Farhan Siddique

Mario Barbatti

Zhonghua Cui

Hans Lischka

Adélia Aquino

Adelia J A Aquino

Nonadiabatic Dynamics of Charge-Transfer States Using the Anthracene-Tetracyanoethylene Complex as Prototype

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Introduction

The theory and understanding of intermolecular charge transfer (CT) processes [START_REF] Mulliken | Donor-acceptor complexes[END_REF][START_REF] Metzger | The cohesive energy of tetrathiafulvalenium 7, 7, 8, 8-tetracyanoquinodimethanide (TTF TCNQ) as a function of charge transfer[END_REF] is a fascinating topic because of the wide appearance and significance of this phenomenon. CT complexes are attractive candidates for constructing materials with outstanding electrical and optical properties 3- 6 . Mixed stacked donor (D)/acceptor (A) CT systems have been extensively investigated in recent research due to their promising features as semi-conductuctors [START_REF] Valeev | Effect of electronic polarization on charge-transport parameters in molecular organic semiconductors[END_REF][START_REF] Hu | Impact of C-H••• X (X= F, N) and π-π interactions on tuning the degree of charge transfer in F6tnap-based organic binary compound single crystals[END_REF] to be used, for example, as biosensors in medicine [START_REF] Chaubey | Mediated biosensors[END_REF][START_REF] Xu | In Intermolecular charge transfer in organic donor-acceptor systems for optical storage applications[END_REF][START_REF] Jiang | Fluorescent detection of zinc in biological systems: recent development on the design of chemosensors and biosensors[END_REF] or as for the constructions of ferroelectric crystals [START_REF] Tayi | Room-temperature ferroelectricity in supramolecular networks of charge-transfer complexes[END_REF][START_REF] Narayanan | Ferroelectric polarization and second harmonic generation in supramolecular cocrystals with two axes of charge-transfer[END_REF] . The formation of the CT states generated from aggregates of π-conjugated donor polymers and electron acceptors (mostly fullerenes) is also a major step in organic photovoltaic systems, [START_REF] Yu | Polymer photovoltaic cells: enhanced efficiencies via a network of internal donor-acceptor heterojunctions[END_REF][START_REF] Liang | Highly efficient solar cell polymers developed via fine-tuning of structural and electronic properties[END_REF][START_REF] Thompson | Polymer-fullerene composite solar cells[END_REF] in which light absorption by the donor and exciton formation in the π-conjugated chain [START_REF] Brédas | Molecular understanding of organic solar cells: the challenges[END_REF] act as primary processes.

Theoretical investigations provide useful access to the description of the CT states, their energetic relation to the locally excited excitonic states, and their electronic response to changes in the environment [START_REF] Akimov | Theoretical insights into photoinduced charge transfer and catalysis at oxide interfaces[END_REF][START_REF] Stein | Reliable prediction of charge transfer excitations in molecular complexes using time-dependent density functional theory[END_REF][START_REF] Liao | DFT calculations and spectral measurements of charge-transfer complexes formed by aromatic amines and nitrogen heterocycles with tetracyanoethylene and chloranil[END_REF][START_REF] Borges | Ab initio modeling of excitonic and charge-transfer states in organic semiconductors: the PTB1/PCBM low band gap system[END_REF][START_REF] Cui | Analysis of charge transfer transitions in stacked π-electron donor-acceptor complexes[END_REF][START_REF] Chen | The characterization of charge transfer in excited states of extended clusters of π-stacked donor and acceptor complexes in lock-arm supramolecular ordering[END_REF] .

Tetracyanoethylene (TCNE) is an interesting acceptor, which forms prototypical complexes with aromatic hydrocarbons such as benzene, naphthalene, or anthracene as donors. [START_REF] Hanazaki | Vapor-phase electron donor-acceptor complexes of tetracyanoethylene and of sulfur dioxide[END_REF][START_REF] Merrifield | Cyanocarbon chemistry. II. 1 Spectroscopic studies of the molecular complexes of tetracyanoethylene[END_REF][START_REF] Masnovi | Electron transfer from anthracenes. Comparison of photoionization, charge-transfer excitation and electrochemical oxidation[END_REF] It has been found [START_REF] Aquino | Intermolecular interactions and charge transfer transitions in aromatic hydrocarbon-tetracyanoethylene complexes[END_REF] that the CT in the ground state is relatively small and amounts to about 0.2 e, but pronounced CT bands are observed on electronic excitation, and the CT states usually form the lowest excited states [START_REF] Aquino | Intermolecular interactions and charge transfer transitions in aromatic hydrocarbon-tetracyanoethylene complexes[END_REF] . The theoretical description frequently faces severe problems since the widely used time-dependent density functional theory (TD-DFT) shows, for many functionals, an artificial over-stabilization of the CT states. Therefore, range-separated functionals [START_REF] Vydrov | Assessment of a long-range corrected hybrid functional[END_REF][START_REF] Dreuw | Long-range charge-transfer excited states in time-dependent density functional theory require non-local exchange[END_REF][START_REF] Iikura | A long-range correction scheme for generalized-gradientapproximation exchange functionals[END_REF][START_REF] Adamo | The calculations of excited-state properties with time-dependent density functional theory[END_REF][START_REF] Laurent | TD-DFT benchmarks: a review[END_REF] have been developed to circumvent this problem. On the other hand, ab initio, wave function-based methods do not show this bias but are usually computationally more expensive.

A method that has been shown as a good compromise in terms of accuracy and computational efficiency is the second-order algebraic diagrammatic construction (ADC(2)) method 33 34 , which has been successfully used in many calculations of excited states of π-conjugated systems [START_REF] Aquino | The charge-transfer states in a stacked nucleobase dimer complex: A benchmark study[END_REF][START_REF] Panda | Electronically excited states in poly (pphenylenevinylene): Vertical excitations and torsional potentials from high-level Ab initio calculations[END_REF][START_REF] Shi | High-level theoretical benchmark investigations of the UV-vis absorption spectra of paradigmatic polycyclic aromatic hydrocarbons as models for graphene quantum dots[END_REF][START_REF] Li | Comparison of LC-TDDFT and ADC (2) methods in computations of bright and charge transfer states in stacked oligothiophenes[END_REF] . For more information on the applicability of the ADC(2) method, also see the overviews given in Refs 39, 40. A crucial role for the success of this method can be found in combination with the resolution-of-the-identity (RI) approach [START_REF] Hättig | Geometry optimizations with the coupled-cluster model CC2 using the resolution-of-the-identity approximation[END_REF][START_REF] Hättig | CC2 excitation energy calculations on large molecules using the resolution of the identity approximation[END_REF] , which lows efficient handling of the two-electron integrals. Using empirical scaled opposite-spin (SOS) [START_REF] Jung | Scaled opposite-spin second order Møller-Plesset correlation energy: an economical electronic structure method[END_REF][START_REF] Hellweg | Benchmarking the performance of spin-component scaled CC2 in ground and electronically excited states[END_REF] corrections, the interactions of TCNE and the aromatic compounds benzene, naphthalene, and anthracene have been investigated at SOS second-order Møller-Plesset perturbation theory [START_REF] Møller | Note on an approximation treatment for many-electron systems[END_REF] (SOS-MP2) and SOS-ADC(2) level [START_REF] Aquino | Intermolecular interactions and charge transfer transitions in aromatic hydrocarbon-tetracyanoethylene complexes[END_REF] .

