Fabien Picard

Jean-François Llitjos

Marine Diefenbronn

Driss Laghlam

Gabriel Seret

Anastasia Sokoloff

Alain Cariou

Florence Dumas

Olivier Varenne

Alessandro Amaddeo
email: alessandro.amaddeo@aphp.fr

Sonia Khirani

Annick Frapin

Theo Teng

Lucie Griffon

Brigitte Fauroux

The balance of thrombosis and hemorrhage in STEMI patients with or without associated cardiac arrest: An observational study

Keywords:

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

Introduction

The prevalence of obstructive sleep apnoea syndrome (OSA) in children varies widely from 0.1 to 13% [START_REF] Bixler | Sleep disordered breathing in children in a general population sample: prevalence and risk factors[END_REF]. OSA is thus a relatively common disease in children with important neurobehavioral, cardiovascular and metabolic consequences [START_REF] Marcus | Diagnosis and management of childhood obstructive sleep apnea syndrome[END_REF]. OSA in children is classically associated with adenoidal and tonsillar hypertrophy and adenotonsillectomy represents the first-line treatment [START_REF] Marcus | Diagnosis and management of childhood obstructive sleep apnea syndrome[END_REF]. However, success rate ranges from 50 to 80% according to different studies [START_REF] Kaditis | Obstructive sleep disordered breathing in 2-to 18-year-old children: diagnosis and management[END_REF][START_REF] Lee | Polysomnographic findings after adenotonsillectomy for obstructive sleep apnoea in obese and non-obese children: a systematic review and meta-analysis[END_REF].

Residual OSA after adenotonsillectomy is more frequent in obese children [START_REF] Marcus | A randomized trial of adenotonsillectomy for childhood sleep apnea[END_REF] or in children with associated conditions such as congenital craniofacial malformations, neurological or neuromuscular diseases [START_REF] Kaditis | Obstructive sleep disordered breathing in 2-to 18-year-old children: diagnosis and management[END_REF]. In these patients, OSA may be corrected by continuous positive airway pressure (CPAP) [START_REF] Marcus | Diagnosis and management of childhood obstructive sleep apnea syndrome[END_REF][START_REF] Kaditis | Obstructive sleep disordered breathing in 2-to 18-year-old children: diagnosis and management[END_REF]. However, the efficacy of CPAP is counterbalanced by its poor compliance. Indeed, numerous studies report suboptimal compliance with CPAP use ranging from 3.3 to 5.3 h/night despite behavioral programs and close follow-up [START_REF] O'donnell | Compliance rates in children using noninvasive continuous positive airway pressure[END_REF][START_REF] Marcus | Adherence to and effectiveness of positive airway pressure therapy in children with obstructive sleep apnea[END_REF]. Low compliance has been associated with low maternal education [START_REF] Difeo | Predictors of positive airway pressure therapy adherence in children: a prospective study[END_REF], obesity [START_REF] Marcus | Adherence to and effectiveness of positive airway pressure therapy in children with obstructive sleep apnea[END_REF] and Down syndrome [START_REF] Brooks | Relationship between sleep, sleep apnea, and neuropsychological function in children with Down syndrome[END_REF].

Few alternatives to CPAP are available. Medical therapy with nasal steroids and antileukotriene drugs have proved their efficacy in case of moderate residual OSA after adenotonsillectomy [START_REF] Wang | The effect of montelukast on mild persistent osa after adenotonsillectomy in children: a preliminary study[END_REF]. Surgical or orthodontic treatments such as mandibular distraction [START_REF] Gürsoy | Five-year follow-up of maxillary distraction osteogenesis on the dentofacial structures of children with cleft lip and palate[END_REF] or rapid maxillary expansion [START_REF] Vale | Efficacy of rapid maxillary expansion in the treatment of obstructive sleep apnea syndrome: a systematic review with meta-analysis[END_REF] are reserved to selected patients with anatomical malformation.

High-flow nasal cannula (HFNC) is a noninvasive ventilatory device that is currently used for the treatment of acute and chronic respiratory failure in adults and children [START_REF] Leeies | High-flow oxygen via nasal cannulae in patients with acute hypoxemic respiratory failure: a systematic review and meta-analysis[END_REF][START_REF] Hutchings | Heated humidified high-flow nasal cannula therapy in children[END_REF]. HFNC consists of the delivery of high-flowing heated and humidified air through the nose, with a fraction of oxygen (FiO2) that may be set from 21% to nearly 100%. Moreover, the nasal cannula used with HFNC is less invasive and more comfortable than nasal masks or nasal prongs used for CPAP therapy. A few case series have reported the use of HFNC in children with OSA but none of the studies analyzed long term outcome and objective compliance [START_REF] Joseph | High-flow nasal cannula therapy for obstructive sleep apnea in children[END_REF][START_REF] Mcginley | A nasal cannula can be used to treat obstructive sleep apnea[END_REF][START_REF] Hawkins | High-flow, heated, humidified air via nasal cannula treats CPAP-intolerant children with obstructive sleep apnea[END_REF] The aim of the present study was to test the hypothesis that children with OSA who do not comply with CPAP may accept overnight HFNC. The secondary hypothesis was that HFNC was able to correct OSA on a respiratory polygraphy (RP).

Material and methods

Patients

This prospective study was conducted between January and December 2016 at the noninvasive ventilation (NIV) and sleep unit of Necker Children's Hospital in Paris. All consecutive patients, aged 0-18 years, treated with CPAP for a severe OSA defined as an AHI>10 events/h, and not compliant with home CPAP therapy, defined by a CPAP use <2 h/night, after at least 4 weeks of an 'optimal' CPAP trial, were considered eligible for the study. An 'optimal' CPAP trial included the trial of different interfaces and CPAP settings, therapeutic education of the patient and the caregivers and/or behavioral interventions. Patients in an unstable medical condition or with non-French-speaking parents were excluded.

Medical records, demographic data and results of the baseline RP were collected for each patient. RP were performed using CID 102* (Cidelec, Angers, France) and Alice 6 (Respironics, Carquefou, France). The recorded data included nasal airflow, oronasal thermal flow, body position, body movements, thoracic and abdominal movements assessed with inductance belts, pulse oximetry (SpO2), continuous video recording and transcutaneous carbon dioxide (CO2) recording.

Respiratory events were scored according to the American Academy of Sleep Medicine (AASM) manual and following update [START_REF] Berry | Rules for scoring respiratory events in sleep: update of the 2007 AASM Manual for the Scoring of Sleep and Associated Events. Deliberations of the Sleep Apnea Definitions Task Force of the American Academy of Sleep Medicine[END_REF]. Written informed consent was obtained before enrolment from patient's parents or from the patient's legal guardian. The protocol was approved by the local ethical committee (CPP Ile de France XI, no. 2016-A00305-46).

HFNC initiation program

The HFNC initiation program was performed in an outpatient setting with the MyAirvo device (Fisher & Paykel Healthcare, Auckland, New Zealand) and lasted approximately 2 h. As all the patients (if aged >2-3 years) and caregivers had already received a therapeutic education on CPAP, which includes the explanation of the results of the RP, the HFNC initiation program was focused on HFNC and the differences with CPAP. Accordingly, during the first 15 min, the principles of HFNC were explained to the patient and the caregivers by a pediatric pulmonologist and a nurse specialized in NIV and therapeutic education. The size of the nasal cannula was chosen in order to obtain a cannula-to-nares ratio of approximately 80% and in any case <90%, in order to prevent high pressures, as recommended by the manufacturer. The device was set at an arbitrary initial flow of 1 L/kg/min with a maximal flow of 20 L/min, a temperature of 34° and a FiO2 of 21% (no additional oxygen was used). Then, the patient tried the nasal cannula with the device during the following 30 minutes. After this initial trial, the parents (and the patient if aged >4-5 years) were trained to put on and take off the nasal cannula and to switch on and off the device.

Afterwards, the nurse reviewed the use and maintenance of the nasal cannula and device with the parents. The hour counter of the device was recorded in order to allow the monitoring of the objective compliance with the HFNC. The patient was then discharged home and was encouraged to use the HFNC every night for the entire night. A follow-up phone call was made after 1 week and the parents were asked to contact the NIV unit by phone or email earlier in case of any problem at home. An in-hospital titration RP was planned once the patient slept with the HFNC for at least 4 h per night for at least 1 week. RP was performed as described above, with the exception that nasal pressure could not be directly measured due to the nasal cannula. A pressure signal was obtained by the outlet of a T-tube, originally created by the manufacturer to add nebulization to the device. A follow-up visit was planned 1 month after the titration RP. Objective compliance (number of hours' use per night) was evaluated from the device hours counter after 1 month. If the objective compliance was less than 2 h per night, the patient was considered to refuse the treatment and HFNC was stopped. If the patient slept with HFNC more than 4 h per night, follow-up visits at 3 months and then every 6 months were planned.

Data are presented as mean and standard deviation. Student paired t-test (parametric test) was used to compare pre-and post-treatment RP. A P-value less than 0.05 was considered statistically significant.

Results

Eight patients, mean age 8.9 ± 6.2 years, six females and two males, were included in the study (Table 1). Six patients had Down syndrome, one patient had Pfeiffer syndrome with the youngest patient (patient no. 1) being a 2-month-old girl with Pierre Robin syndrome. All the patients had severe OSA with a mean AHI of 33±22 events/h (range 10-64 events/h) (Table 2).

Anthropometric data, the type of cannula, and the post titration HFNC flow rate are reported in Table 1.

