Tzu-Hao Chiu

Jian-Hong Liao

Franck Gam

Isaac Chantrenne

Samia Kahlal

Jean-Yves Saillard

C W Liu

All-Selenolate-Protected Eight-Electron Platinum/Silver Nanoclusters

First atomically and structurally precise platinum/silver superatoms protected by Se-donor ligands were synthesized in high yield by adopting ligand replacements on [PtAg20{S2P(O n Pr)2}12] (3) with 12 equiv. of di-alkyl diselenophosph(in)ates. Structures of [PtAg20{Se2P(OR)2}12] (R = n Pr (1a), i Pr (1b) and [PtAg20{Se2P(CH2CH2Ph)2}12] (2) were accurately determined by single-crystal X-ray diffraction to reveal an eight-electron [Pt@Ag12] 4+ icosahedral core embedded within a cube of eight silver(I) atoms and wrapped into a shell of twelve diselenophosph(in)ates. While the lowest energy absorption band of the Se-derivatives is red-shifted to longer wavelengths in comparison with S-analogue, it is blue-shifted in the emission spectra. DFT and TD-DFT calculations rationalize their electronic structures as that of 8-electron superatoms, with their HOMO and LUMO being the 1P and 1D levels, respectively. The two UV-vis lowest bands are associated with 1P→1D MMCT transitions. The blue shift observed for their S-analogue results from a larger HOMO-LUMO gap in the case of dithiolate ligands.

Metal nanoclusters (NCs) with precise compositions and structures are currently the subject of intense investigations due to the original properties associated with their specific nature. [1][2][3][4][5][6][7] In order to get a clear understanding of these properties, a precise knowledge of the NC structure is compulsory. Under this guideline, NC synthesis by doping hetero metals is very promising due to their potential property tuning controlled by dopants. 8 Alloying group 11 metals with each other usually produces partial substitutions in several positions of the metal framework because of the close parentage of group 11 metals. 9 On the other hand, when doping silver NCs with Pd and Pt, the group 10 metal tends to locate at the center of the polyhedral Ag kernel. [10][11] Fully structurally characterized examples include PtAg9, 10l PtAg14, 10m PtAg20, 10f PtAg24, 10g PtAg26, 10h PtAg28, 10c,d PtAg31, 10a and PtAg42. 10k Most of them are supported by (or a combination of) thiolates, tertiary phosphines, halides or alkynes. So far, no selenolate-passivated platinum/silver NC has been reported.

Although more and more ligand-supported group 11-doped NCs have been reported, still only very few examples of silver or silver-rich nanoclusters protected by Se-donor ligands are known. 12,14 Most of them originate from our group, such as the structurally characterized 8electron silver superatoms [Ag20{Se2P(O i Pr)2}12], 14a and [Ag21{Se2P(OEt)2}12] +14a and their gold/silver alloy relatives [AuAg20{Se2P(OEt)2}12] + , 14a [AuAg20{Se2P(O i Pr)2}12] + 14b and [Au3Ag18{Se2P(O i Pr)2}12] + . 14b An atomically precise, 18-electron silver superatom, [Ag44(SePh)30] 4-, primarily characterized by ESI-MS and theoretically certified by DFT calculations, was reported by Pradeep, Negishi, and co-workers in 2013. 13 The latest example is the 20-electron superatom, Cd12Ag32(SePh)36, published by Bootharaju et al. in 2019. 15 Thus the synthesis of silver-rich superatomic alloys protected by Se-donor ligands remains challenging and a convenient synthetic methodology is needed.

In the past few years we have developed a method of high-yield synthesis of Se-stabilized copper hydrides and silver-rich NCs by using a ligand exchange methods. 14,16 It seemed natural to extend this approach to the design of Pt/Ag NCs, with the hope that not only the target alloys could be isolated, but also that we could explore the possibility of fine-tuning their optical properties. 14 Another incentive in this endeavor is the frequently observed structural re-arrangement of metal cores, 14 which indeed generate pseudo-isomers, primarily arising from the different bite distances of the chelating dithiolate and diselenolate ligands. 17 Herein, we report the synthesis by ligand exchange reaction, characterization, crystal and electronic structures of three 8-electron Ptdoped diselenophosph(in)ates-protected silver superatoms. All three alloys have a similar Pt@Ag20 metallic framework of Th symmetry. This high symmetry is favored by diselenophosphate/diselenophosphinate (dsep/dsepi) bite distance, which is ~0.3 Å larger than that of dithiophosphate (dtp), the latter favoring the lower C2 symmetry for the Pt@Ag20 core. 10f Furthermore, the new compounds display different optical properties compared to their sulfur analogue. To the best of our knowledge these are the first, homoleptic Pt/Ag alloy cluster compounds protected entirely by Se-donor ligands.

A one-pot synthesis of PtAg20(dsep)12 can be achieved by the reaction of twelve equiv. of [NH4][dsep] with [PtAg20{S2P(O n Pr)2}12] 10f (3) in THF at 0°C. The solution undergoes a color change from deep brown to orange. After rota-evaporation, the products were purified by hexanes to remove [NH4][S2P(O n Pr)2], then PtAg20{Se2P(OR)2}12 (R = n Pr, 1a; i Pr, 1b) can be obtained as deep orange solids in 85% yield after drying under vacuum. [PtAg20{Se2P(CH2CH2Ph)2}12] (2) was produced via a similar procedure by using dsepi 18 instead of dsep ligands. These experiments must be performed in dark since the three resulting compounds are highly sensitive to light.

The X-ray structures of 1a, 1b, and 2 are very similar. That of 1a is depicted on Fig. 1a. The PtAg20 core can be described as a Pt-centered Ag12 icosahedron inscribed in a cube made of 8 Ag atoms, each of them capping a triangular face of the icosahedron, in such a way that the whole Pt@Ag12@Ag8 framework is of ideal Th symmetry (Fig. 1b). The PtAg20 core is stabilized by twelve dsep/dsepi ligands located on the twelve edges of the cube (Fig. 1c). The trimetallic triconnectivity (µ3: η 2 , η 1 ) of the ligands destroys the centrosymmetry (Fig. S1), lowering the ideal symmetry of the whole cluster to T. It is noteworthy that the sulfur precursor of 1 and 2, namely [PtAg20{S2P(O n Pr)2}12] 10f (3) is of C1 symmetry. Similarly, [Ag21{S2P(O i Pr)2}12] + , which is of C3 symmetry, can be transformed into [Ag21{Se2P(OEt)2}12] + of T symmetry via ligand replacement. 14a Nowadays the ligand-exchange-induced structure transformation (LEIST), proposed by Jin, 19 has become topical in the field of NC science. 20 However, structure transformations induced by selenolate ligands remain scarce. 12,14 Moreover, most of the works focused on LEIST involve nuclearity changes, that reported here maintains the whole cluster stoichiometry.

The averaged Pt-Agico and Agico-Agico distances in 1a are 2.759(4) Å and 2.901(9) Å, respectively, showing strong Pt-Ag and Ag-Ag interactions in the superatomic core. The Agico-Agcap distances (avg. 2.959(8) Å) are slightly longer than Agico-Agico ones. All the metrical data of 1a are similar to that of 1b and 2 (Table S1). Astonishingly, the Pt-Agico and Agico-Agico distances observed in 3 (2.7601(7) Å and 2.9022(9) Å) 10f are similar to that in 1 and 2. It clearly demonstrates that the nature of the protected ligands barely affects the metal-metal distances within the superatomic PtAg12 core. The Agcap-Se distances (avg. 2.617(10) Å) are shorter than the Agico-Se ones (avg. 2.658(7) Å). The Se…Se bite distances (avg. 3.688(2) Å, 3.691(3) Å, and 3.665(3) Å in 1a, 1b, and 2, respectively) are longer than that in 3 (avg. 3.412(4) Å). Two probable factors account for the transformation of the metal framework from C2 to Th upon ligand exchange. Firstly, the longer ligand bite distances in PtAg20(dsep/desepi)12 releases the constrains around the metallic framework. Secondly, all the Se-protected superatomic clusters can only be synthesized by ligand exchange. In the course of the ligand replacement, the eight outer Agcap atoms are allowed to move away from each other to minimize electrostatic repulsions. As a result, they arrange themselves in a cubic array, with all Agcap…Agcap distances larger than 5 Å. Intriguingly the reverse ligand-exchange reaction from dsep to dtp does not happen, suggesting that the stronger M-Se bonding may also play a role on the alloy stabilization.

Being of non-centrosymmetrical T symmetry, the molecular structures of 1 and 2 are chiral. Unfortunately, they came out as racemic mixtures (Fig. S2-S3). We therefore could not obtain any chiroptical responses of 1 and 2 from CD spectroscopy, including for 1a, which crystallizes in a non-centrosymmetrical space group (P21) and contains only one enantiomer in its unit cell (Fig. 1d), but is likely a mixture of rightand left-handed crystals. The 31 P{ 1 H} NMR spectrum of 1a displays one chemical shift centered at 74.1 ppm and flanked by two sets of selenium satellites (ca. 609 and 709 Hz in Fig. 2a). The data suggest that the as-synthesized NC is highly symmetric in solution and that the two selenium atoms bound to the same phosphorus are not only chemically, but also magnetically inequivalent, a reflection on their binding modes (µ3: η 2 , η 1 in Fig. 2b) derived from the X-ray structure (vide supra). Similar results are found for 1b and 2 (1b: 67.3 ppm, 1 JPSe = 597, 711 Hz; 2: 31.9 ppm 454, 554 Hz; Fig. S4-S5). The positive-ion ESI mass spectrum of 1a displays one intense band at m/z 6145.3, corresponding to the adduct ion [1a + Ag + ] + (calc. m/z: 6145.1 in Fig. 2c). In the ESI-MS spectrum of 2, there is one ion peak at 3625.6 corresponding to [2 + Ag + + H + ] 2+ (calc. m/z: 3626.7 in Fig. S6). All simulated isotopic patterns match well with the experimental observations. The UV-vis spectrum of 1a shows two bands centred at 388 and 466 nm (391, 470 nm for 1b; 414, 484 nm for 2) with the larger molar absorptivity observed at the former band (Table 1). Compared with 3, the absorption bands in 1a are red-shifted (Fig. 3). The stability of PtAg20(dsep)12 in solution was further explored by time-dependent absorption spectroscopy (Fig. S7-S9). Compounds 1a, 1b and 2 exhibit high stability in 2-MeTHF solution under the strict exclusion of light. The two major absorption bands retain both their shape and intensity over the course of one week when the solution is kept in dark. Under light, decomposition starts within 30 minutes, and the intensity of the two absorption bands decreases with time. Eventually, the absorption bands are barely discernable after two hours, indicating that three the PtAg20 NCs are highly sensitive to visible light. The 77K photoluminescent (PL) spectra of 1a, 1b and 2 (Fig. 3, S10-S11) in 2-MeTHF exhibit one structureless emission band centered at 624 nm, 621 nm, and 629 nm, respectively. They are blue-shifted relative to their dithiophosphate-protected relative 3. The time-resolved photoluminescence spectrum (77K) of 1a, 1b, and 2 exhibit a single exponential decay curve (Fig. S12-S14). The lifetimes (τ) of 1a and 1b are 8.5 μs and 8.4 μs, which are slightly shorter than in 2 (τ = 12.3 μs). With emission lifetimes in the microsecond range, phosphorescence is suggested. The corresponding Stokes shifts in 1a (5400 cm -1 ) and 1b (5200 cm -1 ) are slightly larger than in 2 (4800 cm -1 ). The smaller Stokes shift in 2 indicates that there is less non-radiative relaxation (vibrational relaxation) than for 1a and 1b, i.e. better emission efficiency. As a result, the weak orange emission of 2 can be barely seen in solution at ambient temperature, but that 1a and 1b were not observed. The lifetime of 2 at ambient temperature, which is possibly quenched by the solvent, was largely decreased. Relevant photophysical data of 1 and 2 are reported in Table 1.