Computed CT bands gave good agreement with experimental measurements, and solvent effects could be well-represented at the level of the conductor-like screening model (COSMO) [START_REF] Klamt | The COSMO and COSMO-RS solvation models[END_REF][START_REF] Lunkenheimer | Solvent effects on electronically excited states using the conductor-like screening model and the second-order correlated method ADC (2)[END_REF] using dichloromethane as solvent. An interesting feature was observed in these investigations when calculating vertical emission processes from the S1 state. Significant stabilization of the S1 state by the solvent was found for all three complexes, which led, especially for the anthracene case, to a zero-energy gap between excited and ground states. This circumstance opens the possibility for fluorescence quenching due to ultrafast radiationless deactivation to the ground state.

The purpose of the present work is to perform a photodynamical characterization of the radiationless deactivation of the TCNE/anthracene complex to the ground state. The motivation for the choice of the TCNE/anthracene complex (Figure 1) lies in the fact that it offers the possibility of a prototypical study of the sequence of events starting from a bright, locally excited π-π* state followed by the internal conversion to lower-lying CT states, and final decay to the ground state. The reason for the choice of the anthracene complex was also given by the observation in our previous static calculations on the series of TCNE complexes with benzene, naphthalene and anthracene [START_REF] Aquino | Intermolecular interactions and charge transfer transitions in aromatic hydrocarbon-tetracyanoethylene complexes[END_REF] that with increasing size of these polycyclic aromatic hydrocarbons the fluorescence energy systematically decreased, and that for the anthracene complex this energy was practically zero. This fact indicated rapid internal conversion to the ground state. This strong stabilization of the CT state was closely connected to solvent effects and did not occur in gas phase.

According to our previous calculations, this should have happened for dichloromethane. To assure the internal conversion process in the present, time-consuming on-the-fly dynamics, we decided to use as solvent water with an even larger dielectric constant.

The photodynamical studies have been performed through decoherence-corrected fewest switches surface hopping dynamics [START_REF] Tully | Molecular dynamics with electronic transitions[END_REF] for two different environments, the isolated complex and aqueous solution. The SOS-ADC(2) method has been used as a reliable method to describe CT complexes, as discussed above. Solvent effects have been included explicitly at the atomistic level by means of the quantum mechanical/molecular dynamics (QM/MM) approach [START_REF] Bakowies | Hybrid models for combined quantum mechanical and molecular mechanical approaches[END_REF][START_REF] Sherwood | Hybrid quantum mechanics/molecular mechanics approaches[END_REF][START_REF] Lin | QM/MM: what have we learned, where are we, and where do we go from here?[END_REF][START_REF] Ruckenbauer | Nonadiabatic excited-state dynamics with hybrid ab initio quantum-mechanical/molecular-mechanical methods: solvation of the pentadieniminium cation in apolar media[END_REF] . These nonadiabatic dynamics simulations are well suited to provide insight into the details of the deactivation processes of CT states and about the role, polar solvation is playing.

Computational Details

Optimization of ground-state geometries was performed with the SOS-MP2 method, and excited states were calculated with the SOS-ADC(2) method using the RI approach and the SV(P) basis set. [START_REF] Hättig | Structure optimizations for excited states with correlated second-order methods: CC2 and ADC (2)[END_REF] Solvation effects for water were treated for vertical excitations using the conductorlike screening model (COSMO) [START_REF] Klamt | The COSMO and COSMO-RS solvation models[END_REF][START_REF] Lunkenheimer | Solvent effects on electronically excited states using the conductor-like screening model and the second-order correlated method ADC (2)[END_REF] based on a continuum approach. The values of  = 78.39 [START_REF] Malmberg | Dielectric Constant of Water from 0 to 100 C[END_REF] for the relative dielectric constant and n = 1.33 [START_REF] Eisenberg | Equation for the refractive index of water[END_REF] for the refractive index of water were used. Ground state optimizations using COSMO were performed with the PTE scheme [START_REF] Cammi | Quantum cluster theory for the polarizable continuum model. I. The CCSD level with analytical first and second derivatives[END_REF] . For the nonadiabatic dynamics in water and the vibrational broadening of the UV spectra (see below), an additive QM/MM scheme [START_REF] Senn | QM/MM methods for biological systems[END_REF] with water as the solvent has been applied as described in Refs [START_REF] Ruckenbauer | Nonadiabatic excited-state dynamics with hybrid ab initio quantum-mechanical/molecular-mechanical methods: solvation of the pentadieniminium cation in apolar media[END_REF][START_REF] Ruckenbauer | Nonadiabatic photodynamics of a retinal model in polar and nonpolar environment[END_REF] . In this approach, the total region S of the solute/solvent system is divided into an inner region I for the QM part and an outer MM region O for the solvent. The total energy of the entire system S is written as

( ) ( ) ( ) ( ) ( ) el vdW b+vdW el QM/MM QM MM MM MM ( , ) , E S E I E I O E I O E O E O = + + + + , (1)
where the superscripts denote electronic (el), van der Waals (vdW), and bonding (b).

The QM part consisted of the TCNE/anthracene complex. It was included in a spherical cluster of 1000 water molecules, and the initial packing was performed using the PACKMOL program [START_REF] Martínez | PACKMOL: a package for building initial configurations for molecular dynamics simulations[END_REF] .

To keep the gross density during the dynamics constant, the entire cluster was constrained into a spherical boundary for the entire time of the simulation. The radius of the sphere was 19.3 Å to obtain a water density of 0.997 g/L [START_REF] D'ans | Taschenbuch für Chemiker und Physiker[END_REF] . Concerning the MM part, the OPLS/AA force field 60 has been used for the complex, and the SPC model 61 has been chosen for the water molecules. The effective point charges of the water molecules were included in the quantum mechanical

Hamiltonian within an electrostatic embedding scheme. More technical details on the calculations can be found in Refs [START_REF] Ruckenbauer | Nonadiabatic excited-state dynamics with hybrid ab initio quantum-mechanical/molecular-mechanical methods: solvation of the pentadieniminium cation in apolar media[END_REF][START_REF] Ruckenbauer | Nonadiabatic photodynamics of a retinal model in polar and nonpolar environment[END_REF] .

Initial conditions for the surface hopping dynamics and sampling points for calculating the vibrational broadening of UV spectra were computed in the framework of the nuclear ensemble method [START_REF] Crespo-Otero | Spectrum simulation and decomposition with nuclear ensemble: formal derivation and application to benzene, furan and 2-phenylfuran[END_REF][START_REF] Yamazaki | Molecular mechanisms of the photostability of indigo[END_REF] . For the gas phase, 150 sampling points were created from a harmonic-oscillator Wigner [START_REF] Crespo-Otero | Spectrum simulation and decomposition with nuclear ensemble: formal derivation and application to benzene, furan and 2-phenylfuran[END_REF][START_REF] Barbatti | The UV absorption of nucleobases: semi-classical ab initio spectra simulations[END_REF] distribution. Ten states were computed for each of these geometries. In the case of the aqueous solution, the sampling of geometries was carried out by means of a mixed scheme applying the Wigner distribution for the QM part and performing a classical molecular dynamics (MD) to sample solvent structures. In short, the following steps were executed: (i) sample solvent structures were obtained from ground state trajectory keeping the QM solute fixed at the equilibrium structure; (ii) initial conditions were calculated for the isolated QM system from a Wigner distribution; (iii) the equilibrium QM structure in each of the solvent samples was replaced by different displaced structure from the Wigner distribution; and (iv) each structure was rethermalized for 10 ps. In this way, a sampling of the QM part with appropriate consideration of quantum mechanical zero-point energies has been achieved and combined with MD sampling for the solvent. For more details, see the discussion in Refs [START_REF] Ruckenbauer | Nonadiabatic excited-state dynamics with hybrid ab initio quantum-mechanical/molecular-mechanical methods: solvation of the pentadieniminium cation in apolar media[END_REF][START_REF] Ruckenbauer | Nonadiabatic photodynamics of a retinal model in polar and nonpolar environment[END_REF] . The initial conditions, created in that way, were also used for the calculation of the spectra using the nuclear ensemble method.