Five (62%) patients achieved a good compliance (7 h 10 min ±0 h 36 min/night) with HFNC and underwent a titration RP with HFNC at a flow rate of 5 L/min in the youngest patient (patient no. 1), 15 L/min for patients no. 2 and no. 3, and 20 L/min for patients no. 4 and no. 5 (Table 2).

HFNC was associated with a normalization of the AHI in four patients and a significant reduction in the AHI in patient no. 1 (from 27 to 6 events/h) (Fig. 1). HFNC was also associated with a significant improvement in minimal SpO2 (p=0.02) and oxygen desaturation index (ODI) (p=0.03).

Because of the persistence of a moderate residual OSA in patient no. 1, the HFNC flow rate was increased from 5 to 10 L/min. This patient underwent cleft palate repair at 8 months of age. An RP confirmed the resolution of OSA with a normalization of the AHI on room air 2 months after the palate repair, allowing the weaning from HFNC. Patient no. 2 had an adenoidectomy after 1 year of HFNC. HFNC was continued for a further 6 months and was withdrawn after the correction of OSA on a follow-up RP. Patient no. 3 continued HFNC for 2 months and then underwent a craniofacial LeFort III advancement surgery. She developed a postoperative tracheal stenosis and required a tracheotomy. Patients no. 4 and no. 5 are still on HFNC with a good compliance after a follow-up of 20 and 28 months, respectively. No adverse events related to HFNC were reported for the five patients.

The three oldest patients with Down syndrome did not comply with HFNC. Each of these three patients presented severe neurocognitive impairment with behavioral problems. An orthodontic treatment was started in patient no. 6 with an RP planned at the end of the treatment.

CPAP was restarted in patient no. 7 without success. Patient no. 8 had a follow-up RP 18 months later which showed a spontaneous slight improvement inOSA with an AHI of 8 events/h.

Discussion

Our study shows that a good compliance as well as a correction of OSA may be obtained with HFNC in selected children with OSA not compliant with CPAP. HFNC may be used as a rescue therapy for children not compliant with CPAP.

McGinley et al. were the first to report the use of HFNC for OSA in 11 adults with mild to severe OSA [START_REF] Mcginley | A nasal cannula can be used to treat obstructive sleep apnea[END_REF]. The authors used a modified air compressor coupled to a humidifier that was able to generate a maximum flow rate of 20 L/min with a temperature of 31-33°C. They showed that an airflow of 20 L/min improved the AHI and sleep characteristics. Using the same protocol in 12 obese children, they then showed that HFNC was able to reduce the AHI with results comparable to those obtained with CPAP [START_REF] Mcginley | Effect of a highflow open nasal cannula system on obstructive sleep apnea in children[END_REF]. However, none of these patients were subsequently treated with the HFNC and therefore no data about adherence to treatment is available. Another study reported the use of HFNC in five children with OSA due to different underlying diseases who did not comply with CPAP (N = 4) or who could not be treated with CPAP because of facial deformities (N = 1) [START_REF] Joseph | High-flow nasal cannula therapy for obstructive sleep apnea in children[END_REF]. HFNC was able to correct AHI and nocturnal gas exchange. However, no information is available on the type of device used and, importantly, even if all the patients continued the treatment at home, no data on compliance is available. Finally, a recent study reported the results of the use of the MyAirvo device in 10 children with moderate to severe OSA, non-adherent or not eligible for CPAP treatment [START_REF] Hawkins | High-flow, heated, humidified air via nasal cannula treats CPAP-intolerant children with obstructive sleep apnea[END_REF]. All patients had underlying disorders such as Down syndrome, craniofacial abnormalities, and obesity. A titration polysomnography (PSG) was performed on the day of HFNC initiation and showed a significant improvement of the AHI which decreased from a median of 11.1 (8.7-18.8) to 2.1 (1.7-2.2) events/h (p=0.02). However, neither the flow rates nor the information on compliance and long-term follow up are provided. The present study is thus the first to report objective compliance and long-term outcome.

A good compliance was achieved in five of the eight (62%) patients. One could argue that this is a low rate. First of all, the reported failure rate of CPAP treatment in children is high. In a standard care program in the USA, which represents a classical initiation program for most centers in the world, only 68% of the patients initiated on CPAP attended the follow-up clinic visit and only 38% of them had a titration PSG [START_REF] Riley | Financial analysis of an intensive pediatric continuous positive airway pressure program[END_REF]. More recently, among 90 children who were started on CPAP, 24 (27%) did not present to follow-up visits or to PSG, meaning that only 73% of patients adhered to treatment [START_REF] Machaalani | Objective adherence to positive airway pressure therapy in an Australian paediatric cohort[END_REF]. Although these data may not be directly compared to ours because of a different organization of the healthcare system, we believe that a success rate of 62% may be considered as acceptable as all our patients were not compliant to CPAP treatment. This good compliance may be explained by the nasal cannula used with HFNC. Indeed, the cannula used with HFNC are softer and lighter, and are thus better accepted and tolerated than any other commercial CPAP nasal mask or nasal pillows. The good comfort of the HFNC was recently reported in nine children with OSA who did not tolerate any CPAP interface [START_REF] Overbergh | The Optiflow TM interface for chronic CPAP use in children[END_REF].

A correction of OSA was observed in the five compliant patients in the present study.

Previous studies in children suggested that HFNC may be more efficient in patients with predominant hypopneas rather than apneas [START_REF] Joseph | High-flow nasal cannula therapy for obstructive sleep apnea in children[END_REF][START_REF] Hawkins | High-flow, heated, humidified air via nasal cannula treats CPAP-intolerant children with obstructive sleep apnea[END_REF]. However, this hypothesis needs further evidence. Moreover, HFNC at a flow of 20 L/min has been shown to reduce apneas as well as hypopneas [START_REF] Mcginley | A nasal cannula can be used to treat obstructive sleep apnea[END_REF] as we observed in three of our patients who had an increased obstructive apnea index (OAI) which normalized with HFNC.

Several mechanisms may contribute to the improvement of obstructive respiratory events during HFNC. The direct measurement of pharyngeal pressure during HFNC therapy for OSA has never been performed due to technical difficulties. However, it has been demonstrated that when the cannula occlude less than 90% of the nares, as in the present study, the airway pressure is below 4 cmH2O [START_REF] Arora | Nasopharyngeal airway pressures in bronchiolitis patients treated with high-flow nasal cannula oxygen therapy[END_REF]. The flow currently used for treating OSA at different ages in our and other studies do not produce a pressure higher than 4-5 cmH2O, which is a level below the minimal starting pressure proposed for titration studies [START_REF] Force | Clinical guidelines for the manual titration of positive airway pressure in patients with obstructive sleep apnea[END_REF]. Because positive airway pressure seems insufficient to treat OSA, some authors proposed additional mechanisms of action. The slight increase in pharyngeal pressure achieved with the HFNC increases lung volumes, leading to a stabilization of upper airways patency through genioglossus muscle activation during stable non-rapid eye movement sleep [START_REF] Sériès | Increasing the functional residual capacity may reverse obstructive sleep apnea[END_REF]. Moreover, the delivery of a humidified and warm airflow on nasopharynx mucosa may stimulate nasopharyngeal mechano-or thermoreceptors and elicit an increase in upperairway patency [START_REF] Sozansky | The physiological mechanism for sensing nasal airflow: a literature review[END_REF]. Finally, HFNC may increase anatomical dead space washout which improves gas exchange [START_REF] Sivieri | Carbon dioxide washout during high flow nasal cannula versus nasal CPAP support: An in vitro study[END_REF]. For all these reasons, HFNC may be efficient in correcting hypopneas as well as apneas, at least in pediatric patients.

Our study has some limitations. We decided to perform titration study via RP. We acknowledge that this exam is less sensitive than PSG as it may overestimate total sleep time and it does not consider arousal-related respiratory events. However, in our unit all RPs are attended by trained staff and include video recording. Therefore, total sleep time was determined by the absence of body movements and by the changes in breathing patterns. The number of patients is small with predominantly children with Down syndrome. However, these patients are known to have a poor CPAP compliance and often severe OSA. A good objective compliance (mean night use of 8 h 46 min) was observed in 11 patients meaning that eight non-compliant patients have few remaining therapeutic options available. For these patients, HFNC may thus represent an interesting rescue treatment. We chose the MyAirvo device (Fisher & Paykel Healthcare, Auckland, New Zealand) because it was the only HFNC device approved for home care use in France at the time of the study. However, this device has been developed for home oxygen therapy with a technology that is not optimal for OSA monitoring. Adherence data were directly taken from the screen of the device, which shows the total time divided by the number of days of use. Unfortunately, the system does not consider the number of nights per months of use, which is an important parameter when considering adherence to treatment. There are no alarms in case of displacement of the cannula during the night. Moreover, the device does not provide a real-time monitoring of generated pressure, exposing the patient to potential pressure overshooting. In order to avoid this risk of overpressure, we deliberately chose a cannula-to-nares ratio of less than 0.9 which has been shown to prevent overshooting in bench studies models [START_REF] Sivieri | Effect of HFNC flow rate, cannula size, and nares diameter on generated airway pressures: an in vitro study[END_REF]. Finally, MyAirvo system does not have an integrated battery. This means that in case of break down, the patient is at risk of sleeping with the nares partially occluded by a cannula without any flow. This may be dangerous in infants who have an exclusive nasal breathing.

In conclusion, a good compliance as well as a correction of OSA may be obtained with HFNC in selected children with OSA not compliant to CPAP. Larger studies are needed in order to compare adherence between CPAP and HFNC on different patient profiles. However, HFNC may be used as a rescue therapy for children not compliant with CPAP. An improvement in the devices currently available is also needed in order to adapt their characteristics to the needs of children with OSA.

