with cervical cancer with low-volume disease in the sentinel node: a retrospective multicenter cohort study[END_REF]. An association between lympho-vascular space involvement and the presence of MMs in SLNs was illustrated.

In a multi-centered retrospective study of 645 patients with an early stage cervical cancer, 85.3% of patients with macrometastasis, 82.6% with MMs, 52% with ITCs and 10.5% with negative pelvic nodes received adjuvant therapy (RT+CT) [START_REF] Cibula | Prognostic significance of low volume sentinel lymph node disease in early-stage cervical cancer[END_REF]. The two groups of patients with macrometastasis and MMs could not be statistically distinguished in the effect on overall survival rate (p=0.886). Both these groups were significantly different from the group of patients with negative lymph node status (p=0.001) [START_REF] Cibula | Prognostic significance of low volume sentinel lymph node disease in early-stage cervical cancer[END_REF]. Finally, Zaal et al. recently published a retrospective analysis of the same data of women who underwent SLNs biopsy followed by pelvic LNs dissection. Interestingly, among the patients diagnosed with MMs in the sentinel node, the overall survival was significantly better if more than 16 non-SNs were removed. This result could not be observed among women with macrometastases or women without SLN disease [START_REF] Zaal | Pelvic lymphadenectomy improves survival in patients with cervical cancer with low-volume disease in the sentinel node: a retrospective multicenter cohort study[END_REF]. This study, despite his retrospective and multicenter nature limitations, identifies a subgroup of patients with MMs LNs who might be treated differently from women without nodal disease or women with LNs macrometastases.

Despite the lack of data, most teams offer adjuvant radiotherapy. These results should be further validated, preferably by randomized controlled studies in this specific population.

Prognostic value and treatment options for women diagnosed with LNs ITCs

We have little data for ITCS. We said before for MMS, than Cibula observed the same prognosis in patients with positive LNs for MMs and MACs in terms of overall survival. Any prognostic significance for positive nodes with ITCs was found [START_REF] Cibula | Risk of micrometastases in nonsentinel pelvic lymph nodes in cervical cancer[END_REF].

In addition, Marchiolé et al. found that the relative risk of recurrence in women with SLN MMs and ITCs was 2.30 (CI 1.65, 3.20, p < 0.01) and 2.22 (CI 1.30,3.80, p = 0.09), respectively. Using bivariate model analysis, MMs were the only independent risk factor for recurrence [START_REF] Marchiolé | Clinical significance of lympho vascular space involvement and lymph node micrometastases in early-stage cervical cancer: A retrospective case-control surgico-pathological study[END_REF].

The prospective SENTICOL study of 139 patients, unlike previous retrospective studies, showed no impact of isolated tumor cells in sentinel lymph nodes on disease-free survival.

The discovery of isolated tumor cells did not change oncological management, therefore these patients did not receive additional adjuvant therapy [START_REF] Guani | Recurrence risk after sentinel lymph node biopsy in patients with early cervical cancer: Follow-up results after 3 years of a prospective study (SENTICOL)[END_REF].

Summary

In general we have much more data for MMs than for ITCs. The available knowledge in the literature is inadequate to assess the prognostic effect of LNs MMs and ITCs in women with cervical cancer. Unfortunately, there is a scarce data to conclude whether the diagnosis of MMs or ITCs should influence the treatment decisions as occurs in LNs MACs. Despite the insufficient data that exists regarding treatment options for women diagnosed with LNs MMs/ITCs, whether detected by pathological or molecular biology techniques, it is unlikely that a large trial, in which patients with small nodal disease would be randomly assigned to treatment versus observation, will be feasible. We are forced to await long-term follow up from further prospective cohort studies. In the meantime, with the limitations of the current evidence regarding MMs/ITCs presented here, we and other groups continue diagnosing women with small nodal disease by using ultrastaging technique and prefer to treat them as an high risk group.

  [START_REF] Van De Lande | SCC-Ag, lymph node metastases and sentinel node procedure in early stage squamous cell cervical cancer[END_REF].Yuen et al. concluded that molecular SLNs metastasis based on elevated SCC-Ag mRNA level is the best predictor of recurrence in a study where 178 samples were assessed by quantitative reverse transcription-PCR assay. High-risk types of HPV are etiological factors in cervical cancer. Yet, the impact of positive HPV DNA on prognosis and survival in histopathologically negative lymph nodes remains uncertain. In one of the first studies regarding HPV DNA positive LNs in cervical cancer patients, Czeglédy et al.

  . A retrospective review of the medical records of 62 patients who underwent radical hysterectomy and lymphadenectomy for FIGO stage IA2-IB2 cervical cancer showed a recurrence rate of 50% (2/4 patients) among patients who were diagnosed with LNs MMs compared with 6.7% (3/45 patients) in patients without MMs in a mean follow up time of 39.4 months [19]. Marchiolé et al. performed a retrospective histological analysis of 2 groups: 26 cases who recurred in a median time of 36.8 months and the second series were 26 cases matched for age, histological sub-type, surgicopathological stage and maximal tumor diameter, who did not recur after a median follow-up of 122 months. The relative risk of recurrence was shown to be 2.44 (1.58-3.78, P < 0.01) in the in the presence of LNs MMs [40]. The retrospective study by Colturado et al. has reviewed patients with stage IB1, IB2,

Table 1 .

 1 Rate of micrometastases in apparently negative nodes in studies using molecular biology methods.

Table 2 .

 2 Prognosis of women diagnosed with early stage cervical cancer LNs MACs/MMs/ITCs compared with patients without LNs involvement (N0)

Conflict of Interest statement

All authors disclose no financial and personal relationships with other people or organizations that could inappropriately bias their work.

Author Contribution