In the nonadiabatic surface hopping dynamics, the fewest switches algorithm [START_REF] Tully | Molecular dynamics with electronic transitions[END_REF] corrected for decoherence effects (α = 0.1 Hartree 65 ) was used. Nonadiabatic interactions were computed according to the numerical scheme proposed by Hammes-Schiffer and Tully 66 , and using the local diabatization approximation [START_REF] Plasser | Analysis of excitonic and charge transfer interactions from quantum chemical calculations[END_REF][START_REF] Granucci | Direct semiclassical simulation of photochemical processes with semiempirical wave functions[END_REF] . Nonadiabatic interactions between excited states were calculated employing the approximate derivative couplings orbital derivative (OD) [START_REF] Ryabinkin | Fast numerical evaluation of time-derivative nonadiabatic couplings for mixed quantum-classical methods[END_REF] method. Since the ADC(2) method does not describe the multireference character of the ground state well at intersection structures with S0, an approximate criterion has been adopted to estimate the S1/S0 crossings in the form of a stopping condition which is activated when the S1/S0 energy gap falls below 0.2 eV. The corresponding time step was taken as an estimate of the S1/S0 crossing time. It was found previously that this procedure reproduced the nonadiabatic relaxation dynamics quite well [START_REF] Plasser | Surface hopping dynamics with correlated single-reference methods: 9H-adenine as a case study[END_REF] . The maximum simulation time was 1 ps for both gas and solution phases. The velocity Verlet algorithm [START_REF] Verlet | Computer "experiments" on classical fluids. I. Thermodynamical properties of Lennard-Jones molecules[END_REF][START_REF] Swope | A computer simulation method for the calculation of equilibrium constants for the formation of physical clusters of molecules: Application to small water clusters[END_REF] with a time step of 0.5 fs was used for the integration of Newton's equations.

The Andersen-Lowe thermostat [START_REF] Andersen | Molecular dynamics simulations at constant pressure and/or temperature[END_REF] was applied to keep the temperature at 298 K in the aqueous solution. The action of the thermostat was restricted to the solvent molecules. The usual protocol to select initial conditions for dynamics considering a target diabatic character in the adiabatic state manifold is based on the difference of oscillator strengths between the states in the excitation region. Nevertheless, in the current case, such protocol did not help to select a target diabatic state due to the similarity of the oscillator strengths of the states (see Table 1). For this reason, we decided to start all trajectories from S4, which should maximize the contribution of local excitation in the initial state composition. 109 trajectories were started in each case. For the calculation of the line shapes of the spectra, the excitation at each sample point was broadened by a Lorentzian with a broadening parameter of 0.1 eV.

The value of charge transfer from fragment A to fragment B for excited states using the descriptor q(CT) [START_REF] Plasser | Analysis of excitonic and charge transfer interactions from quantum chemical calculations[END_REF] for a given electronic transition was analyzed by means of transition density

matrices 0 ,[ ] n AO D defining the descriptor 𝛺 𝐴𝐵 𝑛 as     ( )     ( ) 0 , 0 , 1 , 2 n AO AO AO n AO n AB ab ab aA bB D S S D   =  (2)
where n labels the electronic state, and S [AO] is the AO orbital overlap matrix. n AB  represents the contribution of charge transfer from fragment A to fragment B (for A≠B), and the contributions of the same-fragment excitations (for A = B). The total CT character for a system with multiple fragments is given by: ( )

1 , n AB A B A n q CT  =   (3)
n  is the total sum of the charge transfer numbers for all pairs of A and B. If q(CT) = 1e, a complete charge transfer of one electron has occurred while for q(CT) = 0 the transition is a locally excited or Frenkel excitonic state.

The nonadiabatic dynamics calculations were performed with the NEWTON-X program system [START_REF] Barbatti | Newton-X: a surface-hopping program for nonadiabatic molecular dynamics[END_REF] . The QM calculations were all performed with TURBOMOLE [START_REF] Ahlrichs | Electronic structure calculations on workstation computers: The program system turbomole[END_REF] ; for the MM calculations, TINKER [START_REF] Ponder | An efficient newton-like method for molecular mechanics energy minimization of large molecules[END_REF] was used. Natural Transition orbitals (NTOs) [START_REF] Martin | Natural transition orbitals[END_REF] and values q(CT) for the charge transfer from anthracene to TCNE in excited states have been calculated using the TheoDORE [START_REF] Plasser | Analysis of excitonic and charge transfer interactions from quantum chemical calculations[END_REF][START_REF] Plasser | New tools for the systematic analysis and visualization of electronic excitations. I. Formalism[END_REF][START_REF] Plasser | New tools for the systematic analysis and visualization of electronic excitations[END_REF] program.

Results and Discussions

The UV spectrum

Vertical excitation energies, oscillator strengths, and CT values q(CT) are collected in Table 1 for the five lowest excited singlet states of the anthracene-TCNE complex for the gas phase and for continuum solvation. Table S1 of the Supporting Information (SI) shows excitation energies for all 10 states calculated in the gas phase, and Table S2 gives the corresponding results for continuum solvation. In Tables S3 andS4, the vertical excitations of the monomers anthracene and TCNE, respectively, are collected for comparison.

In the gas phase, the first two excited states of the anthracene-TCNE complex (Table 1) show a strong CT character with almost a full charge transferred to the TCNE. The LE states follow as states S3 and S4. They are localized on anthracene, while S5 and S6 have CT character again (Figure 2). The excitation energies of the two LE states on anthracene agree well with the ones for the isolated anthracene shown in Table S3. Excitations in TCNE start at 5.37 eV for the isolated compound (Table S4). The first state with significant TCNE character appears in the complex as S9 (5.55 eV). In water (Table 1), the first three excited states have a strong CT character. S4 and S5 are locally excited states on anthracene, and S6 has CT character. The effect of the solvent is to stabilize the CT states by 0.3 -0.5 eV, which brings the CT(III) state to the third excited state. The LE states are practically unaffected by the polar solvation. This situation in terms of state ordering and character of states is in quite good agreement with the results obtained in Ref. [START_REF] Aquino | Intermolecular interactions and charge transfer transitions in aromatic hydrocarbon-tetracyanoethylene complexes[END_REF] using a much larger cc-pVTZ basis. Differences in terms of excitation energies amount to a few tenths of an eV.

