Satoko Hakeda-Suzuki

Sandra Berger-Müller

Tatiana Tomasi

Tadao Usui

Shin-Ya Horiuchi

Tadashi Uemura

Takashi Suzuki
email: suzukit@neuro.mpg.de

Golden Goal Collaborates with Flamingo in Conferring Synaptic-Layer Specificity in the Visual System

Neuronal connections are often organized in layers, which contain synapses between neurons that have similar functions. In Drosophila, R7 and R8 photoreceptors, which detect different wavelengths, form synapses in distinct medulla layers. The mechanisms underlying the specificity of synaptic-layer selection remain unclear. We found that Golden Goal (Gogo) and Flamingo (Fmi), two cell-surface proteins involved in photoreceptor targeting, functionally interact in R8s. Our genetic studies indicated that Gogo promotes R8 adhesion to the temporary layer M1, whereas Gogo and Fmi collaborate to mediate axon targeting to the final layer M3. Structure-function analysis suggests that Gogo and Fmi interact with intracellular components through the Gogo cytoplasmic domain. Moreover, Fmi is also required in a subset of target cells for R8 axon targeting. We propose that Gogo acts as a functional partner of Fmi for R8 axon targeting and that the dynamic regulation of their interaction specifies synaptic-layer selection of photoreceptors.

INTRODUCTION

The establishment of well-defined synaptic connections between specific neurons is critical for information processing in the brain. Synapses are often arranged into structures which reflect a functional organization of synaptic contacts [1][START_REF] Huberman | Molecular and cellular mechanisms of lamina-specific axon targeting[END_REF] . In the visual system for instance, nearby photoreceptors connect to nearby columns in the target region, preserving the spatial relationships between the visual world and its representation in the brain 1,[START_REF] Sanes | Design principles of insect and vertebrate visual systems[END_REF] . In addition to this retinotopic organization, different features of a visual stimulus are often decoded into separate synaptic layers [START_REF] Huberman | Molecular and cellular mechanisms of lamina-specific axon targeting[END_REF][START_REF] Sanes | Design principles of insect and vertebrate visual systems[END_REF] . These characteristic columnar and layered structures are often seen in complex nervous systems. How do axons select their specific synaptic layer during development? The mechanisms underlying the formation of synaptic layers are still unclear, although important molecular players have been identified [START_REF] Huberman | Molecular and cellular mechanisms of lamina-specific axon targeting[END_REF][START_REF] Sanes | Design principles of insect and vertebrate visual systems[END_REF][START_REF] Sanes | Many paths to synaptic specificity[END_REF] . In the vertebrate visual system, several homophilic adhesion molecules, such as Sidekicks and Dscams, play a crucial role in layer-specific targeting of photoreceptors by utilizing their binding specificity [START_REF] Sanes | Design principles of insect and vertebrate visual systems[END_REF][START_REF] Yamagata | Dscam and Sidekick proteins direct lamina-specific synaptic connections in vertebrate retina[END_REF][START_REF] Yamagata | Sidekicks: synaptic adhesion molecules that promote lamina-specific connectivity in the retina[END_REF] .

Synaptic connections in the Drosophila visual system are also organized in layers, and synaptic specificity is well investigated in this system. The Drosophila compound eye is made of about 800 units called ommatidia, each containing eight photoreceptors (R1-R8).

R1-R6 axons mediate motion detection and project to the first optic ganglion, the lamina. The photoreceptors responsible for color vision, R7 and R8, innervate the second optic ganglion, the medulla [START_REF] Ting | Visual circuit development in Drosophila[END_REF][START_REF] Mast | The mechanisms and molecules that connect photoreceptor axons to their targets in Drosophila[END_REF] . Each pair of R7 and R8 axons that mediates information from the same point in space forms a columnar structure in the medulla. Importantly, R7 and R8, which respond to different wavelengths, connect to separate medulla layers (M6 and M3, respectively). Thus distinct functional modularities become processed in distinct synaptic layers. Therefore, the Drosophila visual system provides an attractive model to study synaptic-layer selection [START_REF] Sanes | Design principles of insect and vertebrate visual systems[END_REF][START_REF] Ting | Visual circuit development in Drosophila[END_REF][START_REF] Mast | The mechanisms and molecules that connect photoreceptor axons to their targets in Drosophila[END_REF][START_REF] Astigarraga | Missed connections: photoreceptor axon seeks target neuron for synaptogenesis[END_REF] .

In Drosophila, several transmembrane molecules have been shown to regulate synaptic-layer targeting of photoreceptors. Examples include N-Cadherin [START_REF] Lee | N-cadherin regulates target specificity in the Drosophila visual system[END_REF][START_REF] Berger | Systematic identification of genes that regulate neuronal wiring in the Drosophila visual system[END_REF] , two receptor tyrosine phosphatases, LAR [START_REF] Berger | Systematic identification of genes that regulate neuronal wiring in the Drosophila visual system[END_REF][START_REF] Maurel-Zaffran | Cellautonomous and -nonautonomous functions of LAR in R7 photoreceptor axon targeting[END_REF][START_REF] Clandinin | Drosophila LAR regulates R1-R6 and R7 target specificity in the visual system[END_REF] and PTP69D [START_REF] Berger | Systematic identification of genes that regulate neuronal wiring in the Drosophila visual system[END_REF][START_REF] Garrity | Retinal axon target selection in Drosophila is regulated by a receptor protein tyrosine phosphatase[END_REF][START_REF] Newsome | Analysis of Drosophila photoreceptor axon guidance in eye-specific mosaics[END_REF] , a cell adhesion molecule Capricious (Caps) [START_REF] Shinza-Kameda | Regulation of layerspecific targeting by reciprocal expression of a cell adhesion molecule, capricious[END_REF][START_REF] Shishido | Drosophila synapse formation: regulation by transmembrane protein with Leu-rich repeats[END_REF] and a putative receptor Golden goal (Gogo) [START_REF] Berger | Systematic identification of genes that regulate neuronal wiring in the Drosophila visual system[END_REF][START_REF] Tomasi | The transmembrane protein Golden goal regulates R8 photoreceptor axon-axon and axon-target interactions[END_REF] . Although the requirement of each of these proteins has been reported, it is not fully understood how synaptic-layer specificity is achieved by this set of proteins.

Additionally, the seven-pass transmembrane cadherin Flamingo (Fmi) has an important function in axon pathfinding of photoreceptors and is potentially implicated in layer-specific targeting [START_REF] Berger | Systematic identification of genes that regulate neuronal wiring in the Drosophila visual system[END_REF][START_REF] Lee | The protocadherin Flamingo is required for axon target selection in the Drosophila visual system[END_REF][START_REF] Senti | Flamingo regulates R8 axon-axon and axon-target interactions in the Drosophila visual system[END_REF] . Fmi also regulates dendrite formation and planar cell polarity (PCP) [START_REF] Usui | Flamingo, a seven-pass transmembrane cadherin, regulates planar cell polarity under the control of Frizzled[END_REF][START_REF] Gao | Control of dendritic field formation in Drosophila: the roles of flamingo and competition between homologous neurons[END_REF] . In R axon guidance, the primary role of fmi is regulating afferent-afferent interaction in larva and lamina cartridge formation [START_REF] Lee | The protocadherin Flamingo is required for axon target selection in the Drosophila visual system[END_REF][START_REF] Senti | Flamingo regulates R8 axon-axon and axon-target interactions in the Drosophila visual system[END_REF][START_REF] Chen | The cadherin Flamingo mediates level-dependent interactions that guide photoreceptor target choice in Drosophila[END_REF] . Fmi mediates homophilic binding in vitro [START_REF] Usui | Flamingo, a seven-pass transmembrane cadherin, regulates planar cell polarity under the control of Frizzled[END_REF] , however the molecular mechanism of fmi in axon pathfinding remains unclear.

In this study, we first show that Fmi has a specific role in synaptic-layer targeting independent of afferent-afferent interaction. We further asked how the targeting specificity of R8 axons is achieved by Fmi. The broad expression of Fmi in all R axons and in multiple target layers suggests that Fmi does not achieve R8 targeting specificity alone. The phenotypic similarities between gogo and fmi suggested that Gogo might be a functional associate of Fmi that adds the specificity code required for R8 synaptic-layer targeting.

Although we could not demonstrate a direct physical interaction, our results show that Gogo and Fmi can mutually affect their localizations and that they functionally interact in R8s. Our genetic data indicated that Fmi may interact antagonistically with Gogo at the M1 temporary layer, and that Gogo and Fmi cooperate to mediate targeting of R8 axons to M3. We also show that the intracellular signaling for synaptic-layer specificity is primarily mediated by the cytoplasmic domain of Gogo. Finally, the requirement of Fmi in the brain for R8 axon targeting suggested that Fmi in R axons homophilically interacts with Fmi in the target cells.

Taken together, these results indicate that Gogo and Fmi are functional partners in axon guidance, and that their collaboration specifies the synaptic-layer choice of R8 axons.

We propose that synaptic-layer specificity is achieved by combinatorial codes of cell-surface molecules.

RESULTS

Fmi regulates synaptic-layer targeting

To demonstrate that Fmi has a role in synaptic-layer targeting of R8 axons independent of the axon-axon bundling defect seen in larvae [START_REF] Lee | The protocadherin Flamingo is required for axon target selection in the Drosophila visual system[END_REF][START_REF] Senti | Flamingo regulates R8 axon-axon and axon-target interactions in the Drosophila visual system[END_REF] , we generated single fmi mutant R8 axons to minimize axon bundling. Single fmi -/-R8 axons showed targeting defects and abnormally stopped at the M1 layer in the adult medulla (Fig. 1a-d,k).

To assess the requirement of fmi in pupae when R8 layer-targeting occurs, we removed fmi during pupal stages. To achieve this, we used the temperature sensitive repressor Gal80[ts] system [START_REF] Mcguire | Spatiotemporal gene expression targeting with the TARGET and gene-switch systems in Drosophila[END_REF] to control the fmi transgene expression. The initial fmi expression driven at 27°C was sufficient to rescue the R axon bundling phenotype of the fmi mutant in the larval stage (Supplementary Fig. 1). Late third instar larvae were then transfered to 18°C to shut down fmi expression. In the adult, we observed severe R8 targeting defects, albeit the bundling phenotype and the overall structure of the medulla was rescued (Fig. 1e-g).

Together, we showed that fmi has a role in synaptic-layer targeting which is independent of its earlier function in axon-axon interaction.