Intriguingly a general trend is observed on both absorption and emission spectra of the M@Ag20(dsep)12 (M = Ag, Au, Pt) NCs of T symmetry. That is, a spectrum shifting to higher energy upon doping with a hetero-metal at the cluster center. 14a While there is not much difference in the absorption wavelengths between the Au (390, 470 nm) and Pt (388, 466 nm) alloys, even though their cluster charges are different, a significant blue-shift (624 (1a), and 674 nm (AuAg20) is observed in the Pt-doped NCs.

The Ag (3d5/2) XPS spectrum (Fig. S18-S20) exhibits a binding energy (BE) at 367.87, 367.59, and 367.85 eV in 1a, 1b, and 2, respectively. A peak deconvolution can be resolved into two bands, confirming that the average valence state of Ag is between Ag(0) (368.2 eV) and Ag(I) (ca. 1 eV below 368.2 eV). 21 These recorded BEs are comparable with that of 3 (367.50 eV). The elemental analysis (EA) was used to test the purity. For example, the carbon and hydrogen contents of 1b are 13.91% and 2.98%, which are close to the theoretical values (C:14.32%, H:2.8%). The TGA spectra were also employed to test the purity and thermal stability of 1a, 1b, 2 and 3 (Fig. S21-S24). The TGA curves show that 1a, 1b, and 3 start to decompose at 190 ℃, and the curve becomes flat from 210 ℃ to 400 ℃ in which the residual fragment can be calculated as PtAg20E24 (E = Se for 1a, 1b, and 2; E = S for 3). Compound 2 showed better thermal stability, with a decomposition temperature around 300℃. Further weight loss of S/Se occurs when the temperature is elevated above 400 ℃. The experimental weight loss of 29.8%, 29.7%, and 41.5% for 1a, 1b, and 2, respectively, is consistent with the theoretical value of 29.6% (for 1a and 1b) and 40.5% (for 2), calculated as following: [(12ML-24MSe)/(MPt+20MAg+12ML)]*100% (where M represents the mass and L the ligands). The experimental weight loss of 3 (35.8%) matches well with the theoretical value (36.4%).

DFT calculations at the BP86/Def2TZVP level (see Computational Details) were performed on simplified models of 1-2, and 3, respectively, namely [PtAg20{Se2PH2)2}12] (1') and [PtAg20{S2PH2)2}12] (3'). Two geometries were considered, the T arrangement of 1-2 and the C1 structure of 3. 10f In the case of 1', the T geometry was found more stable than the C1 one by 5.5 kcal/mol, whereas in the case of 3' the C1 structure was found the most stable by 1.9 kcal/mol . Although small, these free energy differences are in line with the reported X-ray structures. The optimized T structure of 1' matches with the X-ray structures of 1-2 (Table S1), with slightly larger bond distances, as expected at this level of theory. The metal atomic charges computed within the natural atomic orbital (NAO) formalism are -1.18 (Pt), +0.23 (Agico) and +0.56 (Agcap). The corresponding averaged values of the dithiolato relative 3' are similar: -1.19 (Pt), +0.25 (Agico) and +0.62 (Agcap). That of the homometallic relative [Ag21{Se2PH2)2}12] + of T symmetry are: -0.4 (Agcenter), +0.21 (Agico) and +0.57 (Agcap). The Wiberg bond indices (Table S1) indicate strong, intermediate and weak bonding for the Pt-Agico, Agico-Agico and Agico-Agcap contacts, respectively. These results are consistent with the classical view 10f,14,16 of an 8-electron superatomic icosahedral [PtAg12] 4+ core stabilized by 8 Ag(I) cations in a trigonal planar AgE3 (E = S, Se) coordination mode and twelve formally anionic dsep/dtp ligands. The negative charge on Pt is consistent with the usual polarization of the superatomic 1S level on the icosahedron center. The Kohn-Sham diagram of 1' (T symmetry) is shown in Fig. 4. It exhibits a substantial HOMO-LUMO gap of 1.82 eV, separating the 1P superatomic orbitals from the t component of the 1D set. Indeed, within the T symmetry of the molecule, the 1D level splits into one t and one e components, the latter lying above the former. An Ag-Se antibonding level of a symmetry is intercalated between the two 1D components. The TD-DFT-simulated UV-vis spectrum is shown in Fig. S25. The two bands it displays at 454 and 374 nm are in a very good agreement with their experimental counterparts reported for 1-2 (Table 1). That of lowest energy corresponds to a HOMO→LUMO, i.e. 1P→1D(t), transition and the other one is of dominant 1P→1D(e) character. Therefore, both transitions are associated with significant intraicosahedral Pt→Ag transfer. Replacing Se by S in 1' and keeping the T high symmetry of the cluster induces a blue shift of the two low-energy bands to 430 and 357 nm, respectively, as the result of a larger HOMO-LUMO gap. In the less symmetrical 3', these two bands are merged into a single one peaking at 408 nm, 10f in a pretty good agreement with experiment. The TDDFT-computed CD spectrum of one of the 1' enantiomers is also shown in Fig. S25. Its major features match with the two computed absorption bands and therefore are of important MMCT nature. The minor but non-negligible contribution of the chiral ligand shell to the 1P and 1D orbitals (24-33%) is likely to play a significant role in the CD behaviour.

Conclusions

In summary, the first examples of platinum/silver bimetallic alloy NCs protected by Se-donor ligands are reported, with full characterization by single-crystal X-ray diffraction, NMR, ESI-MS, and theoretical analysis. The symmetry of the metal framework is modified from C2 to Th upon changing the passivated shell from S to Se. The Se-passivated PtAg20 NCs are very stable in dark for more than one week, but are highly sensitive to light. Overall, this work demonstrates that the ligand-exchange strategy indeed provides a convenient protocol to synthesize Se-protected alloy NCs, which are difficult to make. Hopefully, such reactions accompanied with structural transformations will soon provide a new insight in the development of structural isomers in the nanoscience domain.

Fig. 1

 1 Fig. 1 (a) Total structure of 1a with n-propoxy groups omitted for clarity. (b) A PtAg12 kernel embedded in a cube made of eight capping Ag atoms. (c) Twelve bridging dsep/dsepi ligands located on each side of the Ag8 cube. (d) The packing diagram of 1a displays one enantiomer in the unit cell (chiral space group P21). Color code: dark red, Pt or PtAg12; violet, Agico; green, Agcap; blue, Se; orange, P.The 31 P{ 1 H} NMR spectrum of 1a displays one chemical shift centered at 74.1 ppm and flanked by two sets of selenium satellites (ca. 609 and 709 Hz in Fig.2a). The data suggest that the as-synthesized NC is highly symmetric in solution and that the two selenium atoms bound to the same phosphorus are not only chemically, but also magnetically inequivalent, a reflection on their binding modes (µ3: η 2 , η 1 in Fig.2b) derived from the X-ray structure (vide supra). Similar results are found for 1b and 2 (1b: 67.3 ppm, 1 JPSe = 597, 711 Hz; 2: 31.9 ppm 454, 554 Hz; Fig.S4-S5). The positive-ion ESI mass spectrum of 1a displays one intense band at m/z 6145.3, corresponding to the adduct ion [1a + Ag + ] + (calc. m/z: 6145.1 in Fig.2c). In the ESI-MS spectrum of 2, there is one ion peak at 3625.6 corresponding to [2 + Ag + + H + ] 2+ (calc. m/z: 3626.7 in Fig.S6). All simulated isotopic patterns match well with the experimental observations.

Fig. 2

 2 Fig. 2 (a) 31 P{ 1 H} NMR spectrum of 1a. (b) The coordination mode (µ3: η 2 , η 1 ) of a dsep/dsepi ligand. (c) Positive-mode ESI mass spectrum of 1a. Insets: experimental (black) and simulated (red) isotopic distribution of the ion peak [1a + Ag + ] + .The UV-vis spectrum of 1a shows two bands centred at 388 and 466 nm (391, 470 nm for 1b; 414, 484 nm for 2) with the larger molar absorptivity observed at the former band (Table1). Compared with 3, the absorption bands in 1a are red-shifted (Fig.3). The stability of PtAg20(dsep)12 in solution was further explored by time-dependent absorption spectroscopy (Fig.S7-S9). Compounds 1a, 1b and 2 exhibit high stability in 2-MeTHF solution under the strict exclusion of light. The two major absorption bands retain both their shape and intensity over the course of one week when the solution is kept in dark. Under light, decomposition starts within 30 minutes, and the intensity of the two absorption bands decreases with time. Eventually, the absorption bands are barely discernable after two hours, indicating that three the PtAg20 NCs are highly sensitive to visible light.

Fig. 3

 3 Fig. 3 Normalized absorption (dash line) and emission spectra (solid line) of [PtAg20{S2P(O n Pr)2}12] (3, blue curve) and [PtAg20{Se2P(O n Pr)2}12] (1a, red curve).

Fig. 4

 4 Fig. 4 Kohn-Sham MO diagram of 1' with atom participations (%) indicated as follows: Pt/Agico)12/(Agcap)8/(ligand)12

Table 1 .

 1 Photophysical data of 1a, 1b, 2, and 3.

	Comp.	State	Absorbance λabs (nm), ε (M -1 cm -1 )	Excitation λex (nm)	Emission λem (nm)	Stokes Shift (cm -1 )	Life time τ (μs)
	1a	2-Me-THF (298 K) 2-Me-THF (77 K)	388 (35600), 466 (25600) --	--375, 388, 460	--624	5400	--8.5
	1b	2-Me-THF (298 K) 2-Me-THF (77 K)	391 (35900). 470 (28700) --	--374, 390, 463	--621	5200	--8.4
	2	2-Me-THF (298 K) 2-Me-THF (77 K)	414 (36000), 484 (31700) --	414, 485 407, 477	663 629	5600 4800	0.8 12.3
		2-Me-THF (298 K)	369 (43000), 412 (60600)	--	--		--
	3	2-Me-THF (77 K)	--	363, 389, 448	677	9500	18.3

Acknowledgements

This work was supported by the Ministry of Science and Technology of Taiwan (MOST 109-2113-M-259-008; 108-2923-M-259-001), the France-Taiwan ANR-MOST 2018 program (project Nanoalloys) and the GENCI French national computer resource center (grant A0030807367).

Conflicts of interest

There are no conflicts to declare.

ACCEPTED MANUSCRIPT

Table of Content

First 8-e, Pt-Ag superatoms protected by Se-donor ligands were synthesized by ligand replacement reaction on its sulfur analogue. The metal framework of PtAg20 changes from low symmetry (C2) to high symmetry (Th) via rearrangement of five capping silver atoms.


Jitao Xu
email: jitao.xu@limsi.fr

François Yvon
email: francois.yvon@limsi.fr

One Source, Two Targets: Challenges and Rewards of Dual Decoding

Machine translation is generally understood as generating one target text from an input source document. In this paper, we consider a stronger requirement: to jointly generate two texts so that each output side effectively depends on the other. As we discuss, such a device serves several practical purposes, from multi-target machine translation to the generation of controlled variations of the target text. We present an analysis of possible implementations of dual decoding, and experiment with four applications. Viewing the problem from multiple angles allows us to better highlight the challenges of dual decoding and to also thoroughly analyze the benefits of generating matched, rather than independent, translations.