Comparison of the calculated results for aqueous solution with spectroscopic data for dichloromethane given in Table 1 shows a good representation of the first two CT bands found in the experiments. Comparison of available spectroscopic data for anthracene (Table S3) and TCNE (Table S4) demonstrate also good agreement for our calculated results. Table 1. Vertical excitation energies ∆E (eV), oscillator strengths f, charge transfer q(CT) in e, and most important orbital transitions computed for the TCNE/anthracene complex at the ground state minimum using the SOS-ADC(2)/SV(P) method. a H stands for HOMO and L for LUMO; the percentage numbers in parentheses give the weight of the configuration in the linear-response expansion. b cc-pVTZ results from Ref. [START_REF] Aquino | Intermolecular interactions and charge transfer transitions in aromatic hydrocarbon-tetracyanoethylene complexes[END_REF] . c cc-pVTZ results from Ref. [START_REF] Aquino | Intermolecular interactions and charge transfer transitions in aromatic hydrocarbon-tetracyanoethylene complexes[END_REF] for dichloromethane. d Ref. [START_REF] Masnovi | Electron transfer from anthracenes. Comparison of photoionization, charge-transfer excitation and electrochemical oxidation[END_REF] , for dichloromethane solution.

State

The orbital excitation scheme is illustrated in Figure 2, showing the lowest excited states generated from the list of molecular orbitals (MO) ranging from highest occupied MO (HOMO)

to HOMO-2 and the lowest unoccupied MO (LUMO) to LUMO+2. The optimized structures for the ground state and first excited state in the gas phase using the SOS-MP2/SV(P) and SOS-ADC (2)/SV(P) methods, respectively, are displayed in Figure 3 together with bond and intermolecular distances. The Cartesian coordinates are collected in the SI, Table S6. The intermolecular distance increases from 3.14 Å in the ground state to 3.25 Å in S1.

The change in the C=C bond distance from 1. The nuclear ensemble spectrum of the TCNE/anthracene complex in the isolated and solution phases is shown in Figure 4. There are two absorption bands in the region of up to 4 eV.

The first band originates from CT transitions. LE and CT transitions contribute to the second band system. At even higher energies, an intensive band consisting of LE transitions, which is partially shown in Figure 4. In the case of the gas phase, the band maxima are found at 2.19 eV and 3.80 eV while they are located at ~1.68 eV and 3.72 eV in solution. The redshift induced by solvation is 0.51 eV for the first (CT) peak while it is much less (0.08 eV) for the second peak. The positions and relative intensities of the vertical emissions for solution are given in Figure 4 as well. See Tables S1 andS2 for numeric values for gas phase and solution.

Figure 4: Photoabsorption cross-section in Å 2 molecule -1 as a function of the excitation energy (eV); 150 points and 10 states were calculated for both gas and aqueous solution. Red vertical lines (arbitrary units) show the excitation energies for each transition and their character in solution.

The vertical emission energy (Eemission) from the geometry optimized in the S1 state to S0 is compared in Table 2 with the vertical absorption (Evert) and the adiabatic (Eadiab) S0/S1 excitation energy. The latter is computed as the difference between the S0 and S1 energies computed at the respective optimized geometries. In the gas phase, the geometry relaxation in S1 reduces its energy by 0.45 eV, yielding 1.44 eV emission energy. This relaxation renders a substantial Stokes shift (Evert -Eemission) of 0.94 eV. In water, Eemission is negative, indicating that solvation stabilizes the S1 CT state considerably, but also that the description SOS-ADC(2) is breaking down since it is not capable of taking into account the multireference character of the ground state at the intersection [START_REF] Tuna | Assessment of approximate coupled-cluster and algebraic-diagrammatic-construction methods for ground-and excited-state reaction paths and the conical-intersection seam of a retinal-chromophore model[END_REF] , as already discussed in the Computational Details. a In this transition, the energy of the excited state computed with linear response is lower than that of the ground (reference) state. b Optimized S1 geometry of the isolated complex used.

The Photo-dynamics of the Anthracene-TCNE Complex

The dynamics simulations have been started in both the gas and aqueous solution phases from S4, which is predominantly a LE state. Figure 5 shows the evolution of the average state occupations over all trajectories. In the gas phase, the decay of the initial S4 state occurs within a few fs leading to a concomitant rapid initial increase of S3, which subsequently decreases steadily In solution, the S4 (LE) state also decays very fast. It reaches an occupation of <10% in less than 15 fs. Similar to the gas phase, a maximum in the S2 occupation is observed at ~130 fs. In contrast to the gas phase, in solution, the S1 state decays rapidly to S0, which is populated at the end of the simulation time by 90%.

The CT behavior within a trajectory is shown in Figure 6 for both gas and solution phases. shows stronger variations in the gas phase, but both states do not play an important role overall since the above discussion showed that they decay rapidly to lower states. From Figure 6a, a similar dynamic behavior of the CT character of S1 and S2 can be seen, irrespective of their actual occupation. Except for a short period in the beginning, the evolution of the CT in the active state (Figure 6c, gas phase) displays a continuous strong CT character while switching from one CT state to the other. In solution (Figure 6b), the density of CT states comprises now three states (S1-S3). Figure 6c in gas and Figure 6d in solution phases, clearly show this behavior of active state switching of CT from the S4 state to the S1 state. Figure 6d shows an interesting feature of initial strong oscillations in q(CT) and switches between different states. The discussion of Figure 6 just above showed that, except for an initial phase, the character of the lowest excited states is dominated by CT character. Therefore, we did not follow their decay individually but treated them globally as one CT entity. To estimate their combined excited-state lifetime, we fitted the quantity

( ) ( ) 0 1 e f t f t =- , (4)
where ( )

0
ft is the occupation of S0, with the exponential decay function

( ) ( ) ( )/ 0 1 1 1 Le t t t N t N N e -- = + - . (5)
In this equation, N1 is the fraction of the population that does not decay to the ground state in the ultrafast process, tL is the latency time until the hops to S0 start, and t e is the exponential time constant. The excited-state lifetime is given as 10 eL tt  =+. Because our simulations are limited to 1 ps, we do not have enough information to predict the long time scale fate of the system fully.

Thus, for both phases, we fitted the data twice, keeping N1 fixed at zero (which implies the hypothesis that the entire excited state population goes to the ground state through the same ultrafast process) and optimizing it (which leaves open the possibility that part of the excited state population decays through another, longer process).

We have also estimated the time 32  to transfer the population from the high excited states .

t t N t e e     - -  =-  - (6)
Table 3. Time constants derived from fitting the gas phase and solution dynamics, according to Eqs. ( 5) and ( 6).

1

N L t (fs) e t (fs) 10  (fs) 21  (fs) 32  (fs)
Gas phase (N1 free) 0. In both gas and water phases, the S2 state is populated within 100 fs, as indicated by 32

 in Table 3. This time marks the birth of the CT state. In the gas phase, it takes about 395 fs for the S2 state to transfer the population to S1. In water, however, this process is faster, taking 266 fs.

In both water and the gas phases, the latency time before the first transition to the ground state is about 110 -120 fs (Table 3). In the gas phase, if N1 is optimized, it yields the value N1 = 0.59, indicating that a substantial amount of population may remain excited and does not follow the ultrafast decay mechanism. In this case, the ultrafast excited-state lifetime  is 2.1 ps. Forcing 100% decay to the ground state (N1 fixed at 0) enhances the lifetime considerably to 5.4 ps. In water, the situation is completely different. Either optimizing of fixing N1, the results are similar.

If N1 is optimized, one finds that only about 8% of the population may remain in the excited state.