Fig. 1 .

 1 Fig. 1. Apnea-hypopnea index (AHI) at baseline and with high flow nasal canula (HFNC).

Table 1 .

 1 Anthropometric data, type of nasal cannula, and final high-flow nasal cannula (HFNC) flow rate.

		Gender	Age (years)	Weight (kg)	Height (m)	Underlying disorder	Type of cannula	HFNC flow rate (L/min)
	Patient 1 Female	0.1	4.9	0.60	Pierre Robin sequence	Infant	5
	Patient 2 Female	1.8	9.4	0.70	Down syndrome	Pediatric	15
	Patient 3 Female	6.4	19.5	1.10	Pfeiffer syndrome	Pediatric	15
	Patient 4	Male	7.6	29	1.20	Down syndrome	Adult Small	20
	Patient 5	Male	9.2	27	1.20	Down syndrome	Adult Medium	20
	Patient 6 Female	12	76	1.50	Down syndrome	Adult Medium	20
	Patient 7 Female	16.2	44	1.30	Down syndrome	Adult Medium	20
	Patient 8 Female	17.3	85	1.50	Down syndrome	Adult Medium	20

Table 2 .

 2 Respiratory polygraphy data during spontaneous breathing and with high-flow nasal cannula.

	Spontaneous breathing

Table 1 : Baseline clinical and biological characteristics of STEMI patients with or

 1

	without associated OHCA. Abbreviations: ECMO: extracorporeal membrane oxygenation;
	IABP : intra-aortic balloon pump ; LVEF: left ventricular ejection fraction; WBC: white
	blood cells; AST: aspartate aminotransferase; ALT: alanine aminotransferase , aPTT:
	activated partial thromboplastin time.
			STEMI with OHCA (n :
		STEMI (n : 549)	146)	p value
	Age, years, mean ± SD	61 (±12.4)	61.3 (±12.8)	0.78
	Male, % (n)	80.5% (442)	84.9 % (124)	0.22
	Smoking, % (n)	45.2 % (248)	48.6 % (71)	0.46
	Hypertension, % (n)	39.7% (218)	26.0 % (38)	0.002
	Diabetes mellitus, % (n)	26.8% (147)	13.0 % (19)	0.0005
	Hypercholesterolemia, % (n)	36.2% (199)	19.2 % (28)	< 0.0001
	Family history of coronary artery disease, % (n)	20.9 % (115)	2.0 % (3)	< 0.0001
	ECMO, % (n)	0.9 % (5)	4.1% (6)	0.006
	IABP, % (n)	4.3 % (24)	9.6% (14)	0.01
	Impella, % (n)	0.1 % (1)	0% (0)	0.61
	Shockable rhythm, % (n)	N/A	59.6% (87)	N/A
	Therapeutic temperature management, % (n)	N/A	72.6% (106)	N/A
	Public location of cardiac arrest, % (n)	N/A	41.8% (61)	N/A
	Present witness of cardiac arrest, % (n)	N/A	89 % (130)	N/A
	Resuscitation by the witness, % (n)	N/A	56% (93)	N/A
	No Flow time, mean ± SD	N/A	3.5 (±4.5)	N/A
	Low Flow time, min, mean ± SD	N/A	21.7 (±17.1)	N/A
	LVEF, %, mean ± SD	48.3 (±11.2)	34.2 (±14.6)	< 0.0001
	WBC, x10*3/mm3, mean ± SD	11.9 (±4.5)	18.6 (±7.3)	< 0.0001
	Hemoglobin, g/dL, mean ± SD	13.9 (±5.6)	13.1 (±2.0)	0.007
	Platelets, x10*3/mm3, mean ± SD	229 (±79)	227 (±87)	0.83
	Creatinine, µmol/, mean ± SD	83.1 (±43.1)	105.6 (±0.7)	< 0.0001
	Troponin peak, ng/l, mean ± SD	5239 (±7908)	5692 (±9187)	0.60
	Creatine kinase, IU/l, mean ± SD	1957 (±4615)	2307 (±2986)	0.39
	AST, mmol/l, mean ± SD	202 (±546)	352 (±402)	0.0004
	ALT, mmol/l, mean ± SD	71 (±306)	199 (±230)	< 0.0001
	aPTT ratio, mean ± SD	4.1 (±2.6)	3.3 (±2.4)	0.001
	Prothrombin time, %, mean ± SD	79.3 (±17.3)	64.6 (±17.8)	< 0.0001
	pH, mean ± SD	N/A	7.21 (±0.15)	N/A
	Lactates, mmol/L, mean ± SD	N/A	4.9 (4.5)	N/A

Table 2 : Baseline procedural characteristics and treatment of STEMI patients with or

 2

	without associated OHCA. Abbreviations: LAD: left anterior descending artery; LCX: left
	circumflex artery; RCA: right coronary artery; LMCA: left main coronary artery; TIMI:
	thrombolysis in myocardial infarction; DAP: dose area product; PCI: percutaneous coronary
	intervention.
			STEMI with OHCA
		STEMI (n : 549)	(n : 146)	p value
	Number of coronary arteries with significant lesion, %			0.47
	(n)
	1, % (n)	43.7% (240)	40.4% (59)
	>1, % (n)	56.3% (309)	59.6% (87)
	Culprit coronary artery, % (n)			< 0.0001
	LAD	47.7% (262)	60.3% (88)
	LCX	13.1% (72)	17.1 % (25)
	RCA	37.9% (208)	19.2% (28)
	LMCA	1.3% (7)	3.4% (5)
	No reflow, % (n)	11.7 % (64)	5.5% (8)	0.03
	Final TIMI flow, mean ± (SD)	2.9 (±0.5)	2.8 (±0.7)	0.07
	Stent implantation, % (n)	91% (502)	88.3% (129)	0.25
	Bare-metal stent, % (n)	22.1% (111)	46.5% (68)	< 0.0001
	Drug-eluting stent, % (n)	71% (391)	41.8% (61)	< 0.0001
	Total length of implanted stent, mm, mean ± (SD)	25.6 (±13.2)	28.5 (±18.3)	0.10
	Stent diameter, mm, mean ± (SD)	3.11 (±0.45)	3.03 (±0.43)	0.07
	Thromboaspiration use, % (n)	36.6 % (201)	34.9% (51)	0.71
	Total DAP, Gy.cm 2 , mean ± (SD)	7498 (±7279)	8315 (±9561)	0.36
	Angioscopy duration, min, mean ± (SD)	11.58 (±8.5)	13.11 (±8.7)	0.07
	Antiplatelet treatment characteristics
	Aspirin, % (n)	100 % (549)	100 % (146)	0.61
	P2Y12 inhibitors	98.9% (543)	83.6% (122)	< 0.0001
	Clopidogrel, % (n)	27.7% (152)	16.4% (24)	0.06
	Ticagrelor or Prasugrel, % (n)	71.2% (391)	67.1 % (98)	0.06
	None, % (n)	1.1 % (6)	16.4% (24)	< 0.0001
	Switch of P2Y12 inhibitors therapy during
	hospitalization, % (n)
	Clopidogrel to Ticagrelor or Prasugrel, % (n)	2.7% (15)	0.7% (1)	0.14
	Ticagrelor or Prasugrel to Clopidogrel, % (n)	5.6% (31)	0.7% (1)	0.01
	Anti-P2Y12 interruption, % (n)	0.4% (2)	2.7% (4)	0.006
	Glycoprotein IIbIIIa inhibitors, % (n)	40.6% (223)	38.3% (56)	0.62
	Post-PCI anticoagulation therapy, % (n)	9.4 % (52)	30.8% (45)	< 0.0001
	Thrombolysis therapy prior-PCI, % (n)	0 % (0)	3.4 % (5)	< 0.0001
	Shock needing vasoactive therapies	3% (17)	65.0% (95)	< 0.0001

Table 3 : Thirty-days clinical outcomes of STEMI patients with or without associated

 3

	OHCA. Abbreviations: BARC: Bleeding Academic Research Consortium; GUSTO: Global
	Utilization Of Streptokinase And Tpa For Occluded Arteries; TIMI: Thrombolysis in
	Myocardial Infarction
			STEMI with OHCA
		STEMI (n : 549)	(n : 146)	p value
	All-cause death, % (n)	3.1 % (17)	47.9% (70)	< 0.0001
	Any bleeding, % (n)	8.6 % (47)	29.5% (43)	< 0.0001
	BARC 3-5 bleeding, % (n)	3.3 % (18)	19.2% (28)	< 0.0001
	BARC 1-2 bleeding, % (n)	5.3% (29)	10.3% (15)	0.03
	GUSTO « severe » bleeding, % (n)	1.5 % (8)	9.6% (14)	< 0.0001
	GUSTO «mild» or «moderate» bleeding, % (n)	7.1% (39)	19.9% (29)	< 0.0001
	TIMI «major» bleeding, % (n)	1.8% (10)	11.6% (17)	< 0.0001
	TIMI «minor» bleeding, % (n)	6.2% (34)	17.1% (25)	< 0.0001
	Stent thrombosis, % (n)	3.1% (17)	12.3% (18)	< 0.0001
	Definite, % (n)	2.6% (14)	7.5% (11)	0.004
	Probable, % (n)	0.5 % (3)	4.8% (7)	< 0.0001
	Hospitalization duration, days, mean ± (SD)	5.6 (±5.0)	12.6 (±13.4)	< 0.0001

Table 4 : Univariate and multivariate analysis of factors associated with BARC 3-5 bleeding in STEMI patients with OHCA.