Introduction

Neural Machine Translation (NMT) is progressing at a rapid pace. Since the introduction of the first encoder-decoder architecture [START_REF] Sutskever | Sequence to sequence learning with neural networks[END_REF][START_REF] Cho | On the properties of neural machine translation: Encoder-decoder approaches[END_REF], then completed with an attention mechanism [START_REF] Bahdanau | Neural machine translation by jointly learning to align and translate[END_REF][START_REF] Vaswani | Attention is all you need[END_REF], the performance of NMT systems is now good enough for a growing number of services, both for the general public and the translation industry. Not only are neural translation systems better, they are also more versatile and have been extended in many ways to meet new application demands. This is notably the case with multilingual extensions [START_REF] Firat | Multi-way, multilingual neural machine translation with a shared attention mechanism[END_REF][START_REF] Ha | Toward multilingual neural machine translation with universal encoder and decoder[END_REF][START_REF] Johnson | Google's multilingual neural machine translation system: Enabling zero-shot translation[END_REF], which aim to develop systems capable of processing multiple translation directions with one single model.

Another common situation for MT applications is the multi-source / multi-target scenario, where source documents in language S l need to be published in several target languages T 1 l , T 2 l , . . . . This is, for instance, what happens in multilingual institutions, or with crowdsourced translations of TV shows. The multi-source way [START_REF] Franz | Statistical multi-source translation[END_REF] f I could do that again if you want . e1 只要 你 愿意 我 可以 重复 一遍 。 e2 もう 一 回 やり ましょ う か e1 Je peux le refaire si vous le voulez . e2 . voulez le vous si refaire le peux Je e1 Ich kann das noch mal machen , wenn Sie wollen . e2 Ich kann das noch mal machen , wenn du willst .

Table 1: Instances of Dual Decoding: multi-target translation ( § 3), bi-directional decoding ( § 4), variant generation ( § 6). [START_REF] Schwartz | Multi-source translation methods[END_REF][START_REF] Zoph | Multi-source neural translation[END_REF] to handle this generates a first translation into target language T 1 l , which, once revised, can be used in conjunction with the original source to generate the translation into language T 2 l . The expected benefit of this approach is to facilitate word disambiguation.

An alternative, that we thoroughly explore here, is to simultaneously generate translations in T 1 l and T 2 l , an approach termed multi-target translation by [START_REF] Neubig | Multi-target machine translation with multisynchronous context-free grammars[END_REF]. While the same goal is achieved with a multilingual system translating independently in T 1 l and T 2 l , several pay-offs are expected from a joint decoding: (a) improved disambiguation capacities (as for multi-source systems); (b) a better collaboration between the stronger and the weaker decoders; (c) more consistent translations in T 1 l and T 2 l than if they were performed independently. As it turns out, a dual decoder computing joint translations can be used for several other purposes, that we also consider: to simultaneously decode in two directions, providing a new implementation of the idea of [START_REF] Watanabe | Bidirectional decoding for statistical machine translation[END_REF]; [START_REF] Finch | Bidirectional phrase-based statistical machine translation[END_REF]; to disentangle mixed language (code-switched) texts into their two languages [START_REF] Xu | Can you traducir this? Machine translation for code-switched input[END_REF]; finally, to generate coherent translation alternatives, an idea we use to compute polite and impolite variants of the same input (Sennrich et al., 2016a). Considering multiple applications allows us to assess the challenges and rewards of dual decoding under various angles and to better evaluate the actual agreement between the two decoders' outputs. Our main contributions are the following: (i) a comparative study of architectures for dual decoding ( § 2); (ii) four short experimental studies where we use these architectures to simultaneously generate several outputs from one input ( § 3- § 6); (iii) practical remedies to the shortage of multi-parallel corpora that are necessary to implement multi-target decoding; (iv) concrete solutions to mitigate exposure bias between two decoders; (v) quantitative evaluations of the increased consistency incurred by a tight interaction between decoders. An additional empirical finding that is of practical value is the benefits of exploiting multi-parallel corpora to finetune multilingual systems.

2 Architectures for Dual Decoding

Model and Notations

In our setting, we consider the simultaneous translation of sentence f in source language S l into two target sentences e1 and e2 in languages 1 T 1 l and T 2 l . In this situation, various modeling choices can be entertained [START_REF] Le | Dualdecoder transformer for joint automatic speech recognition and multilingual speech translation[END_REF]:

P (e
where T = max(|e 1 |, |e 2 |), and we use placeholders whenever necessary. The factorization in Equation (1) implies a joint event space for the two languages and a computational cost we deemed unreasonable. We instead resorted to the second (dual) formulation, that we contrasted with the third one (independent generation) in our experiments. Note that thanks to asynchronous decoding, introduced in Section 2.4.2, we are also in a position to simulate other dependency patterns, where each symbol e 2 t is generated conditioned on e 1 <t+k , e 2 <t , thus reproducing the chained model of [START_REF] Le | Dualdecoder transformer for joint automatic speech recognition and multilingual speech translation[END_REF].

Attention Mechanism

Our dual decoder model implements the encoderdecoder architecture of the Transformer model of [START_REF] Vaswani | Attention is all you need[END_REF]. In this model, the input to each attention head consists of queries Q, keyvalue pairs K and V. Each head maps a query and a set of key-value pairs to an output, computed as a weighted sum of the values, where weights are based on a compatibility assessment between query and keys, according to (in matrix notations):

Attention(Q, K, V) = softmax( QK T √ d k )V, (4)
with d k the shared dimension of queries and keys. Note that Q, K and V for each head are obtained by linearly transforming the hidden states from previous layer with different projection matrices.

Proposal for a Dual Decoder

We chose to implement Equation ( 2) with a synchronous coupling of two decoders sharing the same encoder. An alternative would be to have the two decoders share the bottom layers and only specialize at the upper layer(s) for one specific language: we did not explore this idea further, as it seemed less appropriate for the variety of applications considered. Figure 1 illustrates this design. Compared to a standard Transformer, we add a cross attention layer in each decoder block to capture the interaction between the two decoders.

Denoting the output hidden states of the previous layer for each decoder as H 1 l and H 2 l , the decoder cross-attention is computed as: 2

H 1 l+1 = Attention(H 1 l , H 2 l , H 2 l ) H 2 l+1 = Attention(H 2 l , H 1 l , H 1 l ), (5)
where Attention is defined in Equation ( 4). The two decoders are thus fully synchronous as each requires the hidden states of the other in each block to compute its own hidden states. The decoder crossattention can be inserted before or after the encoderdecoder attention. Preliminary experiments with these variants have shown that they were performing similarly. We thus only report results obtained with the decoder cross-attention as the last attention component of a block (see Figure 1).

Synchronous Beam Search

Full Synchronous Mode

Our decoding algorithm uses a dual beam search.

Assuming each decoder uses its own beam of size k, the cross-attention between decoders can be designed and implemented in multiple ways: for instance, one could have each hypothesis in decoder 1 attend to any hypotheses in decoder 2, which would however create an exponential blowup of the search space. Following [START_REF] Zhou | Synchronous bidirectional neural machine translation[END_REF], we only compute the attention between 1best candidates of each decoder, 2-best candidates in each decoder, etc. This heuristic ensures that the number of candidates in each decoder beam remains fixed. There is however an added complexity, due to the fact that the ranking of hypotheses in each decoder beam evolves over time: the best hypothesis in decoder 1 at time t may no longer be the best at time t + 1. Preserving the consistency of the decoder states therefore implies to recompute the entire prefix representation for each hypothesis and each decoder at each time step, thus creating a significant computing overhead. 3We also explored other implementations where each candidate prefix in one beam always attends to the best candidate in the other beam or attends to the average of all candidates. These variants ended up delivering very similar results, as also found in [START_REF] Zhou | Synchronous bidirectional neural machine translation[END_REF]. For simplicity reasons, we use the first scheme in all experiments.

Relaxing Synchronicity

Simultaneously generating symbols in two languages is a very strong requirement, and may not bring out all the benefits of dual decoding, especially when the two target languages have different word orders. We relax this assumption by allowing one decoder to start generating symbols before the other: this is implemented by having the delayed decoder generate dummy symbols for a fixed number of steps before generating meaningful words, a strategy akin to the wait-k approach in spoken language translation [START_REF] Elbayad | Efficient Wait-k Models for Simultaneous Machine Translation[END_REF].

A more extreme case of delayed processing is when one decoder can access a complete translation in the other language. In our implementation, this is simulated with partial forced decoding, where one translation is predefined, while the other is computed. We explored this in two settings: (a) within a two-pass, sequential procedure, where the output of step 1 for decoder 1 is fully known and fixed when computing the second output of decoder 2; (b) using a reference translation in one of the decoder, implementing a controlled decoding where the output in T 2 l not only translates the source, but does so in a way that is consistent with the reference translation in T 1 l . These strategies are used in Sections 3 and 6.

Training and Fine-tuning

Training this model requires triplets of instances comprising one source and two targets. Given a set of such examples D = {(f , e 1 , e 2 ) i , i = 1 . . . N }, the training maximizes the combined loglikelihood for the two target sequences:

L(θ) = D ( |e 1 | t=1 log P (e 1 t |e 1 <t , e 2 <t , f , θ) + |e 2 | t=1 log P (e 2 t |e 2 <t , e 1 <t , f , θ)), (6)
where θ represents the set of parameters.

As multi-parallel corpora are not as common as bilingual ones, we also considered a two-step procedure which combines bilingual and trilingual data. In a first step, we train a standard multilingual model (one monolingual encoder, one bilingual decoder), where tags are used to select the target language [START_REF] Johnson | Google's multilingual neural machine translation system: Enabling zero-shot translation[END_REF]. This only requires bilingual data {(f , e 1 ) i , i = 1 . . . N 1 } and {(f , e 2 ) j , j = 1 . . . N 2 }. We then initialize the dual decoder model with pre-trained parameters and fine-tune with the trilingual dataset. Both decoders thus start with the same pre-trained decoder. The decoder cross-attention matrices cannot benefit from pre-training and are initialized randomly. During fine-tuning, tags are no longer necessary as both target translations are required.

3 Multi-target Machine Translation

Data

We first evaluate our dual decoder model on the multi-target MT task for three directions: English to German/French (En→De/Fr), German to English/French (De→En/Fr) and English to Chinese/Japanese (En→Zh/Ja). Similarly to [START_REF] Wang | Synchronously generating two languages with interactive decoding[END_REF][START_REF] Hao He | Synchronous interactive decoding for multilingual neural machine translation[END_REF], we use the IWSLT17 dataset4 [START_REF] Cettolo | WIT3: Web inventory of transcribed and translated talks[END_REF] as our main test bed.5 Pre-training experiments additionally use WMT20 De-En, De-Fr, En-Zh, En-Ja and WMT14 En-Fr bilingual datasets. 6 We use the IWSLT tst2012 and tst2013 as development sets and test our model on tst2014.7 Table 2 For WMT data, we discard sentence pairs with invalid language tag as computed by fasttext language identification model8 [START_REF] Bojanowski | Enriching word vectors with subword information[END_REF]. We tokenize all English, German and French data using Moses tokenizer.9 Chinese and Japanese sentences are segmented using jieba10 and mecab11 respectively. For En→De/Fr and De→En/Fr, we use a shared source-target vocabulary built with 40K Byte Pair Encoding (BPE) units (Sennrich et al., 2016b) learned on WMT data with subword-nmt.12 For En→Zh/Ja, we build a 32K BPE model for En and a joint 32K BPE for Zh and Ja, both learned on the WMT data.13

Experimental Settings

We implement the dual decoder model using fairseq14 [START_REF] Ott | fairseq: A fast, extensible toolkit for sequence modeling[END_REF], 15 with a hidden size of 512 and a feedforward size of 2048. We optimize with Adam, set up with a maximum learning rate of 0.0007 and an inverse square root decay schedule, as well as 4000 warmup steps. For finetuning models, we use Adam with a fixed learning rate of 8e-5. For standard Transformer models, we share the decoder input and output matrices, while for dual decoder models, we share all four input and output decoder matrices [START_REF] Press | Using the output embedding to improve language models[END_REF][START_REF] Inan | Tying word vectors and word classifiers: A loss framework for language modeling[END_REF]. All models are trained with mixed precision and a batch size of 8192 tokens on 4 V100 GPUs. Pre-training last for 300k iterations, while all other models are trained until no improvement is found for 4 consecutive checkpoints on the development set. Performance is computed with SacreBLEU [START_REF] Post | A call for clarity in reporting BLEU scores[END_REF].

