Sarah Benghanem

MD Marine Paul

MD Julien Charpentier

Said Rouhani

MD Omar Ben

Hadj Salem

MD Lucie Guillemet

MD Stéphane Legriel

MD Wulfran Bougouin

PhD Frédéric Pène

PhD Jean Daniel Chiche

PhD Jean-Paul Mira

PhD Florence Dumas

PhD Alain Cariou
email: alain.cariou@cch.aphp.fr

Value of EEG reactivity for prediction of neurologic outcome after cardiac arrest: Insights from the Parisian registry

Keywords: cardiac arrest, prognosis, therapeutic hypothermia, reactivity, electroencephalography, persistent coma Cerebral Performance Category EEG: Electroencephalogram ESM: Electronic Supplementary Material FPR: False Positive Rate GCS: Glasgow Coma Scale ICU: Intensive-Care-Unit IQR: Interquartile-Range NPV: Negative Predictive RASS: Richmond-Agitation-Sedation-Scale

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

Introduction

A vast majority of patients who are resuscitated after a cardiac arrest (CA) are still comatose after return of spontaneous circulation (ROSC) because of transient or definitive anoxic-ischemic brain injury. Among these comatose patients, a significant proportion of them remain unconscious after rewarming from targeted temperature management (TTM) and discontinuation of sedation. Despite the improvement of post-CA care, most of these comatose patients will die following withdrawal of life-sustaining treatment (WLST) for irreversible post-anoxic encephalopathy [START_REF] Lemiale | Intensive care unit mortality after cardiac arrest: the relative contribution of shock and brain injury in a large cohort[END_REF][START_REF] Bougouin | Characteristics and prognosis of sudden cardiac death in Greater Paris: population-based approach from the Paris Sudden Death Expertise Center (Paris-SDEC)[END_REF]. European guidelines recommend to delay the initiation of WLST until exclusion of potential confounders, focusing mostly on residual sedation [START_REF] Nolan | European Resuscitation Council and European Society of Intensive Care Medicine 2015 guidelines for post-resuscitation care[END_REF][START_REF] Sandroni | Prognostication in comatose survivors of cardiac arrest: an advisory statement from the European Resuscitation Council and the European Society of Intensive Care Medicine[END_REF]. Indeed, predictive value of clinical indicators can be influenced by remaining effects of sedatives, as illustrated by clinical studies that reported a high rate of delayed awakening [START_REF] Paul | Delayed awakening after cardiac arrest: prevalence and risk factors in the Parisian registry[END_REF][START_REF] Mulder | Awakening and withdrawal of life-sustaining treatment in cardiac arrest survivors treated with therapeutic hypothermia*[END_REF]. Consequently, a combination of prognostic tools that are independent of sedation should always be combined to avoid premature WLST.

Electroencephalogram (EEG) is broadly used both as a diagnostic and a prognostic tool in post-CA comatose patients [START_REF] Friberg | Survey on current practices for neurological prognostication after cardiac arrest[END_REF]. This bedside investigation is widely available, noninvasive and provides real-time investigation of electrical brain activity. In post-CA comatose patients, EEG may reveal a large spectrum of abnormalities carrying prognostic information, such as malignant patterns indicating brain injury. In addition, ischemia-induced neuronal damage is associated with decreased amplitude and slowing of background EEG activity, and these abnormalities are correlated with the degree of neuronal injury [START_REF] Bauer | EEG patterns in hypoxic encephalopathies (postcardiac arrest syndrome): fluctuations, transitions, and reactions[END_REF]. Data from the literature are heterogeneous regarding the predictive value of EEG non-reactivity for prediction of a poor outcome in post-anoxic patients [START_REF] Sivaraju | Prognostication of post-cardiac arrest coma: early clinical and electroencephalographic predictors of outcome[END_REF][START_REF] Crepeau | Value analysis of continuous EEG in patients during therapeutic hypothermia after cardiac arrest[END_REF][START_REF] Fugate | Predictors of neurologic outcome in hypothermia after cardiac arrest[END_REF][START_REF] Juan | Clinical evolution after a non-reactive hypothermic EEG following cardiac arrest[END_REF][START_REF] Amorim | Malignant EEG patterns in cardiac arrest patients treated with targeted temperature management who survive to hospital discharge[END_REF]. By contrast, some authors have reported that the presence of EEG reactivity could also identify patients with good outcome [START_REF] Tsetsou | Multimodal Outcome Prognostication After Cardiac Arrest and Targeted Temperature Management: Analysis at 36 °C[END_REF][START_REF] Rossetti | Electroencephalography Predicts Poor and Good Outcomes After Cardiac Arrest: A Two-Center Study[END_REF]. However, most of these prior studies have included heterogeneous populations and did not permit to assess the interaction between sedation and background reactivity.

The aim of the present study was to evaluate the prognostic value of EEG reactivity for neurological outcome after CA and to assess the potential confounding effect of sedation.

Materials and Methods

Population

We considered for inclusion all consecutive adult patients who were admitted between January 2007 and October 2016 in the intensive care unit (ICU) of the Cochin University Hospital (Paris, France) with coma (Glasgow coma scale [GCS] ≤ 8) after resuscitation from CA and in whom at least one EEG was performed during coma. We excluded patients investigated for brain death diagnosis and those who had an EEG performed after awakening.

Patients who died within 48 hours post-CA (before any reliable neurological examination could be made) were also excluded. Patients' next of kin were informed that data were collected for clinical research purposes. According to the French legislation, the institutional review board waived the need for written informed consent.

Data collection

We performed a retrospective analysis of prospectively collected data from our CA registry that has previously been described [START_REF] Lemiale | Intensive care unit mortality after cardiac arrest: the relative contribution of shock and brain injury in a large cohort[END_REF][START_REF] Dumas | Immediate percutaneous coronary intervention is associated with better survival after out-ofhospital cardiac arrest: insights from the PROCAT (Parisian Region Out of hospital Cardiac ArresT) registry[END_REF]. The following data were recorded for each participant: patient's characteristics and cardiac arrest management data using Utstein style [START_REF] Perkins | Cardiac Arrest and Cardiopulmonary Resuscitation Outcome Reports: Update of the Utstein Resuscitation Registry Templates for Out-of-Hospital Cardiac Arrest: A Statement for Healthcare Professionals From a Task Force of the International Liaison Committee on Resuscitation (American Heart Association, European Resuscitation Council, Australian and New Zealand Council on Resuscitation, Heart and Stroke Foundation of Canada, InterAmerican Heart Foundation, Resuscitation Council of Southern Africa, Resuscitation Council of Asia); and the American Heart Association Emergency Cardiovascular Care Committee and the Council on Cardiopulmonary, Critical Care, Perioperative and Resuscitation[END_REF], in-hospital parameters including, TTM modalities, type of sedation, timing for discontinuation of sedation, clinical indicators of neurological status (pupillary light reflex and motor component of the GCS, status epilepticus, timing for awakening) and short-latency somatosensory evoked potentials (SSEP). Length of ICU stay, cause of death and vital status at 1 year were also reported. For the present study, we used the first EEG performed during the ICU stay.

ICU management

Our management protocol for patients admitted after CA has previously been described (see ESM) and did not change throughout the study period except for sedation [START_REF] Paul | Comparison of two sedation regimens during targeted temperature management after cardiac arrest[END_REF].

In the absence of contraindication, TTM was immediately started after ICU admission, targeting a core temperature of 32-34°C for 24 hours using an external cooling device. For sedation, we used midazolam and fentanyl between 2007 and 2014. Following updated guidelines, we then shifted towards propofol and remifentanil. The Richmond Agitation-Sedation Scale (RASS) was assessed every 3 hours and the sedation level was titrated to maintain a RASS level of -5 (no response to voice or physical stimulation) all along the TTM period. Sedatives were interrupted after rewarming.