In such a case, the excited state lifetime is 0.45 ps. Restricting N1 to zero produces only a tiny enhancement of the lifetime, to 0.51 ps. In water, the margin of error for the 10 lifetime is ±46 fs for a 90% confidence interval. In the gas phase, this margin of error is ±113 fs for the same confidence interval.

Analysis of the structural modifications due to excitation to the S1 minimum (Figure 3) indicated that with an extension by 0.09 Å, the C=C stretching in TCNE is one of the most prominent coordinates subject to changes. The center-of-mass (c.m.) distance is another interesting indicator for the description of the excited-state dynamics since it increases significantly from 3.14 Å in the S0 minimum to 3.25 Å in the S1 minimum (Figure 3). It should be mentioned that comparable increases in the intermolecular bond distances on CT were observed for several other D/A complexes as well. [START_REF] Cui | Analysis of charge transfer transitions in stacked π-electron donor-acceptor complexes[END_REF] The evolution of the c.m. distances between TCNE and anthracene are shown in Figure 8. The The c.m. distance is of special interest since it will cover a larger range of geometries. In Figure 10, we plotted the potential energy profile along the rigid displacement of the c.m. coordinate. In the gas phase (Figure 10a), the S0 and S1 curves remain well separated, whereas a crossing is found in the SOS-ADC(2)-COSMO case (Figure 10b) due to the strong stabilization of the CT state by the solvent. As already mentioned, ADC(2) is not appropriate to describe this crossing between the states; however, outside of its neighborhood, the reference state (always denoted S0/ClS) always has closed-shell (ClS) character and should be adequate for performing SOS-ADC(2) calculations.

It is very interesting to note that in aqueous solution, two minima can co-exist for the ground state: the ordinary, neutral one, with a charge transfer of about 0.3 e, and an ion-pair (IPr).

The intermolecular distance in this ion pair is about 0.5 Å larger than for the closed-shell dimer.

Both cases have approximately the same stability. This situation resembles closely the one found previously for the interaction of para-chloranil with tetramethyl-para-phenylenediamine [START_REF] Cui | Analysis of charge transfer transitions in stacked π-electron donor-acceptor complexes[END_REF] , where intersections between S1 and S0 were found as well. The photoexcitation acts as a tool to populate both minima directly in an ultrafast mechanism as our simulations show. A similar mechanism has been put forward for organic charge-transfer crystals [START_REF] Collet | Laser-induced ferroelectric structural order in an organic charge-transfer crystal[END_REF] , where laser-induced ferroelectric order was achieved by switching from the neutral to the ion-pair state. Reference energy is calculated for the optimized S0 geometry.

Conclusions

Surface hopping dynamics simulations have been performed on the benchmark system TCNE/anthracene to elucidate the photodynamical deactivation processes of LE and CT states.

The process is schematically illustrated in Figure 11. The dynamics starts from the S4 LE state in anthracene, which is followed by the formation of CT states within less than 100 fs. From that time on, CT states involving the transfer of almost an entire electron dominate the dynamics. The S2 CT state reaches the maximum occupation within ~110-130 fs and depopulates towards S1, also a CT state, within 0.3 -0.4 ps. In S1, the gas phase dynamics shows that radiationless decay may take several picoseconds. In water, however, the deactivation to S0 happens within only 0.5 ps. The In the gas phase, the eventual deactivation of the complex should restore the original ground state. In water, however, a completely different picture emerges. Due to the strong stabilization of the ion-pair CT state, the ultrafast deactivation of the complex should create two different ground-state populations, one in the original neutral state and another in the ion-pair state.

Although the existence of these competing ground states is clear, limitations in the computational methodology at the S1/S0 crossing used here preclude us from making estimates of the populations in each one.

We expect that time-resolved spectra of TCNE in the gas phase and in water should show markedly distinct features, following the scheme in Figure 11. In both environments, the spectra should feature an initial sub-picosecond time constant relative to the LE→CT transfer and relaxation. Nevertheless, the gas phase spectrum should give rise to a long time constant (several ps), which should not be present in the measurement in water.

The formation of a ground-state population of ion-pairs may have a profound implication for the design of photodevices. Such stable ion-pairs may have low recombination rates, which could increase the charge migration and separation yields. It is conceivable that such ion-pair states can also be created in solid-state, as long as the electrostatic environment is strongly polar. It is clear that the TCNE/anthracene complex is only one step toward understanding this phenomenon, and larger and more sophisticated models for the description of nonadiabatic processes are necessary.

Supporting Information

Additional Supporting Information may be found in the online version of this article concerning extended data for vertical excitations for the dimer and the monomers, and Cartesian coordiantes of optimized structures.

Figure 1 :

 1 Figure 1: Top (a) and side (b) views of the anthracene-TCNE complex in the ground state.

Figure 2 .

 2 Figure 2. MO scheme and the characterization of the vertical transitions for the anthracene-TCNE complex in gas phase; red lines indicate CT transitions, and black lines refer to localized excitation in anthracene.

  38 Å in S0 to 1.46 Å in S1 due to CT populating the antibonding * orbital is most prominent. Another characteristic change due to CT in S1 is a symmetric torsion across the C=C bond of TCNE with the electronegative nitrogen ends pointing more to anthracene.

Figure 3 .

 3 Figure 3. Bond distances (Å) and center-of-mass distances (Å) (dashed line) for (a) the S0 state optimized at SOS-MP2/SV(P) level and (b) the S1 structure optimized at SOS-ADC(2)/SV(P) level in the gas phase. The trans-CCCC torsional angle in TCNE is 175 in S0 and 166 in S1.

  until the end of the simulation time. The state occupation then switches to S2, which has a maximum at around 110 fs and decreases to about 15% within the simulation time of 1 ps. The decrease in S2 is accompanied by a steady increase in S1. The occupation of the S0 state increases slowly due to the decay of the S1 state.

Figure 5 :

 5 Figure 5: Average state occupation for the photo-dynamics of the anthracene-TCNE complex a) for the gas phase and b) for water.

Figure 6 a

 6 Figure 6 a and b show the CT values for all states calculated during the dynamics and provide a good overall picture of possible CT character. On the other hand, Figure 6 c and d focus on the CT character of the active state, i.e., the state that is actually occupied at a given time in the surfacehopping dynamics. S4 has, most of the time, LE character (Figure 6a,b), both in the gas phase and solution. S3 shows stronger variations in the gas phase, but both states do not play an important

Figure 6 :

 6 Figure 6: Charge transfer character during the photo-dynamics of anthracene-TCNE for a selected trajectory a) all states for gas phase, b) all states for solution phase, c) active state in the gas phase, d) active state in solution.

S4 and S3 to S2 and the time 21 

 21 to transfer the population from S2 and S1. These times were obtained by fitting the S2 occupation with the unimolecular decay function

Figure 7

 7 displays the mean of the C=C distance taken over all trajectories for each time step and shows the range of the amplitudes in the form of the standard deviation. The dynamics starts off with a mean value close to the 1.37 Å obtained for the C=C distance at the ground state minimum of the complex (Figure3), both for the gas phase and for aqueous solution. The distance increases rapidly after electronic excitation and oscillates around ~1.47 Å, a value close to the S1 minimum value of 1.46 Å. The standard deviation amounts to 0.08 Å in the gas phase and to a slightly smaller value of 0.06 Å in solution. The oscillations appear more pronounced in water, especially toward the end of the simulation time, possibly also to the statistical uncertainty caused by the smaller number of surviving trajectories.