Fmi cytoplasmic domain is dispensable for R8 axon targeting

To determine whether Fmi can transmit the guidance signal inside the growth cone, we tested if the cytoplasmic domain of Fmi is required for R8 axon pathfinding. To do so, we developed a strategy to make a conditional knock-out of fmi. We used FLPase and available FRT lines flanking fmi to flip-out fmi. This technique is referred to as FRTs located in cis for conditional knock-out (FLICK) throughout this study (Supplementary Fig. 2, see Methods).

Using this method, we knocked out fmi specifically in the eye with eyFLP, and we could rescue the fmi mutant phenotype with Fmi lacking its intracellular domain (FmiΔIntra transgene) (Fig. 1h-j). Since the cytoplasmic domain of Fmi is dispensable, Fmi may have other interactors to convey the guidance signal intracellularly. We tested whether core PCP complex proteins [START_REF] Vladar | Planar cell polarity signaling: the developing cell's compass[END_REF] , which are known to interact with Fmi, have any role in R8 axon targeting. Mutant R8 axons for frizzled (fz), van gogh (vang, also known as strabismus), dishevelled (dsh) and prickle (pk) showed completely normal targeting, suggesting that PCP proteins do not play a role in R8 axon targeting (Supplementary Fig. 3a-d).

gogo and fmi have similar neuronal phenotypes

Previous studies reported that gogo mutants show R8 pathfinding and targeting defects [START_REF] Tomasi | The transmembrane protein Golden goal regulates R8 photoreceptor axon-axon and axon-target interactions[END_REF] that are similar to those in fmi mutants. To check whether these phenotypes in adults result from the same defects during development, we compared gogo and fmi mutants at different pupal stages. R8 axons target their synaptic layer in a two-step manner [START_REF] Ting | Drosophila N-cadherin functions in the first stage of the two-stage layerselection process of R7 photoreceptor afferents[END_REF] : they first halt at the temporary layer M1 until 50APF, and then start extending filopodia to their final target layer M3. Like in gogo mutants, the majority of R8 axons fail to send filopodia towards the M3 target layer at this stage in fmi mutants [START_REF] Tomasi | The transmembrane protein Golden goal regulates R8 photoreceptor axon-axon and axon-target interactions[END_REF] (Supplementary Fig. 4). Additionally, gogo mutants have similar defects to fmi mutants [START_REF] Lee | The protocadherin Flamingo is required for axon target selection in the Drosophila visual system[END_REF][START_REF] Chen | The cadherin Flamingo mediates level-dependent interactions that guide photoreceptor target choice in Drosophila[END_REF] in R1-6 synaptic target selection in lamina cartridges (Fig. 2a,b,e). These defects in gogo mutants are not secondary to the R8 axon pathfinding phenotype, since R1-6 target selection is still abnormal when R8s are specifically rescued in gogo mutants (Fig. 2c-e). Moreover, we found that gogo mutants show similar defects to fmi mutants in dendrite formation of embryonic md neurons [START_REF] Gao | Control of dendritic field formation in Drosophila: the roles of flamingo and competition between homologous neurons[END_REF][START_REF] Kimura | Potential dual molecular interaction of the Drosophila 7-pass transmembrane cadherin Flamingo in dendritic morphogenesis[END_REF] . Dendrites overgrew and 8.3% of the segments showed dorsal midline crossing (Fig. 2f-h). However, the functions of these two proteins do not perfectly overlap, since the gogo mutant does not exhibit a PCP phenotype [START_REF] Tomasi | The transmembrane protein Golden goal regulates R8 photoreceptor axon-axon and axon-target interactions[END_REF] (Supplementary Fig. 3e-g). The similarities between gogo and fmi neuronal phenotypes suggested that gogo and fmi may act in the same pathway during neuronal development.

In addition, Gogo and Fmi showed striking similarities in their expression patterns (Fig. 2i-u). Especially in R axons in the third instar larval optic lobe, Gogo and Fmi stainings essentially overlapped (Fig. 2i-q). Similarly to Fmi [START_REF] Lee | The protocadherin Flamingo is required for axon target selection in the Drosophila visual system[END_REF] , Gogo strongly localized to the youngest axons which innervate the outmost part of the crescent shape of the optic lobe (Supplementary Fig. 5). Notably, Fmi was also strongly expressed in the target region, whereas Gogo was mainly detectable in R axons in third instar larvae and throughout the first half of pupal stages (Fig. 2r-u). Altogether, despite some small differences, the striking similarities in the neuronal phenotype and the overlapping expression patterns suggested that Fmi interacts with Gogo in the development of the nervous system.

gogo and fmi interact genetically in neuronal development

As a first indication of gogo and fmi genetic interaction, we observed the impact of the mutations on the lethality. The fmi hypomorphic mutation (fmi[E86]/fmi[E59]) reduced the survival rate to 16.2%. Removing one copy of gogo in this background resulted in a further reduction of the survival rate to around 5% (Fig. 3a), suggesting a cooperation of these two genes.

To further assess the genetic interaction between gogo and fmi, we overexpressed both proteins under the control of the photoreceptor specific driver GMR-Gal4. We used a moderate gogo overexpression line, UAS-gogoT1 [START_REF] Tomasi | The transmembrane protein Golden goal regulates R8 photoreceptor axon-axon and axon-target interactions[END_REF] . When Gogo or Fmi were overexpressed separately, R7 axons targeted the proper layer M6 (Fig. 3b-d). When both proteins were cooverexpressed, however, almost half of R7 axons stopped at the M3 layer where R8 axons normally terminate (Fig. 3b,e). This effect was not observed when Fz, an interactor of Fmi [START_REF] Chen | Asymmetric homotypic interactions of the atypical cadherin flamingo mediate intercellular polarity signaling[END_REF] , was co-overexpressed with Fmi, suggesting that the synergistic effect between Gogo and Fmi is specific (Supplementary Fig. 6). To test whether this redirecting phenotype still occurs with R7 specific expression, we overexpressed Gogo and Fmi in R7s, but not in R8s, using the GMR-FLP technique (see Methods). Although the penetrance was reduced (possibly due to the perdurance of the Gal80 repressor protein), we still observed R7 mistargeting to M3 (Fig. 3b,f). This suggests that Gogo and Fmi act together to recognize and to adhere to the M3 target layer.

We further tested the genetic interaction in the loss-of-function situation. To exclude the contribution of Fmi from the brain, we used the FLICK system to generate a fmi hypomorphic mutant only in R axons, with the rest of the animal remaining wild type (fmi[E86]/+) (see Methods). In these flies, R8s only occasionally innervated neighboring medulla columns and made abnormal contacts with other R8 axons (Fig. 3g,i). The incidence of the R8 defects increased significantly when one copy of gogo was removed in the fmi[E86] hypomorphic background (Fig. 3h,i). This suggests that gogo and fmi genetically interact in R8 neurons in the loss-of-function situation.

If fmi and gogo act in the same genetic pathway, we should not see an additive effect in the double mutant. The double knock-out of fmi and gogo using the eyFLP-FLICK system showed a failure of R8s to target M3 layer; rather, R8s stay at the M1 layer like in the fmi mutant (Fig. 3j-l, see Methods). There is a slight difference in the way R8s interact with the M1 temporary layer (Supplementary Fig. 7, see also discussion), however the phenotype of R8s failing in extending their filopodia to the M3 layer at 55APF (After Puparium Formation) are very similar among gogo, fmi single and the double mutants (Supplementary Fig. 4).

Altogether, these results suggest that gogo and fmi function in the same pathway when R8 targets the M3 layer, and their collaboration confers specificity to the synaptic-layer selection of R8 axons.

We also observed a robust genetic interaction in dendrite formation. The dorsalmidline crossing phenotype in the fmi hypomorph mutant (15.3% per segment) was significantly enhanced by removing one copy of gogo (29.6%) (Fig. 3m-p). This indicates that gogo and fmi collaborate not only in axons but also more generally in neuronal processes including dendrites.

Gogo and Fmi colocalize at cell-cell contacts in S2 cells

S2 cells transfected with Fmi form aggregates due to Fmi homophilic interactions, and Fmi

often accumulates at sites of cell-cell contacts. The transmembrane PCP proteins Vang and Fz have been shown to colocalize with Fmi at boundaries between adjacent cells [START_REF] Strutt | Differential stability of flamingo protein complexes underlies the establishment of planar polarity[END_REF] . When Gogo and Fmi were co-transfected, Gogo was enriched at sites of cell contacts together with Fmi (Fig. 4a-c,p). This effect was specific to Gogo and Fmi, since neither Gogo accumulated with another cadherin E-Cad (Fig. 4d-f,p), nor did Unc5 30 , another transmembrane receptor which structurally resembles Gogo, co-accumulate with Fmi (Fig. 4g-i,p). We found that Gogo colocalizes with Fmi without the cytoplasmic portion of both proteins, indicating that colocalization is mediated by their transmembrane or extracellular domains (Fig. 4j-l,p). A chimeric protein consisting of the extracellular domain of Gogo and the transmembrane and cytoplasmic domains of Unc5 (Gogo Ecto ::Unc5 TM+Cyto ) colocalized with Fmi at cell-cell contacts (Fig. 4m-o,p), suggesting that the ectodomain of Gogo mediates the colocalization with Fmi.

To investigate whether Gogo and Fmi mediate heterotypic interaction in trans, we performed an aggregation assay by mixing Gogo-expressing cells with Fmi-expressing cells.

Cells transfected with Gogo were not more included in the aggregates generated by Fmi than the control cells transfected with mCD8 (Supplementary Fig. 8a,b), which suggests that Gogo and Fmi interact in cis.

Gogo and Fmi interact in cis in vivo

To assess the interaction of Gogo and Fmi in vivo, we used the wing epithelial cells, which have well-organized cell borders where Fmi accumulation is easily visible. At midpupal stage (28APF), Fmi is normally localized apically at adherens junctions on the proximal and distal borders. When we ectopically overexpressed Gogo, Fmi localization was disrupted at the apical pole of the cells (Fig. 5a,c) and Fmi was relocated to the entire surface of lateral membranes (Fig. 5b,d) without disrupting adherens junction components (Supplementary Fig. 9). This suggested that Gogo binds and recruits Fmi to the baso-lateral membrane (Fig. 5e).