 4 Abbreviations: same as Tables 1,2 and 3.

	Univariate Analysis	Odds ratio	95% CI	p
	Age	0.996	0.964-1.029	0.791
	Diabetes	1.615	0.529-4.932	0.400
	Dyslipidemia	0.653	0.207-2.061	0.467
	Smoker	1.070	0.469-2.440	0.872
	Hypertension	0.935	0.362-2.415	0.890
	LVEF	0.972	0.943-1.002	0.072
	P2Y12 inhibitors therapy	0.349	0.077-1.585	0.173
	Clopidogrel	2.395	0.907-6.327	0.078
	Ticagrelor or prasugrel	0.889	0.375-2.106	0.789
	Post-PCI anticoagulation therapy	2.496	1.059-5.88	0.037
	Glycoprotein IIbIIIa inhibitors use	3.091	1.321-7.234	0.009
	Stent thrombosis	0.490	0.106-2.269	0.362
	Thromboaspiration	1.802	0.780-4.163	0.168
	Bare-metal stent	0.747	0.502-2.612	0.747
	Drug-eluting stent	0.717	0.310-1.655	0.435
	Hemoglobin	0.725	0.228-2.311	0.587
	WBC	0.946	0.817-1.094	0.453
	Platelets	0.997	0.992-1.003	0.343
	Creatinine level	0.994	0.983-1.005	0.291
	Multivariate analysis
	Clopidogrel	0.249	0.05 -1.24	0.09
	Post-PCI anticoagulation therapy	3.11	1.22-7.98	0.02
	Glycoprotein IIbIIa inhibitors use	4.16	1.61-10.79	0.003
	Thromboaspiration	1.51	0.61-3.78	0.38

Table 5 : Univariate and multivariate analysis of factors associated with all-cause death in STEMI patients with OHCA

 5 . Abbreviations: same as Tables 1,2 and 3.

	Univariate Analysis	Odds ratio	95% CI	P
	Age	1.050	1.021-1.080	<0.001
	LVEF	0.978	0.954-1.001	0.061
	Any bleeding	0.904	0.443-1.847	0.783
	BARC 3-5 bleeding	1.301	0.569-2.973	0.533
	P2Y12 inhibitors therapy
	Clopidogrel	1.6	0.659-3.885	0.299
	Ticagrelor or prasugrel	0.198	0.093-0.423	<0.001
	Post-PCI anticoagulation therapy	1.205	0.595-2.439	0.605
	Glycoprotein IIbIIIa inhibitors use	0.701	0.358-1.374	0.301
	Stent thrombosis	3.193	1.075-9.488	0.037
	Total stent length	0.997	0.969-1.026	0.861
	Thromboaspiration	0.579	0.289-1.160	0.123
	Bare-metal stent	1.273	0.662-2.447	0.470
	Drug-eluting stent	0.618	0.322-1.184	0.147
	Hemoglobin	1.284	0.644-2.560	0.478
	WBC	1.046	0.922-1.186	0.487
	Multivariate analysis
	Age	1.05	1.02-1,09	0,004
	LVEF	1.00	0.97-1.02	0.72
	Ticagrelor or Prasugrel	0.20	0.08-0.46	<0.001
	Stent thrombosis	5.62	1.61-19.65	0.007
	Thromboaspiration	0.54	0.24-1.23	0.12
	Drug-eluting stent	0.49	0.23-1.06	0.07

Acknowledgements

The research of Brigitte Fauroux is supported by the Association Française contre les Myopathies (AFM), Assistance Publique-Hôpitaux de Paris, ADEP Assistance, ASV Santé, Elivie, Asten and Université Paris Descartes -Paris V.

Conflict of interest

All the authors declare that they have no conflict of interest with this manuscript. No funding was secured for this study.


Alessandra Biancolillo
email: alessandra.biancolillo@uniroma1.it

Federico Marini

Angelo Antonio

D ' Archivio

Dr Alessandra Biancolillo

Geographical discrimination of red garlic (Allium sativum L.) using fast and non-invasive Attenuated Total Reflectance-Fourier Transformed Infrared (ATR-FTIR) spectroscopy combined with chemometrics

Keywords: Garlic, ATR-FTIR, geographical classification, PLS-DA, Multi-block, Multi-block, SO-PLS, SO-CovSel

Four varieties of red garlic (Allium sativum L.) cultivated in different Italian territories, Sulmona (Abruzzo), Proceno and Castelliri (Lazio), and Nubia (Sicily), were analysed by Attenuated Total Reflectance-Fourier Transformed Infrared (ATR-FTIR) spectroscopy. ATR-FTIR spectra of bulbils and bulbil tunics were separately acquired and processed by Partial Least Squares Discriminant Analysis (PLS-DA) with the aim of classifying the garlic samples on the basis of their geographical origin. Finally, two multi-block strategies (based on Sequential and Orthogonalized Partial Least Squares and Sequential and Orthogonalized Covariance Selection, coupled with Fisher's Linear Discriminant Analysis) have been applied in order to test whether a joint analysis of data could lead to higher prediction rates. Eventually, the best results were achieved by the multi-block approach based on SO-PLS, which allows obtaining a total classification rate of 95% (corresponding to one misclassified sample over 20) in external validation.

Introduction

Garlic (Allium sativum L.) has been worldwide employed as food condiment and herbal medicine for millennia. Apart from the common culinary use of fresh leaves or cloves, commercial products obtained by various processing methods, including oil maceration, dehydration and lyophilisation, are today marketed for therapeutic purposes [START_REF] Ramirez | Analytical methods for bioactive sulfur compounds in Allium: An integrated review and future directions[END_REF]. Historical references dating back to 4000 years mention diffuse use of garlic in ancient civilizations, from religious and superstition rituals to prevention and cure of infections and diseases (Corzo-Martínez et al., 2007).

Bio-activity of garlic, including anti-inflammatory, antimicrobial, cardioprotective, anticancer and antidiabetic action, has been demonstrated in the last decades by epidemiological and clinical studies (Corzo-Martínez et al., 2007;[START_REF] Martins | Chemical composition and bioactive compounds of garlic (Allium sativum L.) as affected by pre-and post-harvest conditions: A review[END_REF][START_REF] Yun | Potential therapeutic effects of functionally active compounds isolated from garlic[END_REF][START_REF] Shukla | Cancer chemoprevention with garlic and its constituents[END_REF].

Unique pungent aroma and most of the medical properties attributed to garlic by traditional and modern medicine are related to distinctive organosulfur compounds.

Great attention is paid to the relation between the cultivar and geographical origin of garlic and its metabolomic profile, with specific reference to the aroma precursors and other bio-active constituents [START_REF] Lu | Determination of total phenolic content and antioxidant activity of garlic (Allium sativum) and elephant garlic (Allium ampeloprasum) by attenuated total reflectance-fourier transformed infrared spectroscopy[END_REF][START_REF] Beato | Changes in Phenolic Compounds in Garlic (Allium sativum L.) Owing to the Cultivar and Location of Growth[END_REF][START_REF] Khar | Evaluation of garlic ecotypes for allicin and other allyl thiosulphinates[END_REF][START_REF] Montaño | Effect of Genetic Characteristics and Environmental Factors on Organosulfur Compounds in Garlic (Allium sativum L.) Grown in Andalusia, Spain[END_REF]. Moreover, traditional garlic varieties cultivated in given territories are appreciated by an increasing number of consumers because of their peculiar taste, aroma and functional properties compared to commercial products. In Europe, quality and geographical identity of some traditional garlic varieties of Italy (Aglio di Voghiera and Aglio Bianco Polesano), France (Ail violet de Cadours, Ail fumé d'Arleux, Ail blanche de Lomagne and Ail de la Drôme) and Spain (Ajo Morado de Las Pedroñeras) have been officially recognised in recent years through the attribution of PDO (Protected Designation of Origin) or PGI (Protected Geographical Indication) mark (European Commission, Agriculture and Rural Development, 2019). In particular, in the last years, different consortia in Italy have been constituted to valorize and preserve the traditional varieties of specific territories. Beside the protection provided by institutions (through laws and regulations) a wide effort has been put in developing analytical methodologies aimed at authenticating and tracing food specialties awarded of quality marks, for example [START_REF] Lastra-Mejías | Cognitive chaos on spectrofluorometric data to quantitatively unmask adulterations of a PDO vinegar[END_REF]D'Archivio, et al. 2019a;Biancolillo et al., 2018a;[START_REF] Rocha | Multi-elemental analysis as a tool for characterization and differentiation of Portuguese wines according to their Protected Geographical Indication[END_REF][START_REF] Giannetti | Flavour fingerprint for the differentiation of Grappa from other Italian distillates by GC-MS and chemometrics[END_REF][START_REF] Mora | Implicit reaction vs explicit emotional response: Protected designation of origin in apple cider[END_REF][START_REF] Firmani | Authentication of "Avola almonds" by near infrared (NIR) spectroscopy and chemometrics[END_REF]. In this context, analytical/chemometric approaches for the classification of garlic according to the cultivar and/or the geographical origin are essential tools to unveil commercial frauds arising from the intentional substitution of varieties cultivated in specific territories by commercial products.