Neurological prognostication and WLST

Neurological status was evaluated twice-daily by ICU physicians until death or ICU discharge. Sedation level was assessed by nurses every 3 hours and awakening was defined by the presence of 3 consecutive RASS scores of at least -2 (patient briefly awakens with eye contact to voice), as previously reported [START_REF] Ely | Monitoring sedation status over time in ICU patients: reliability and validity of the Richmond Agitation-Sedation Scale (RASS)[END_REF]. In patients who were still comatose 72 hours after sedation discontinuation, a multimodal prognostication protocol was used, according to current guidelines (ESM) [START_REF] Nolan | European Resuscitation Council and European Society of Intensive Care Medicine 2015 guidelines for post-resuscitation care[END_REF][START_REF] Nolan | European Resuscitation Council guidelines for resuscitation 2005. Section 1. Introduction[END_REF][START_REF] Wijdicks | Quality Standards Subcommittee of the American Academy of Neurology. Practice parameter: prediction of outcome in comatose survivors after cardiopulmonary resuscitation (an evidence-based review): report of the Quality Standards Subcommittee of the American Academy of Neurology[END_REF][START_REF] Nolan | Part 1: Executive summary: 2010 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations[END_REF]. Burst suppression or refractory status epilepticus were part of our algorithm. Importantly, EEG reactivity was not part of our prognostication algorithm during the study period.

EEG assessment:

Intermittent EEG was recorded for at least 20 minutes by a skilled technician using a standardized protocol with 19 externals electrodes. In order to facilitate the interpretation of tracings, physicians in charge specified several clinical data to the board-certified neurologists, such as GCS, pupillary and corneal reflexes, presence of myoclonus, timing of sedation weaning, and drugs administered during EEG recording.

Presence of EEG reactivity was defined as the reproducible evidence of increase or decrease in amplitude or frequency of EEG waveforms. Appearance of muscle activity or eye blink artifacts was not considered as reactive patterns. The reactivity was tested at bedside in a standardized way, by auditory stimulation (hands clap, patient name call) and nociceptive (pressure on the nail or nipple pinching only if no reactivity was obtained with other stimuli) every 2 minutes during EEG recording. [START_REF] Tsetsou | EEG reactivity to pain in comatose patients: Importance of the stimulus type[END_REF] EEG findings were collected from the daily report of neurologists in the medical record of each patient, and tracings were not re-analyzed. EEG patterns were retrospectively classified by two authors (and a third one in case of disagreement) based on the American Clinical Neurophysiology Society (ACNS) terminology [START_REF] Hirsch | American Clinical Neurophysiology Society Societyal Neurophysiology SocietySocietylogy: 2012 version[END_REF]. Highly malignant pattern was defined as electrical status epilepticus, burst suppression or suppression. Electrical status epilepticus was considered as continuous rhythmic discharges like spikes, or spike-waves; burst suppression was defined as isoelectric voltage background with burst, and suppression as isoelectric voltage or suppressed background (<10 microvoltages) without discharges waveforms. Malignant patterns were defined as malignant periodic or rhythmic patterns, malignant background (discontinuous background, low voltage background <20 microvoltages) or non-reactive EEG. Finally, EEG was considered benign in the absence of any malignant features stated above and with the presence of reactivity EEG.

The primary outcome was the neurological status at ICU discharge using the cerebral performance categories (CPC) scale, dichotomized in good (CPC 1 or 2) or poor (CPC 3-5) outcome. [START_REF] Jennett | Assessment of outcome after severe brain damage[END_REF] Statistical Analysis Continuous variables were summarized using medians and interquartile range (IQR), and categorical variables were reported as proportions.

We first investigated the association between relevant patients' characteristics and neurologic outcome (CPC 1-2 vs CPC 3-5), including patient demographics, Utstein variables and EEG patterns. We performed Pearson's Chi2 test for categorical variables, and Wilcoxon test, when appropriate, for continuous variables. Secondly, a multivariate analysis was performed to assess independent predictors of poor outcome using logistic regression including factors with a p-value <0.15 in univariate analysis. Specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV) and false positive rate (FPR) corresponding to 1-specifity using an exact binomial 95% confidence interval (CI), were calculated in order to assess the predictive performance of each clinical and EEG indicator. We evaluated the predictive values for unfavorable outcome of non-reactivity, benign pattern, highly malignant pattern, malignant pattern on EEG, and bilaterally absence of N20 potentials on SSEP. To take into account the influence of potential confounders on non-reactive EEG, we stratified analysis according to the presence or absence of sedation and according to presence or absence of TTM use during EEG recording.

Finally, we assessed the predictors of EEG non-reactivity, using logistic regression including factors associated with EEG non-reactivity identified in univariate analysis with a P value<0.15.

All tests were two-sided, with P <0.05 considered statistically significant. We performed analysis using STATA/SE 14.0 software (College Station, TX, USA).

Results

Patients:

From January 2007 to October 2016, 1426 patients were admitted after CA, of them 529 patients had at least one EEG performed during their ICU stay and were potentially eligible for the present analysis. Among them, 101 patients were excluded for the following reasons: 16 patients died during the first 48 hours; 45 patients were already awake at time of EEG recording; in 40 additional patients, the indication of EEG was confirmation of a brain death diagnosis (Figure 1). Patients' characteristics are described in Table 1 for the 428 patients retained in the analysis. Patients were mostly male (71%), with a median age of 63 years. Initial cardiac rhythm was shockable in 212 (49%) patients, and epinephrine was used during resuscitation in 320 (76%) cases. TTM was used in 96% of patients, with a median duration of sedation 1.1 (0.7-1.5) days, using mostly midazolam (81%). A poor outcome was observed in 344/428 patients (80%) at ICU discharge and in 342/411 patients (83%) at 1 year. Causes of death are listed in ESM.

EEG and SSEP findings:

Patients' status at time of EEG recording is detailed in Table 2 and EEG results according to neurologic outcome are reported in Table 3. Median time to EEG assessment was 3 days (2-4). Two-hundred and forty-three patients (57%) were weaned from sedation at time of EEG recording: the median delay between CA and EEG examination was 4 days [START_REF] Nolan | European Resuscitation Council and European Society of Intensive Care Medicine 2015 guidelines for post-resuscitation care[END_REF][START_REF] Sandroni | Prognostication in comatose survivors of cardiac arrest: an advisory statement from the European Resuscitation Council and the European Society of Intensive Care Medicine[END_REF] in patients assessed after weaning of sedation versus 1 day (1-2) in patients assessed with ongoing sedation p <0.001.

In the whole population, 209/409 (51%) patients had a non-reactive EEG. Nonreactive EEG was far less frequent in patients with good outcome as compared with those with poor outcome (7% vs 63%, p<0.001). Characteristics of the 6 patients who had a good outcome despite non-reactivity on EEG are listed in ESM.

Based on the ACNS nomenclature, 113/428 patients (26%) had highly malignant patterns, 162/428 (38%) had malignant patterns and 153/428 patients (36%) had a benign EEG. As compared with the good outcome group, malignant patterns were more frequently observed in the poor outcome group (9.5% vs 45%, p<0.001), and all highly malignant patterns were observed in the poor outcome group (0% vs 33%).

EEG for prediction of poor outcome:

PPV of a non-reactive EEG for prediction of a poor outcome was 97.1% (93.6-98.9) (Table 4). The PPV reached 98.3% (94.1-99.8) if reactivity was tested in non-sedated patients (p=0.41). In multivariate analysis, ongoing sedation during EEG was not associated with a higher risk of non-reactive EEG (OR 1.3; IC95 (0.8-2.1) p=0.35) (ESM).

In multivariate analysis, non-reactive EEG was significantly associated with a higher risk of poor outcome (OR 12.56; IC 95% (4.7-33.6) p <0.001). Predictive values of other EEG patterns for poor outcome are reported in Table 4 and ESM.

EEG patterns for prediction of a good outcome:

The PPV of a benign EEG to predict a good outcome was 49.7% (41.5-57.9). This PPV increased up to 66.2% (54.3-76.8) when sedation was present, as compared to benign EEG without sedation, with PPV of 34.2% (23.9-45.7) p<0.001. In the subgroup of patients under TTM and still sedated during EEG recording (n=96), this PPV increased to 72.9% p=0.33. (ESM).

Discussion

In this population of post-CA comatose patients, we found that EEG non-reactivity was observed in half of the population, being predictive of unfavorable outcome. Sedation had a modest effect on the predictive value of EEG non-reactivity, with a slightly higher PPV and lower FPR even if this difference was not significant. This would suggest that sedation might partly alter EEG reactivity, a fact that should be taken into account in the interpretation of EEG for neuroprognostication after CA. Interestingly, predictive value of a benign reactive EEG for good outcome seems to be better if EEG was assessed earlier, despite ongoing sedation.