We then tested if Gogo could relocalize Fmi in trans or in cis, by generating fmi mutant clones. Fmi was not relocalized to the basolateral membrane of the abutting cells when fmi was absent from the gogo-overexpressing cells (Fig. 5f-i), suggesting that Gogo does not interact with Fmi in trans (Fig. 5j). On the contrary, Fmi in Gogo-overexpressing cells was still relocalized on lateral cell membranes abutting fmi mutant cells (Fig. 5k-n), which indicates that Gogo relocates Fmi in cis (Fig. 5o). These data support our hypothesis of the physical interaction between Gogo and Fmi (direct or indirect) and indicate that they interact in cis.

Gogo accumulation at the growth cone is dependent on Fmi

We next tested whether Gogo and Fmi localizations are reciprocally dependent on each other also in growing R axons. We found that, in fmi mutant R axon growth cones, Gogo accumulation is reduced compared with the surrounding wild type axons (Fig. 5p-s). This strongly indicates that Gogo and Fmi interact endogenously in R axon growth cones, and that it stabilizes Gogo accumulation. It is noteworthy that Gogo localization at the growth cone is not completely dependent on Fmi, since at 24APF, we could detect Gogo accumulation at fmi -/-R growth cones (Supplementary Fig. 10a-d).

We did not, however, observe a mutual dependency of Gogo or Fmi localization at the cell body and along the axonal shaft of photoreceptors, and Fmi accumulation in gogo mutant R axon tips was normal (Supplementary Fig. 10e-m). Thus, it seems that the trafficking and the transport of Gogo and Fmi to the axons occur mostly independently of each other, and that they only interact at the tip of axons after transport.

Gogo cytoplasmic domain is crucial for the M3 targeting

Since FmiΔIntra could rescue the R8 axon targeting defects, it is possible that the interacting partners of Fmi convey the signal received by Fmi ectodomain to the intracellular space. As Gogo interacts with Fmi in axon pathfinding, and since GogoΔC cannot rescue the gogo mutant phenotype in R axon targeting [START_REF] Tomasi | The transmembrane protein Golden goal regulates R8 photoreceptor axon-axon and axon-target interactions[END_REF] , we anticipated that Gogo may transduce guidance information inside the growth cone.

To test this, we used the experiment of gogo and fmi overexpression in R neurons, which showed R7 redirecting to the M3 layer (see Fig. 3e). When we overexpressed both FmiΔIntra and GogoΔC, R7 extended normally to the M6 layer, suggesting that Gogo and Fmi interact with intracellular components (0.0% at 25°C; Fig. 6a,d and Supplementary Fig. 11a,b,g). Overexpression of Fmi full length and GogoΔC resulted only in a subtle R7 premature stopping phenotype (7.2% at 25°C; Fig. 6b,d), whereas FmiΔIntra and Gogo full length overexpression induced a much stronger phenotype (41.0% in 25°C; Fig. 6c,d). The expression level of each of these transgenes was comparable (Supplementary Fig. 11c-f).

Therefore, we concluded that Gogo and Fmi transmit the M3 targeting information to downstream components primarily through the cytoplasmic domain of Gogo. This indicates that intracellular transduction of the axon guidance signal may be the major role of Gogo when it collaborates with Fmi.

Fmi is required in the target area for R8 axon targeting

Fmi functions through homophilic interaction both in vivo and in vitro. We wondered whether Gogo and Fmi in R8 axons bind to Fmi on the target cells to achieve the proper innervation of R8s. It was shown using the ELF system that Fmi seems to be required in the target area, but the outcome was somewhat modest (5.6% bundling, 0.4% stopping) [START_REF] Bazigou | Anterograde Jelly belly and Alk receptor tyrosine kinase signaling mediates retinal axon targeting in Drosophila[END_REF] .

Using a variety of Gal4 lines driving FLPase in lamina and/or medulla neurons, we used the FLICK system to knock out fmi in the target area. Among them, gcm-Gal4 FLICK flies showed robust R8 axon targeting defects (Fig. 7a,c and Supplementary Table 1). As monitored by Act<stop<nLacZ 32 , FLP-out occurred in a subset of brain cells including almost all lamina neurons, but not in R cells (Fig. 7b). In gcm-Gal4 fmi FLICK flies, Fmi expression was indeed strongly reduced in the medulla at early and mid-pupal stages (Fig. 7d-w). The morphology of the medulla neuropiles seems to be normal (Fig. 7h,m,r,w,x and Supplementary Fig. 12). The requirement of Fmi in the brain strongly suggests that Gogo and Fmi on R8 axons interact with Fmi in target cells in trans (Supplementary Fig. 13a).

Fmi antagonizes Gogo adhesion to the M1 temporary layer

From the in vivo co-overexpression data, the interaction between Gogo and Fmi seems to specifically mediate M3 layer recognition of R8 axons. In our former work, we showed that Gogo overexpression induces a stronger affinity of R8 axons to the M1 layer [START_REF] Tomasi | The transmembrane protein Golden goal regulates R8 photoreceptor axon-axon and axon-target interactions[END_REF] . How can this be explained? We hypothesized that there might be a different function of Gogo and Fmi when R8 interacts with the temporary M1 layer. We tried to address this issue by changing the balance of Gogo and Fmi activity level in R8 axons. We first overexpressed gogo in fmi hypomorph R8s. If the Gogo overexpression phenotype (blob-like structure at M1) is also mediated by Fmi, the M1 blobs would be suppressed by the partial removal of fmi. We observed, however, the opposite effect: more R8s stopped at the M1 layer compared to the moderate gogo overexpression or the fmi hypomorphic mutant phenotypes (Fig. 8a-d).

On the contrary, when the expression of fmi was mildly elevated (with GMR-fmi) in the Gogo overexpression situation, M1 blobs were strongly reduced (Fig. 8e-g). This suggests that Gogo alone promotes adherence to the M1 layer, but when Gogo interacts with Fmi at the mid-pupal stage, Fmi inhibits the Gogo-M1 interaction, and Gogo and Fmi act together to mediate R8 association with the M3 layer (Supplementary Fig. 13b). Whereas the ligand on the M3 layer is likely to be Fmi, the presumptive ligand on the M1 layer is unknown. Thus, it seems that there exists a complicated regulation of the Gogo-Fmi interaction and that Gogo alone has a different function than when it is associated with Fmi.

DISCUSSION

Interaction between Gogo and Fmi

In this study, we have suggested that the transmembrane receptor Gogo physically interacts (directly or indirectly) with the atypical cadherin Fmi in cis to cooperatively guide R8 axons to their correct target. However, we have been unable to demonstrate a robust direct interaction between Gogo and Fmi by coimmunoprecipitation, Bimolecular Fluorescent Complementation (BiFC) [START_REF] Saka | Visualizing protein interactions by bimolecular fluorescence complementation in Xenopus[END_REF] or Proximity Ligation Assay (PLA) 34 (Supplementary Figs. 14 and 15, see also Supplementary Note 1). The failure in coimmunoprecipitation is probably due to technical difficulties in solubilizing the seven-pass transmembrane Fmi and maintaining a huge complex during the procedure (Fmi is about 400 kD). Nevertheless, a close interaction of these proteins is supported by three lines of evidence. First, ectopic expression of Gogo in wing epithelial cells is able to relocate Fmi in cis. Second, Fmi and Gogo colocalize at cell-cell contacts in cultured cells via their ectodomains. Finally, Gogo accumulation at the growth cone is strongly reduced in fmi mutant R axons, suggesting that Fmi is at least partially required to localize/stabilize Gogo at the growth cone through a close association.

It has been suggested that Fmi binds homophilically in cis [START_REF] Kimura | Potential dual molecular interaction of the Drosophila 7-pass transmembrane cadherin Flamingo in dendritic morphogenesis[END_REF] . We also found that Gogo forms oligomers in cultured cells (Supplementary Fig. 8e). These observations suggest that even if Gogo and Fmi physically interact with each other, they may multimerize and form a protein cluster. Alternatively, Gogo-Gogo, Gogo-Fmi and Fmi-Fmi interactions may happen separately at distinct locations and have different functions.

Gogo and Fmi in neuronal development

Fmi controls the nervous system development broadly. It regulates axon guidance, but also synaptic target selection and dendritic field development 22-23, 27, 35-37 . The phenotypic similarities and the genetic interactions of gogo and fmi in diverse aspects of neuronal development in Drosophila suggest that the collaboration of Gogo and Fmi is a general molecular mechanism.

Interestingly, however, in the dendrites of multi-dendritic neurons, it has been reported that the ectodomain deletion of Fmi (FmiΔN) is able to partially rescue the fmi dorsal-overgrowth phenotype in dendrites, but FmiΔC cannot [START_REF] Kimura | Potential dual molecular interaction of the Drosophila 7-pass transmembrane cadherin Flamingo in dendritic morphogenesis[END_REF] . On the contrary, in R8 axons, FmiΔN cannot rescue (Supplementary Fig. 16), but FmiΔIntra can (Fig. 1j). These observations indicate that the underlying molecular mechanisms may be different between axons and dendrites. It will be therefore interesting to investigate the molecular mechanisms of Gogo in dendrite formation to decipher the general principles versus unique, diversified mechanisms mediated by the Gogo-Fmi interaction.

Mechanisms of axon targeting by the Gogo-Fmi interaction

What is the function of Gogo when interacting with Fmi? We can envision three scenarios that are not mutually exclusive. First, Fmi homophilic adhesion properties change when it is associated with Gogo. Second, Gogo mediates intracellular signaling to transduce axon pathfinding information in the growth cone. Third, Gogo adds a specificity code to the Fmi-Fmi homotypic asymmetric interaction. To test the first scenario, we used a cell aggregation assay mixing Fmi-expressing cells with cells co-expressing Gogo and Fmi. Since the two populations of cells were equally distributed in the aggregates (Supplementary Fig. 8c,d),

Gogo seems not to have an effect on Fmi homophilic adhesion in S2 cells. The second scenario is supported by the fact that the Gogo cytoplasmic domain mediates the R7 cooverexpression phenotype, and that Fmi cytoplasmic domain is dispensable for R8 axon pathfinding. Additionally, the interaction between Gogo and Fmi seems to add molecular specificity to R8 axons, allowing them to recognize the proper layer M3, suggesting the third scenario. Fmi seems to be the "cue" on the target layers, since elimination of Fmi from a population of brain cells, but not from photoreceptors, resulted in targeting defects in R8 axons. This suggests that Fmi-Fmi homotypic interactions take place between R8 axons and the target cells (Supplementary Fig. 13a). However, the interaction seems to be asymmetric, since Gogo is not required in the brain for R axon pathfinding [START_REF] Tomasi | The transmembrane protein Golden goal regulates R8 photoreceptor axon-axon and axon-target interactions[END_REF] .