Figure 7 .

 7 Figure 7. Time evolution of the averaged distance (Å) of a) the C=C bond in TCNE in the gas phase (black line) and b) of the C=C bond in TCNE in water (red line). The green lines show the standard deviation around the mean value.

  mean c.m. distance in gas starts at 3.15 Å and increases steadily in a slight oscillatory motion and reaches a limiting value of ~3.3 Å, somewhat smaller than the S1 minimum values of 3.25 Å. In solution, the c.m. distances rise to a higher initial maximum at ~200 fs. This increase is followed by strongly damped oscillations.

Figure 8 :

 8 Figure 8: Center-of-mass distances between TCNE and anthracene for all trajectories as a function of simulation time. The black line shows the average distance taken over all trajectories. a) gas phase and b) in solution.

Figure 8

 8 Figure 8 also gives indications of the role the c.m. distance is playing concerning the hopping mechanism to the ground state. Inspecting this figure, one notes that, especially in the water case, the crossing between S1 and S0 (within our threshold criterion of 0.2 eV) tends to occur for c.m. distances above the mean distance. To investigate this point systematically, histograms of the c.m. and C=C distances, respectively, at stopping time are shown in Figure 9. For both coordinates, the same picture arises: the distances at S1/S0 crossing time are significantly enlarged with respect to the mean value calculated over the entire dynamics. Only a few values are located below the mean value.

Figure 9 .

 9 Figure 9. Histograms of the c.m. distance a) for the gas phase and b) for water, and of the C=C distance of TCNE c) for gas phase and d) for water at the S1/S0 hopping time in comparison to the mean distances during the dynamics (vertical black lines).

Figure 10 .

 10 Figure 10. S0/ClS and S1/IPr potential energy curves for the rigid displacement along the line connecting the c.m. of anthracene and TCNE in the S1 optimized gas-phase structure for a) gas phase and b) water using SOS-ADC(2). Note that S0/ClS and S1/IPr represent a diabatic notation with the former standing for the neutral (closed-shell) state and the latter for the ion-pair CT state.

  most prominent geometrical changes due to dynamics in the S1 CT state are the extension of the intermolecular TCNE-anthracene c.m. distance by 0.4 Å and the elongation of the C=C distance in TCNE by 0.09 Å. TCNE gets slightly bent with the N atoms moving towards anthracene. In anthracene, smaller changes in the CC bonds of 0.02 Å occur, which led to almost equal bond lengths in S1.

Figure 11 .

 11 Figure 11. Schematic representation of the photodynamical process involving the LE, CT and closed-shell (ClS) states.

Table 2

 2

	. Vertical absorption energy to S1 (Evert), adiabatic S0/S1energy (Eadiab) and vertical
	emission energy (Eemission) from S1 to S0 for the TCNE/anthracene complex in gas phase and in
	aqueous solution (COSMO).
		Evert (eV) Eadiab (eV)	Eemission (eV)
	Gas phase	2.38	1.93	1.44
	Water (COSMO)	1.98	0.88 a	0.51 a,b

Acknowledgments

We are grateful for support from the School of Pharmaceutical Science and Technology (SPST), Tianjin University, Tianjin, China, including computer time on the SPST computer cluster Arran. Z. C. acknowledges financial support from the Beijing National Laboratory for Molecular Sciences (BNLMS201910). M. B. thanks the support from the projects ANR PRC WSPLIT (ANR-17-CE05-0005-01) and Equip@Meso (ANR-10-EQPX-29-01).

Author Information

Corresponding Authors *E-mail: adelia.aquino@univie.ac.at hans.lischka@univie.ac.at ORCID Adélia J. A. Aquino: 0000-0003-4891-6512

Hans Lischka: 0000-0002-5656-3975


est destinée au dépôt

The evolution of mechanism underlying the allocation of resources and consequences on the shape of trade-offs in multicellular organisms

Salomé Bourg

To cite this version:

Salomé Bourg. The evolution of mechanism underlying the allocation of resources and consequences on the shape of trade-offs in multicellular organisms. Populations and Evolution [q-bio.PE]. Université de Lyon, 2019. English. NNT : 2019LYSE1266. tel-02611778v2


Keywords: larval density, crowding, microbiota, metabolic wastes, uric acid, ammonia 34

HAL is

139 In a first experiment, we generated a broad range of larval densities: 1, 5, 20, 60, 100, 140 200, 300, 500, and 1000 eggs per mL of food (see Fig. S1). To generate these nine 141 density treatments, a total of 300, 300, 400, 480, 800, 1600, 2400, 4000, and 8000 142 eggs were counted and deposited in 150,30,10,4,4,4,4 and 4 replicated vials 143 respectively, each containing 2 mL of food. We did not adjust the number of deposited 144 eggs to account for embryo mortality.

145 We characterized development, phenotypes, and behavior of individuals from all these 146 larval densities. For metabolic wastes measurements and microbiota characterization, 147 we selected among the nine densities three densities showing contrasted phenotypes 148 and referred as LD (low density; 5 eggs.mL -1 ), MD (medium density; 60 eggs.mL -1 ) and 149 HD (high density; 300 eggs.mL -1 ). For these three densities, we measured ammonia 150 and uric acid in L3 larvae and in the food that sustained their development. We 151 collected larvae and food samples over a time window of 4h and at the specific 152 occurrence peak of L3 instar for each larval density, in order to avoid sampling outlier 153 individuals. Larvae samples were L3 instar individuals picked on the surface of the 154 medium, and food samples consisted in cubes of 5x5x5 mm, i.e including the whole 155 depth of the medium. Samples were then transferred in autoclaved tubes with sterile 156 tools, immediately snap-frozen in liquid nitrogen, and stored at -80°C until use.

  desired larval density treatments (see below). Eggs manipulation was performed 135 identically in all treatments, standardizing time spent under the stereomicroscope

  , MD and HD conditions, the bacterial composition of L3 larvae and of food that 193 sustained their development was characterized using 16S Illumina MiSeq sequencing.194 To remove external bacteria in larvae, pools of 10 individuals were surface-sterilized 195 with successive baths and quick vortex in 2.7% hypochlorite for two minutes, 70% 196 ethanol for two minutes, and rinsed twice in autoclaved miliQ water. DNA extraction 197 was performed in six independent replicates using FastDNA spin kit (MP Biomedicals), 198 according to manufacturer's instructions. We used PCRs to amplify V3/MiSeq[START_REF] François | Olfactory epithelium changes in germfree mice[END_REF]. Thirty thermal cycles at 65°C annealing 203 temperature were performed. The PCR products were purified and loaded onto the 204 Illumina MiSeq cartridge (Illumina, San-Diego, CA, USA) according to manufacturer'height were analyzed using non-linear models (NLS) as described in233 Henry, Renault and Colinet (2018). Briefly, we adapted the non-linear logistic equation 234 proposed by[START_REF] Börger | Quantifying individual differences in dispersal using net squared 543 displacement -Oxford Scholarship[END_REF] such as: 235 𝑀𝑎𝑠𝑠 = 𝑑 + 𝑎 1 + exp [ density -𝑏 𝑐 ] 236 where (a+d) corresponds to the asymptotic mass at density=0, b is the inflection point 237 expressed in density units, c is the range of the curve on the density axis, and d is the 238 asymptotic mass at the highest density; and 239 𝑃𝑢𝑝𝑎𝑡𝑖𝑜𝑛 ℎ𝑒𝑖𝑔ℎ𝑡 = 𝑎 1 + exp [ 𝑏 -𝑑𝑒𝑛𝑠𝑖𝑡𝑦 𝑐 ] 240 where a corresponds to the asymptotic mass at the highest density, b is the inflection 241 point expressed in density units, c is the range of the curve on the density axis. 242 Ammonia and uric acid contents were analyzed using one-way ANOVA followed by 243 post-hoc Tukey tests. In the waste-supplementation experiment, development duration 244 was analyzed using Kruskal-Wallis test followed by post-hoc Dunn's test with 245 Benjamini-Hochberg correction, and pupation height was analyzed using one-way 246 ANOVA followed by post-hoc Tukey tests. 247 Sequencing data were analyzed using a custom pipeline. Raw pair-end sequences 248 files from Illumina were assembled using Flash software (Magoč and Salzberg 2011) 249 using at least a 10bp-overlap between the forward and reverse sequences, allowing 250 5% of mismatch. Dereplicating, denoising, clustering and chimera removing steps were 251 sequentially performed using Galaxy tool "FROGS" (Escudié et al. 2016). A 252 comparison of normalized reads to the lower sample vs non-normalized reads showed 253 it provided similar results in both cases. Thus, we kept non-normalized reads as 254 normalization may induce statistical bias (McMurdie and Holmes 2014). Taxonomic was affected by sample type and by larval density, whatever the 308 considered index (Fig. 2A-D; S3; S4