The first step of neuroprognostication after CA is based on neurological examination performed after exclusion of confounders, particularly residual sedation [START_REF] Samaniego | Sedation confounds outcome prediction in cardiac arrest survivors treated with hypothermia[END_REF]. In this context, other reliable tools are needed. Bilateral absence of N20 potentials on SSEP is highly correlated with poor neurologic outcome [START_REF] Sandroni | Predictors of poor neurological outcome in adult comatose survivors of cardiac arrest: a systematic review and meta-analysis. Part 2: Patients treated with therapeutic hypothermia[END_REF]. However, SSEP have a low sensitivity and remain underused [START_REF] Grossestreuer | Factors associated with post-arrest withdrawal of life-sustaining therapy[END_REF]. Conventional EEG is more widely available, contributes to diagnose persistent coma with subclinical seizure and serves also as a prognostic tool after CA. In our population, EEG was performed in 37% of patients admitted for a CA, a rate that is in line with previous studies [29 ,30].

The present work highlights that sedation might interfere with reactivity assessment even the results are not statistically significant. In our population, 43% of EEG recordings were performed with ongoing sedation, mostly because EEG was performed during the first 24 hours. This is concordant with other recent studies suggesting that EEG within the first 24 hours after ROSC might be associated with a good prognostic accuracy [START_REF] Cloostermans | Continuous electroencephalography monitoring for early prediction of neurological outcome in postanoxic patients after cardiac arrest: a prospective cohort study[END_REF]. In fact, sedation could probably be a confounding factor for absence of EEG reactivity, with a trend to a lower PPV regarding prediction of poor neurologic outcome. As a matter of fact, midazolam and propofol are not only sedatives drugs but they are also active against seizures and can modify EEG patterns [START_REF] Brophy | Guidelines for the evaluation and management of status epilepticus[END_REF]. Some studies highlighted that sedation increases suppression ratio and decreases amplitude-integrated EEG [START_REF] Backman | Highly malignant routine EEG predicts poor prognosis after cardiac arrest in the Target Temperature Management trial[END_REF][START_REF] Drohan | Effect of sedation on quantitative electroencephalography after cardiac arrest[END_REF]. In a clinical study in which 8/22 post-CA patients with burst suppression survived with good outcome, all cases were possibly confounded by the concomitant use of propofol [START_REF] Amorim | Malignant EEG patterns in cardiac arrest patients treated with targeted temperature management who survive to hospital discharge[END_REF]. Physicians should cautiously interpret the lack of reactivity during this period because of the risk of interference with sedation [START_REF] Sivaraju | Prognostication of post-cardiac arrest coma: early clinical and electroencephalographic predictors of outcome[END_REF][START_REF] Juan | Clinical evolution after a non-reactive hypothermic EEG following cardiac arrest[END_REF]. This is mostly important when the first tracing is non-reactive in a sedated patient. We reported 6 patients with an initial non-reactive EEG who later awoke and who had a favorable outcome. Interestingly, all these 6 patients were sedated with midazolam and EEG assessment was performed 1 day after CA in median, with ongoing sedation in 67% of cases. We could probably explain our statistically negative results with the under estimation of sedative effect on EEG reactivity because EEG was not systematically performed in all comatose post-CA patients.

We reported the presence of a highly malignant EEG pattern in 26% of patients, which was a very strong EEG indicator of poor outcome, in agreement with previous studies [START_REF] Rossetti | Electroencephalography Predicts Poor and Good Outcomes After Cardiac Arrest: A Two-Center Study[END_REF][START_REF] Backman | Highly malignant routine EEG predicts poor prognosis after cardiac arrest in the Target Temperature Management trial[END_REF]. However this should be interpreted cautiously since presence of a status epilepticus was considered as a highly malignant pattern and was a part of our criteria for WLST although some study described good outcome after this event [START_REF] Legriel | Prognostic value of electrographic postanoxic status epilepticus in comatose cardiac-arrest survivors in the therapeutic hypothermia era[END_REF]. A malignant EEG pattern was strongly associated with poor outcome but with a higher rate of false positive rate, confirming that detection of low voltage and discontinuous background could be difficult [START_REF] Westhall | Standardized EEG interpretation accurately predicts prognosis after cardiac arrest[END_REF].

Concerning prognostication of good outcome, we found a moderate predictive value of a reactive EEG (39%) and a benign EEG (50%) in comparison with Rossetti et al. reporting a 72% PPV of presence of reactivity on early EEG and 86.2% PPV of early benign EEG for good outcome [START_REF] Rossetti | Electroencephalography Predicts Poor and Good Outcomes After Cardiac Arrest: A Two-Center Study[END_REF]. Our population differed significantly since we only included patients who had an EEG performed in case of neurological concern. This may have selected a population with a higher risk of severe brain injury. This may explain that only 20% of our patients recovered a CPC 1-2 level as opposed to 50% in Rossetti's population. Second, the diversity of reactivity testing modalities and interpretation with a possible inter-rater variability could also explain these differences [START_REF] Westhall | Interrater variability of EEG interpretation in comatose cardiac arrest patients[END_REF]. Third, timing for EEG assessment was different.

Interestingly, the PPV of a benign EEG regarding good outcome seems to be better when a benign EEG was obtained earlier, despite ongoing sedation and TTM.

Our study has several strengths. First, we studied a large number of patients with inclusion criteria in concordance with current guidelines for neuroprognostication. Second, reactivity was tested in a standardized way by the same technician and the interpretation was blinded to other prognostic tools and performed by the same electrophysiologists all along the study period. Both were external consultants, thus limiting the risk of bias. Moreover, definition of EEG reactivity respected ACNS's standardized critical care EEG terminology [START_REF] Hirsch | American Clinical Neurophysiology Society Societyal Neurophysiology SocietySocietylogy: 2012 version[END_REF].

This study also has some limitations. First, this is a retrospective and monocentric study in which the timing for EEG assessment was not strictly uniform, even if the median delay from CA was 3 days as recommended. EEG recordings performed under sedation were obtained earlier as compared with those performed without ongoing sedation. The difference may have influenced the results on sedation effect on EEG. EEG patterns may fluctuate and evolve, and temporal dynamic changes of EEG over time are common [START_REF] Bauer | EEG patterns in hypoxic encephalopathies (postcardiac arrest syndrome): fluctuations, transitions, and reactions[END_REF]. Second, categorization between highly malignant, malignant or benign patterns was scored retrospectively according to the detailed EEG report. We cannot exclude the risk of missing data in EEG description. Third, neurophysiologists who interpreted EEG tracings were not totally blinded for all clinical data and it could have influenced their interpretation. However, they were blinded to outcome at time of EEG interpretation. Fourth, there is a risk of subjective assessment of EEG reactivity [START_REF] Tsetsou | EEG reactivity to pain in comatose patients: Importance of the stimulus type[END_REF][START_REF] Admiraal | International consensus on EEG reactivity testing after cardiac arrest: Towards standardization[END_REF][START_REF] Azabou | Value and mechanisms of EEG reactivity in the prognosis of patients with impaired consciousness: a systematic review[END_REF]. Quantitative methods might increase reproducibility and objectivity of EEG reactivity assessment, though they are rarely used in routine practice [START_REF] Noirhomme | Automated analysis of background EEG and reactivity during therapeutic hypothermia in comatose patients after cardiac arrest[END_REF][START_REF] Liu | Electroencephalography reactivity for prognostication of post-anoxic coma after cardiopulmonary resuscitation: A comparison of quantitative analysis and visual analysis[END_REF]. Fifth, we did not use continuous EEG in our study. However, it has been reported that two standard EEG recordings of 20-30 min duration, including reactivity testing, are as informative as continuous EEG, while being less demanding on resources [START_REF] Crepeau | Value analysis of continuous EEG in patients during therapeutic hypothermia after cardiac arrest[END_REF]41]. Then, we changed our sedation protocol during the 9 years collection period, which could have theoretically influenced our results. Sixth, self-fulfilling prophecy is a common bias in this setting. The treating physicians were not blinded to the results of reactivity testing. However WLST decisions were not based on EEG reactivity assessment, since it was not part of our prognostication algorithm during the study period. Median ICU length of stay was 7 days in the group of patients with poor outcome, illustrating the low risk of early WLST in this population. Finally, the outcome was assessed at ICU discharge for analysis while longterm outcome would have been more adapted.