Overall, we propose that: 1) Gogo alone promotes adherence between R8 axons and the M1 layer [START_REF] Tomasi | The transmembrane protein Golden goal regulates R8 photoreceptor axon-axon and axon-target interactions[END_REF] (Supplementary Fig. 13b); 2) at mid-pupal stages, Fmi acts antagonistically with Gogo at the M1 layer, and Gogo and Fmi collaborate to mediate R8 targeting to the M3 layer (Supplementary Fig. 13b); 3) at the M3 layer, Fmi on the target cells mediates homophilic interaction with Fmi on R8 axons (Supplementary Fig. 13a). Fmi could be detected on R8 axons when R8s extend their tip to the M3 layer, if Fmi protein level was reduced from surrounding neuropils, consistent with the idea that Gogo and Fmi act together to guide the M3 targeting growth cones (Supplementary Fig. 17). The above model is supported by mainly five lines of evidence: 1) co-overexpression of Fmi and Gogo retargets R7 axons to the M3 layer (Fig. 3e,f), 2) removing Fmi from presumptive target cells induces R8 stopping at the M1 layer (Fig. 7a), 3) a combination of Gogo overexpression and fmi hypomorphic background induces more R8 axon stopping at M1 layer than each of these genotypes individually (Fig. 8c), 4) the Gogo overexpression phenotype is suppressed by mild fmi overexpression (Fig. 8e). Taking into account that gogo overexpression in a fmi hypomorph could not enhance the axon bundling which is typical in fmi mutant axons, it is unlikely that gogo overexpression acts merely as dominant-negative on Fmi function. 5) In fmi mutants, R8 commonly stalls at the M1 layer, whereas in gogo mutants [START_REF] Tomasi | The transmembrane protein Golden goal regulates R8 photoreceptor axon-axon and axon-target interactions[END_REF] or in the double mutants, R8 has a tendency to stray at the M1 layer (Supplementary Fig. 7). We think that this difference is due to a reduced adhesion of R8 to M1 in gogo mutants, which is not impaired in fmi mutants.

The cell identity of the M3 layer which is recognized by Gogo and Fmi in R8 axons is not clear. We knocked-out fmi almost completely from the lamina neurons. Although the R8 stopping phenotype is not completely penetrant, we see substantial R8 stopping at the M1 layer, indicating that the lamina neurons might be the target cells where Fmi functions as a ligand. Lamina neurons innervate into medulla layers during early pupal stages. Their processes take over R8 axons when R8 axons rest at the temporary layer, and they arborize between developing R7 and R8 termini [START_REF] Ting | Drosophila N-cadherin functions in the first stage of the two-stage layerselection process of R7 photoreceptor afferents[END_REF][START_REF] Nern | Local N-cadherin interactions mediate distinct steps in the targeting of lamina neurons[END_REF] . Interestingly, L3 lamina neurons spread their terminal processes at the M3 layer [START_REF] Takemura | Synaptic circuits of the Drosophila optic lobe: the input terminals to the medulla[END_REF] (see also Fig. 7x). The functional significance of L3 neurons in this context should be addressed in the future. In any case, it seems that mutual interactions between lamina neuron processes and R axons account for the two step targeting mechanism of R8 axons [START_REF] Ting | Drosophila N-cadherin functions in the first stage of the two-stage layerselection process of R7 photoreceptor afferents[END_REF][START_REF] Nern | Local N-cadherin interactions mediate distinct steps in the targeting of lamina neurons[END_REF] .

LEGENDS

Figure 1 fmi regulates synaptic-layer targeting and does not require its cytoplasmic domain.

(a) Horizontal image of the wild type adult medulla. R axons are labeled with mAb24B10 (red), R8 axons with Rh6-GFP (green) and medulla-layers with anti-N-Cad (blue). Medulla layers are indicated by white dashed lines. (b,c) Images of ey3.5FLP fmi -/-mosaic adult medulla. WT axons are labeled with GMR-mCD8mKOrange (red), R8 axons with Rh6-GFP (green) and medulla layers with anti-N-Cad (blue). Single fmi -/-R8 axons (green without red) abnormally stopped at the M1 layer (arrows). The quantification of the phenotype of single fmi -/-R8 axons is shown in d. (e-g) Temporal fmi -/-rescue using Gal80[ts]. The eye specific fmi -/-mutants were generated by FLICK (see Supplementary Fig. 2) and the fmi transgene was expressed using ey3.5FLP, Act<CD2<Gal4 and UAS-fmi. The expression level of the fmi transgene was controlled by tub-Gal80 [ts]. The labeling is identical to a. The fmi -/-phenotype was rescued at 27°C (e) whereas there was no rescue at 18°C (g). (f) When the temperature was shifted from 27°C to 18°C at the late third instar larval stage, R8 axons abnormally stopped at M1 (arrows). (h-j) fmi -/-ey3.5FLP FLICK (h) was successfully rescued by GMR-Gal4 UAS-fmi (i) or by GMR-Gal4 UAS-fmiΔIntra (j). The labeling is identical to a. (l-n) Magnification of the region shown in i. R axons marked with glass-LacZ (red in o-q), overlap with the Fmi (white in p) or Gogo signal (white in q). (r-t) Gogo (green) and Fmi (magenta) expression in 24 APF pupa. At this stage, Gogo and Fmi are expressed in R axons, but Fmi becomes more apparent in brain neuropils between R7-R8 axons (white brackets). m,s-w). Fmi in gcm-Gal4 fmi -/-FLICK medulla (e,f,o,p) is significantly reduced compared to the control (j,k,t,u), whereas the level in the lobula stays the same (arrows). The arrowheads indicate the M3 layer. The intensity of the Fmi staining is also indicated in pseudocolors (g,l,q,v). The medulla layers (anti-N-Cad) appear normal in gcm-Gal4 fmi -/- FLICK flies (h,r) compared to the control (m,w). (x) WT and fmi -/-L3 neurons were marked with GFP using dacFLP and an L3 specific Gal4 driver (9-9Gal4). They target their correct layer M3. The scale bars represent 50μm (b), 10μm (d,n) and 5μm (x). Molecular cloning. UAS-Gogo Ecto ::Unc5 TM+cyto -GFP plasmid (residues 1-699 of Gogo and 502-1072 of Unc5) and UAS-SIGHA-Fmi EctoΔCad plasmid (signal peptide from wg followed by HA tag and residues 1398-2527 of Fmi) were constructed using standard PCR-based cloning methods and the gateway system (Invitrogen). Unc5 related constructs contain the additional residues SF (HindIII) after the transmembrane domain, since they were amplified from the UAS-SIGHA-Unc5d plasmid [START_REF] Keleman | Short-and long-range repulsion by the Drosophila Unc5 netrin receptor[END_REF] . For GMR-fmi, NotI-SfiI fragment including full length fmi was cut out from CAD47B/pGEM9Zf9(-), subcloned into ENTRY vector, and subsequently recombined into Gateway based CaSpeR-GMR vector. For the BiFC constructs, VN154m9 and VC155 fragments [START_REF] Saka | Visualizing protein interactions by bimolecular fluorescence complementation in Xenopus[END_REF] were cloned into the UAST vector. The UAS-Fmi-VN, UAS-Gogo-VC, and UAS-mCD8-VC plasmids were made using standard PCR-based cloning methods.

Fly strains and genetics. Flies were kept in standard Drosophila media at 25°C, except for the temporal rescue experiment (27°C and 18°C) and overexpression of gogo and fmi transgenes (20°C). To perform temporal rescue, flies were shifted from 27°C to 18°C at late 3 rd instar larvae. To generate clones overexpressing gogo in the wing, flies were heat-shocked for 30min at 37°C at day 3 after egg laying (AEL). To produce fmi mutant clones in the wing disc, larvae were heat-shocked for 1hr at 37°C at day 3 AEL. Wing discs were dissected out at 28APF. The following fly stocks and mutant alleles were used: gogo[D869], gogo[H1675],

Immunochemistry. Whole mount brain dissection and antibody stainings were done as described [START_REF] Wu | A protocol for dissecting Drosophila melanogaster brains for live imaging or immunostaining[END_REF] . For agarose sections, the proboscis was removed and flies were fixed with 4% formaldehyde overnight. Then, they were embedded in 7.0% standard agarose in PBS at 60-80°C. 50-80 μm sections were cut using a vibrating blade microtome (LeicaVT1000S). This was followed by standard antibody staining procedures. Primary antibodies were used as follows: mAb24B10 (1:50 dilution, DSHB), monoclonal mouse anti-Fmi (#74; 1:20, DSHB), rat anti-Elav (7E8A10; 1:100, DSHB), rabbit anti-Gogo (1:50), rat anti-N-Cad (Ex#8, 1:50, DSHB), rat anti-E-Cad (DCAD2 1:100, DSHB), rabbit anti-GFP conjugated with Alexa488

(1:200, Molecular probe), rabbit anti-Myc (1:200, Gramsch), mouse anti-Myc (9E10, 1:200, Santa Cruz), rat anti-HA (3F10, 1:2000, Roche), rat anti-mCD8 (1;200 Caltag), guinea pig anti-Sens (gift from H. Bellen), rabbit anti-lacZ(1;200, Cappel), mouse anti-LacZ (1:1000, Promega) and chicken anti-lacZ (1:1000, ABcam). Secondary antibodies: Series of Alexa488, Alexa568, Alexa633 conjugated goat anti-mouse, goat anti-rabbit, goat anti-rat, goat antichicken secondary antibodies were used (1:200, Invitrogen). EM sections were done according to standard procedures for Drosophila lamina sections. Adult flies were transferred in 70% ethanol for 30s, and heads were cut off in PBS. After removing the proboscis, heads were transferred into the fixative (2.5% glutaraldehyde in PBS) overnight. Heads were then post-fixed in Dalton solution (1% osmium tetroxide, 1% potassium dichromate, 0.85% sodium chloride) and further dehydrated and embedded in Epon. Ultra-thin section (70nm) were obtained with an Ultracut Ultramicrotome (Reichert-Jung) and counterstained with Uranyl acetate and Lead citrate. Images were collected with a magnification of 6000x using Zeiss Em10 and JEOL TEM 1230 microscopes and the number of R cell terminals per cartridge was counted. 66 cartridges were scored in the wild type, 91 in the gogo mutant and 57 for the gogo R8 specific rescue. dendrites extend on 2D planes just underneath the epidermis, Z-series of dorsal front images were projected into 2D images which were then used for quantification.