We call the dual decoder models dual. To study the effectiveness of dual decoding, we also train a simplified multi-task model [START_REF] Dong | Multi-task learning for multiple language translation[END_REF], implementing the independent model of Equation (3) without decoder cross-attention. For indep, the only interaction between outputs is thus a shared loss computed on multi-parallel data. Baseline Transformer models trained separately on each language pair are denoted by base.

Results

We evaluate the performance of models trained only with trilingual data, as well as models pre-trained in a multilingual way. Table 3 shows that the indep model outperforms the base model in all directions, demonstrating the benefits of jointly training two independent decoders. The same gain is not observed for the dual model, for which results in some directions are even worse than the baseline. Table 3: BLEU and similarity scores of multi-target models. Similarity scores (SIM) are computed as the crosslingual similarity between the two target translations. Pseudo (ps) refers to models trained from scratch with synthetic reference data. FT indicates models fine-tuned from the pre-trained multilingual (multi) model. FT+ps refers to models fine-tuned using synthetic reference data.

One explanation is that dual decoding suffers from a double exposure bias, as errors in both decoders jointly contribute to derail the decoding process.

We get back to this issue in Section 4.

To test this, we use the base model to translate source texts f into targets ê1 and ê2 , which are then merged with the original data to build a pseudo-trilingual training set. For fair comparison, we only use half of the translations from each target language, yielding a pseudo-trilingual dataset {(f 1/2 , ê1

1/2 , e 2 1/2 ), (f 2/2 , e 1 2/2 , ê2 2/2 )} that is as large as the original data. We see in Table 3 that these artificial translations almost close the gap between the independent and dual decoders.

Initializing with pre-trained models (Section 2.5) brings an additional improvement for both methods, thus validating our pre-training procedure (see bottom of Table 3). They confirm that dual decoders can be effectively trained, even in the absence of large multi-parallel data. These results also highlight the large gains of fine-tuning on a tri-parallel corpus, which improves our baseline multilingual models by nearly 5 BLEU points on average.

We additionally experiment fine-tuning pretrained models with the synthetic pseudo-trilingual data. This setting (FT+ps in Table 3) does not bring any gain in translation quality: for the indep model we see a small loss due to training with noisy references; for dual, it seems that mitigating exposure bias is less impactful when starting from well-trained models.

Complements and Analysis

The value of dual decoding is to ensure that translations ê1 and ê2 are more consistent than with independent decoding. To evaluate this, we compute the similarity scores (SIM) between these two translations using LASER. 16 As shown in As explained in Section 2.4, the dual decoder model is not limited to strictly synchronous generation and accommodates relaxed variants (as well as alternative dependency patterns) where one decoder can start several steps after the other. We finetune "wait-k" dual models from the pre-trained model with k = 3 for En→De/Fr and evaluate the effects on performance. As shown in Table 4, the BLEU scores are slightly improved for both targets when either side is delayed by 3 steps. These results suggest that depending on language pairs, the information flow between decoders can be benefi- cial from a small amount of asynchronicity.

Our implementation also enables to have one decoder finish before the other begins. We thus experiment a sequential decoding strategy (see Section 2.4.2), in which we first compute the complete translation in one target language (with the dual model), then decode the other one. In this case, the second decoding step has access to both the source and the other target sequence. This decoding strategy does not require any additional training and is applied directly during inference.

We decode both dual and dual FT models with this strategy. Results in Table 4, obtained with both automatic and reference translations in one language, show that this technique is able to improve the dual model on both French and German translations, while only slightly improves the French translation for the dual FT model. Sequential decoding with reference in one language provides the other decoder with the ground truth, which therefore alleviates the exposure bias problem suffered by dual models. However, combining results of FT models in Table 3 and4, we see that fine-tuned models are less sensitive to errors made during decoding. This again shows the benefit that dual models actually obtain from pretrained models.

Bi-directional MT

Bi-directional MT [START_REF] Finch | Bidirectional phrase-based statistical machine translation[END_REF][START_REF] Zhou | Synchronous bidirectional neural machine translation[END_REF]Liu et al., 2020b) aims to integrate future information in the decoder by jointly translating in the forward (left to right, L2R) and in the backward (right to left, R2L) directions. Another expectation is that the two decoders, having different views of the source, will deliver complementary translations. Dual decoding readily applies in this setting, with one decoder for each direction, with the added benefit of generating more coherent outputs than independent decoders. We evaluate this added consistency by reusing the experimental setting (data, implementation and hyperparameters) of Section 3, and by training 4 bi-directional systems, from English into German, French, Chinese and Japanese. Similar to [START_REF] Zhou | Synchronous bidirectional neural machine translation[END_REF], we output the translation with the highest probability, inverting the translation if the R2L output is picked.

We first train models on tri-parallel corpora obtained by adding an inverted version of the target sentence to each training sample. In this setting, the dual model again suffers a clear drop of BLEU scores as compared to indep model (Table 5). We again attribute this loss to the impact of the exposure bias, as can be seen in Table 5, where the loss in BLEU score of the dual system is accompanied by a very large increase in consistency of the outputs (+31.1). We therefore again introduce pseudo-parallel targets, where one of the two targets is automatically generated with the base model. This was also proposed in [START_REF] Zhou | Synchronous bidirectional neural machine translation[END_REF][START_REF] Wang | Synchronously generating two languages with interactive decoding[END_REF][START_REF] Zhang | Synchronous bidirectional inference for neural sequence generation[END_REF][START_REF] Hao He | Synchronous interactive decoding for multilingual neural machine translation[END_REF]. Similar to the pseudo-data described in Section 3.3, we generate a pseudo dataset in which each original source sentence occurs just once. This means that the forward and backward training target sentences are not always deterministically related, which forces each decoder to put less trust on tokens from the other direction. We also consider the pseudo-dup data, in which each source sentence is duplicated, occurring once with the reference data in each direction. Results in Table 5 show that this method again closes the gap between indep and dual, and yields systems that surpass the baseline by about 1 BLEU point in the pseudo setting, and by 1.5 BLEU point in the pseudo-dup setting.

By computing the BLEU score between the two output translations, we can also evaluate the increment of consistency incurred in dual decoding. These scores are reported in Table 5 (column Cons) and show to a +13.4 BLEU increment when averaged over language pairs, thereby demonstrating the positive impact of dual decoding.

MT for Code-switched Inputs

In this section, we turn to a novel task, consisting in translating a code-switched (CSW) sentence (containing fragments from two languages) simultaneously into its two components. An example in Table 6 for French-English borrowed from [START_REF] Carpuat | Mixed language and codeswitching in the Canadian hansard[END_REF] illustrates this task. Code-switching is an important phenomenon in informal communications between bilingual speakers. It generally consists of short inserts of a secondary language which are embedded within larger fragments in the primary language. When simultaneously translating into these two languages, we expect the following "copy" constraint to be satisfied: every word in the source text should appear in at least one of the two outputs.

Our main interest in this experiment is to assess how much dual decoding actually enforces this constraint. As tri-parallel corpora for this task are scarce [START_REF] Menacer | Machine Translation on a parallel Code-Switched Corpus[END_REF], we mostly follow [START_REF] Song | Code-switching for enhancing NMT with pre-specified translation[END_REF][START_REF] Xu | Can you traducir this? Machine translation for code-switched input[END_REF] and automatically generate artificial CSW sentences from regular parallel data. Working with the En-Fr pair, we use the WMT14 En-Fr data to generate training data as well as a CSW version of the newstest2014 test set. Approximately half of the test sentences are mostly English with inserts in French, and mostly French with inserts in English for the other half. We use the same pretraining procedure as in Section 3.2 and evaluate with csw-newstest2014 data.

Table 7 reports overall BLEU scores, as well as scores for the 'primary and 'secondary' part of the test set for each target language. These results show that indep and dual systems, which are both able to translate French mixed with English and English mixed with French, achieve performance that is comparable to the base model, which, in this experiment, is made of two distinct Transformer models, one for each direction.

We also measure how well the constraint expressed above is satisfied. It stipulates that every Table 7: BLEU scores of CSW translation models tested on the csw-newstest2014 data that we generated. Small numbers are scores computed separately on the two parts of the test set where the target language is primary or secondary (second).

token in a CSW sentence should be either copied in one language (and translated into the other), or copied in both, which mostly happens for punctuations, numbers or proper names. Our analysis in Table 8 shows that the base model is more likely to reproduce the patterns observed in the reference, notably is less likely to generate two copies for a token than the other systems. However, indep and, to a larger extend, dual, are able to reduce the rate of lost tokens, i.e. of source tokens that are not found in any output. This again shows that the interaction between the two decoders helps to increase the consistency between the two outputs. 8: Analysis of the "copy" constraint. "Exclusive" refers to the percentage of test tokens appearing in only one hypothesis. "Both" and "Punctuations" are for tokens and punctuations+digits appearing in both hypotheses, and "Lost" is for tokens not found in either.

Generating Translation Variants

As a last application of dual decoding, we study the generation of pairs of consistent translation alternatives, using variation in "politeness" as our test bed. We borrow the experimental setting and data of Sennrich et al. (2016a). 17 The training set contains 5.58M sentences pairs, out of which 0.48M are annotated as polite and 1.06M as impolite. The rest is deemed neutral. 18 Using this data, we generate triparallel data as follows. We first train a tag-based NMT with politeness control as in Sennrich et al. (2016a) and use it to predict the polite counterpart of each impolite sentence, and vice-versa. We also include an equivalent number of randomly chosen neutral sentences: for these, the polite and impolite versions are identical. The resulting 3-way corpus contains 3.07M sentences. Similar to the multitarget task (Section 3), we fine-tune a pre-trained model with this data until convergence. We use the test data of Sennrich et al. (2016a) as development set and test our model on the testyou set, which contains 2k sentences with a second-person pronoun you(r(s(elf))) in the English source. The annotation tool distributed with the data is used to assess the politeness of the output translations. Table 9 (top) reports the performance of the pretrained model. ref refers to the annotation result of the reference German sentences. none is translated without adding any tags to the source text, while pol and imp are translated with all sentences tagged respectively as polite and impolite. The oracle line is obtained by prefixing each source sentence with the correct tag. These results show the effectiveness of side constraints for the generation of variants: for both polite and impolite categories, the pretrained model generates translations that mostly satisfy the desired requirement. Results of the fine-tuned dual decoder models are in Table 9 (bottom): we see that both models are very close and generate more neutral translations and also slightly improve the BLEU scores compared to the pre-trained model.