Various analytical techniques, such as 1 H high resolution magic angle spinning-nuclear magnetic resonance spectroscopy [START_REF] Ritota | Traceability of Italian garlic (Allium sativum L.) by means of HRMAS-NMR spectroscopy and multivariate data analysis[END_REF], infrared spectroscopy [START_REF] Lu | Determination of total phenolic content and antioxidant activity of garlic (Allium sativum) and elephant garlic (Allium ampeloprasum) by attenuated total reflectance-fourier transformed infrared spectroscopy[END_REF], high performance liquid chromatography [START_REF] Montaño | Effect of Genetic Characteristics and Environmental Factors on Organosulfur Compounds in Garlic (Allium sativum L.) Grown in Andalusia, Spain[END_REF], high resolution mass spectrometry [START_REF] Hrbek | Authenticity assessment of garlic using a metabolomic approach based on high resolution mass spectrometry[END_REF] and electronic nose [START_REF] Trirongjitmoah | Classification of garlic cultivars using an electronic nose[END_REF], were applied to characterise garlic for traceability purposes. In these investigations, the organo-sulphur compounds and other metabolome components, such as amino acids, fatty acids, organic acids and sugars were recognised as promising traceability indicators to assess the garlic provenance or variety. In addition, garlic cultivated in different countries [START_REF] Smith | Determination of the country of origin, of garlic (Allium sativum) using trace metal profiling[END_REF][START_REF] Vasi | Statistical Analysis of Mineral Concentration for the Geographic Identification of Garlic Samples from Sicily (Italy), Tunisia and Spain[END_REF] or close regions (D'Archivio et al., 2019b) were well discriminated using the trace multi-element profile determined by atomic spectroscopy. However, most of the above analytical methods are relatively complex, expensive, time-consuming and require specialized skills. Moreover, a preliminary sample treatment is often necessary, which, apart from further increasing complexity and cost of the characterisation method, may also alter the metabolomic profile of garlic. Attenuated Total Reflectance-Fourier Transformed Infrared (ATR-FTIR) spectroscopy, by contrast, is a relatively simple, fast, cheap and non-invasive technique applicable to both liquids and solids without any complex sample pre-treatment. About garlic, ATR-FTIR spectroscopy was previously used to quantify the total phenol content and antioxidant activity with the aim of differentiating the samples grown in different US states [START_REF] Lu | Determination of total phenolic content and antioxidant activity of garlic (Allium sativum) and elephant garlic (Allium ampeloprasum) by attenuated total reflectance-fourier transformed infrared spectroscopy[END_REF], but the ATR-FTIR spectra, rather than directly on the garlic samples, were acquired from methanolic extracts.

In the light of these considerations, the aim of the present work is to test the potentiality of ATR-FTIR for geographical traceability purposes. In particular, red garlic varieties cultivated in four distinct areas of Italy, namely Sulmona (Abruzzo), Castelliri and Proceno (Lazio), and Nubia (Sicily), were analyzed and the observed spectra were handled by chemometrics. These red garlic ecotypes have been chosen because of their valuable characteristics; additionally, those cultivated in Sulmona and Nubia were also included by Slow Food Foundation for Biodiversity (Slow Food Foundation, 2019) in the list of local plant varieties to safeguard. At first, ATR-FTIR spectra of bulbils and tunics were separately analysed by Partial Least Squares-Discriminant Analysis (PLS-DA) [START_REF] Sjöström | PLS discriminant plots[END_REF]; this approach is widely and satisfactorily applied for authentication of agro-food, in particular handling Infrared (IR) data (Biancolillo & Marini, 2018b). Finally, the two data blocks were jointly analyzed by multi-block classifiers, in order to test whether data fusion strategies would provide more accurate models, allowing a deeper comprehension of the system. Consequently, IR spectra were analyzed by Sequential and Orthogonalized Partial Least Square (SO-PLS) [START_REF] Naes | Path modelling by sequential PLS regression[END_REF] or Sequential and Orthogonalized Covariance Selection (SO-CovSel) (Biancolillo et al., 2019a) coupled with Fisher's Linear Discriminant Analysis (LDA).

Materials and methods

Garlic samples

Bulbs of red garlic varieties cultivated in 2017 in four Italian sites, Sulmona (Abruzzo), Castelliri and Proceno (Lazio), and Nubia (Sicily), were kindly donated by producers working in the respective territories that assured the geographical origin of the samples. In particular, 81 garlic cloves were analyzed with the tunics; of these, 20 were from Castelliri, 20 from Proceno, 20 from Nubia and 21 from Sulmona. Eventually, spectra were collected on 82 skinned samples (21 were from Castelliri, 20 from Proceno, 19 from Nubia and 22 from Sulmona). Only 69 cloves were analyzed on both compartments (i.e., either with and without tunic). Of these, 19 were from Castelliri, 20 from Proceno, 17 from Nubia and 13 from Sulmona. Samples were acquired in July-September 2017, stored under typical domestic conditions (bulbils were located into a box which allowed their perspiration, not exposed under direct light and in a cool room) and analyzed before December to avoid variations in the composition due to aging or sprouting.

ATR FT-IR measurements

The infrared spectra of garlic cloves and clove tunics were separately recorded on a PerkinElmer Spectrum Two™ (PerkinElmer, Waltham MA, USA) FT-IR spectrometer consisting in a deuterated triglycine sulfate (DTGS) detector and a PerkinElmer Universal Attenuated Total Reflectance (uATR) accessory equipped with a single bounce diamond crystal. Each spectrum was registered from 4000 cm -1 to 400 cm -1 with 1 cm -1 instrumental resolution and ten scans were averaged per spectral replicate. The background was collected with the crystal exposed to the air. Before each measurement, the ATR crystal was cleaned with methanol and air-dried. ATR FT-IR spectra were collected on intact freshly peeled cloves by contacting a flat part of the clove with the ATR crystal.

A consistent force was applied using the pressure monitoring system integrated with the instrument to maximize the spectrum intensity but avoiding crushing the clove. The ATR FT-IR spectra of clove tunics were recorded separately following the same procedure.

- --------------------------------------------Insert Figure 1 approx. Here------------------------------------The observed ATR-FTIR spectra (shown in Figure 1) display the typical vibration patterns of the plant constituents (proteins, fats and sugars) [START_REF] Schulz | Identification and quantification of valuable plant substances by IR and Raman spectroscopy[END_REF][START_REF] Movasaghi | Transform Infrared (FTIR) Spectroscopy of Biological Tissues[END_REF] and reflect the composition of garlic clove and tunics. The garlic clove is mainly composed by water (65%), followed by carbohydrates (28%, mainly fructans), sulphur compounds (1-4%), proteins (2%), fibres (1.5%) and free amino acids (1-1.5%) [START_REF] Rahman | Garlic and aging: New insights into an old remedy[END_REF], while polysaccharides (cellulose, hemicellulose and pectin) and lignin are the main constituents of the bulbil skin [START_REF] Kallel | Polysaccharide from garlic straw: Extraction, structural data, biological properties and application to beef meat preservation[END_REF][START_REF] Reddy | Isolation and characterization of cellulose nanocrystals from garlic skin[END_REF]. The broad band centred at about 3290 cm -1 can be assigned to the N-H stretching of proteins and O-H stretching of carbohydrates and water, while the two sharp signals at 2920 and 2850 cm -1 are associated to symmetric and antisymmetric C-H stretching vibrations, respectively. The spectral region between 1200 and 900 cm -1 , although showing different intensity and fine structure in the spectra of cloves and skins, takes origin from coupled C-C, C-O stretching and C-O-H, C-O-C deformation modes of oligo-and polysaccharides. The distinctive band at 1025 cm -1 in particular can be assigned to the vibrational frequency of CH2OH groups of carbohydrates. In the same spectral region, the S=O stretching of sulfoxides may contribute to the signal at about 1090 cm -1 (Nikolić et al., 2011), well visible in cloves but not in skin samples. The band at about 1160 cm -1 arises from the glycosidic linkage (C-O-C) vibrations.

The weak bands in the region 880-900 cm -1 can be attributed to the C-O-C skeletal modes of carbohydrates and polysaccharides, the signal at 894 cm -1 in particular being diagnostic of ??-(1 → 4)-glycosidic bonds. The band at 1735-1740 cm -1 observed in both clove and skin samples are typical of the C=O stretching vibration of polysaccharides and cellulose. The signal at 1640 cm -1 observed in the spectrum of cloves can be assigned to the bending vibration of water and to the stretching of carbonyl of proteins (amide I), while the signals at 1552 cm -1 and 1252 cm -1 can be attributed to the amide II and amide III bands (associated with coupled C-N stretching and N-H bending vibrations of the peptide group). Additionally, the stretching vibrations of aliphatic and aromatic double bonds fall in the region 1640-1500 cm -1 as well. The signal at 1225-1230 cm -1 of the skin spectra can be attributed to the stretching vibrations of C-O bonds in lignin [START_REF] Stark | Techniques for Characterizing Lignin[END_REF]. The band observed at about 1600 cm -1 in skin samples, partially overlapped to the amide I band of proteins in the clove spectra, can be assigned to the asymmetric stretching of carboxylate groups of amino acids, proteins or polysaccharides. The signals due to the O-H stretching of adsorbed water and asymmetric stretching of lignin aryl rings also fall in this spectral region, while the band at about 1510 cm -1 can be assigned to the symmetric stretch of the aromatic groups of lignin [START_REF] Stark | Techniques for Characterizing Lignin[END_REF]. The two sharp signals at 1460 and 1470 cm -1 observed in clove samples and sometimes superimposed, and the bands at 1427 cm -1 and 1328 cm -1 are due to O-H deformation vibration, and to various vibrational modes of CH2 groups of lipids, polysaccharides and proteins. The two weak signals at about 720 and 727 cm -1 observed in the clove spectra but not in the skin samples can be attributed to C-S stretching vibrations of di-alkyl sulphides and disulphides [START_REF] Minzhen | Identification and Characterization of volatile Organic Compounds of Fresh Plant Using Headspace Combined with Surface-Enhanced Raman Scattering[END_REF] and to C-H deformation vibration.