Lethality test. For the lethality test, we genotyped each mutant animal at first to second instar larval stages using Kr-GFP, CyO or Kr-GFP, TM3 balancer, and we transfered 20 larvae to a single fresh apple juice plate with a thin spread of yeast paste and raised at 25°C.

We counted the number of hatched adults on 14 days AEL for controls and on 17 days AEL for mutants.

Assessment of R8 axonal phenotypes. R8 axons were visualized in confocal-stack images of whole mount brains. (Fig. 3i): the number of bundling axons was divided by the total thickness scanned, and the average per 10μm section was calculated. (Fig. 7c): although the stopping of R8 axons was clearly visible (see arrows in Fig. 7a'), a precise quantification was hampered by the overlapping nature of R8 axons before entering the medulla columns.

Therefore, we compared the number of innervating R8 axon between the wild type and the gcm fmi -FLICK flies. In the wild type, there are 34.60 R8 axons (Rh6-GFP positive) per 10μm stack (113 μm in total thickness), whereas in gcm fmi -FLICK flies, there are 28.38 R8 axons (216μm in total thickness). From these numbers we assessed that 18.0% of R8 axons are stopping at the M1 layer. (Fig. 8d): Summation of R8s innervating into the medulla and blobs without axons (arrowheads in Fig. 8c) was considered as total R8 axon number. (Fig. 8g): percentage of blob-formation per total axon number was calculated. The statistical significance of quantification results was tested using Microsoft Excel.

  (k) Schematics of the optic lobe development. Representative individual neurons are depicted in blue, whereas neuronal regions are color coded (retina: yellow; lamina: green; medulla: red). Scale bars represent 20μm.

Figure 2

 2 Figure 2 Phenotypic similarities between gogo and fmi. (a-c) EM sections of lamina

  (u) Magnification of the region shown in r. The scale bars represent 2μm (a) and 20μm (f, i, r).

Figure 3

 3 Figure 3 gogo genetically interacts with fmi. (a) Survival rates of the indicated genotypes.

Figure 4

 4 Figure 4 Gogo and Fmi colocalize at cell-cell contacts. (a-o) Protein accumulation at cell

Figure 5

 5 Figure 5 Gogo interacts with Fmi in cis in wing cells. (a-d,f-i,k-n) Images of gogo-

Figure 6

 6 Figure 6 Gogo and Fmi interact with intracellular components through the Gogo cytoplasmic

Figure 7

 7 Figure 7 Fmi is required in target cells for R8 synaptic-layer targeting. (a) Medulla of gcm-Gal4 fmi -/-FLICK flies. The labeling is the same as in Figure 1a. R8s frequently stop at the

Figure 8

 8 Figure 8 Antagonistic interaction between Gogo and Fmi at the M1 layer. The labeling is the

ACKNOWLEDGEMENTS

We thank Narin Hengrung for helping with BiFC experiments, Igor Siwanowicz for helping with agarose sections and Marianne Braun for EM sections. We thank I. Salecker, T. Clandinin, J. Axelrod, D. Strutt, B. Dickson, K. Keleman, H. Oda, H. Bellen, J. Morante, J. Smith, DSHB, DGRC Kyoto, Exelixis Collection at Harvard and Blomington Stock Center for reagents, and FlyBase for information resources. We thank Ruediger Klein, Frank Schnorrer, Joachim Egea for suggestions and comments, and the Suzuki lab members for critical discussions. This work was supported by the Max Planck Society and the individual grant from the Deutsche Forschungsgemeinschaft (T.S.).

Imaging. Images were obtained with Olympus FV-1000 or Leica SP2 confocal microscopes, and processed with Adobe Photoshop and Illustrator. Notums were imaged with a Leica MZ95 stereomicroscope attached with a CCD camera (Leica DFC320). Cross section images were created using Imaris software.

Cell culture and transfection

All expression constructs contained the UAS promoter and were co-transfected with pActin5C-Gal4. S2 cells were transiently transfected with Cellfectin (Invitrogen) according to the manufacturer's protocol. Cells were attached to IBIDI slides (μ-slide IV 0.4) for 1hr, fixed for 30min, permeabilized with 0.1% Triton X-100 in PBS, and stained with the following antibodies: with mouse anti-Fmi (#74; 1:50, DSHB), rabbit anti-Gogo (1:20), and rat anti-mCD8 (MCD0800, 1:400, Invitrogen).

Cell aggregation assay.

For the cell-cell contact accumulation assay, 72hr after transfection cells were diluted to obtain a concentration of 10 6 cells/ml and agitated on a rotary shaker for 2hr. To quantify protein accumulation at cell-cell contacts, we used Photoshop to calculate the fluorescence intensities of selected areas at cell-cell borders and on membranes which were not contacting other cells. The background fluorescence was subtracted, and the ratio of the fluorescence intensities between cell-cell contact membranes and free membranes was calculated.

Quantification of midline crossing in md neurons. 20-22hrs AEL embryos were collected

on apple juice-agar plates and dechorionated manually. Selected embryos were mounted in glycerol for fluorescence microscopy (Merck). Images were collected with a laser scanning confocal microscopes and processed with Adobe Photoshop (Adobe Systems). Because Co-immunoprecipitation. 48hr after transfection, cells were washed twice in PBS, and lysed with a dounce homogenizer in lysis buffer (1% NP-40, 150mM NaCl, 50mM Tris, pH 7.5) containing protease inhibitors (Roche). After centrifugation at 13,000g for 15min, 250μg of lysate diluted in lysis buffer was incubated with 20μl of anti-myc, anti-V5, or anti-HA agarose beads (Sigma) for 2hr at 4°C. The beads were washed three times with 750μl of lysis buffer and incubated with Leammli loading buffer at 65°C for 30min. The beads were removed by centrifugation and the samples were loaded on a 3-8% Tris-acetate gel (Invitrogen) and analysed by western blotting. Myc-tagged constructs were detected by mouse anti-Myc (9E10, 1:100, Santa Cruz), Gogo-GFP by mouse anti-GFP (JL-8, 1:1000, Clonetech), Fmi-V5 by mouse anti-Fmi (#74; 1:50, DSHB) or mouse anti-V5 (1:5000, Invitrogen), HA-Fmi EctoΔCad by rat anti-HA (3F10, 1:1000, Roche).

Bimolecular Fluorescent Complementation (BiFC). S2 cells were transfected with VN and

VC constructs with Act-Gal4 using a low concentration of DNA (0.02μg per well in 24-well plates). 48h after transfection, cells were transferred to slides, fixed, and stained.

Proximity Ligation Assay (PLA)

PLA probes were made for anti-N-Cad (Ex#8, DSHB) and rat anti-mCD8 (MCD0800, Invitrogen) using the Duolink probemaker kit (Olink Bioscience). 72hrs after transfection, cells were fixed as described above and incubated with the PLA probes. The PLA reaction was done according to the DuolinkII Fluorescence protocol (Olink Bioscience).

AUTHOR CONTRIBUTIONS

Supplementary Item & Number

Title or Caption

Supplementary Figure 1 Control of the fmi transgene expression by the Gal80[ts] system

Supplementary Figure 2 Efficiency of the FLICK system for generating conditional knock-outs.

Supplementary Figure 3 The core PCP complex genes do not regulate R8 axon targeting.

Supplementary Figure 4 R8 projections in gogo and fmi mutants at different pupal stages Supplementary Figure 5 Characteristic localization of Gogo in third instar R axons Supplementary Figure 6 No gain-of-function interaction between fmi and fz Supplementary Figure 7 gogo and fmi mutant phenotypes in R8 axons show a mild qualitative difference at the M1 layer. Supplementary Figure 8 Gogo does not interact with Fmi in trans, nor modulate Fmi-Fmi interaction in cis, and Gogo forms oligomers in S2 cells Supplementary Figure 9 Adherens junctions are not disrupted in gogo-overexpressing cells Supplementary Figure 10 The interaction of Gogo and Fmi do not affect their trafficking nor overall axonal localization Supplementary Figure 11 Overexpression phenotype and expression of GogoΔC and FmiΔC transgenes.

Supplementary Figure 12

Morphology of the medulla layers in gcm-FLICK fmi -/-flies.

Supplementary Figure 13

Models for Gogo-Fmi collaboration Supplementary Figure 14 Co-immunoprecipitation did not show evidence for robust physical interaction.

Supplementary Figure 15 BiFC and PLA showed unspecific interaction on the cell surface Supplementary Figure 16 The extracellular domain of Fmi is required for R8 axon targeting.

Supplementary Figure 17

Fmi can be detected on R axons in the mid-pupal stage.

Supplementary Table 1 Gal4 strains used for FLICK in the target area of R axons Supplementary Table 2 The genotypes of the flies Supplementary Note 1

Consideration on methods for detecting the physical interactions of Gogo and Fmi


Luis Martinez

; Miguel

Ariane ; Dupont-Kieffer

José Viegas

Manuel

An integrated application of Zoning for Mobility Analysis and Planning: the case of Paris Region

Keywords: zoning, transport demand modelling, policy design and assessment

aggregation allows analyses within the administrative and political boundaries but with a more disaggregated perspective. A case study based on the Mobility Survey for the Paris Metropolitan Area in 2001 is developed to illustrate this new approach to zoning. The magnitude of those resulting improvements is very significant especially when compared with the already existing zoning as the department zoning or the IAURIF zoning. It then shows that more attention should definitely be given to this initial process in the transport planning studies.

INTRODUCTION

TAZs [Traffic Analysis Zone, Transportation Analysis Zone, Traffic Assignment Zone] have been pointed out as one of the keystones on Transportation Planning studies. Nevertheless, defining a good set of TAZ is still one of the transportation unsolved problems.

Transportation Planning studies have used transport demand models over the past four decades to forecast travel demand for short and long term planning. Transport Demand models typically follow a four-step process of trip generation, trip distribution, modal split, and network or trip assignment (Ortúzar and Willusen, 2001). The steps are chained in a sequence, and the outputs of each step become inputs of the following step.