Figure 4 :

 4 Figure 4: Conceptual visualization of hypothetical nitrogen cycle in the lab D. melanogaster system. (A)Larva is metabolizing nutrients, producing uric acid in the process. Larva then degrades uric acid into intermediate products and finally to ammonia or urea through the uricolytic pathway before defecating in the food. (B) Larva is metabolizing nutrients, producing uric acid in the process. This uric acid is directly excreted in the hindgut or defecated in the food, before microorganisms scavenge it into ammonia and urea. 301x80mm (150 x 150 DPI)

  Figure S4: nMDS ordinated distances of bacterial communities in LD, MD and HD conditions, split by sample type (larvae or food). Plots A-D respectively show Jaccard, Bray-Curtis, unifrac, and weighted unifrac distances. Dots: sequencing replicates, colored by rearing density. Ellipses: 95% confidence zones. Plot B is identical to the Fig. 2C in article.

Fig S5 :

 S5 Fig S5: Relative abundance heatmap of the second-level functional KEGG pathways identified by PICRUSt2, in larvae and food samples, at each larval density level.

Fig S6 :

 S6 Fig S6: Relative abundance of purine metabolism and urea cycle KEGG modules (N=6 per sample type per density), in larvae and food samples from LD, MD and HD conditions. Boxes: first and third quartiles of the distribution. Black horizontal line: median of the distribution. We found no significant differences between any treatments.

Table S1 :

 S1 PERMANOVA table showing the effect of larval density and sample type, whether alone or in interaction, on different distance metrics describing the beta diversity between bacterial communities.

				FEMS Microbiology Ecology				Page 38 of 78
	Distance	Factors	Df Sum s of squares Mean squares	F Model	R2	Pr(>F)
	Jaccard	density	2	0,307	0,154	4,542	0,163 2e-04	***
		sample_type	1	0,307	0,307	9,086	0,163 1e-04	***
		density:sample_type	2	0,254	0,127	3,755	0,135 1e-03	***
		Residuals	30	1,015	0,034	0,539
		Total	35	1,884	1,000
	Bray-Curtis	density	2	1,727	0,863	6,402	0,205 1e-04	***
		sample_type	1	1,023	1,023	7,581	0,122 1e-04	***
		density:sample_type	2	1,612	0,806	5,974	0,192 1e-04	***
		Residuals	30	4,046	0,135	0,481
		Total	35	8,407	1,000
	Unifrac	density	2	0,191	0,096	3,977	0,154 0.0016	**
		sample_type	1	0,199	0,199	8,268	0,160 0.0001 ***
		density:sample_type	2	0,132	0,066	2,746	0,106 0.0173	*
		Residuals	30	0,722	0,024	0,580
		Total	35	1,244	1,000
	Weighted unifrac density	2	1,297	0,649	29,267	0,493 0.0001 ***
		sample_type	1	0,188	0,188	8,460	0,071 0.0011	**
		density:sample_type	2	0,484	0,242	10,928	0,184 0.0001 ***
		Residuals	30	0,665	0,022	0,252
		Total	35	2,634	1,000
				ScholarOne Support 1-434/964-4100

Supplementary material

Functional profiling of the bacterial metacommunity

We extrapolated functional characteristics of bacterial communities from their taxonomic composition using the PICRUSt2 program ver 2.2.0 (Douglas et al. 2019). We used the same OTU tables than those used for taxonomic diversity analyses as inputs (build with SILVA 132 16S database), and then run the PICRUSt2 script with default settings (picrust2_pipeline.py). We extracted KEGG Orthology (KO) abundances and normalized them relative to the total expression level in each individual sample. We grouped KO in KEGG pathways and KEGG modules [START_REF] Kanehisa | KEGG: Kyoto Encyclopedia of Genes and Genomes[END_REF] and calculated functional differences between treatments using Kruskal-Wallis test followed by post-hoc Dunn's test with Benjamini-Hochberg correction.

Supplementary figures


Invasive fungal diseases during COVID-19:

We should be prepared.

Gangneux JP 1 , Bougnoux ME 2 , Dannaoui E 3 , Cornet M 4 , Zahar JR 5 .

1-Univ

Invasive fungal diseases during COVID-19: We should be prepared

The epidemic of respiratory infection due to the new coronavirus SARS-CoV-2 that emerged by the end of 2019 in China is now pandemic and associated with a huge number of deaths. The mortality rate greatly varies between countries, with an unexplained high rate in some of them. Among various causes of morbidity and mortality in COVID-19 patients, the frequency and impact of co-infections has still been poorly studied, particularly in patients with an acute respiratory distress syndrome (ARDS).

Invasive fungal infections (IFI) during COVID-19 are still rarely reported and may be underdiagnosed

Some gripping epidemiological points must be underlined: 3). (iii) Mortality. We still don't know exactly how fungal co-infection impacts on mortality but are aware of the dramatic impact of influenza/IPA co-infection with a mortality reaching 23% in some European centers [START_REF] Verweij | Influenza Coinfection: Be(a)ware of Invasive Aspergillosis[END_REF][START_REF] Van De Veerdonk | Influenza-Associated Aspergillosis in Critically Ill Patients[END_REF]. In the study by Schauwvlieghe, the 3-month mortality rate of influenza is 51 % when associated with IPA and 28% without IPA (3).

(i)
Besides, the particular pathophysiology of COVID-19 may also account for unprecedented comorbidity with IFI. First, the high aggressive feature of the SARS-CoV-2 virus to the lung tissue and the large bilateral alveolo-interstitial lesions make the occurrence of IFI very likely, specifically those with a primary pulmonary entry and an airborne route of infection such as IPA, pneumocystosis (PjP) and mucormycosis [START_REF] He | Coronavirus Disease 2019 (COVID-19): What we know?[END_REF]. Second, absolute number of T lymphocytes, CD4+T and CD8+T cells are markedly lower in severe COVID-19 cases than moderate cases, associated with markedly higher levels of IL-2R, IL-6, IL-10, TNF-alpha and some other inflammatory markers [START_REF] Chen | Clinical and immunologic features in severe and moderate Coronavirus Disease[END_REF].