Conclusion

Among comatose patients after CA, a non-reactive EEG was predictive of unfavorable outcome but this indicator has to be used in a multimodal approach for prognostication.

Assessment of reactivity was slightly influenced by concomitant sedation. Conversely, early benign EEG seems to be a promising tool for prediction good outcome but further studies are required to confirm this finding.

Tables and Figures

Figure 1 :

 1 Figure 1: Flow chart

  Figure 1: Flow chart

Table 1 :

 1 Patients' characteristics

Table 2 :

 2 Status at time of EEG recording

Table 3 :

 3 EEG and SSEP findings

Table 4 :

 4 Prognostic value of EEG parameters and SSEPs for poor outcome

Table 1 : Patients' characteristics Patients' characteristics All patients n=428 CPC 1-2 n=84 CPC 3-5 n=344 p

 1

	Male gender, n (%)	304 (71)	66 (77)	(69)	0.15
	Age > 63 years, n (%)	214 (50)	32 (38)	(53)	0.02
	CA in a public area, n (%)	136 (32)	35 (42)	(29)	0.03
	Witnessed CA, n (%)	375 (89)	75 (93)	(88)	0.26
	Bystander CPR, n (%)	255 (61)	49 (61)	(61)	0.96
	Initial shockable rhythm, n (%)	212 (49)	64 (76)	(43)	<0.001
	Use of epinephrine, n (%)	320 (76)	35 (44)	(84)	<0.001
	Dose of epinephrine >2 mg, n (%)	165 (39)	18 (22)	(43)	0.001
	Time from CA to CPR >5 min, n (%)	201 (52)	31 (40)	(55)	0.02
	Time from CPR to ROSC >20 min, n (%)	210 (50)	27 (33)	(55)	<0.001
	Serum lactate at admission >4.5 mmol.L-1, n (%)	183 (49)	17 (23)	(55)	<0.001
	TTM, n (%)	412 (96)	80 (95)	(97)	0.58
	Post-resuscitation shock, n (%)	209 (48)	44 (52)	(48)	0.47
	Duration of sedation, days, median (IQR)	1.1 (0.7-1.5)	1.1 (0.6-1.6)	1.1 (0.7-1.5)	0.68
	ICU length of stay, days, median (IQR)	7.2 (5.2-9.9)	8.9 (5.5-13.9)	(5.2-9.0)	0.008

Acknowledgments:

We thank Nancy Kentish-Barnes for her help in preparing the manuscript.

Conflict of interest statement:

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Status at time of EEG recording

All patients n=428


Aya Zoghlami

Yassin Refahi

Christine Terryn

Gabriel Paës
email: gabrielpaës@inra.fr

Multimodal characterization of acid-pretreated poplar reveals spectral and structural parameters strongly correlate with saccharification

Keywords: Lignocellulose, Recalcitrance, Multiscale, Pretreatment, Hydrolysis

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

Introduction

The environmental effects of climate change and fast depletion of fossil resources [START_REF] Trends | Challenges and Opportunities in the Implementation of the Sustainable Development Goals[END_REF] have promoted the development of alternative energy sources and bio-based chemicals and polymers. Lignocellulosic biomass (LB) is an important feedstock to produce such sustainable products and can be a solution to increasing concerns over energy demand and climate change without compromising global food security [START_REF] Yuan | Production of bioethanol and value added compounds from wheat straw through combined alkaline/alkaline-peroxide pretreatment[END_REF].

LB is mainly composed of cellulose and hemicelluloses which are carbohydrate polymers and of lignin which is a highly branched phenylpropanoid polymer. The different biomass feedstocks (mainly wood and grass biomass species) have significant differences in proportion of the main constituents (Zhao et al., 2012a). While cellulose and lignin are more abundant in woody biomass, grass biomass has higher proportion in hemicelluloses.

LB includes dedicated energy crops growing on low-quality soil such as miscanthus or switchgrass. Also considered as LB resources are agricultural wastes such as cereal straw, bagasse and forest biomass such as forest and mill residues and woody crops such as poplar and pine.

Main issue in the transformation of LB is that it is naturally recalcitrant to deconstruction into bio-based products because of its complex structure and chemical composition (Zhao et al., 2012b). Several factors underlying LB recalcitrance have been identified such as the content of lignin/hemicellulose/cellulose [START_REF] Studer | Lignin content in natural Populus variants affects sugar release[END_REF], cellulose crystallinity [START_REF] Xu | Effect of crystallinity on pretreatment and enzymatic hydrolysis of lignocellulosic biomass based on multivariate analysis[END_REF], the degree of cellulose polymerization [START_REF] Hallac | Analyzing cellulose degree of polymerization and its relevancy to cellulosic ethanol[END_REF]) and pore size/density [START_REF] Meng | Insights into the effect of dilute acid, hot water or alkaline pretreatment on the cellulose accessible surface area and the overall porosity of Populus[END_REF]To overcome the recalcitrance of lignocellulose, a pretreatment step is essential to facilitate LB enzymatic hydrolysis. The pretreatment changes the physical and/or chemical structure of LB and facilitates the conversion of polysaccharides into fermentable sugars by enzymes by increasing accessibility to enzymes [START_REF] Mccann | Biomass recalcitrance: a multi-scale, multi-factor, and conversion-specific property[END_REF]. A wide range of pretreatment methods have been developed which can be classified into physical (e.g. milling, irradiation (e.g. Gammaray)), chemical & physico-chemical methods (e.g. explosion (with steam), acid (sulfuric)), and biological (fungi and actinomycetes) methods. Pretreatment is an expensive step in LB conversion process and the choice of the pretreatment type depends on the biomass species, due to variations in the content and composition of lignin and hemicellulose among LB species.

Dilute Acid Pretreatment (DAP) is one of the most commonly used industrial methods of pretreatment due to its relatively low cost and its efficiency particularly with hardwoods [START_REF] Silveira | Current pretreatment technologies for the development of cellulosic ethanol and biorefineries[END_REF]. The DAP is mostly performed using 0.4-2.0% (w/w) H2SO4 at a temperature of 160-220°C [START_REF] Cao | Chemical transformations of Populus trichocarpa during dilute acid pretreatment[END_REF]. It solubilizes hemicelluloses, disrupts the lignin structure, and increases cellulose accessibility to enzymes [START_REF] Santos | Role of hemicellulose removal during dilute acid pretreatment on the cellulose accessibility and enzymatic hydrolysis of compositionally diverse sugarcane hybrids[END_REF] favoring subsequent enzymatic hydrolysis. The influence and efficiency of the DAP depends on the pretreatment temperature, acidity (pH) and residence time. An indication of pretreatment harshness can be represented by a single value called CSF (Combined Severity Factor) [START_REF] Lee | Efficiencies of acid catalysts in the hydrolysis of lignocellulosic biomass over a range of combined severity factors[END_REF]. Despite extensive studies investigating the influence of the DAP on LB physico-chemical structure and morphology, the effect of the DAP at cellular and tissular scale remains yet poorly explored.

In this article, we have selected poplar (Populus nigra x deltoides) as a model of hardwood to investigate the effect of DAP pretreatment on LB at multiple scales under different pretreatment severities. Indeed, poplar is the first woody plant to have its genome sequenced [START_REF] Chang | Field guide to plant model systems[END_REF], it has a fast growth rate, an easy in vitro cultivation and vegetative propagation as well as extensive geographical distribution [START_REF] Meng | An In-Depth Understanding of Biomass Recalcitrance Using Natural Poplar Variants as the Feedstock[END_REF].

Moreover, the possibility of performing genetic modifications in poplar turns it into one of the most promising species for reducing LB recalcitrance [START_REF] Lee | Efficiencies of acid catalysts in the hydrolysis of lignocellulosic biomass over a range of combined severity factors[END_REF]. In order to investigate the effect of DAP pretreatment on poplar, compositional, structural and topochemical changes of poplar samples during DAP and cellulose digestibility were measured. This multimodal analysis was carried out using different microscopy techniques including advanced fluorescence microscopy techniques, chemical composition analysis by confocal Raman microscopy, scanning electron microscopy, and enzymatic hydrolysis to monitor the changes of substrate properties. Correlation between chemical / structural information and digestibility was finally done to highlight the impacts of specific markers on recalcitrance.