The key elements of a transport demand model are a study area divided into zones (called TAZ), and a transportation network in which each zone is represented by a centroid. TAZs represent areas from which and to which trips are allocated in a transport demand model. A centroid represents the "centre of activity" of a zone and the origin and destination for all trips to and from the zone [START_REF] Chang | Effects of zoning structure and network detail on traffic demand modeling[END_REF]. Trips generated between zones are assigned to the transportation network through connectors joining centroids and the physical network. Because zones and centroids are defined and used at the beginning of the modelling process, they affect the subsequent outputs, particularly the trip assignments on the network [START_REF] Chang | Effects of zoning structure and network detail on traffic demand modeling[END_REF].

In most Transportation Planning studies, a lot of effort is put in data collection, estimation of parameters and sophistication of models, but the issue of zoning rarely merits similar attention, normally being done on top of administrative units or "by common-sense".

Dividing the territory in zones (for the purpose of Transport Modelling, and namely for the definition of O/D matrices), is a process of discretisation of space:

With great advantages for the simplicity of the models and for the interpretation and communication of results. But with insufficiently recognized (and managed) problems of loss of information in the process.

Geographical information is lost in the process of substituting (concentrating) the real origins and destinations of trips (which occur over a continuous space) by an artificial point in each zone, the centroid. In doing this, two types of loss occur:

Intra-zonal trips cannot be processed (as they now start and end in the same point).

The travelling paths close to the real origins and to destinations of the trips are subject to large errors, implying that traffic load estimates in the lower hierarchy links of the transportation network are highly unreliable.

If loss of information was only geographical, the solution would be simplified in terms of possible solutions: Adopting a high number of small zones. But this leads to a head-on collision with the loss of statistical precision, due to the fact that O/D matrices are always estimated through sampling processes, most frequently through direct survey of travellers and /or by traffic counts on network links [START_REF] Viegas | Effects of the modifiable areal unit problem on the delineation of traffic analysis zones[END_REF]. Adopting many small zones would lead to two problems:

Width of Confidence intervals of the flow estimates.

Percentage of matrix cells with zero flow.

At the same time, the TAZ concept has not been clearly defined, and their multiple uses sometimes are confused. Different zoning systems can be defined for different uses. Clear examples of that are the zoning system developed for sampling and computation of the expansion coefficient in a Mobility Survey, the zoning system developed for Transportation and Urban Planning analysis, and the zoning system defined for transportation gas emissions in Environmental assessments, which do not necessarily have to be exactly the same, but are often considered in the scientific literature as only one.

Lack of rigor about the TAZ concept led to a non uniform terminology in the scientific literature. Most authors use the Traffic Analysis Zones term, but the domain of this terminology is not clearly defined. Some authors use the Transportation Analysis Zones term, usually associated with the Transportation and Land Use Planning. The term of Traffic Assignment Zones is also often used in scientific literature and technical studies linked to traffic forecasting models (e.g. transportation 4 step classical model). Other terms as Traffic Zones, Travel Analysis Zones or just zones are also used by some authors, normally associated with the O/D matrixes establishment and traffic assignment in the transportation 4 steps model [START_REF] Martínez | Zoning Decisions in Transport Planning and Their Impact on the Precision of Results[END_REF]Martínez, Viegas et al., 2009).

Through four decades of research, the scientific literature has established some guidelines and constraints to the definition of TAZ. Table I presents a complete set of the constraints discussed in the literature. Some contradictions and difficulties in their implementation are discussed in detail.

Constraints References

Trip generation/attraction homogeneity [START_REF] Baass | Design of Zonal Systems for Aggregate Transportation Planning Models[END_REF][START_REF] O'neill | Developing Optimal Transportation Analysis Zones Using GIS[END_REF][START_REF] Ding | Impact analysis of spatial data aggregation on transportation forecasted demand[END_REF][START_REF] Ding | The GIS-based human-interactive TAZ design algorithm: examining the impacts of data aggregation on transportation-planning analysis[END_REF]Ortúzar and Willusen, 2001;[START_REF] Chang | Effects of zoning structure and network detail on traffic demand modeling[END_REF] Contiguity and convexity of zones [START_REF] O'neill | Developing Optimal Transportation Analysis Zones Using GIS[END_REF][START_REF] Ding | GIS-Based Traffic Analysis Zone Design[END_REF][START_REF] Ding | Impact analysis of spatial data aggregation on transportation forecasted demand[END_REF][START_REF] Ding | The GIS-based human-interactive TAZ design algorithm: examining the impacts of data aggregation on transportation-planning analysis[END_REF] Compactness of TAZ shapes [START_REF] Baass | Design of Zonal Systems for Aggregate Transportation Planning Models[END_REF][START_REF] O'neill | Developing Optimal Transportation Analysis Zones Using GIS[END_REF][START_REF] Ding | GIS-Based Traffic Analysis Zone Design[END_REF][START_REF] Ding | Impact analysis of spatial data aggregation on transportation forecasted demand[END_REF][START_REF] Chang | Effects of zoning structure and network detail on traffic demand modeling[END_REF]) Exclusiveness (no doughnuts or islands) of zones [START_REF] O'neill | Developing Optimal Transportation Analysis Zones Using GIS[END_REF][START_REF] Ding | GIS-Based Traffic Analysis Zone Design[END_REF][START_REF] Ding | Impact analysis of spatial data aggregation on transportation forecasted demand[END_REF][START_REF] Ding | The GIS-based human-interactive TAZ design algorithm: examining the impacts of data aggregation on transportation-planning analysis[END_REF]) Equity in terms of trip generation (small standard deviation across zones) [START_REF] O'neill | Developing Optimal Transportation Analysis Zones Using GIS[END_REF][START_REF] Ding | GIS-Based Traffic Analysis Zone Design[END_REF][START_REF] Ding | Impact analysis of spatial data aggregation on transportation forecasted demand[END_REF][START_REF] Ding | The GIS-based human-interactive TAZ design algorithm: examining the impacts of data aggregation on transportation-planning analysis[END_REF] Adjustment of TAZ boundaries to political, administrative, or statistical boundaries [START_REF] Baass | Design of Zonal Systems for Aggregate Transportation Planning Models[END_REF][START_REF] O'neill | Developing Optimal Transportation Analysis Zones Using GIS[END_REF][START_REF] Ding | GIS-Based Traffic Analysis Zone Design[END_REF][START_REF] Ding | Impact analysis of spatial data aggregation on transportation forecasted demand[END_REF][START_REF] Ding | The GIS-based human-interactive TAZ design algorithm: examining the impacts of data aggregation on transportation-planning analysis[END_REF]Ortúzar and Willusen, 2001;[START_REF] Chang | Effects of zoning structure and network detail on traffic demand modeling[END_REF] Respect of physical separators [START_REF] O'neill | Developing Optimal Transportation Analysis Zones Using GIS[END_REF][START_REF] Ding | GIS-Based Traffic Analysis Zone Design[END_REF][START_REF] Ding | Impact analysis of spatial data aggregation on transportation forecasted demand[END_REF][START_REF] Ding | The GIS-based human-interactive TAZ design algorithm: examining the impacts of data aggregation on transportation-planning analysis[END_REF]) Decision makers" preferences are considered in determining the number of TAZ [START_REF] Ding | GIS-Based Traffic Analysis Zone Design[END_REF][START_REF] Ding | Impact analysis of spatial data aggregation on transportation forecasted demand[END_REF][START_REF] Ding | The GIS-based human-interactive TAZ design algorithm: examining the impacts of data aggregation on transportation-planning analysis[END_REF]Ortúzar and Willusen, 2001) Avoid main roads as zone boundaries (Ortúzar and Willusen, 2001) Zone size is selected such that the aggregation error caused by the assumption that all activities are concentrated at the centroid is not too large (geographical precision) (Ortúzar and Willusen, 2001) Minimization of intra-zonal trips [START_REF] Baass | Design of Zonal Systems for Aggregate Transportation Planning Models[END_REF][START_REF] Crevo | Impacts of zonal reconfigurations on travel demand forecasts[END_REF]) Maximization of the statistical precision of the estimation of the OD matrix cells [START_REF] Openshaw | Optimal Zoning Systems for Spatial Interaction Models[END_REF] It is difficult to consider and implement all of the above criteria in a single process of TAZ design because some rules contradict others [START_REF] O'neill | Developing Optimal Transportation Analysis Zones Using GIS[END_REF][START_REF] Ding | GIS-Based Traffic Analysis Zone Design[END_REF]. Some of these rules are a consequence of the use of a fixed zoning scheme over time, and of different study scales and purposes. In the scientific literature, it has been stipulated that transport demand modelling requires constant spatial data aggregation in TAZ along the entire process, from data collection to the trip assignment step [START_REF] Chang | Effects of zoning structure and network detail on traffic demand modeling[END_REF].

The number of zones problem can be solved through the development of a hierarchical zoning system, as in London Transportation Studies (Ortúzar and Willusen, 2001), where sub-zones are aggregated into zones, which in turn are combined into districts, traffic boroughs, and finally a sector. This facilitates an analysis of different types of decisions at the appropriate level of detail (Ortúzar and Willusen, 2001).

Unfortunately, predetermined zoning systems do not take into account the ongoing changes of land use (spatially and temporarily), which can deeply affect TAZ homogeneity and compactness, producing significant misestimates of trip generation and OD matrices (Edwards, 1992).

These ongoing spatial and temporal land use changes demonstrate that current traffic management models, which are sometimes directly based on the results of the data collection process, and suppress the trip generation and distribution steps of the classical travel demand model [START_REF] Ding | The GIS-based human-interactive TAZ design algorithm: examining the impacts of data aggregation on transportation-planning analysis[END_REF], should use a different zoning scheme from the transportation demand forecasting models [START_REF] Openshaw | Algorithms for Reengineering 1991 Census Geography[END_REF].

A better solution to the data collection zoning system constraint is the development of survey processes, in which the trip ends are geocoded. This data collection process requires the establishment of an initial zoning system, as with all other data collection processes, for the determination of expansion coefficients of each trip (Ortúzar and Willusen, 2001); however, after the conclusion of this process, travel data is not attached to this or any other zoning system, but only to their geospatial coordinates [START_REF] Chapleau | Conducting Telephone Origin-Destination Household Surveys with an Integrated Informational Approach[END_REF]. Each study that uses this database can then develop a new zoning scheme that better fits the study scale and goals, resulting in more flexibility for the transportation analyst and a higher utility of the available database [START_REF] Chapleau | Conducting Telephone Origin-Destination Household Surveys with an Integrated Informational Approach[END_REF][START_REF] Trepanier | Object-oriented and totally disaggregate analysis of origin-destination household survey data[END_REF].