What we still don't know

-Numerous studies will be implemented in ICU patients, particularly in patients with ARDS, because a performant diagnosis for IFI will usually be available. It will be more complicated to evaluate the dynamic of colonization, the risk of chronic and allergic fungal diseases, and at a lower extent the risk of invasive fungal diseases in non-hospitalized patients while chronic respiratory diseases and long-term corticosteroid therapy may be part of risk factors.

J o u r n a l P r e -p r o o f

-The balance between underlying disease and COVID-19 as attributable risk factors for fungal infection will be difficult to appreciate. That's why large surveys and registries with sufficient description of the patient characteristics are mandatory. -Will we propose the optimal diagnostic tools to ensure a correct survey? In particular, numerous laboratories have stopped some manipulations of at risk respiratory samples such as direct examination, or galactomannan determination in respiratory samples.

Besides, the performances of some blood biomarkers such as galactomannan, beta-Dglucan or DNA detection are well evaluated in neutropenic patients but far less in other conditions.

What we should do

In France, IFI account for a high risk of mortality in patients with co-morbidities from 9.2% to 40% depending on the fungal disease [START_REF] Bitar | Population-based analysis of invasive fungal infections, France, 2001-2010[END_REF]. IA is notably diagnosed in neutropenic patients, patients under chemotherapy, particularly for hematological malignancies, prolonged corticosteroid therapy or biotherapy, HSCT allografts or solid organ transplantation, or chronic respiratory diseases. PCP is an opportunistic infection diagnosed in lymphopenic patients, patients co-infected with HIV, and patients suffering from hematological malignancies, solid organ transplantation or chronic respiratory diseases. Invasive mucormycosis is increasingly reported (thanks to the improvement of diagnostic tools) in susceptible patients such as those suffering from diabetes, hematological malignancies, solid organ transplantation or chronic respiratory diseases and superficial injuries in burned patients or after local traumatism. Using local and literature data, the global burden of severe fungal infection is estimated approximately 1,000,000 (1.47%) cases in France each year [START_REF] Gangneux | LIFE program, the Société française de mycologie médicale SFMM-study group. An estimation of burden of serious fungal infections in France[END_REF].

We believe that microbiological diagnosis should be promptly adapted to the current unprecedented situation. In accordance with the preliminary data available concerning the occurrence of IFI in COVID-19 patients, the French High Council for Public Health (HSCP -Haut Conseil de la santé publique) recommends to systematically screen for fungal pathogens in patients admitted with pneumoniae [START_REF]Avis relatif à la prise en charge des cas confirmés d'infection au virus SARS[END_REF]. Among the 5 first well described French patients, one is co-infected with Aspergillus flavus [START_REF] Lescure | Clinical and virological data of the first cases of COVID-19 in Europe: a case series[END_REF].

In this context, vigilance is essential and we consider that a large panel of tools must be proposed to better characterize the epidemiology of IFI during this striking pandemic:

-local implementation of diagnostic tools to optimize the early diagnosis in order to allow a prompt specific antifungal treatment and to optimize the management of the patients. A 2-step process could associate an efficient syndromic molecular approach (qPCR for Aspergillus, Pneumocystis jiroveci, and mucorales) associated to culture for respiratory samples. In case of positivity of any of these tests, a confirmation step with blood biomarkers will be implemented depending on the positive results, with serum galactomannan and/or serum beta-D-glucan and/or cryptococcal antigenemia and/or blood qPCR for Aspergillus or mucorales, -national multicentric studies that aim to explore the risk of fungal co-infection during COVID-19 with joint consortia of ICU and Mycology specialists. The French Society for Medical Mycology (SFMM) will be supportive for these studies, -contribution to national (Centre National de Référence Mycoses Invasives et Antifongiques, https://www.pasteur.fr/fr/sante-publique/CNR/les-cnr/mycoses-invasives-antifongiques) and international registries such as the one endorsed by the European Confederation of Medical Mycology (ECMM, https://data.castoredc.com/).

Our collective goal is :

-To provide original and still unknown epidemiological data focused on fungal infections during COVID-19; better evaluate the incidence and the dynamic of fungal infection in the course of COVID-19, particularly during the ICU stay.

-To improve the diagnosis, in proposing an efficient syndromic molecular approach for fungal respiratory infection during ARDS that can be shared with all hospitals receiving COVID-19 patients -To optimize immediate COVID-19 patient management, with a real-time screening in order to introduce as early as possible a targeted treatment. First-line treatment for aspergillosis, pneumocystosis and mucormycosis are far different and empirical treatments will be avoided as much as possible. Depending on the epidemiological data obtained, preventive strategies such as antifungal chemoprophylaxis and environmental measures could be envisaged with the aim to decrease morbidity and mortality.

2 - 3 - 4 - 5 -

 2345 Rennes, CHU Rennes, Inserm, EHESP, Irset (Institut de recherche en santé environnement et Travail) -UMR_S 1085, CHU Rennes, 2, rue Henri Le Guilloux, 35033 Rennes, France. Electronic address: jean-pierre.gangneux@univ-rennes1.fr. Service de microbiologie clinique, unité de parasitologie-mycologie, hôpital Necker-Enfants-Malades, AP-HP, Centre -université de Paris, France; Unité biologie et pathogénicité fongiques -Institut Pasteur, Paris, France. Service de microbiologie, unité de parasitologie -mycologie, hôpital européen Georges-Pompidou, Centre -université de Paris, AP-HP, France. Service de parasitologie -mycologie, CHU de Grenoble, TIMC-IMAG TheREX, université Grenoble-Alpes, France. Unité de prévention du risque infectieux, groupe hospitalier Paris Seine-Saint-Denis, hôpital Avicenne, UFR SMBH, université Paris 13, France. KEYWORDS: Covid Aspergillus; Covid Pneumocystis; Covid aspergillosis; Covid fungal; Covid mucorales; Covid pneumocystosis

Pr

  Jean-Pierre Gangneux MD, PhD Président de la Société Française de Mycologie Médicale CHU Rennes, Inserm, EHESP, IRSET (Institut de recherche en santé environnement et Travail) -UMR_S 1085, Université de Rennes 2 rue Henri le Guilloux, 35033 Rennes Tél : +33-(0)2-99-28-42-68 Email: jean-pierre.gangneux@univ-rennes1.fr Dr Marie-Elisabeth Bougnoux MD, PhD Unité de Parasitologie-Mycologie -Service de Microbiologie clinique Hôpital Necker -Enfants Malades, AP-HP.Centre -Université de Paris Unité Biologie et Pathogénicité Fongiques -Institut Pasteur, Paris Dr Eric Dannaoui MD, PhD Unité de Parasitologie -Mycologie. Laboratoire de Microbiologie Hôpital Européen Georges Pompidou, AP-HP.Centre -Université de Paris Pr Muriel Cornet MD, PhD Service de Parasitologie -Mycologie. CHU de Grenoble TIMC-IMAG TheREX,Université Grenoble-Alpes Pr Jean Ralph ZAHAR, MD, PhD Unité de Prévention du risque Infectieux GH Paris Seine Saint-Denis -Hôpital Avicenne -UFR SMBH -Université Paris 13