Chemometric Analysis

The ATR-FTIR signals collected as described in the previous section have been analyzed by means of chemometric tools, in order to classify samples according to their geographical origin. To achieve this goal, three classification methods have been employed: PLS-DA, in order to handle the two data blocks individually, and SO-PLS-LDA or SO-CovSel-LDA, to achieve a simultaneous analysis of both sets of signals.

Partial Least Square-Discriminant Analysis (PLS-DA)

Discriminant classification methods discern samples on the basis of their mutual differences.

Applying these approaches, the multi-dimensional samples-space is entirely divided into classregions, and each object will be assigned to one specific category. One of the first discriminant classifiers proposed is the Linear Discriminant Analysis by Fisher. This approach, despite it performs well and it is still widely used, presents a considerable limitation: it can be used only when the data matrix is invertible. This condition is rarely met, in particular working with instrumental data, where the number of variables is likely higher than the number of samples.

Among the different methods developed in order to enable the application of discriminant analysis on ill-conditioned data matrices, Partial Least Square-Discriminant Analysis (PLS-DA) [START_REF] Sjöström | PLS discriminant plots[END_REF][START_REF] Ståhle | Partial least squares analysis with cross-validation for the two-class problem: a Monte Carlo study[END_REF] is probably one of the most widely applied. One of the main reasons for its diffusion is that it is suitable to handle highly correlated variables (e.g., spectroscopic data), which makes it applicable on non-invertible data blocks. PLS-DA exploits the PLS algorithm [START_REF] Wold | The multivariate calibration problem in chemistry solved by the PLS method[END_REF] to solve a classification problem as if it were a regression one [START_REF] Barker | Partial least squares for discrimination[END_REF]; mathematically, this corresponds to estimate Eq.1:

= + (1)
Where is the data matrix of measures collected on samples, and are the regression coefficients and the residuals, respectively, and is the so-called Dummy Matrix, a binary matrix (of dimensions × , where is the number of the analyzed samples and is the number of categories present into the system) encoding the class-belonging. Once Eq.1 is solved (i.e., once the calibration model is built), whenever new measures ( ) are collected on unknown samples, it is possible to predict their class-belonging (solving = ). Nevertheless, , i.e., the matrix collecting the responses predicted on new samples, is not categorical, but made of continuous values, so that it is necessary to define a criterion for class-attribution. Accordingly, classification may be accomplished, e.g., by assigning the sample to the category corresponding to the highest value of the predicted response, by application of LDA either on scores or on predicted responses or by a bayesian approach based on Gaussian mixture modeling (Perez et al., 2009).

Sequential and Orthogonalized Partial Least Square-Linear Discriminant Analysis (SO-

PLS-LDA)

Sequential and Orthogonalized Partial Least Square-Linear Discriminant Analysis (SO-PLS-LDA) [START_REF] Biancolillo | Combining SO-PLS and linear discriminant analysis for multi-block classification[END_REF] is a multi-block discriminant classifier whose algorithm has been developed combining a multi-block regression method, SO-PLS [START_REF] Naes | Path modelling by sequential PLS regression[END_REF], and Fisher's Linear Discriminant Analysis, where SO-PLS is used to reduce the dimensionality of the data blocks (solving problems related to ill-conditioned matrices) prior the application of the discriminant approach.

In order to create a SO-PLS-LDA model, first of all it is necessary to build the SO-PLS one; given two predictor blocks, and , and a dummy , this can be done applying the procedure described in [START_REF] Naes | Path modelling by sequential PLS regression[END_REF]. Briefly, is fitted to by PLS (obtaining the -scores ), is orthogonalized with respect to and then the resulting matrix ( ) is used to predict (by PLS) the residuals from the previous regression. Finally, the predictive model is calculated summing up the outcomes of the two PLS-models (the reader is addressed to [START_REF] Naes | Path modelling by sequential PLS regression[END_REF]) for more details).

Once the SO-PLS model is built, classification is achieved by applying LDA on the predicted (or on the row-augmented scores) [START_REF] Biancolillo | The sequential and orthogonalised PLS regression (SO-PLS) for multi-block regression: theory, examples and extensions[END_REF].

Sequential and Orthogonalized-Covariance Selection-Linear Discriminant Analysis (SO-

CovSel-LDA)

As the name suggests, Sequential and Orthogonalized-Covariance Selection-Linear Discriminant Analysis (SO-CovSel-LDA) (Biancolillo et al., 2019a) is a multi-block classification method strictly linked to SO-PLS-LDA. In fact, the two approaches have similar algorithm, but in SO-CovSel-LDA the feature reduction is operated by a variable selection method called Covariance Selection (CovSel) [START_REF] Roger | CovSel: variable selection for highly multivariate and multi-response calibration, application to IR spectroscopy[END_REF]. CovSel is a feature reduction approach developed to select variables in a regression context; in fact, it points out the predictors which contribute the most to the estimation of a response. Briefly, considering a predictor block , used to estimate a response ,

Covariance Selection iteratively selects the -variables presenting the highest covariance with the response. As a consequence, the main divergence between SO-PLS and SO-CovSel, is that, in the latter, calculations are based on the original variables instead on the scores (Biancolillo et al., 2019a). Considering the two predictor blocks case above-mentioned ( and ), in order to create a SO-CovSel model, the first step consists in selecting variables from the -block (by CovSel), obtaining the reduced matrix , which is used to estimate the by ordinary least squares (OLS).

Then, is orthogonalized with respect to , obtaining . Covariance Selection is then used to select the -variables which contribute the most to the prediction of the residuals ( ) from the previous regression, obtaining , , which is used to estimate by OLS. Finally, the predicted is calculated by summing up the individual predictions made by the two regressions.

Once the SO-CovSel model is created, LDA can be applied on the predicted . Also in this case, in order to solve the classification problem, the is a binary matrix encoding class-belongings.

Results and discussion

After the collection of spectra, IR signals were exported in MatLab (The Mathworks, Natick, MA; version 2015b) for the analysis. In order to pursue external validation of the models, spectra are reorganized into a training set, for the optimization of the calibration model, and a test set, for validation, by the Duplex algorithm [START_REF] Snee | Validation of regression models: methods and examples[END_REF] (more details about the division are reported below).

In Figure 1 the average spectra collected on cloves (in red) and on tunics (in blue) are shown. From the figure it is straightforward the IR signals collected on the different compartments of garlic are slightly different, in particular in specific absorption ranges; a wider discussion over the interpretation of the spectra is reported in the related sections.

Independently on the classifier used, different pretreatments have been tested on training spectra: bare mean centering, 1 st or 2 nd derivative (following the Savitzsky-Golay approach, using 19 points window and a second or third order for the interpolating polynomial, respectively) [START_REF] Savitzky | Smoothing and differentiation of data by simplified least squares procedures[END_REF], Standard Normal Variate (SNV) [START_REF] Barnes | Standard normal variate transformation and detrending of near-infrared diffuse reflectance spectra[END_REF] and combinations of SNV an derivatives; the most suitable preprocessing approach (together with the optimal complexity, i.e., the number of latent variables) has been defined as the one leading to the lowest classification error in a 7-fold cross-validation procedure. Even if not always explicitly mentioned, data is assumed to be mean-centered prior the creation of any model.

PLS-DA analysis on tunics

As above-mentioned, the IR signals were divided into a calibration and a validation set. The 81 signals collected on tunics were divided into a training set of 61 samples (15 belonging to Class Castelliri, 15 appertaining to Class Proceno, 15 for Class Nubia and 16 from Sulmona) and a test set of 20 samples (5 per each category). Then, six different pretreatments (listed in Table 1) have been tested on data, and an equal number of PLS-DA models were calculated (on training samples); the preprocessing approaches, the number of latent variables (LVs) extracted and the average crossvalidated classification errors (%) are reported in Table 1.

- --------------------------------Insert Table 1 approx. here- ------------------------------------------------After inspection of Table 1, the optimal calibration model has been built on data pretreated by SNV, 1 st derivative and mean centering; this PLS-DA model, applied on the test set (preprocessed accordingly) provided a classification rate of 75% (corresponding to 5 misclassified test samples over 20; of these, 2 belong to class Castelliri, 2 to class Sulmona and 1 appertains to class Nubia).

After the creation of any PLS-based model, in order to give a depth insight into the data set under study, it is possible to calculate the Variable Importance in Projection (VIP) indices [START_REF] Wold | PLS: partial least squares projections to latent structures[END_REF] to understand which variables contribute the most to the model; generally, each spectral variable presenting a VIP index higher than 1 is considered relevant. A graphical representation of VIP analysis is shown in Figure 2; one plot per class is displayed in order to show which variables characterize each category.

----------------------------------Insert Figure 2 approx. here--------------------------------------------------

In Figure 2, the black solid lines represent the average training signals (offset to avoid overlapping and make them visible) whereas the selected variables are highlighted as bold colored dashed lines:

red for Class Castelliri, blue for Class Proceno, green for Class Nubia and cyan for Class Sulmona.

From the figure it is possible to see that, as expected, the most relevant instrumental features are more or less the same among the four categories; in fact, independently on the class, variables presenting VIP indices higher than 1 are those around 2849 cm -1 and 2920 cm -1 , ascribable to the symmetric and antisymmetric stretching of the C-H bond, those in the spectral range between 1640 cm -1 and 1629 cm -1 , attributable to the stretching of carboxylate groups, variables around 1417cm -1 ascribable to CH bending and some from 1181 cm -1 to 914 cm -1 , linked to the absorptions caused by CH deformations, skeletal stretching of C-O and C-C.