The complexity involved in TAZ definition underlies the need of further research on this topic. This research, building on previous research by the authors, tries to develop a new TAZ delineation methodology and a GIS based application, which does not only suits to one purpose (e.g. traffic assignment or 4 steps model definition), but that can also integrate under the same tool, different zoning schemes for different types of studies, allowing a more comprehensive analysis of mobility within Transport Planning Studies.

The new TAZ delineation approach is determined not only by the trips generation and distribution patterns, but is also constrained by other indicators that characterise other mobility and accessibility components. The integration of these indicators allows a holistic picture of mobility within a region and to identify at the very precise level the main zones of activities and traffic exchanges.

In order to be effective for mobility analysis and policy purposes, this kind of approach cannot only rely on the original cell grid unit but a hierarchical aggregation should be set up to ensure integration between the several resulting zoning schemes for different types of analysis that can be performed to the mobility and land use patterns of a region.. This new methodology will be tested on the Mobility Survey for the Paris Metropolitan Area in 2001 (EGT 2001), which will allow and easier illustration of the capabilities of this new modelling and planning tool.

PARIS REGION AND PRESENTATION OF THE DATA BASE

Ile-de-France is one of the twenty-six administrative regions of France, composed mostly of the Paris metropolitan area with the size of 12,211 Km 2 (0,86% in Paris Municipality). Created as the "District of the Paris Region" in 1961, it was renamed after the historic province of "Isle de France" in 1976, when its administrative status was aligned with the other French administrative regions created in 1972.

The Paris Region corresponds also to a NUTS2 EU statistical division of the territory, and it is formed by 8 NUT3, which do also correspond the department administrative division.

The Ile-de-France region is formed by 1,281 communes (20 of then inside Paris Municipality -Arrondissements), which is the French smallest administrative division.

Brief presentation of the Paris Region Patterns

In 2006, according to OECD Regional Statistical Database, Paris region with a population of 11,490,969 inhabitants, accounts for one fifth of the French population and with a GDP of 523,138 mil.USD is the wealthiest part of France (GDP/ capita: 46,797 USD). Even if other parts of France, especially in the South of the Loire Valley, are economically dynamic and attractive, Paris region remains a dynamic pole with a GDP yearly growth (1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005) of 2.5% and the major labour pool, with an employment rate of 59.5% and an unemployment rate of 9. 5% (2006). The active population is highly qualified with 26.2% of the population with at most lower secondary education (0-2 years after graduate), and 33.5% of the population with an upper secondary education (3-4 years after graduate) and at last 40.3% of the population with a tertiary education (5-6 years after graduate) and the job market is mainly oriented towards tertiary activities.

The analysis of the number of inhabitants according to the residential location shows that in all the departments of the Parisian region, the residential population has increased from The jobs are mainly located in the centre and the west of the region, as to say in Paris and the Hauts-de-Seine, and housing places in the east. In the recent years, the firms have relocated their headquarters from inside Paris to the suburbs due the high level rents in the French capital.

Due to the high density of the Parisian Region and a cluster location of business and administrative activities and housing, the mobility is a key issue in the most important economic and labour pool of France.

Brief presentation of the Paris Mobility Survey

A large survey is regularly conducted on transportation behaviour of the households living in Paris region. It contains various survey instruments in order to describe the means of transport owned by the household, commuting lifestyle, daily mobility, long distance trips, etc. Data collected in each of these instruments can be analyzed separately. The purpose of this survey is to describe the trips made by households and individuals who are living in Paris and the nearest departments, as well as their use of public and private transport means. The survey is conducted for a week day. We focus on internal trips as to say the trips which origin and destination are within the Parisian region (Ile de France).

The It shall be noticed that the origin and destination of each trip are geo-coded allowing the analysis of the Parisian mobility within different zoning.

Due the fact that only the 2001 survey was geocoded, our research was carried only with the latter. This mobility survey contains 23,656 respondents from different households, sampling a total of 81,386 trips (0.23% of the total trips). The trip ends, and the residential location of the respondent are geocoded using a 300 square meters grid cell. It is also possible to identify from the figure, the existence of four main axes of mobility to reach Paris centre, corresponding to locations with better accessibility to the public transport network and the road network.

There also a significant number of communes that do present a low number of trips generated, reflecting locations with lower population density and activities concentration within the Ile-de-France region. 9

TAZ DELINEATION ALGORITHM

This section of the paper presents a summary of the original TAZ Delineation Algorithm that has been developed in previous research of the authors [START_REF] Martínez | TAZ Delineation and Information Loss in Transportation Planning Studies[END_REF][START_REF] Martínez | Zoning Decisions in Transport Planning and Their Impact on the Precision of Results[END_REF]Martínez and Viegas, 2009;Martínez, Viegas et al., 2009) and the application of the methodology to the Paris Region mobility survey (EGT 2001).

The methodology for zones delineation is defined to reduce the noise level of the data for traffic modelling, and at the same time, to minimise the geographical error of the trip end location.

The methodology defines zones such that:

Zone boundaries correspond to places with very low generation of trips (reducing the probability of misallocating trips to zones near zones boundaries); intra-zonal trips are minimised; the definition of zones with a very low number of trips or very large area (high geographical error) is avoided; the density of trip production inside a zone should be as homogeneous as possible.

TAZ methodology

The methodology for the determination of zones starts by the aggregation of the geocoded trip ends (origin and destination) into a (relatively fine) cell grid. The cell grid can be variable and depends on the size of the study area and the precision intended for the study [START_REF] Viegas | Effects of the modifiable areal unit problem on the delineation of traffic analysis zones[END_REF]. As a reference, for the Lisbon Metropolitan Area, a square cell grid a 200 m side length was used.

A thin plate spline1 was used to smooth the resulting surface and interpolate for cells without observations. The result of this analysis is presented in Figure III, where it is possible to identify the high concentration of trip ends in the Lisbon city centre and near some locations at the Lisbon municipality boundary. The TAZ delineation algorithm is defined by five different constraints and an objective function with two variables. These constraints can be divided in two groups: Those derived from the algorithm (4 constraints) and the geographic constraint for the TAZ border delineation (avoiding overlapping between zones). The constraints derived from the algorithm are:

1. The total origins ( i O ) or destinations ( i D ) of trips of each TAZ should be greater than 70%2 of the average origins or destinations of trips by zone (total origins or destinations of trips divided by the number of zones). This is a requirement for quasi-homogeneity of trip quantities across zones, which indirectly controls the relative statistical error of the resulting zones. 2. Each TAZ area should be at least 70%3 of the size of the influence area4 of local predefined "highest peaks,"5 which avoids the formation of zones with very low geographic precision.

3. The average statistical (relative) error in the estimation of OD flow matrix cells should be lower than 50%, which directly controls the statistical precision of each TAZ (see ( 1) and ( 2)).

N p SWCI x p p 1 n Z p SWCI N 1 i ij STAT _ ij ij 2 ij i (1) (2)
4. The number of zones should fall within the range previously defined by the analyst, which forces the algorithm to follow the analyst"s preferences. This constraint is also used as one of the stopping criteria of the algorithm when it cannot be satisfied because the given range of is too high for the available number of cells and data.

The objective function of the TAZ algorithm contains two different components: The density of trips and the percentage of intra-zonal trips of each zone. This objective function tries, simultaneously, to optimise these variables by minimizing the standard deviation of the density of trips (across cells inside each TAZ), thus leading to more homogeneous zones and minimizing the sum of the percentages of intra-zonal trips across all zones. The algorithm requires setting some parameters, which are important for the establishment of constraints, as well as for the optimization of some additional features of the algorithm (e.g., the optimal number of zones for a given range, which depends on some macroscopic indicators that will be presented below).

These parameters are:

The definition of the range of number of zones (searching for the most suitable solution)compulsory input; minimum size of the influence area of local "highest peaks" for TAZ delineation; definition of a core problem area6compulsory input; percentage of zones belonging to the core problem area; maximum proportion of areas between zones in the core problem area and in the "rest of the world;" some parameters of the indicators (macroscopic indicators) used to define the optimal number of zones of the given range. This optimal number is found with the help of a multi-criteria additive function, which is defined below.

If the user omits some of these parameters, and they are not compulsory, the algorithm uses the default values of those parameters in the algorithm 7 (e.g. percentage of zones belonging to the core problem area equal to 50%).

From the administrative zoning to the TAZ zoning: a better understanding of geography and mobility

After the initial presentation of the TAZ Delineation Algorithm, this point presents an assessment of the geography and mobility patterns of Ile-de-France region with two main goals:

Assess the relevance of some indicators for the mobility analysis of the region in order to include them in the new TAZ Delineation Algorithm; Compare the resulting spatial structures with the existing planning zoning scheme for the Ile-de-France region (IAURIF). 6 The core problem area parameter allows the analyst to establish different levels of relevance inside the modelling area, which does not need to be used, and considers this parameter to be equal to the entire modelling area.

7 For an overview of the TAZ delineation algorithm and a detailed presentation of the mathematical and data flow of the algorithm, presentation allowing a deeper understanding of its mechanics, see Martinez, L., Viegas, J., TAZ Delineation Application for Transport Planning Studies: A New approach Applied to the Ile-de-France-Technical report for DEST/INRETS, September 2009 (not published but available on request).

13

With this purpose, it was developed a cluster analysis of these indicators using zoning scheme resulting from the TAZ Delineation Algorithm, having in mind the future application of this zoning scheme for the hierarchical procedure.

The zoning scheme used is presented in Figure V. This TAZ configuration presents 1500 and was developed for traffic assignment purposes. Table II presents some indicators of this zoning scheme. The zoning scheme presents overall good quality indicators, with the exception of the statistical precision indicators, derived from the large number of zones used for traffic assignment purposes.

INTRODUCING A HIERARCHICAL ZONING APPROACH

The new approach introduces the concept of hierarchical zoning, due to the fact the zoning schemes result from previously aggregated zones and not from square grid cells. This new methodology allows integrating results from previous stages to make models computing and assessment compatible.

The concept of hierarchical zoning has been already use in literature and in practical application as in the London TAZ system (Ortúzar and Willusen, 2001); however a systematic and algorithmic approach has not been already developed.

The new methodological approach for zones delineation is defined in order to keep the goals of the original algorithm (reduce the noise level of the data for traffic modelling, and at the same time, to minimise the geographical error of the trip end location), but also include some new indicators relevant of transport policy assessment in order to measure the homogeneity within the same zone.