As discussed in Section 2.4.2, our dual decoder model can delay one decoder until the other is finished. We redo the same sequential decoding procedure as in Section 3.4. Results in Table 9 (bottom) indicate that given the full translation of impolite variations, the dual model tends to generate less neutral sentences but more polite ones. The same phenomenon is also observed in the other direction. This implies that the output variations can be better controlled with sequential decoding.

Related Work

The variety of applications considered here makes it difficult to give a thorough analysis of all the related work, and we only mention the most significant landmarks.

Multi-source / Multi-target Machine Translation Multi-source MT was studied in the framework of SMT, considering with a tight integration (in the decoder), or a late integration (by combining multiple hypotheses obtained with different sources). This idea was revisited in the Neural framework [START_REF] Zoph | Multi-source neural translation[END_REF]Liu et al., 2020a). Setting multilingual MT aside [START_REF] Dabre | A survey of multilingual neural machine translation[END_REF], studies of the multi-target case are comparatively rarer [START_REF] Neubig | Multi-target machine translation with multisynchronous context-free grammars[END_REF]. Notable references are [START_REF] Dong | Multi-task learning for multiple language translation[END_REF], which introduces a multi-task framework; [START_REF] Wang | Three strategies to improve one-to-many multilingual translation[END_REF], which studies ways to strengthen a basic multilingual decoder; while closer to our work, [START_REF] Wang | Synchronously generating two languages with interactive decoding[END_REF] consider a dual decoder relying on dual self-attention mechanism. Related techniques have also been used to simultaneously generate a transcript and a translation for a spoken input [START_REF] Anastasopoulos | Tied multitask learning for neural speech translation[END_REF][START_REF] Le | Dualdecoder transformer for joint automatic speech recognition and multilingual speech translation[END_REF] and to generate consistent caption and subtitle for an audio source [START_REF] Karakanta | Between flexibility and consistency: Joint generation of captions and subtitles[END_REF].

Bi-directional Decoding is an old idea from the statistical MT era [START_REF] Watanabe | Bidirectional decoding for statistical machine translation[END_REF][START_REF] Finch | Bidirectional phrase-based statistical machine translation[END_REF]. Instantiations of these techniques for NMT are in [START_REF] Zhang | Asynchronous bidirectional decoding for neural machine translation[END_REF][START_REF] Su | Exploiting reverse target-side contexts for neural machine translation via asynchronous bidirectional decoding[END_REF], where asynchronous search techniques are considered; and in [START_REF] Zhou | Synchronous bidirectional neural machine translation[END_REF][START_REF] Wang | Synchronously generating two languages with interactive decoding[END_REF][START_REF] Zhang | Synchronous bidirectional inference for neural sequence generation[END_REF] where, similar to our work, various ways to enforce a tighter interaction between directions are considered in synchronous search, while Liu et al. (2020b) also study ways to increase the agreement between L2R and R2L directions. More recently, [START_REF] Hao He | Synchronous interactive decoding for multilingual neural machine translation[END_REF] combines multi-target and bi-directional decoding within a single architecture, where, in each layer and block, all cross-attentions are combined with a single hidden state; four softmax layers are used for the output symbols in a proposal that creates an even stronger dependency between decoders than what we consider here.

Code-switching is an important linguistic phenomenon in bilingual communities that is getting momentum within the natural language processing communities [START_REF] Sitaram | A survey of code-switched speech and language processing[END_REF]. Several tasks have been considered: token-level language identification [START_REF] Samih | Multilingual code-switching identification via LSTM recurrent neural networks[END_REF]), Language Modeling (Winata et al., 2019), Named Entity Recognition [START_REF] Aguilar | Named entity recognition on code-switched data: Overview of the CALCS 2018 shared task[END_REF], Part-of-Speech tagging [START_REF] Ball | Part-of-speech tagging for code-switched, transliterated texts without explicit language identification[END_REF] and Sentiment Analysis [START_REF] Patwa | SemEval-2020 task 9: Overview of sentiment analysis of code-mixed tweets[END_REF]. Machine Translation for CSW texts is considered in [START_REF] Menacer | Machine Translation on a parallel Code-Switched Corpus[END_REF].

Conclusion and Future Work

In this paper, we have explored various possible implementations of dual decoding, as a way to generate pairs of consistent translation. Dual decoding can be viewed as a tight form of multi-task learning, and, as we have seen, can be effectively trained using actual or partly artificial data; it can also directly benefit from pre-trained models. Considering four applications of MT, we have observed that dual decoding was prone to exposure bias in the two decoders, and we have proposed practical remedies. Using these, we have achieved BLEU scores that match those of a simple multi-task learners, and display an increased level of consistency.

In our future work, we plan to consider other strategies, such as scheduled sampling [START_REF] Bengio | Scheduled sampling for sequence prediction with recurrent neural networks[END_REF][START_REF] Mihaylova | Scheduled sampling for transformers[END_REF], to mitigate the exposure bias. Another area where we seek to improve is the relaxation of strict synchronicity in decoding. We finally wish to study more applications of this technique, notably to generate controlled variation: controlling gender variation [START_REF] Zmigrod | Counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology[END_REF] or more complex form of formality levels, as in [START_REF] Niu | Controlling neural machine translation formality with synthetic supervision[END_REF], are obvious candidates.

A Details of Data for Multi-target and Bi-directional Machine Translation

We use the IWSLT17 dataset as training data.

We use IWSLT17.TED.tst2012 and IWSLT17.TED.tst2013 as development set and test our model on IWSLT17.TED.tst2014 and IWSLT17.TED.tst2015. The original data is not entirely multi-parallel. Therefore, we extract the shared English sentences from En-De and En-Fr data with the corresponding translation to build a truly trilingual corpus. The En→Zh/Ja trilingual data is built similarly. We use WMT20 De-En, De-Fr, En-Zh, En-Ja and WMT14 En-Fr bilingual data for our pretraining experiments. For De-En, De-Fr and En-Fr, we discard the ParaCrawl data and use all the rest. For En-Zh, we only use News Commentary, Wiki Titles, CCMT corpus and WikiMatrix data. For En-Ja, we use all data except ParaCrawl and TED talks. The latter is our trilingual data that we do not use in our pre-training stage. For all WMT data, we discard sentence pairs with invalid language tag as computed by fasttext language identification model 19 [START_REF] Bojanowski | Enriching word vectors with subword information[END_REF]. Detailed statistics for the WMT data that we have actually used for each language pair are in Table 11.

To generate the pseudo data, taking En→De/Fr as an example, we first train individual Transformer models for En→De and En→Fr using the trilingual data. We then use the En→De model to translate half of the English source f 1/2 into German ê1/2 and use the En→Fr model to translate the other half of the English source f 2/2 into French ê2/2 , thus obtaining the pseudo-trilingual dataset {(f 1/2 , ê1 1/2 , e 2 1/2 ), (f 2/2 , e 1 2/2 , ê2 2/2 )} that is as large as the original data. Pseudo datasets for De→En/Fr and En→Zh/Ja are generated similarly.

For Bi-directional translation, we reuse the same trilingual data as described above. Pseudo data is generated by first training individual En→De L2R and En→De R2L Transformer models; we then follow the same procedure as above. The En→De R2L system is trained on En-De trilingual data with the German reference simply inverted. Pseudo data for the other language pairs is generated similarly.

B Details of Data for Code-switched Input Translation

We use the same WMT14 En-Fr data as in previous section to generate artificial code-switched sentences. These are obtained by randomly replacing small chunks in one sentence by their translation according to the following procedure. We first compute word alignments between parallel sentences using fast_align 20 (Dyer et al., 2013) in two directions, then apply a standard symmetrization procedure. Using the algorithm of [START_REF] Josep | Reordered search, and tuple unfolding for Ngram-based SMT[END_REF], we then identify bilingual phrase pairs (f , e) extracted from the symmetrized word alignments under the condition that all alignment links outgoing from words in e reach a word in f , and vice-versa.

For each pair of parallel sentence, we first randomly select the primary language; then the number of substitutions r to perform using an exponential distribution as: P (r = k) = 1 2 k+1 ∀k = 1, . . . , rep, (7) where rep is the maximum number of replacements. We also make sure that the actual number of replacements never exceed half of either the original source or target sentence length, adjusting the actual number of replacements as:

n = min( S 2 , T 2 , r), (8)
Table 12: BLEU and similarity scores of multi-target models on tst2015. Similarity scores (SIM) are computed as the cross-lingual similarity between the two target translations. Pseudo (ps) refers to models trained from scratch with synthetic reference data. FT indicates models fine-tuned from the pre-trained multilingual (multi) model. FT+ps refers to models fine-tuned using synthetic reference data.

where S and T are respectively the length of the source and target sentences. We finally choose uniformly at random r phrase pairs and replace these fragments in the primary language by their counterpart in the secondary language.

A shared vocabulary built with a joint BPE of 32K merge operations is used for CSW source as well as for English and French targets.

C Details of Data for Generating Translations of Varying Formalities

We reuse the data of Sennrich et al. (2016a). 21 The training data consists of OpenSubtitles2012 En-De data with 5.58M sentence pairs, out of which 0.48M of German reference are annotated as polite and 1.06M as impolite. The rest is deemed neutral. The annotation tool is based on the ParZu dependency parser 22 and an annotation script that is also released with the data. Polite/Impolite tags are based on an automatic analysis of the German side according to rules described in (Sennrich et al., 2016a). The test set that we use as development set is a random sample of 2000 sentences from OpenSubtitles2013. We use the testyou set as our main test set, which consists of 2000 random sentences also extracted from OpenSubtitles2013 where the English source contains a 2nd person pronoun you(r(s(elf))).

We built shared vocabulary with a joint BPE of 32K merge operations. When fine-tuning the dual decoder models, we also randomly extract an equivalent number of neutral sentences as the polite and impolite ones, i.e. 1.54M. Reference of neutral sentences is thus identical for both polite 21 http://data.statmt.org/rsennrich/ politeness/ 22 https://github.com/rsennrich/ParZu and impolite targets. The overall fine-tuning data thus comprises 3.07M sentences.

D More Results of tst2015 for Multi-target Translation

Table 12 reports results for the multi-target translation experiments of Section 3 using the IWSLT tst2015, a setting that is also used in [START_REF] Hao He | Synchronous interactive decoding for multilingual neural machine translation[END_REF].

  they are getting out of the closet e 1 : In other words, they are getting out of the closet e 2 : autrement dit, ils sortent du placard

Table 2 :

 2 summarizes the main statistics for trilingual training and test data.

		Original De Original Fr 3-way
	Train	209522	236653	205397
	Dev	2693	3083	2468
	tst2014	1305	1306	1168
		Original Zh Original Ja 3-way
	Train	235078	226834	213090
	Dev	3064	3024	2837
	tst2014	1297	1285	1214

Number of lines in the trilingual IWSLT data. English is used to identify trilingual sentences and is therefore not shown in this table.

Table 3 ,

 3 dual model generate translations that are slightly more similar on average than the indep model: as both translate the same source into the same languages, similarity scores are always quite high.