Material and methods

Sample preparation

Poplars (Poplus nigra x deltoides) of about 2 years old were collected in Estrées-Mons, France. The collected poplar dried stems were cut into fragments of 0.4 cm wide, 2 cm long, 0.2 cm thickness using a razor blade. Poplar fragments were pretreated in a batch mode using mineralization bombs equipped with Teflon cups. 500 mg of poplar samples were presoaked in 2% (v/v) sulfuric acid (solution at a ratio of 1:30) and incubated in an oil bath at 170°C for 10, 15 and 20 minutes corresponding to three combined severity factor (CSF) of 2.4, 2.5 and 2.7, respectively. These conditions were chosen because a lower temperature did not provide significant modifications of the tissue architecture while higher temperature strongly damaged the tissues so that microscopy analysis was no longer possible. The CSF integrates the reaction time, temperature and acid concentration into a single value and represents the pretreatment harshness. The CSF is computed using the formula CSF = log 10 (t. exp[(TH-TR)/14.75] -pH) as previously defined [START_REF] Chum | Organosolv pretreatment for enzymic hydrolysis of poplars. 2. Catalyst effects and the combined severity parameter[END_REF], where t is the reaction time in minutes, TH is the temperature in °C, TR is a reference temperature (typically 100°C) and pH is the acidity of the aqueous solution. Following DAP pretreatment of the samples, the Teflon cups were immersed in an ice bath for 5 min. The pretreated fragments were then washed three times with a 50% ethanol solution and three times with deionized water until the pH of the wash reached 7.0 to stop the acid hydrolysis. The samples were dried for 24 hrs at room temperature. To acquire microscopy images, the dried fragments were sectioned in the transverse plane from the xylem using a sliding microtome 40µm thickness. All sections used for microscopic analysis were made 0.5 cm thickness starting from the edge to be sure to have a homogeneous effect of the pretreatment.

To perform chemical composition analysis and enzymatic saccharification, the dried samples were milled into particles with 80 µm diameter (granulometry measurement). Untreated fragments were cut using a sliding microtome into sections of 40 µm thickness or milled into 80 µm diameter particles for imaging, chemical composition and enzymatic saccharification analysis.

Chemical composition and enzymatic saccharification

The chemical composition including moisture, ash, lignin, and carbohydrates' contents of untreated and pretreated poplar samples was determined using the methods already described [START_REF] Herbaut | Multimodal analysis of pretreated biomass species highlights generic markers of lignocellulose recalcitrance[END_REF]. The enzymatic hydrolysis assays were performed on both untreated and pretreated samples using a commercial cellulase preparation Cellic ® CTec2 (Novozymes A/S Bagsvaerd, Danemark), selected for its hydrolysis efficiency with a cellulase activity of 195 FPU/mL measured by the filter paper method. Enzymatic saccharification of poplar samples (2 % w/v) was carried out in 10 mL acetate buffer (0.05M, pH 5) for 72 hrs at 50°C and 200 rpm containing 0.02 % sodium azide and the Cellic ® CTec2 cocktail with a final enzyme concentration of 20 FPU/g of dry matter.

The reaction mixtures (buffer and sodium azide) were pre-incubated for 30 min at 50°C and 200 rpm. The enzymatic hydrolysis was then initiated by adding the enzymatic cocktail. During the enzymatic hydrolysis, the samples were kept at 50°C for 72 hrs.

Aliquots were taken at different timepoints: 0, 0.5, 1, 2, 4, 6, 8, 24, 32, 56 and 72 hrs to compute hydrolysis kinetics (Auxenfans et al., 2017a). The concentration of glucose released from enzymatic hydrolysis in the supernatant was determined using a highperformance anionic exchange chromatography (HPAEC-PAD, Dionex) to calculate the cellulose conversion from the untreated and pretreated poplar, as follows:

Cellulose conversion (%) = Amount of released glucose/Amount of cellulose in samples before hydrolysis x 100 All experiments and analysis were carried out three times (biological triplicate).

Scanning electron microscopy (SEM)

To investigate and compare the surface morphology changes between untreated and pretreated samples, the samples were observed using a binocular microscope (Stemi 2000-C, Zeiss,Germany), then imaged by scanning electron microscopy (SEM) using an environmental tabletop electron microscope Hitachi TM-1000 (Japan) in low-vacuum mode. SEM pictures of untreated and pretreated poplar sections were acquired at, 600x and 3000x magnifications.

Confocal Raman microscopy (CRM)

The untreated and pretreated poplar sections were pre-incubated in water for 30 min and then placed on a quartz slide with a drop of ultrapure water and sealed with a cover slip (0.17 mm thickness) to avoid evaporation during the Raman signal detection. Raman spectra were acquired with a microspectrometer LabRam ARAMIS (Horiba Jobin-Yvon, Villeneuve d'Ascq, France). The laser source was a diode at 785 nm to avoid fluorescence [START_REF] Zeng | In situ label-free imaging of hemicellulose in plant cell walls using stimulated Raman scattering microscopy[END_REF], with 13mW power on sample. The microspectrometer was coupled to a microscope BX41 (Olympus, France) equipped with a motorized (x, y) stage. All measurements were recorded using a 100x water immersion objective (LUMPlanFI, NA = 1.0, Olympus, France). The laser and the light scattered by sample were collected through the same objective. A confocal pinhole rejects signals from out-of-focus regions of the sample. A multichannel charge-coupled-channel device (1024x256 pixels) was used to detect the Raman Stokes signal dispersed by a holographic grating (1200 lines/mm) [START_REF] Gierlinger | Imaging of plant cell walls by confocal Raman microscopy[END_REF]Chen et al., 2016a). Raman spectra were recorded with LabSpec 5 software (Horiba, Jobin Yvon, Villeneuve d'Ascq, France) in the specific spectral range 950-1700 cm -1 . Three samples for each condition of pretreatment were analysed and for each sample ten spectra were recorded on different regions of cell wall (cell corner (CC), compound middle lamella (CML) and secondary wall (SW)). These spectra were averaged for each sample. Then, a pre-treatment of Raman spectra was performed with a homemade interface in Matlab were smoothed using Savitzky-Golay function (5 points, 3 rd polynomial order) and baseline corrected (3 rd polynomial order). Bands were assigned according to the literature are summarized in Table 1.

Laser Scanning Confocal Microscopy for 3D imaging

3D image acquisition

Poplar cross sections were incubated in 0.05 M acetate buffer at pH 5 for 30 min prior to mounting in the same medium on a microscope slide and covered with a cover slip (0.17 mm thickness). Using a confocal laser scanning microscope (Leica TCS SP8, Germany) equipped with 63× oil-immersion objective (NA = 1.4), z-stacks (0.3µm) of both untreated and pretreated poplar samples were acquired at scan speed of 400 Hz. A 405 nm laser (4% intensity) was used for imaging cell wall sample autofluorescence by detecting fluorescence emission on the 415-700 nm range using the HyD detector in counting mode.

The z-stacks were acquired with a resolution of 512 x 512 pixels. The microscope parameters and image resolution were optimized to avoid sample photo-bleaching.

For each pretreatment condition (CSF = 2.4, 2.5, 2.7) as well as untreated samples, zstacks on three different randomly selected areas of three different samples were acquired.

Image analysis and 3D segmentation of confocal images

Acquired z-stacks were processed using an automated 3D segmentation and quantification pipeline. The original z-stacks were then saved in tif files with customized tags which guaranteed that the metadata (e.g. voxel dimensions, file type) were saved properly. The images were then denoised using Alternating Sequential Filter (ASF) and Gaussian filters [START_REF] Willis | Cell size and growth regulation in the Arabidopsis thaliana apical stem cell niche[END_REF][START_REF] Michelin | Spatio-temporal registration of 3D microscopy image sequences of Arabidopsis floral meristems[END_REF]. To segment the filtered z-stack in order to identify individual cell walls, the z-stack was first segmented using a 3D watershed algorithm whose seeds were determined using the h-minima operator which computed local minima regions in the denoised z-stacks. The 3D watershed algorithm provided a 3D image in which voxels (volumetric pixel) of the same cell were labeled by a unique integer as cell identifier. Thresholding was then used to compute cell walls by replacing the voxels of the segmented images with background value (fixed to 1) if the intensity of the corresponding voxels on filtered z-stack was below a global threshold. The segmentations were visually inspected for segmentation error (over-segmentation, under-segmentation, missed cell, or shape error) and the segmentation parameters were subsequently optimized [START_REF] Willis | Cell size and growth regulation in the Arabidopsis thaliana apical stem cell niche[END_REF].