The new methodology defines zones such that:

Zone boundaries correspond to places with very low generation of trips (reducing the probability of misallocating trips to zones near zones boundaries); intra-zonal trips are minimised; the definition of zones with a very low number of trips or very large area (high geographical error) is avoided; the homogeneity of the selected indicators within the same zone is maximized.

The selected indicators to use to measure the zone homogeneity were defined in the previous section. At the same time, the new aggregation unit are no longer grid cells, which will result in a different set of rule for the vicinity assessment. These rules, as in the base algorithm, try to avoid the delineation of zones with complex spatial structures, which could undermine the applicability of the model in complex urban structures, as well as the assessment of its results, even for an experienced transportation analyst.

New formulation of the TAZ

The main changes introduced by the new algorithm in order to use the new hierarchical approach are:

The definition of new vicinity rules based irregular boundaries overlap;

The definition of a new objective function and constraints set for the algorithm.

The introduction of new vicinity rules in the TAZ hierarchical procedure is due to the possibility of use of irregular borders zones as seed for the aggregation process. The algorithm starts, as the original algorithm, by identifying the local "highest peaks" and their surrounding area, sorts them by decreasing magnitude, and then uses a local search algorithm for the design of the zones. This search is performed for each peak considering a defined set of rules. These search rules were developed to avoid the delineation of zones with complex spatial structures, which could undermine the applicability of the model in complex urban structures, as well as the assessment of its results, even for an experienced transportation analyst. This local search algorithm presents now a new set of rules, which use as main indicator the percentage of TAZ boundary perimeter overlap between the already formed zones and the candidate zone, considering as minimum overlap percentage 5% of the perimeter of the already formed TAZ.

The new rule for vicinity assessment, presents a simple mathematical definition, however the computational burden needed for the dynamic computing of the TAZ boundary perimeter overlap is considerably higher that the original regular grid search rules.

The objective function of the Hierarchical TAZ algorithm contains two different components as original algorithm: the percentage of intra-zonal trips each zone and an aggregated Euclidean distance measure of the new set of indicators within each zone.

The Euclidean distance function is computed after a hipergeometric standardisation of the indicators (N) as presented in (3). The distance function is then computed by the difference between the standardized indicators (NS) of the already aggregated zones and the zone under analysis. The distance of each indicator is then weighted by multiplying the standardized distanced by a weight factor of each indicator (wn) (considered equal among all indicators by default). This distance function (DNj) is presented in (4).

i i i i i N min N max N N max NS (3) i i i j wn NS DN (4)
This objective function tries, simultaneously, to optimise these variables by minimizing the distance measure of the new indicators (across cells inside each TAZ), thus leading to more homogeneous zones and minimizing the sum of the percentages of intra-zonal trips across all zones. These components can have minimum values at different points, with trade-offs solved through the use of a ranking function, which minimises the sum of the rankings of the two variables.

After the establishment of the new TAZ Delineation hierarchical procedure, an application of the new algorithm will be presented for the Ile-de-France region based on the EGT 2001 survey.

The hierarchical procedure will use as base zoning scheme the TAZ system with 1500 zones. This TAZ configuration was developed using the original TAZ Delineation Algorithm and had as main purpose the development of a zoning scheme appropriate to traffic assignment models.

In order to test the new hierarchical algorithm, two different tests were performed:

A 50 zones scheme that could be applied for transport planning and energy consumption and emissions assessment in the transport sector; A 75 zones scheme for the same purposes.

A new mapping

The obtained results are presented in Figure XI and Figure XII. The presented results map the spatial distribution of the emission factor, which leads to a configuration where the agglomeration ring around Paris and the some secondary agglomerations present the highest values of this coefficient. These results are coherent with the assessment presented in the previous section, which is due to the greater private car use in areas outside Paris.

It is relevant to notice that the borders of the resulting zones cross frequently the administrative limits of the departments, which indicates urban developments and activity interactions that not always follow the administrative divisions.

The larger zones are located in more rural areas with more disperse land use patterns and lower trip generation/attraction rates. This fact leads to a trade-off between statistical precision and homogeneity of zones and the geographical precision, which for planning purposes is not as relevant as for traffic assignment models.

Comparing the two zoning schemes developed, a significant overlap between zones can be observed. The main differences result from the subdivision of some zones located within the Petite Couronne, which can present a higher geographical precision for a greater number of zones. The global indicators as the average equivalent radius change from 4646 m to 3842 m, and the average statistical error from 0.51 to 0.70. The new TAZ shows that the centroids of the zones are not the administrative centres but the economic centres which are close to transport networks nodes.

Both resulting zoning systems seem adequate for transport planning assessment.

CONCLUSIONS AND FUTURE DEVELOPMENTS

In most Transport Planning studies, a lot of effort is put in data collection, estimation of parameters and sophistication of models, but the issue of zoning rarely merits similar attention, normally being done on top of administrative units or "by common-sense". The results obtained show that zoning is not a trivial matter giving the significant consequences it may have for the generation of statistical and geographical errors. This study introduces a new methodology of zones delineation that deals with the existent trade-offs in the process.

The introduction of the new Hierarchical Delineation Algorithm enhances the capabilities of the original algorithm, leading to more flexible TAZ delineation process that can be used for multiple purposes.

The application developed for this study focuses on new indicators that are considered determinants of mobility. These new indicators are used to measure the homogeneity within a zone, leading to zoning schemes that are more suited for transport planning.

Applying the new hierarchical zoning procedure over the results of the original TAZ Delineation Algorithm, ensures the conservation of the "good traffic assignment properties" of the original algorithm and simultaneously gather areas with similar values in relevant land use, accessibility and energy consumption attributes.

The results show a great potential in the use of this integrated approach that can easily combine previous studies to other layers of analysis and more comprehensive and holistic assessments. This step forward allows simultaneously achieving good zoning schemes for different purposes and models and using their outputs as inputs for different models due to the hierarchical design used.

The use of this algorithm for different case studies (Lisbon Metropolitan Area and the Ile-de-France region) means a step forward on the validation of this procedure. Testing the in a different database allowed the tuning of the model and the assessment of the sensitivity of the results to different travel patterns and land use distributions.

Four main directions are envisaged for the next steps of this research:

Look at the consequence of these different zoning strategies on the traffic load estimates on roads of intermediate and lower hierarchies, especially after the implementation of all the local refinements to the base algorithm.

Investigate the consequences of these findings for the process of matrix estimation from traffic counts, being aware that the assignment of a trip to a zone directly affects the TAZ definition process. For this reason, studies based on traffic counts might have a simultaneous definition of the zoning system and the OD matrix, and not as a exogenous and pre-defined process. This iterative process until the convergence of 24 the zoning systems and OD matrix might have a similar structure to the k-clustering algorithms, where the centroids of the clusters change position with every iteration. Economically quantify the improvements in statistical precision and the reduction of percentage of intra-zonal trips obtained by the use of the algorithm. This significant decrease of information loss can lead to the use of smaller samples in data collection or to a significant increase of the quality of the data used in typical transportation planning studies, obtaining more robust results for the same input data and costs. Evaluate the possible application of the algorithm with other indicators resulting from zonal data rather travel vector data (e.g., population vs. employment). Both resulting zoning systems seem adequate for transport planning assessment.

Figure I -

 I Figure I -Map of the Ile-de-France communes

  1976 to 2001, except in Paris (75) , except in Paris (75) where the population decreased by 5%. The most important raise occurred in theSeine-et-Marne department (+ 58% between 1976 and 2001), in the East of Paris region and in the North West area, in the Val d"Oise department(+ 41% between 1976 and 2001). Even so, Paris remains the most populated place with 1.97 millions of inhabitants followed by the Hauts-de-Seine department, closed to the La Défense business, with 1.31 millions of inhabitants. These latter departments are those with the less important non working population. The share of non working individuals has significantly and continuously increased in the departments of Seine-Saint Denis and the Val d"Oise department, in the North of Paris.

Figure

  Figure II presents the spatial distribution of the origins of trips of the EGT 2001 mobility survey. It is easy to notice a higher concentration of trip origins within the central departments of the study area and mainly within the Paris Municipality.

Figure

  Figure II -Trip origins of the EGT 2001 mobility survey

Figure

  Figure III -Total origins and destinations in Lisbon Municipality 3D view

  These components can have minimum values at different points, with trade-offs solved through the use of a ranking function, which minimises the sum of the rankings of the two variables (see FigureIV).If the ranking function retrieves the same result for different cases, the objective function considers the result with the lower trip density standard deviation to be the "most suitable". The decision tree used for the objective function is presented in Figure IV.

Figure

  Figure IV -Decision tree for determining the optimum of the TAZ algorithm objective function

Figure

  Figure V -1500 zones scheme of the Ile-de-France region (TAZ Delineation Algorithm)

Figure

  Figure VI -Population density spatial distribution of the Ile-de-France region

Figure

  Figure VII -Public transport accessibility spatial distribution on the Ile-de-France region

Figure

  Figure IX -Cluster analysis with the 81 profiles for the Ile-de-France region

Figure

  Figure XI -TAZ 50 zones (Application of the Hierarchical TAZ Delineation Algorithm)

Table I -

 I Constraints to the definition of TAZ

  analysis of the trips, the travel time budget and the travel distance budget, of the most wealthy and densely populated region of France, is based on the data collected by 4 Surveyed individuals within the household (e.g. work, age, place of work);3. Trip characterisation (e.g. Origin, Destination, Mode, Time, purpose).

	Household mobility surveys EGT carried on the Paris region in 1976/77, 1983/84, 1991/92
	et 2001/02.
	For the 4 surveys, the sample size is around 18,000 households representative of Ile de
	France population and the answer rate is around 57% with an average of 10,000
	respondents (10,478 answers in 2001). The questionnaire contains three parts:
	1. Household composition (HSD) (e.g. address, income, motorization, number of
	persons);
	2.

th WCTR, July 11-15, 2010 -Lisbon, Portugal

The thin plate spline is the two-dimensional analog of the cubic spline in one dimension. It is the fundamental solution to the biharmonic equation. The name "thin plate spline" refers to a physical analogy involving the bending of a thin sheet of metal.

This value was obtained for the case study after calibration using different size grid cells.

This value was obtained for the case study after calibration using different size grid cells.

The surrounding area size is a parameter of the algorithm that is defined by the user.

The concept of influence area of local "highest peaks" is defined as the minimum size that an analyst establishes for the specific modeling problem.

thWCTR, July 11-15, 2010 -Lisbon, Portugal

th WCTR, July 11-15, 2010 -Lisbon, Portugal