	Model	En-De En-Fr
	dual	28.5	38.8
	dual De+Fr auto	28.7	39.3
	dual De+Fr ref	28.7	39.6
	dual FT	30.0	42.3
	dual FT De wait-3	30.2	42.4
	dual FT Fr wait-3	30.4	42.6
	dual FT De+Fr auto	30.0	42.5
	dual FT De+Fr ref	30.0	42.4
	Table 4: BLEU scores for asynchronous decoding: se-
	quential decoding on the dual model trained from
	scratch (top), wait-k models fine-tuned on the pre-
	trained model and sequential decoding on the dual FT
	model (bottom) for the direction En→De/Fr. Results
	using sequential decoding for one decoder are obtained
	in a second decoding pass using either automatic (auto)
	or reference (ref) translations.

Table 5 :

 5 Results of bi-directional MT models trained with actual data (top) and synthetic data (bottom). The consistency score (Cons) is an averaged BLEU score between the forward and backward translations.

	16 https://github.com/facebookresearch/
	LASER

Table 6 :

 6 Dual decoding for a CSW sentence.

Table 9 :

 9 Results of politeness MT models. Tags are used for the pre-train model to generate the desired variant. Decoders (Dec) of indep and dual compute two translations in one decoding step, while the results using sequential decoding for one decoder are obtained with the 2-step procedure of Section 2.4.2.

	Model	Tag	neutral pol	imp BLEU
		ref	438	525 1037	-
	pre-	none	1914	16	70	17.7
	train pol	479	1518	3	20.9
		imp	22	0	1978	24.1
		oracle	551	406 1043	30.2
		Dec	neutral pol	imp BLEU
	indep pol	528	1470	2	21.0
		imp	82	0	1918	24.4
	dual	pol	541	1457	2	21.3
		imp	97	0	1903	24.3
	seq imp pol	531	1467	2	21.2
	seq pol imp	84	0	1916	24.4

Table 10 :

 10 Table 10 summarizes the statistics for the trilingual training and test data. Statistics of extracted trilingual IWSLT data. English is used to extract trilingual sentences therefore not shown in this table.

		Original De Original Fr 3-way
	Train	209522	236653	205397
	Dev	2693	3083	2468
	tst2014	1305	1306	1168
	tst2015	1080	1210	1074
		Original Zh Original Ja 3-way
	Train	235078	226834	213090
	Dev	3064	3024	2837
	tst2014	1297	1285	1214
	tst2015	1205	1194	1132

Table 11 :

 11 Statistics of WMT bilingual data used in pretraining experiments for multi-target translation.

	19 https://dl.fbaipublicfiles.com/
	fasttext/supervised-models/lid.176.bin

In our applications, these do not always correspond to actual natural languages. We keep the term for expository purposes.

For simplicity, we omit the other sub-layers (self attention, encoder-decoder cross attention, feed forward and layer normalization).

In our experiments, the dual decoder was about twice as slow as the two independent ones.

https://wit3.fbk.eu/2017-01-c

Subject to some filtering to obtain a fully multi-parallel set of sentences.

See http://statmt.org.

For comparison with (He et al., 2021), we also report the results on tst2015 in Appendix D.

https://dl.fbaipublicfiles.com/ fasttext/supervised-models/lid.176.bin

https://github.com/moses-smt/ mosesdecoder

https://github.com/fxsjy/jieba

https://taku910.github.io/mecab/

https://github.com/rsennrich/ subword-nmt

Full experimental details are in Appendix A.

https://github.com/pytorch/fairseq

Our implementation is open-sourced at https:// github.com/jitao-xu/dual-decoding.

http://data.statmt.org/rsennrich/ politeness/

See details in Appendix C.

Acknowledgements

This work was granted access to the HPC resources of IDRIS under the allocation 2021-[AD011011580R1] made by GENCI. The authors wish to thank Josep Crego for his comments and discussions. We would also like to thank the anonymous reviewers for their valuable suggestions. The first author is partly funded by Systran and by a grant from Région Ile-de-France.


Camilla Pires De Souza

Bernardo Dias

Maria Alice
email: alice@eq.ufrj.br

Zarur Coelho

Rodrigo Volcan Almeida

Jean-Marc Marc Nicaud
email: jean-marc.nicaud@inrae.fr

Construction of wild-type Yarrowia lipolytica IMUFRJ 50682 auxotrophic mutants using dual CRISPR/Cas9 strategy for novel biotechnological approaches

Keywords:

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

Introduction

Yarrowia lipolytica is one of the most widely studied non-conventional oleaginous yeasts due to its valuable and diverse biosynthetic potential among different wild-type strains of industrial relevance [START_REF] Egermeier | Metabolic flexibility of Yarrowia lipolyticagrowing on glycerol[END_REF]. It has emerged as an excellent cell factory for safe and high-level production of enzymes [START_REF] Dulermo | Using a vector pool containing variable-strength promoters to optimize protein production in Yarrowia lipolytica[END_REF], unusual fatty acids [START_REF] Park | Optimization of odd chain fatty acid production by Yarrowia lipolytica[END_REF], organic acids [START_REF] Rywińska | High-yield production of citric acid by Yarrowia lipolyticaon glycerol in repeated-batch bioreactors[END_REF], polyalcohols [START_REF] Carly | Enhancing erythritol productivity in Yarrowia lipolytica using metabolic engineering[END_REF], aromas [START_REF] Celińska | Yarrowia lipolytica: the novel and promising 2-phenylethanol producer[END_REF], carotenoids [START_REF] Larroude | A synthetic biology approach to transform Yarrowia lipolytica into a competitive biotechnological producer of β-carotene[END_REF] and bioemulsifiers [START_REF] Souza | Production of bioemulsifiers by Yarrowia lipolytica in sea water using diesel oil as the carbon source[END_REF] from different sources. Over the years, this yeast became a model in research on fungal dimorphic transition [START_REF] Martinez-Vazquez | Identification of the transcription factor Znc1p, which regulates the yeast-to-Hypha transition in the dimorphic yeast Yarrowia lipolytica[END_REF], recombinant protein secretion [START_REF] Nicaud | Protein expression and secretion in the yeast Yarrowia lipolytica[END_REF], microbial oil accumulation [START_REF] Beopoulos | Yarrowia lipolytica as a model for bio-oil production[END_REF] and hydrophobic substrates degradation [START_REF] Sabirova | The "LipoYeasts" project: Using the oleaginous yeast Yarrowia lipolytica in combination with specific bacterial genes for the bioconversion of lipids, fats and oils into high-value products[END_REF]. All these features have boosted the development of novel and efficient synthetic biology tools such as modular cloning systems [START_REF] Wong | YaliBricks, a versatile genetic toolkit for streamlined and rapid pathway engineering in Yarrowia lipolytica[END_REF][START_REF] Egermeier | Golden Gate-based metabolic engineering strategy for wild-type strains of Yarrowia lipolytica[END_REF] and CRISPR/Cas9 technology that facilitate the manipulation of Y. lipolytica genome [START_REF] Schwartz | Standardized markerless gene integration for pathway engineering in Yarrowia lipolytica[END_REF][START_REF] Holkenbrink | EasyCloneYALI: CRISPR/Cas9-based synthetic toolbox for engineering of the yeast Yarrowia lipolytica[END_REF].

In the CRISPR/Cas9 method, a single-guide RNA (sgRNA) complexed with a Cas9 nuclease can recognize a precise DNA sequence and generate a double-strand break (DSB) which is repaired by homologous recombination (HR) or non-homologous end-joining (NHEJ) mechanism [START_REF] Gao | Multiplex gene editing of the Yarrowia lipolytica genome using the CRISPR-Cas9 system[END_REF]. The repair introduces small indels resulting in frameshift mutations that can cause disturbance of gene function, incorporation of an exogenous DNA or restoration of the original allele [START_REF] Kosicki | Dynamics of indel profiles induced by various CRISPR/Cas9 delivery methods[END_REF]. To date, several strategies CRISPR/Cas9-based have been developed for genetic improvement of microbial cell factories, since industrial biotechnology needs to meet the growing demands and expand the diversity of molecules that can be produced by non-model microorganisms [START_REF] Donohoue | Advances in industrial biotechnology using CRISPR-Cas systems[END_REF]. Some wild-type strains with great advantage for specific bioproduction processes, such as Ashbya gossypii [START_REF] Jiménez | One-vector CRISPR/Cas9 genome engineering of the industrial fungus Ashbya gossypii[END_REF], Kluyveromyces marxianus [START_REF] Cernak | Engineering Kluyveromyces marxianusas a robust synthetic biology platform host[END_REF], Aspergillus niger [START_REF] Nødvig | A CRISPR-Cas9 system for genetic engineering of filamentous fungi[END_REF] and also Y. lipolytica have already been accurately engineered through CRISPR/Cas9 systems without limitations related https://doi.org/10.1016/j.enzmictec.2020.109621 Received 28 November 2019; Received in revised form 23 May 2020; Accepted 7 June 2020 to selection markers [START_REF] Larroude | A set of Yarrowia lipolyticaCRISPR/Cas9 vectors for exploiting wild-type strain diversity[END_REF].

Y. lipolytica IMUFRJ 50682 is a Brazilian wild-type strain isolated from an estuary in Guanabara Bay of Rio de Janeiro [START_REF] Hagler | Yeasts from marine and estuarine waters with different levels of pollution in the state of rio de janeiro, Brazil[END_REF] with noticeable application in bioconversion and bioremediation [START_REF] Coelho | Yarrowia lipolytica: an industrial workhorse[END_REF]. This strain displayed peculiar surface characteristics related to the hydrophobic cell wall and high adhesion to non-polar solvents [START_REF] Amaral | Cell surface characterization of Yarrowia lipolytica IMUFRJ 50682[END_REF]. Y. lipolytica IMUFRJ 50682 is also able to secrete a particular bioemulsifier, a lipid-carbohydrate-protein complex, in the presence of glucose, glycerol, n-alkanes and oils [START_REF] Amaral | Production and characterization of a bioemulsifier from Yarrowia lipolytica[END_REF][START_REF] Zarur Coelho | Factorial design to optimize biosurfactant production by Yarrowia lipolytica[END_REF]. Recently, IMUFRJ 50682 strain showed ability to consume isoprenoids and aromatic hydrocarbons as the group of naphthalenes and phenanthrene [START_REF] Martins | Evaluation of crude oil degradation by Yarrowia lipolytica[END_REF] and to be used as a low-cost biocatalyst for esterification reactions of fatty acids present in acid raw materials for biodiesel synthesis [START_REF] Da Silva | Biocatalytic esterification of fatty acids using a low-cost fermented solid from solid-state fermentation with Yarrowia lipolytica[END_REF] and generation of commercial esters [START_REF] De Souza | Characterization and application of yarrowia lipolytica lipase obtained by solid-state fermentation in the synthesis of different esters used in the food industry[END_REF]. Although the industrial value of the IMUFRJ 50682 strain is remarkable, it has been little explored due to the lack of information about its genome.

Hence, we employed a combinatorial CRISPR/Cas9-mediated cleavage strategy for construction of a non-inverting auxotrophic mutants Y. lipolytica IMUFRJ 50682 through co-transformation of dual vectors harboring Cas9 nuclease and distinct combinations of sgRNAs targeting upstream and downstream sites of URA3 gene. Further, we determined the cloning efficiency of the auxotrophic derivatives with a fluorescent phenotype, introduced β-carotene biosynthetic pathway, compared the growth profile of the carotenoid recombinant producers and wild-type strain in both rich and synthetic media as well as quantified its production. Future applications of the auxotrophic mutants IMUFRJ 50682 were also discussed.