Quantification of cellular scale structural parameters

Following 3D segmentation of z-stacks, individual cell wall volumes were computed by counting the number of voxels which had the same label and multiplying this number by the voxel volume which was approximately 0.039µ 3 . Cell wall surface area was estimated from triangular meshes of cell wall surfaces obtained using the marching cubes algorithm from the Visualization Toolkit (VTK) [START_REF] Willis | Cell size and growth regulation in the Arabidopsis thaliana apical stem cell niche[END_REF][START_REF] Schroeder | The visualization toolkit: an objectoriented approach to 3D graphics[END_REF]. Then volume, V, and surface area, A, were computed to determine sphericity

= 6 1/3 2/3
, a dimensionless cellular scale parameter, for each individual cell.

Laser Scanning Confocal Microscopy for spectral imaging

Autofluorescence

Spectral images of untreated and pretreated poplar samples were acquired using laser scanning microscope LSM 710 NLO Zeiss (Zeiss SAS, Germany) coupled with a Chameleon TiSa accordable 80 MHz pulsed laser (COHERENT, USA). Samples sections (40 µm) were mounted in 0.5 M acetate buffer at pH 7 and their excitation was performed using a biphoton excitation at 750 nm. Spectral images were acquired using spectral detector (32 channel simultaneously) of the microscope between 420 and 722 nm using a 20x objective (NA = 0.8). Fluorescence images of untreated and pretreated samples were colored based on their spectrum (each of the 32 channels was represented by its corresponding color from blue to red).

Fluorescence lifetime measurement and analysis

The fluorescence lifetime imaging (FLIM) measurements were performed on untreated and pretreated poplar sections between 455 and 655 nm using a spectral time-correlated single photon counting (TCSPC) detector from Becker and Hickl (Becker & Hickl, Berlin, Germany). Data were analyzed using a multi-exponential decay model with 2 components using SPCImage software (Becker & Hickl, Berlin, Germany). Measurement of average fluorescence lifetime was made by applying regions of interest (ROIs) to cell wall regions such as cell corner (CC), compound middle lamella (CML) and secondary wall (SW) for untreated and pretreated poplar using 10 replicate measurements on each of the 3 regions. ROI data were summed to provide estimates of distributions in these regions.

Data and statistical analysis

All the experiments were carried out in triplicate, and the results were expressed as means ± standard deviations. Analysis of variance (ANOVA) was performed on the obtained data followed by a Tukey's post hoc test for comparison between the untreated and pretreated poplar. p-value for statistical difference was set to 0.05. Statistical analyses were performed using the SigmaPlot 12.0 software (Systat Software, Chicago, USA). Kruskal-Wallis test from SciPy library was performed on computed cellular sphericity values from untreated and pretreated datasets.

Results and Discussion

DAP changes LB chemical composition and improves enzymatic saccharification

The chemical composition of biomass samples is a significant factor that affects the sequential enzymatic hydrolysis. In order to better understand the changes induced by the different degree of severity of the DAP, the chemical composition of both untreated and pretreated samples was analysed. Cellulose, hemicelluloses, lignin, moisture and ash content were determined on a dry weight basis (Fig. 1). The untreated poplar contained ca. 40% ± 0.4 cellulose, 21% ± 0.2 hemicelluloses with xylose as the major component (also comprising arabinose, rhamnose, mannose and uronic acids) and 26% lignin, which is consistent with reports from the literature [START_REF] Herbaut | Multimodal analysis of pretreated biomass species highlights generic markers of lignocellulose recalcitrance[END_REF][START_REF] Esteghlalian | Modeling and optimization of the dilute-sulfuric-acid pretreatment of corn stover, poplar and switchgrass[END_REF].

As expected, hemicelluloses solubilisation increased significantly with pretreatment severity. About 70% of the hemicellulose fraction was removed from the pretreated substrate under the most severe pretreatment conditions (CSF 2.7). The decrease in hemicellulose content, proportional to the pretreatment severity, was due to its degradation into other chemicals under higher temperature and acid conditions. The lignin content gradually increased with increasing CSF: this result is due to the concomitant loss of hemicellulose and the formation of condensed lignin products [START_REF] Sannigrahi | Pseudo-lignin and pretreatment chemistry[END_REF]. This could also be due to the formation of pseudo-lignin by the combination of carbohydrates and lignin degradation products [START_REF] Sannigrahi | Pseudo-lignin and pretreatment chemistry[END_REF]. Many studies reported similar changes in carbohydrates and lignin content after DAP (Herbaut et al.,

2018; Chen et al., 2016b).

In order to evaluate the efficiency of DAP on cellulose digestibility, the same samples used for the chemical composition analysis were subjected to a 72 hrs enzymatic hydrolysis using commercial cellulase cocktail Cellic CTec 2 with a loading of 20 FPU/g of dry matter. The effect of the different severity of DAP on poplar digestibility was estimated by determining the percentage of cellulose conversion over 72 hrs (Fig. 2). A remarkable increase in the efficiency of cellulose conversion was observed when DAP severity was increased, which confirms that pretreatment reaction time is a factor affecting the effectiveness of pretreatment [START_REF] An | Combined dilute hydrochloric acid and alkaline wet oxidation pretreatment to improve sugar recovery of corn stover[END_REF]. Indeed, the cellulose conversion of untreated poplar was about 11% ± 0.11 after 72 hrs, then reached 17% ± 0.41 for pretreated poplar at CSF 2.4, 28% ±0.77 (more than 2-fold) for pretreated poplar at CSF 2.5 and 58% ± 2.17 Overall, results confirm that DAP increases the efficiency of enzymes by improving their accessibility, likely due to hemicellulose removal and lignin reorganization.

SEM images reveal effect of DAP on cellular morphology

To better understand the significant enhancement of poplar samples enzymatic digestibility after pretreatment, the morphology of the untreated and pretreated samples was investigated by SEM. Initially, the most apparent effect of the DAP observed by binocular microscope was the color change of samples from yellow to dark brown which was more pronounced in samples pretreated at high severity . Previous studies have reported that this color change originated from chemical degradation of carbohydrates, lignin and wood extractives [START_REF] Negro | Changes in various physical/chemical parameters of Pinus pinaster wood after steam explosion pretreatment[END_REF]. The cell walls of untreated poplar observed by SEM had a rigid and highly ordered structure. The compound middle lamella (CML) and the secondary wall (S2) kept close together. This rigid structure is recognized as a structural organization limiting the accessibility of enzymes to cellulose [START_REF] Mansfield | Substrate and enzyme characteristics that limit cellulose hydrolysis[END_REF]. The pretreated samples showed fiber deformation especially for the pretreated samples at CSF 2.5 and CSF 2.7. Poplar samples pretreated at the highest severity even showed fiber separation (loss of the fibrous network) and formation of cracks emphasized by the detachment between CML and S2. This structural change could be related to the solubilisation of the hemicellulose fraction which is considered as the adhesive between cell wall sublayers [START_REF] Ling | Microstructural and topochemical characterization of thermally modified poplar (Populus cathayaha) cell wall[END_REF].

Raman microscopy shows DAP induced topochemical modifications

Confocal Raman microscopy provides information about the concentration and the spatial distribution of chemical components in cell walls [START_REF] Gierlinger | Chemical imaging of poplar wood cell walls by confocal Raman microscopy[END_REF]. In this study, changes in the spatial distribution of polymers in the cell walls of pretreated poplar were investigated in situ. The average Raman spectra of untreated and pretreated poplar were collected from various regions: cell corner (CC), compound middle lamella (CML), and secondary wall (SW).