Despite the similarities clearly visible in the plot, it is also possible to spot some variables that are selected for the characterization of some categories, but not for others. For instance, spectral variables from 1192 cm -1 to 1292 cm -1 present VIP indices higher than 1 for all the categories except for Class Castelliri, probably indicating a different composition in oligo-and polysaccharides among the diverse categories. More details about spectral absorptions can be found in Section 2.2 and in the related literature.

PLS-DA analysis on cloves

The PLS-DA analysis of spectra collected on cloves has been carried out in the same way as described above for tunics. The 82 signals were divided into a training set of 62 objects (containing 16 samples per Class Castelliri, 15 Proceno, 14 Nubia, and 17 per Class Sulmona) and a test set of 20 samples (5 per category). The same pretreatments have been tested on data, and, also in this case, the most suitable has been defined (together with the optimal complexity) in cross-validation;

results are reported in Table 2.

-

---------------------------------Insert Table 2 approx. here--------------------------------------------------
In this case, the model leading to the lowest classification error in cross-validation is the one built on data after 1 st derivative (and mean centering). When the optimal model was applied to the test set, only 3 over 20 samples were misclassified (correct classification rate: 85%); among these, 2 belong to Class Castelliri and 1 to Class Sulmona.

Also in this case the VIP analysis was pursued in order to inspect the variables contributing the most to the observed differentiation among the geographical origin; the agreement regarding the selected variables among the different classes was strong (plot is not shown); the most relevant variables from the classification point of view were those from 2944 cm -1 to 2837 cm -1 , and some around 1413 cm -1 , and 1757 cm -1 .

Despite the results obtained by the individual analysis of data blocks were quite satisfactory, the above-mentioned multi-block approaches have been used, testing whether it would be possible to improve predictions.

Multi-block analysis

Unfortunately, the IR analysis of both tunics and cloves was not available for all the samples discussed for the PLS-DA analysis; consequently, the multi-block data set has been reduced to the 69 samples which have been analyzed on both compartments.

In order to divide samples into a training and a test set taking into account both blocks of measures, two PCA models have been calculated, one per each set of data. Then, the first 5 principal components extracted by each PCA model were row-wise concatenated; finally, samples were divided into training and test set by the Duplex algorithm [START_REF] Snee | Validation of regression models: methods and examples[END_REF] calculating sample distances in the scores-space defined by the PCs.

Due to the reduction of the available samples, the training set included 49 samples (14 belonging to Class Proceno, 15 from Class Castelliri, 12 appertaining to Class Nubia and 8 of Class Sulmona), and the test set was made of 20 objects (5 objects per class).

As anticipated, two multi-blocks classifiers have been applied at this stage of the work;

independently of the approach used, spectra collected on cloves have been used as first input block, while signals collected on tunics have been used as the second one.

Classification models have been built also using an inverted input order (tunics-data modelled as first block and clove-data as second) but results were slightly worse; consequently, these will not be discussed in the following sections.

SO-PLS-LDA analysis

Building the SO-PLS models, all the possible combinations of the above-mentioned preprocessing approaches have been tested on the two data blocks in a cross-validation procedure (7 cancellation groups). The optimal calibration model is the one calculated using the mean-centered spectra collected on cloves as first input block and signals on tunics as the second one (pretreated by 2 nd derivative and mean centering). The number of components extracted from the two blocks are 4 and 11 for spectra on cloves and tunics, respectively. This model has been applied on the test set (pretreated accordingly) and it provided 100% of correct classification for all categories except for class Sulmona, whose correct classification rate was 80% (corresponding to 1 misclassified sample). The results are graphically shown in Figure 3, where samples are projected onto the space of the first two canonical variates.

------------------------------Insert Figure 3 VIP analysis was pursued on the SO-PLS model, following the embedded strategy described in [START_REF] Biancolillo | Variable selection in multiblock regression[END_REF]; nevertheless, the results were not relevantly different from those previously described for the individual PLS-DA analysis, and therefore they are not reported.

SO-CovSel-LDA analysis

Similarly to SO-PLS-LDA analysis, also for SO-CovSel-LDA, several multi-block models have been built (in a cross-validation procedure) in order to test different combinations of pretreatments;

simultaneously, also the number of variables to be selected per each block is chosen. The optimal model has been calculated on the clove-block pretreated by 1 st derivative whereas the signals collected on tunics were preprocessed by SNV and 1 st derivative. The number of selected variables is 1 and 7 on cloves-and tunics-block, respectively. The application of the calibration model to the test set led to a correct classification rate of 85%, corresponding to 3 misclassified samples in total

(2 object from Class Castelliri and 1 belonging to Class Sulmona assigned to Class Proceno).

As described in the Section 2.3.3, SO-CovSel-LDA naturally provides information about the variables contributing the most to the classification. A visual representation of the selected variables is reported in Figure 4, in particular, the mean spectra collected on cloves and tunics are reported in in Figure 4a and Figure 4b, respectively; selected variables are highlighted by red circles.

-----------------------------------Insert Figure 4 approx. Here-------------------------------------------As expected, the selection provided by CovSel is sharper than the one achieved by VIP analysis;

nevertheless, the two are in agreement. In fact, CovSel selects the variable at 2843 cm -1 in the spectra collected on cloves, and those at 2917 cm -1 and 3285 cm -1 on tunics, probably associated to CH3, CH2, O-H and N-H stretching; variables at 1013 cm -1 , 1034 cm -1 , 1588 cm -1 , 400 cm -1 and 945 cm -1 associable to polysaccharides (for more details the reader is addressed to Section 2.2 and to the related literature).

General overview of the results

In general, all the classification models provided acceptable results, indicating ATR FT-IR coupled with discriminant classifiers could represent a suitable approach for assessing the geographical origin of the investigated cultivars of red garlic. The best results, from the prediction point of view, are provided by a multi-block approach; this outcome is somehow expected, because data fusion strategies are supposed to provide comparable or better results than models built on the individual data blocks (Biancolillo, et al. 2019c). In order to ease a comprehensive overview of the classification rates provided by the different models, they are reported all together in Table 3.

------------------------------------Inset Table 3 approx. here------------------------------------------------

From the table, it is straightforward the most suitable methodology to solve the classification problem under study is SO-PLS-LDA. Concerning the single-block analysis, the best results are provided by the model built on data collected on the cloves. This latter achievement suggests this compartments contains more information suitable for distinguishing the different red garlic ecotypes.

Conclusions

The aim of the present work was to develop a non-destructive approach suitable for distinguishing different cultivars of red garlic according to their geographical origin. In order to achieve this goal,

  approx. here--------------------Looking at the plot, it is possible to recognize a quite clear distinction among the four different classes. In particular, the first canonical variate allows discriminating samples belonging to class Castelliri (red circles, at negative values) from the other three categories; while the second canonical variate allows distinguishing the objects belonging to class Proceno (blue squares) and class Sulmona (green triangle), at negative scores, from those belonging to class Castelliri (red circles) and Nubia (black diamonds) at positive values. From the figure, it is easy to spot the misclassified sample from class Sulmona (green triangle), in fact, this falls closer to the centroid of class Nubia rather than to the one of its own category.

  samples of red garlic harvested in four different Italian towns (Castelliri, Proceno, Nubia and Sulmona) were analyzed by ATR-FTIR spectroscopy and classified. Spectra collection was pursued on both tunics of bulbils and cloves, avoiding any other physical-chemical pretreatment of samples.The data-block obtained were individually analyzed by PLS-DA and involved in multi-block models by the application of SO-PLS-LDA and SO-CovSel-LDA. In general, all the approaches provided good results from the prediction point of view. Concerning the classification pursed on the individual data blocks, the lowest classification error was provided by the PLS-DA model calculated on spectra collected on cloves, which led to the misclassification of three test objects over twenty. Nevertheless, the best results have been provided by a data-fusion strategy, the SO-PLS-LDA approach, which allowed achieving extremely satisfactory results, misclassifying only one sample over the 20 constituting the validation set.

Figure captions Figure 1

 captions1 Figure captions

Figure 2

 2 Figure 2 VIP analysis. Solid black lines represent mean spectra for the four different categories

Figure 3

 3 Figure 3 SO-PLS-LDA analysis: Samples project onto the first to canonical variates. Legend: Red

Figure 4

 4 Figure 4 SO-CovSel Analysis: blue line represents mean spectrum collected on a) cloves b) tunics.

Table 1

 1 PLS-DA analysis on tunics: Cross-validated mean classification errors (%) as function of preprocessing and complexity (LVs). Table 2 PLS-DA analysis on cloves: Cross-validated mean classification errors (%) as function of preprocessing and complexity (LVs).