Materials and methods

Microorganism and culture conditions

Y. lipolytica IMUFRJ 50862 was cultivated at 28 °C in rich YPD medium (1% yeast extract, 2% peptone, 2% glucose; w/v) for growth and synthetic YNB medium (0.17 % yeast nitrogen base without amino acids, 0.5 % NH 4 Cl, 2% glucose; w/v and 50 mM phosphate buffer pH 6.8) for screening of transformants. Alternatively, YPD medium was supplemented with 300 μg.mL -1 hygromycin B (YPD-Hyg) (Corning), 500 μg.mL -1 nourseothricin (YPD-Nat) (Werner BioAgents) or both antibiotics (YPD-Hyg-Nat) for selection of co-transformants and cured strains. Auxotrophic derivatives were tested in YNB medium added of 0.1 % uracil (w/v; YNB-Ura) (Sigma-Aldrich). Escherichia coli DH5α were grown at 37 °C in LB medium (1% tryptone, 0.5 % yeast extract and 1% NaCl; w/v) supplied with 100 μg.mL -1 ampicillin (Sigma-Aldrich) for cloning and propagation of recombinant plasmids. When required, solid media were prepared by adding 1.5 % agar (w/v) to liquid media. All cultures were maintained at -80 °C in glycerol stock.

Assembly of CRISPR-Cas9 vectors

Target sequences for URA3 deletion were predicted by the CRISPOR tool (http://crispor.tefor.net/) [START_REF] Haeussler | Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR[END_REF] based on Y. lipolytica W29 (Accession No. AJ306421.1). Two highest scoring 20 bp sequences targeting URA3transcriptional start site (UTSS) and transcriptional end site (UTES) with low number of predicted off-target and without protospacer adjacent motif (PAM) sequence were selected. sgRNAs were designed as complementary oligonucleotides, flanked by overhangs for plasmid assembly. All oligos were supplied by Eurofins Genomics and were listed in Supplementary File (Table S1).

CRISPR-Cas9 vectors were constructed by Golden Gate Assembly (GGA) [START_REF] Celińska | Golden Gate Assembly system dedicated to complex pathway manipulation in Yarrowia lipolytica[END_REF]. The backbone plasmids were developed prior works [START_REF] Larroude | A set of Yarrowia lipolyticaCRISPR/Cas9 vectors for exploiting wild-type strain diversity[END_REF]. Phosphorylated and annealed sgRNAs were diluted (ratio of 1:200) and then 2 μL was mixed in one-step reaction with 100 ng of acceptor plasmids, 10 U of BsmBI, 3000 U of T7 DNA ligase, 2 μL of DNA ligase buffer and ddH 2 O up to 20 μL. sgRNA-UTSS1 and sgRNA-UTSS2 were separately added to the GGA_NATex_CrisprCas9-yl_RFP reaction, whereas sgRNA-UTES1 and sgRNA-UTES2 were added in the GGA_H-PHex_CrisprCas9-yl_RFP mix. Thermal cycles were applied as follows: [55 °C for 5 min, 16 °C for 5 min] x 30, 50 °C for 5 min, 80 °C for 5 min, 15 °C ∞. The reaction mixture was used for chemically competent E. coli cells transformation and some ampicillin-resistant white colonies were screened for verification of assembled plasmids through BglII digestion. Restriction enzymes, ligases and kinases were obtained from New England Biolabs and performed in accordance with manufacturer's instructions. All plasmids used or constructed in this study were listed in Table S2.

Construction of Y. lipolytica strains

Correct CRISPR/Cas9 vectors were directly used to transform Y. lipolytica IMUFRJ 50682 by adapted lithium acetate method [START_REF] Chen | One-step transformation of the dimorphic yeast Yarrowia lipolytica[END_REF]. Briefly, about 500 ng of each CRISPR-Cas9 vectors were cotransformed. CC_sgRNA-UTSS and CC_sgRNA-UTES plasmids were combined in the same transformation reaction for URA3 excision. The mixture was plated and incubated for 2-4 days. All transformations were performed in duplicate. Genotype verification of URA3 gene disruption were performed by colony PCR (GoTaq® Master Mix, Promega) using Fw-UTSS/Rv-UTES oligos (Table S1). PCR products were purified (Nu-cleoSpin® PCR Clean-up kit, Macherey-Nagel) and then subjected to sequencing (Eurofins Genomics). Multiple alignment of the nucleotide sequences was performed by MAFFT online service (https://mafft.cbrc. jp/alignment/server/) [START_REF] Katoh | MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization[END_REF].

Auxotrophic derivatives were transformed with NotI linearized GGVA-RedStarII plasmid [START_REF] Park | Engineering the architecture of erythritol-inducible promoters for regulated and enhanced gene expression in Yarrowia lipolytica[END_REF] and SfiI digested GGVA-Carotenoid vector [START_REF] Celińska | Golden Gate Assembly system dedicated to complex pathway manipulation in Yarrowia lipolytica[END_REF]. All colonies grown on YNB plate from the transformation with GGVA-RedStarII were cultivated in 96-well microplates containing YPD medium for 24 h in constant shaking and fluorescence was measured with excitation at 485 nm and emission at 528 nm (BioTek Sy-nergy™ Mx). The cloning efficiency was determined as the total colonies in which a fluorescent phenotype was observed by all colonies grown after transformation.

Determination of growth profile

Orange colonies from the transformation with GGVA-Carotenoid were grown in YPD medium at 200 rpm overnight. Cultures were washed, adjusted to an initial OD 750nm of 0.2 in 96-well microplates containing fresh YPD and YNB medium and cultivated at 500 rpm in a microplate shaker (Labstac LS122) for approximately 10 h. Growth was measured as absorbance at 750 nm every 30 min (SpectraMax® M2e). All cultivations were done in duplicate and the maximal specific growth rate (μ max ) were analyzed with one-way ANOVA.

Extraction and quantification of β-carotene

Intracellular β-carotene produced by recombinant IMUFRJ 50682 strains was extracted and measured photometrically at 455 nm (Thermo Scientific Genesys 10S) as published previously [START_REF] Larroude | A synthetic biology approach to transform Yarrowia lipolytica into a competitive biotechnological producer of β-carotene[END_REF]. Then, cultures were inoculated with OD 750nm of 0.1 in 50 mL flasks containing 10 mL of YPD and YNB media. After grown at 200 rpm for 96 h, 500 μL of cultures were washed with ddH2O and 500 μL of glass beads and 1 mL of extraction solution (ethyl acetate and 0.01 % butyl hydroxyl toluene) were added. Cells were disrupted in a BeadBug cell homogenizer (Benchmark Scientific) using a program consisting in 4 cycles of 60 s at 4.000 rpm in the dark, altering with ice incubation until the mixture were colorless. The extract was centrifuged, measured and then converted into concentrations through a standard curve using β-carotene (Sigma-Aldrich) as standard. For determination of dry cell weight (DCW), cells from a known volume were washed and dried at 100 °C. All cultures were performed in duplicate.

Results and discussion

CRISPR/Cas9 technology is a powerful genome-editing tool and several methods based on Type II System have been developed recently [START_REF] Darvishi | Advances in synthetic biology of oleaginous yeast Yarrowia lipolytica for producing non-native chemicals[END_REF]. In dual cleavage strategy, two independent sgRNAs, combined with Cas9 nuclease, induce DNA DSB in different regions which are repaired together by the NHEJ pathway, leading to complete loss of any sequence between target sites [START_REF] Adikusuma | Versatile single-step-assembly CRISPR/ Cas9 vectors for dual gRNA expression[END_REF]. Thus, the present work investigated the efficiency of this strategy in removing all URA3 coding sequence in a promising wild-type strain, Y. lipolytica IMUFRJ 50p@ssw0rd 682, to construct auxotrophic derivatives. URA3 gene encodes orotidine 5′-phosphate decarboxylase, an enzyme that catalyzes the conversion of orotidine 5′-monophosphate (OMP) to uridine 5′-monophosphate (UMP), the last step in pyrimidine biosynthesis [START_REF] Alani | A method for gene disruption that allows repeated use of[END_REF]. URA3 deletion prevents yeast cells from growing in the absence of uracil and this auxotrophy phenotype still remains the best choice for a selective marker in recipient Y. lipolytica strains used in synthetic biology [START_REF] Madzak | Heterologous protein expression and secretion in the non-conventional yeast Yarrowia lipolytica: a review[END_REF][START_REF] Larroude | Synthetic biology tools for engineering Yarrowia lipolytica[END_REF].

To knock out native URA3, two pairs of 20 bp target sequences located in transcriptional initiation sites (sgRNA UTSS1: -79 to -59 bp; sgRNA UTSS2: -139 to -119 bp) and termination regions (sgRNA UTSS1: +888 to +908 bp; sgRNA UTSS2: +1002 to +1022 bp) were predicted (Fig. 1A). Each target sequence was used in the construction of four CRISPR/Cas9 replicative vectors through GGA method. Acceptor plasmids GGA_NATex_CrisprCas9-yl_RFP and GGA_HPHex_CrisprCas9-yl_RFP owned same construction (Fig. S1) with differences only in the target sequence and selection markers to allow us to identify which regions are more favorable for URA3 gene editing in Y. lipolytica IMUFRJ 50682, since gene excision through dual DSB CRISPR/Cas9mediated requires synchronized cleavage of both sites during the same cell cycle [START_REF] Gao | Dual CRISPR-Cas9 cleavage mediated gene excision and targeted integration in Yarrowia lipolytica[END_REF]. CC_sgRNA-UTSS and CC_sgRNA-UTES constructed plasmids were mixed and co-transformed in IMUFRJ 50682 cells. Combination A was done with CC_sgRNA-UTSS1 and CC_sgRNA-UTES1 for excision of 973 bp. Combination B was performed with CC_sgRNA-UTSS1 and CC_sgRNA-UTES2 for deletion of 1.073 bp. Combination C was between CC_sgRNA-UTSS2 and CC_sgRNA-UTES1 for loss of 1.052 bp. Combination D was accomplished with CC_sgRNA-UTSS2 and CC_sgRNA-UTES1 for disruption of 1.152 bp (Fig. 1A).

After 4 days, a total of 101 hygromycin-B and nourseothricin-resistant colonies were observed in all combinations from biological duplicate: A (46 colonies), B (31 colonies), C (2 colonies) and D (22 colonies). Uracil auxotroph phenotype of the co-transformants was tested in YNB, YNB-Ura and YPD media (Fig. 1B). For combination A, 25 colonies were streaked on these media and 40 % (10/25) was unable to grow on YNB. For combination B, 20 colonies were tested and only 5% (1/20) failed to grow in the selective medium. All co-transformants from combinations C and D were cultivated in YNB and were able to grow and, therefore, URA3 knock out did not occur. Each candidate from combinations A (10 colonies -C01, C02, C07, C08, C09, C10, C13, C59, C60 and C62) and B (1 colony -C23) was taken for verification by colony PCR and confirmation via DNA sequencing. No sequence was amplified from C01, C09 and C62 and the other eight candidates (C02, C07, C08, C10, C13, C23, C59, C60) showed amplified sequence ≤ 500 bp confirming URA3 deletion, since amplified sequence from wild-type IMUFRJ 50682 had 1.221 bp length (Fig. 1C). From all sgRNA combinations, only in combinations A (UTSS1 and UTES1) and B (UTSS1 and UTES2) were identified ura3Δ mutants IMUFRJ 50682 with deletion efficiency of 28 % (7/25) and 5% (1/20), respectively (Table 1). Multiple alignment between amplified sequences of native URA3 gene and some auxotrophic mutants from combination A, now named AM07, AM10, AM13 and AM59, showed precise NHEJ repair only in AM59. NHEJ is an error-prone process often associated with small insertions and deletions at the DSB site which regularly occurs 3-4 bp upstream of the PAM sequence [START_REF] Garneau | The CRISPR/cas bacterial immune system cleaves bacteriophage and plasmid DNA[END_REF]. In the UTSS1 regions of AM07, AM10 and AM13 sequences were observed indels: AM07 had deletion of 2 bp, AM10 received addition of 1 bp and AM13 lost 4 bp; totaling a loss of 975, 972 and 977 bp, respectively (Fig. 1D).