The spectral range from 950 to 1700 cm -1 includes bands from the wood components such as cellulose, hemicelluloses and lignin. Band assignment for poplar is shown in Table 1.

Typical bands of lignin are in the region between 1500 cm -1 and 1700 cm -1 with specific peaks at 1604 cm -1 and 1660 cm -1 . The bands at 1036 cm -1 , 1095 cm -1 and 1123 cm -1 are attributed to carbohydrates.

For untreated poplar, the spectra from different cell wall regions presented different features. Lignin bands at 1604 cm -1 , 1275 cm -1 and 1331 cm -1 were higher in the spectra of CC and CML than those in SW, which in accordance with the higher content in lignin of CC. Contrary, the peaks at 1095 cm -1 , 1123 cm -1 and 1150 cm -1 were more pronounced in the SW than in CC and CML, indicating that SW mainly contains polysaccharides.

Previous studies reported similar results (Chen et al., 2016a;[START_REF] Chen | Towards a multi-scale understanding of dilute hydrochloric acid and mild 1-ethyl-3-methylimidazolium acetate pretreatment for improving enzymatic hydrolysis of poplar wood[END_REF][START_REF] Gierlinger | Chemical imaging of poplar wood cell walls by confocal Raman microscopy[END_REF].

DAP caused considerable changes in the band intensity for the majority of Raman peaks, also depending on cell wall regions. The intensity of the band at 1604 cm -1 , which is related to lignin, increased significantly for CC and CML after DAP especially for the pretreated samples at CSF 2.5 and CSF 2.7 . It can be explained by the fact that lignin was more exposed after the removal of hemicelluloses during the DAP. It may also result from the dissolved lignin redeposited onto cell wall surfaces upon cooling after DAP. The increase of the lignin band intensity for the SW was less important which can be explained by the low lignin content in this region. The peak at 1660 cm -1 , which is indicative of coniferyl alcohol and aldehyde (lignin-CAA) [START_REF] Ma | Label-free in situ Raman analysis of opposite and tension wood in Populus nigra[END_REF], has completely disappeared from Raman spectra of pretreated poplar at CSF 2.5 and CSF 2.7 particularly in the CC and CML. One possible explanation is that high severity DAP removes lignin-CAA structures. Previously, it has been reported that lignin is cross-linked with hemicelluloses that embed cellulose fibres and lignin-CAA associates with hydroxyl groups of cellulose and hemicelluloses via hydrogen bonding within cell walls. The removal of hemicelluloses during DAP may disrupt the cross-linked structures. This observation is consistent with the results from a study of the impact of alkali pretreatment on poplar cell walls [START_REF] Ji | Impact of alkali pretreatment on the chemical component distribution and ultrastructure of poplar cell walls[END_REF]. However, a significant decrease in the bands at 992 cm -1 , 1095 cm -1 and 1123 cm -1 was noted, more important in the SW than that in the CML and CC, indicating significant removal of the polysaccharides from the SW region after DAP and correlated with DAP severity. This topochemical analysis reveals that modifications in chemical composition after DAP are heterogeneous throughout the poplar cell walls and stronger with higher DAP severity.

DAP impacts cell wall autofluorescence and fluorescence lifetime

In wood, lignin is the predominant fluorophore exhibiting autofluorescence under UV light excitation [START_REF] Donaldson | Fluorescence lifetime imaging of lignin autofluorescence in normal and compression wood[END_REF]. Fig. 3A-D shows the autofluorescence of untreated and DAP poplar samples. While the spectral confocal image of untreated poplar (Fig. 3A) has a homogenous blue color distribution along the cell wall, this color turns to blue-greenish for pretreated poplar with CSF 2.5 (Fig. 3C) and to green-yellowish for pretreated poplar with CSF 2.7 (Fig. 3D). Changes in autofluorescence are likely to reflect modification of the lignin chemistry during DAP. Indeed, several factors related to lignin are recognized as influencing fluorescence, such as content in lignin and in carbonyl groups, lignin condensation and cross-linkages between lignin and other cell wall components (Auxenfans et al., 2017b). Spectral data were analysed for all the untreated and DAP samples in different cell wall regions like for Raman measurements. The untreated poplar has a narrow emission band centred on the blue spectral range (maximum 478 nm) (Fig. 3E), with the CC and CMC spectra being close to each other and higher than SW spectrum. With the increase of DAP severity, the emission maximum expands in the green and yellow range (Fig. 3F-H) together with an increase in the fluorescence intensity and a distinct separation of the spectra corresponding to the cell wall regions. This result suggests that the DAP changed strongly the natural lignin arrangement, by solubilizing lignin molecules that interact with hemicellulose oligomers to form new fluorescent compounds emitting in the blue, green and yellow spectra. These results are in agreement 3I-L), from green to yellow, meaning that lifetime was decreased with increased DAP severity. At the cell wall level, fluorescence lifetime in SW appeared always slightly longer than in CML and CC for all poplar samples considered (Fig. 4M-P). This can be explained by the differences in the chemical structure of lignin and its molecular environment in these regions, suggesting that both CC and CML regions contain short lifetime component (condensed lignin: p-hydroxyphenyl units) and SW regions contain both short and long lifetime components (loosely packed lignin) [START_REF] Donaldson | Fluorescence lifetime imaging of lignin autofluorescence in normal and compression wood[END_REF][START_REF] Zeng | In situ micro-spectroscopic investigation of lignin in poplar cell walls pretreated by maleic acid[END_REF]. Untreated poplar fluorescence lifetime was about 813 ± 25.22 ps, then increased significantly to 885 ± 29.17 ps for severity of 2.4; p-value < 0.05 (p-value = 0.045) and even reached 966 ± 34.87 ps for severity of 2.5 (p-value = 0.01), suggesting DAP increased the content of loosely-packed lignin by depolymerising dense lignin regions [START_REF] Li | Lignin depolymerization/repolymerization and its critical role for delignification of aspen wood by steam explosion[END_REF]. In contrast, DAP with the highest severity of 2.7 led to a drop of poplar fluorescence lifetime by 40% to 585 ± 30.49 ps (p-value <0.001), suggesting the existence of a threshold effect around CSF 2.5-2.7. Indeed, it seems the population of denser lignin has suddenly extended in all considered cell wall regions because of the extraction of the loosely packed lignin during the solubilisation of hemicellulose and the formation of pseudo-lignin. Such harsh conditions have probably generated lignin degradation products which have been able to repolymerize as highly conjugated polymers with shortened fluorescence lifetime [START_REF] Sannigrahi | Pseudo-lignin and pretreatment chemistry[END_REF][START_REF] Trajano | The fate of lignin during hydrothermal pretreatment[END_REF].

Overall, fluorescence analysis reveals that DAP strongly alters lignin itself and its environment mainly due to partial lignin depolymerization and hemicellulose solubilisation on the range of CSF 2.4-2.5, while higher CSF of 2.7 generates lignin chemical modifications such as lignin condensation and creation of new inter-linkages between fluorophores.

DAP induces significant shape changes at cellular scale

Images obtained using scanning electron microscopy indicated structural changes at cellular scale on pretreated samples compared to untreated samples. However, the drawbacks of SEM imaging including artifacts potentially introduced by sample preparation (drying) and limited spatial dimensions (2D) have led to a mostly qualitative conclusions so far [START_REF] Herbaut | Multimodal analysis of pretreated biomass species highlights generic markers of lignocellulose recalcitrance[END_REF][START_REF] Chen | Towards a multi-scale understanding of dilute hydrochloric acid and mild 1-ethyl-3-methylimidazolium acetate pretreatment for improving enzymatic hydrolysis of poplar wood[END_REF]. To quantitatively investigate the effect of pretreatment at cellular scale and study its correlation with acquired spectral and chemical data, an original method combining 3D confocal acquisition and 3D segmentation and quantification was developed (see Materials and Methods) and the morphological parameters including cell volumes, surface areas and subsequently cellular sphericity, a dimensionless cellular scale structural parameter, were computed. The mean sphericity values were 0.62 ± 0.19, 0.54 ± 0.21, 0.52 ± 0.19, 0.43 ± 0.11 for untreated, and pretreated samples at 2.4 CSF, 2.5 CSF, 2.7 CSF respectively. The quantifications showed that cellular sphericity gradually decreased as the DAP pretreatment severity increased (Kruskal-Wallis test, p-value = 7e-109,) (Fig. 4), with the most striking cell shape alteration at 2.7 CSF. The decreasing trend in cell sphericity with increasing pretreatment severity confirms the morphological changes observed in SEM images and can be explained by the fact that the pretreatment modifies the compact structure of cell walls through dissolution of hemicelluloses and solubilisation and redistribution of lignin therefore decreases cell compactness measured by sphericity. Moreover, sphericity as a function of the computed cell volume over computed surface area, decreases by increasing surface area which has been previously reported [START_REF] Torr | Structural features affecting the enzymatic digestibility of pine wood pretreated with ionic liquids[END_REF][START_REF] Zhang | Understanding the synergistic effect and the main factors influencing the enzymatic hydrolyzability of corn stover at low enzyme loading by hydrothermal and/or ultrafine grinding pretreatment[END_REF]. Our results show that measurements of cell sphericity can provide quantitative useful information about the DAP severity and consequently predict hydrolysis yield. The results also highlight the importance of multi-scale approaches involving cellular and tissular scale in thoroughly investigating LB recalcitrance and shed light on parameters behind LB recalcitrance at scales yet little explored.

Correlation analysis of measured chemical, spectral and structural parameters reveals quantitative relationships across scales

Given the different properties determined for the untreated and DAP poplar samples, results clearly show strong correlations between DAP severity and modifications in chemical composition (in particular lignin) together with structural modifications, in different cell wall regions. Importantly, a threshold effect has been identified as severity was increased from CSF 2.5 to 2.7, revealing that an increase of only 5 minutes of DAP has dramatic consequences on polymer organization as revealed by spectral analysis.

Consequently, these data can be useful to understand the correlation between related factors and saccharification potential. Indeed, lignocellulose chemical and structural complexity limit the establishment of easy and low-cost transformation processes, and many studies have searched to find some factors that could help predicting recalcitrance (Auxenfans et al., 2017b;[START_REF] Chabbert | Fluorescence techniques can reveal cell wall organization and predict saccharification in pretreated wood biomass[END_REF][START_REF] Huang | A precise and consistent assay for major wall polymer features that distinctively determine biomass saccharification in transgenic rice by near-infrared spectroscopy[END_REF][START_REF] Paës | Tracking of enzymatic biomass deconstruction by fungal secretomes highlights markers of lignocellulose recalcitrance[END_REF]. To understand relationships between parameters, we conducted a detailed correlation analysis between quantified parameters from autofluorescence, FLIM, Raman and structural analysis and hydrolysis yield at 72 hrs (Fig. 5).

Among spectral factors, fluorescence lifetime appears as a strong predictor of hydrolysis (correlation values ca. 0.70-0.75), while autofluorescence and Raman data showed much stronger correlations (0.95-1) and can thus be considered as excellent predictors of hydrolysis. Regarding autofluorescence, its capacity to predict hydrolysis was previously demonstrated on different biomass species and different pretreatment types [START_REF] Herbaut | Multimodal analysis of pretreated biomass species highlights generic markers of lignocellulose recalcitrance[END_REF]Auxenfans et al., 2017a), which strengthens its potential universality. This means that autofluorescence, more than fluorescence lifetime, reveals accessibility of enzymes to polysaccharides, probably because it is related to both structure and composition of lignin.

Nonetheless, lifetime analysis is also relevant to understand the chemical modifications of lignin during pretreatment. Raman data are also a good compromise to predict hydrolysis by using specific ratio between lignin and cellulose-related bands, which is directly related to polymer accessibility. Furthermore, cellular scale morphological parameter, namely sphericity is identified which exhibits a strong negative correlation with hydrolysis yield (-0.96, p-value < 0.05). This demonstrates that a structural factor can be measured at cellular scale correlated with DAP severity can predict saccharification. Even though, it was previously demonstrated that nanoscale structural features (porosity, cellulose accessible surface for example) were related to hydrolysis [START_REF] Herbaut | Multimodal analysis of pretreated biomass species highlights generic markers of lignocellulose recalcitrance[END_REF][START_REF] Zhang | Understanding the synergistic effect and the main factors influencing the enzymatic hydrolyzability of corn stover at low enzyme loading by hydrothermal and/or ultrafine grinding pretreatment[END_REF][START_REF] Kruyeniski | Physical and chemical characteristics of pretreated slash pine sawdust influence its enzymatic hydrolysis[END_REF], it is the first time to our knowledge that a cellular scale feature like sphericity can also predict hydrolysis. This reveals some un-commonly studied connections between scales: hydrolysis by enzymes occurs at the nano-scale, but cell shape might be a strong marker of polymer organization.

Conclusions

Multiscale analysis of dilute-acid pretreated poplar samples revealed that several markers could help understanding the effect of pretreatment on polymer composition and organization and subsequently hydrolysis. In particular, autofluorescence appears as a marker previously identified, thus likely to be considered as pivotal. Importantly, the results show that cellular scale structural parameters such as cellular sphericity, seem also to be strongly correlated with hydrolysis which should be further studied using other 20 biomass species and pretreatments, including investigating other structural markers to assay their universality.

  (5-fold) for pretreated poplar at CSF 2.7, which is in agreement with results reported in the literature[START_REF] Meng | Physicochemical Structural Changes of Poplar and Switchgrass during Biomass Pretreatment and Enzymatic Hydrolysis[END_REF][START_REF] Linde | Steam pretreatment of dilute H2SO4-impregnated wheat straw and SSF with low yeast and enzyme loadings for bioethanol production[END_REF][START_REF] Chen | Towards a multi-scale understanding of dilute hydrochloric acid and mild 1-ethyl-3-methylimidazolium acetate pretreatment for improving enzymatic hydrolysis of poplar wood[END_REF].

  with those previously published[START_REF] Coletta | Mapping the lignin distribution in pretreated sugarcane bagasse by confocal and fluorescence lifetime imaging microscopy[END_REF][START_REF] Li | Lignin depolymerization/repolymerization and its critical role for delignification of aspen wood by steam explosion[END_REF] and show how autofluorescence can reveal changes in lignin composition and arrangement after pretreatment. Fluorophore lifetime is the average time between excitation of a fluorophore and emission of a fluorescence photon. Accordingly, fluorescence lifetime imaging microscopy (FLIM) is a technique to determine the spatial distribution of excited state lifetimes in microscopic samples (Van Munster & Gadella, 2005). In complement to autofluorescence, FLIM can advantageously be related to chemical properties of lignin (Auxenfans et al., 2017b ; Chabbert et al., 2018), since a short lifetime reflects high conjugation between fluorophores and vice-versa. Lifetime fluorescence variation could be visualized in the corresponding lifetime color-coded images of the poplar cell walls (Fig.

FiguresFig. 1

 1 Figures

  Fig. 1. Chemical composition of untreated and pretreated poplar samples. Contents are expressed as weight percentages (%w/w) of the dry biomass amount.

Fig. 4 .

 4 Fig. 4. Effect of DAP on poplar samples at cellular scale. (A) 3D projection of confocal image of untreated poplar section. (B) 3D projection of confocal image of pretreated poplar section at CSF 2.7. (C) Rendering of 3D segmentation of the untreated sample confocal stacks. (D) Rendering of 3D segmentation of the pretreated sample confocal stack. (E.1.) Randomly selected cells showing effect of the segmented untreated sample and (E.2.) and the pretreated sample, showing effect of DAP on individual cell shapes. (F) Distribution of cellular sphericity of untreated and pretreated samples with different severity conditions. The boxes represent the interquartile range and the white lines in the boxes represent the medians. The whiskers show the range of values from the highest to the lowest excluding outliers (not shown). N is the number of segmented cells.

Fig. 5

 5 Fig. 5 Pearson's correlation coefficients between quantified spectral, structural factors and hydrolysis yield (p-value <0.05).

Acknowledgements

Valérie Untereiner is acknowledged for her help in performing Raman measurement, Anouck Habrant for her relevant guidance in confocal measurement, François Gaudard for his help in performing the SEM observations and Mickaël Frye for FLIM measurements. This work is supported by Grand Est Region and FEDER (PhD TECMI-4D scholarship project to A.Z.), and INRA ANS Grant LIMO4DE (to Y.R.).

Tables