The choice of target sequences closer to URA3 start (-59 bp) and stop (+888 bp) codon proved to be more favorable for DNA cutting in IMUFRJ 50682 strain, since combinations A and B led to the excision of all gene. Although sgRNAs were designed based on W29 strain, all of them perfectly matched the recognition site in IMUFRJ 50682 strain (Fig. S2), even no auxotrophic mutants have been generated from combinations C and D. According to Graf and colleagues [START_REF] Graf | sgRNA sequence motifs blocking efficient CRISPR/Cas9-Mediated gene editing[END_REF], the specificity of a sgRNA depends on its target sequence and recognition site with ≥ 80 % or ≤ 35 % of guanine-cytosine (GC) content has been shown poorly effective. Overall, Cas9 nuclease has demonstrated preferential binding in sgRNAs with G in the 4 bp directly upstream to the PAM sequence, while thymine (T) and C were unfavorable [START_REF] Wang | Genetic screens in human cells using the CRISPR-Cas9 system[END_REF][START_REF] Doench | Rational design of highly active sgRNAs for CRISPR-Cas9-mediated gene inactivation[END_REF]. The low URA3 deletion frequencies in our study may be due to the presence of such adverse bases in short sequence motifs at the 3′ end of sgRNAs. The UTSS2 and UTES2 are reverse sgRNAs and possess 5′-CTAT-3′ and 5′-CCAT-3′ sequences, respectively, which are rich in TC bases. The UTSS1 and UTES1 sgRNAs have 5′-GATA-3′ and 5′-AGTA-3′ sequences, respectively, with only one T, suggesting correlation between pyrimidine bases near the PAM sequence and Cas9 nuclease performance (Fig. 1E). In addition, analysis in the sgRNA sequences by RNA Structure and RNAfold WEB SERVER platforms indicated the possibility of secondary structure formation in UTSS2, which would prevent the recognition of the target DNA and its cleavage. A substantial variation in the cleavage efficiency in accordance with different genomic targets may be related not only to the inherent quality of the interaction between the sgRNA and the target sequence, but also to the general chromatin accessibility by the Cas9 nuclease, even within the same cell type [START_REF] Yarrington | Nucleosomes inhibit target cleavage by CRISPR-Cas9 in vivo[END_REF]. The combinatorial CRISPR/Cas9-mediated cleavage strategy has proven to be effective for engineering strains without genetic information.

The ura3Δ mutants IMUFRJ 50682 were sequentially cultivated in YNB-Ura medium by three rounds for plasmid curing, purified in YPD plates and verified in YPD-Hyg and YPD-Nat media. Then, AM07, AM10, AM13, AM23 and AM59 auxotrophic mutants were transformed with GGVA-RedStarII plasmid, which contains RedStarII expression cassette driven by pTEF and URA3 gene as selective marker [START_REF] Larroude | A modular Golden Gate toolkit for Yarrowia lipolytica synthetic biology[END_REF], to constitutively express the red fluorescent protein as a gene reporter for verification of cloning efficiency. After 2 days, grown colonies were analyzed by fluorescence phenotype. The cloning rates were satisfactory of 82.6 (19/23), 91.6 (33/36), 100.0 (11/11), 80.0 (4/5) and 89.5 % (17/19) for AM07, AM10, AM13, AM23 and AM59 mutants, respectively, and motivated us to investigate the biotechnological potential of this strain.

In order to verify the applicability of ura3Δ mutants IMUFRJ 50,682 as a chassis for production of non-native value-added compounds, auxotrophic mutants AM07, AM10, AM13, AM23 and AM59 were engineered for β-carotene synthesis. GGVA-Carotenoid plasmid contains the heterologous genes carB and carRP from Mucor circinelloides and native gene GGS1 under the control of constitutive promoter pTEF, for expression of phytoene dehydrogenase, phytoene synthase and geranylgeranyl diphosphate synthase, respectively, and URA3 gene as auxotrophic marker [START_REF] Celińska | Golden Gate Assembly system dedicated to complex pathway manipulation in Yarrowia lipolytica[END_REF]. Three carotenoid recombinant producers, CRP13, CRP23 and CRP59, were selected according to intense orange colour-associated phenotype (Fig. 2A) and growth profile and specific growth rates of them were compared with Brazilian wild-type strain in rich and synthetic media. Throughout cultivation in rich medium, wildtype strain and CRP13, CRP23 and CRP59 recombinants displayed a very similar growth pattern and statistical analysis indicated no significant differences in the μ max (p > 0.05). The IMUFRJ 50682 strain was capable of growing with a μ max of 0.38 h -1 , whereas CRP13, CRP23 and CRP59 recombinants achieved a μ max of 0.34, 0.34 and 0.33,

Table 1

Efficiency of URA3 gene deletion by dual cleavage strategy using different sgRNA combinations. Numbers in parentheses represent screened colonies from biological duplicate. Deletion efficiency is presented as the percentage of positive colonies in genotypic confirmation by the total of uracil auxotrophy phenotype colonies. *Indicate significant differences between specific growth rates of wild-type and carotenoid producers (p > 0.05). WT: wild-type respectively (Fig. 2B). In synthetic medium, the μ max of wild-type strain and CRP13, CRP23 and CRP59 recombinants decreased sightly to 0.22, 0.24, 0.11 and 0.22 h -1 , respectively, and differed significantly between CRP23 and the other strains (p < 0.05) (Fig. 2C). The increased metabolic load by overexpression of GGS1 gene and heterologous expression of carB and carRP genes did not reduced growth capacity of CRP13 and CRP59 recombinants, indicating that the introduction of a new metabolic pathway was efficiently adapted by mutants IMUFRJ 50682. Interestingly, CRP23 strain was able to synthetized 32.4 (4.7 mg/g DCW) and 50.1 mg/L (4.8 mg/ DCW) of β-carotene in YPD and YNB shake-flask cultures, respectively, while the final β-carotene titer of the CRP13 and CRP59 strains reached 30.5 and 41.6 mg/L (3.8 mg/g DCW) and 18.5 (2.4 mg/g DCW) and 36.8 mg/L (3.3 mg/ g DCW) in both media, respectively (Fig. 2D andE). The use of mutants IMUFRJ 50682 as a parental strain led to a higher β-carotene production than other backgrounds, such as Po1d (3.4 mg/g DCW) [START_REF] Larroude | A set of Yarrowia lipolyticaCRISPR/Cas9 vectors for exploiting wild-type strain diversity[END_REF] and CIBTS1176 (2.22 mg/g DCW) [START_REF] Gao | One-step integration of multiple genes into the oleaginous yeast Yarrowia lipolytica[END_REF] strains which were engineered with the same genes and cultivated in similar conditions. These data reveal the metabolic potential of IMUFRJ 50682 strain to be explored.

Combinations of sgRNAs

Total colonies

Phenotype of auxotrophy

As Y. lipolytica displays heterogeneity inter wild-type strains related to different genetics and metabolic patterns, the construction of auxotrophic derivatives from IMUFRJ 50682 will expand the range of applications for this promising strain. The expectation around Y. lipolytica IMUFRJ 50682 is due to its industrially relevant potential in the fields of biocatalysis, bioconversion and bioremediation. Previous studies from our group have demonstrated that this strain exhibits robust metabolism and it is able to degrade alternative substrates, such as compounds from poly(ethylene terephthalate) (PET) production chain, and convert some of them into value-added products with additional sustainability benefits and biotechnological processes economics [50]. As soon, we developed for the first time non-reverting auxotrophic mutants from Y. lipolytica IMUFRJ 50682 and evidenced their ability to produce new compounds of biotechnological interest.

Conclusion

The combinatorial dual CRISPR/Cas9 cleavage strategy proved to be useful and efficient for whole URA3 gene excision in wild-type strain Y. lipolytica IMUFRJ 50682. Auxotrophic mutants demonstrated high cloning frequency by introduction of fluorescent phenotype and were able to synthesize β-carotene efficiently through the expression of homologous and heterologous genes with a similar growth profile compared with the wild-type which motivates us to explore its genetic and physiology in future research.

Fig. 1 .

 1 Fig. 1. URA3 deletion of Yarrowia lipolytica IMUFRJ 50682 by dual cleavage strategy. (A) Schematic representation of paired sgRNA combinations. Numbers in parentheses show fragment size to be lost through dual double-strand break. The position +1 indicate the start codon (ATG) and sgRNA locations are presented by their distance of coding sequence (in base pairs). (B) Screening of auxotrophic mutants IMUFRJ 50682 in YNB, YNB-Ura and YPD plates. Red arrows indicate presence of colonies. (C) Colony PCR of candidates unable to grow on synthetic YNB medium. Fragments with ≤ 500bp indicate URA3 excision. (D) Multiple alignment between amplified sequences (5′→3′) of native URA3 and some auxotrophic mutants from combination A showing gene deletion. Letters in blue represent URA3 transcriptional start site 1 (UTSS1) and URA3 transcriptional end site 1 (UTES1), letters in green exhibit protospacer adjacent motif (PAM) sequence, red arrowheads show predicted cleavage site and indels are underlined. WT: wild-type. C01-62: candidates 01-62. AM07-59: auxotrophic mutants 07-59. (E) PAMproximal bases located in sgRNAs. Letters in green represent PAM sequence, letters in blue indicate sgRNAs sequence, blue arrowheads show sgRNAs direction and letters in red highlight unfavorable bases for Cas9 cleavage (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).

Fig. 2 .

 2 Fig. 2. Yarrowia lipolytica IMUFRJ 50682 engineering for β-carotene synthesis. (A) Carotenoid recombinant producers CRP13, CRP23 and CRP59 showed orange colour during cultivation in liquid media. (B) Growth profile and specific growth rates of wild-type and auxotrophic mutants in rich YPD and (C) synthetic YNB media. (D) β-carotene production in YPD and (E) YNB shake-flask cultures. Data are average of two independent cultivations.*Indicate significant differences between specific growth rates of wild-type and carotenoid producers (p > 0.05). WT: wild-type

Acknowledgements

We are grateful for the National Research Institute for Agriculture, Food and Environment (INRAE), Federal University of Rio de Janeiro (UFRJ) and Higher Education Improvement Coordination (CAPES).

Funding information

This work was financed by grants from Brazilian Federal Agency CAPES (Proc. 88881.188542/2018-01) and French Nacional Agency INRAE. CRediT authorship contribution statement Camilla Pires de Souza: Conceptualization, Methodology, Writingoriginal draft, Writing -review & editing. Bernardo Dias Ribeiro: Writing -review & editing, Supervision. Maria Alice Zarur Coelho: Writing -review & editing, Resources, Supervision. Rodrigo Volcan Almeida: Writing -review & editing, Resources, Supervision, Funding acquisition. Jean Marc Nicaud: Writing -review & editing, Resources, Supervision, Funding acquisition.

Author agreement

I, the Corresponding Author, declare that this manuscript contains original, unpublished results and confirm that it has been read and approved by all the authors. I understand that the Corresponding Author is the sole contact for the Editorial process and is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs.

Declaration of Competing Interest

The authors declare no conflict of interest.

Appendix A. Supplementary data

Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.enzmictec.2020. 109621.