	Preprocessing	LVs	Average classification errors (%-CV)
	Mean Centering (MC)	10	12.3
	1 st Derivative + MC	12	10.2
	2 nd Derivative + MC	7	12.5
	SNV + MC	10	11.8
	SNV + 1 st Derivative + MC	8	8.7
	SNV + 2 nd Derivative + MC	10	12.0

Table 3

 3 Classification rates (on the test set) for the four proposed strategies.

	Method	Classification rates on the test
		set (%)
	PLS-DA on tunics	75.0
	PLS-DA on cloves	85.0
	SO-PLS-LDA	95.0
	SO-CovSel-LDA	85.0


Nelayah

A Chmielewski

C Ricolleau

D Alloyeau

G Wang

J Nelayah
email: jaysen.nelayah@univ-paris-diderot.fr

Nanoscale temperature measurement during temperature controlled in situ TEM using Al plasmon nanothermometry

Keywords: in situ transmission electron microscopy, volume expansion of metal, volume plasmon shift, electron energy loss spectroscopy, local temperature

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

Temperature is a key factor in material science as it inuences the state and properties of matter. To study the eects of temperature on nanomaterials, temperature controlled in situ transmission electron microscopy (TEM) is a powerful technique. It has been successfully applied for years to study the eects of heat stimuli on the states and properties of matter during solid-solid, solid-liquid and solid-gas interactions [START_REF] Saka | In situ heating transmission electron microscopy[END_REF]. For a successful in situ heating TEM experiment, one needs specimen holders than can reach high temperatures without compromising neither thermal nor mechanical stability. In this context, electrically-powered microelectromechanical (MEMS)-based systems have over recent years emerged as ecient sample carriers and high-temperature heaters for in situ heating TEM [START_REF] Creemer | Atomic-scale electron microscopy at ambient pressure[END_REF][START_REF] Allard | A new mems-based system for ultra-high-resolution imaging at elevated temperatures[END_REF][START_REF] Spruit | A review on development and optimization of microheaters for hightemperature in situ studies[END_REF]. These microheaters having compact design with reduced thermal mass (which minimises sample drift), limited power consumption and fast response, enable real-time imaging of the atomic structure of nanomaterials at elevated temperatures in vacuum and ultimately, during their interaction with gases or liquids [START_REF] Creemer | Atomic-scale electron microscopy at ambient pressure[END_REF][START_REF] Allard | Novel mems-based gas-cell/heating specimen holder provides advanced imaging capabilities for in situ reaction studies[END_REF][START_REF] Wu | Advances in sealed liquid cells for in-situ tem electrochemial investigation of lithium-ion battery[END_REF][START_REF] Wu | In situ environmental tem in imaging gas and liquid phase chemical reactions for materials research[END_REF][START_REF] Wittig | In situ investigation of ordering phase transformations in fept magnetic nanoparticles[END_REF][START_REF] Zhang | In situ tem studies of the shape evolution of pd nanocrystals under oxygen and hydrogen environments at atmospheric pressure[END_REF][START_REF] Jiang | Recent advances in gas-involved in situ studies via transmission electron microscopy[END_REF].

To unambiguously establish temperature-structure-property relationships from temperature controlled in situ TEM observations, it is crucial that the local temperature of the sample under observation is known with high accuracy at all times. When MEMS-based microheaters are used, the local sample temperature is not exactly known. It is usually considered equal to an average setpoint temperature determined from ex situ calibration of the heater in vacuum prior to in situ investigation [START_REF] Allard | A new mems-based system for ultra-high-resolution imaging at elevated temperatures[END_REF]. Accessing the actual sample temperatures during an in situ TEM experiment remains a challenging task as this requires thermometry methods that can operate down to the microand nanoscales. For measuring accurately temperatures at such scales, various high spatial resolution thermometry methods are being implemented in dierent areas of research [START_REF] Brites | Thermometry at the nanoscale[END_REF]. For instance, infrared thermography based on Planck blackbody emission with a spatial resolution of 10 microns, and temperature resolution of 0.1 K has been developed in medicine to detect cancer cells [START_REF] Lahiri | Medical applications of infrared thermography: A review[END_REF]. However, due to limited space at the sample location in a TEM column, it is not possible to deploy these micro-and nanoscale thermometry methods in electron microscopy. The space limitation is even more severe when MEMS-based nanoreactors are used due to the higher spatial connement of both the reactive environment and sample. Despite these limitations, successful strategies have been devised lately for determining the sample temperature at the nanoscale during temperature controlled in situ TEM.

A rst strategy is based on TEM imaging of standard phase transitions in an ensemble of metal islands or nanoparticles with well-established transition temperatures. For instance, Begtrup et al. studied the onset of melting and evaporation of small gold NPs to map the temperature gradient over a nanoscale thermal platform operating at elevated temperatures in a TEM [START_REF] Begtrup | Probing nanoscale solids at thermal extremes[END_REF]. Absolute values of the local temperature were determined from nite element analyses adjusted to experimental TEM observations. Using a similar approach, Britlinger et al. achieved nanoscale temperature mapping over a large eld of view by tracking the change in TEM contrast in indium metal islands during their reversible liquid/solid phase transition [14]. The indium probes act as binary thermometers since temperatures could only be mapped above or below the transition temperature close to the melting point of bulk indium (156.6 °C). The main drawback of such approaches based on the study of phase transitions in metals is that the local temperature is accessible over a limited range only, around the transition temperatures considered.

To access the local sample temperature over a wide temperature range with high accuracy, dierent thermometric methods using electron-based spectroscopies have been proposed lately. For example, in thermally controlled in situ gas TEM using MEMS-based nanoreactors, Vandelbo et al.

showed that the local temperature in the microheater area could be deduced by monitoring the change in the density of the reacting gas with temperature using electron energy loss spectroscopy (EELS) [START_REF] Vendelbo | Method for local temperature measurement in a nanoreactor for in situ high-resolution electron microscopy[END_REF]. More specically, by combining quantitative EELS to determine the local gas density and the ideal gas law relating gas density and temperature, they successfully measured the temperature gradients in a nanoreactor operating with 900 Torr of static H 2 at temperatures up to about 600 °C. Recently, using a combination of electron energy loss and gain spectroscopies to study the temperatureinduced phonon frequency shifts in boron nitride akes, Idrobo et al. have been able to measure with nanoscale resolution the local temperature from room temperature up to about 1300 °C of a MEMS-based heating platform [START_REF] Idrobo | Temperature measurement by a nanoscale electron probe using energy gain and loss spectroscopy[END_REF]. For metals with Drude-like behaviour such as aluminium, it has been shown that the temperature dependence of the volume plasmon is explained quantitatively by the volume expansion of the material [START_REF] Abe | Temperature-dependence of the volume-plasmon energy in aluminum[END_REF]. In this case, the temperature dependence of Al volume plasmon energy combined with the free electron model can be used to determine sample temperatures at the nanoscale [START_REF] Mecklenburg | Nanoscale temperature mapping in operating microelectronic devices[END_REF][START_REF] Mecklenburg | Aluminum nanoparticles as ducials for nanoscale temperature measurements[END_REF][START_REF] Miller | Local in situ temperature measurements from aluminum nanoparticles[END_REF]. In [START_REF] Mecklenburg | Nanoscale temperature mapping in operating microelectronic devices[END_REF], by monitoring the shift in the plasmon energy with temperature using low loss EELS, Mecklenburg et al. successfully mapped the temperature gradients (ranging from room temperature to 200 °C) over dierent serpentine aluminium devices with an accuracy better than 10 %.

In the present work, we have used aluminium plasmon-based thermometry technique to measure the local temperatures across the heater region of MEMS-based gas nanoreactors operated between room temperature and 550 °C. Solid Al nanospheres are used as nanoscale temperature probe. The temperature-dependence of their volume plasmon necessary for local temperature determination isobtained from spatially-and time-resolved EELS measurements performed at single particle level. We demonstrate the directness and versatility of our approach by studying the temperature uniformity and stability in the heater region of a gas nanoreactor operating in vacuum and another one in static H 2 . To our knowledge, the application of Al plasmon nanothermometry to measure temperature under gas conditions is new.

Materials and Methods

Temperature dependence of aluminium volume plasmon

In this section, we rst establish the dependence with temperature of the volume plasmon energy of aluminium (metal with free electron behaviour) as a result of its volume expansion. Second, with this dependence known and by tting the experimental linear thermal expansion data for aluminium, we determine the temperature-dependence of the Al volume plasmon energy according to the free electron model.

Aluminium is a very good metal with a high density of free electrons. It has three electrons per atom in its conduction band and exhibits a Drude-like behaviour except around 800 nm (≈ 1.55 eV) due to an intraband transition [START_REF] Gerard | Aluminium plasmonics[END_REF]. According to the free electron model, the energy E p necessary to excite its volume plasmon is given by [START_REF] Raether | Surface-plasmons on smooth and rough surfaces and on gratings[END_REF] (in SI units):

E p (T ) = ω p = n(T )e 2 m 0 (1)
where is the reduced Planck constant, ω p is the plasmon angular frequency, n(T ) is the temperature-dependent density of valence electrons with charge e and mass m and 0 is the vacuum dielectric constant of aluminium. The density of valence electrons n(T ) varies with temperature as a result of the thermal expansion of the metal lattice. Let [α v (T)] be the second-rank thermal expansion tensor of aluminium. The latter having cubic structure, [α v (T)] is isotropic. Therefore, the relative variation of the volume ∆V V , due to change ∆T in temperature, is given by:

∆V V = 3α l ∆T (2)
where α l is the linear thermal expansion coecient of aluminium.

The density of valence electrons n(T ) at a given temperature T with respect to a reference temperature T 0 is thus expressed as:

n(T ) ≈ n(T 0 )[1 -3f (T )] (3)
where

f (T ) = T T 0 α l (T )dT
Combining equations ( 1) and ( 3), we obtain the expression of the volume plasmon of the metal as a function of temperature :

E p (T ) ≈ E p (T 0 ) 1 - 3 2 f (T ) (4)
To evaluate E p (T ) at any temperature, we rst establish an approximate expression for f (T ). For that, we start by tting a polynomial of degree 2 to the experimental data of Al linear expansion coecient from [START_REF] Wilson | The thermal expansion of aluminium : Further experiments[END_REF]. Fig. 1 shows the experimentally available data of Al linear expansion coecient and the polynomial t given by the parametrized model After least-squares minimization between 24 and 600 °C, the values of the coecients of the t are : α 0 = 2.299 ± 0.016 × 10 -5 °C-1 , α 1 = 1.63 ± 0.13

α l (T ) = α 0 + α 1 (T - T 0 ) + α 2 (T -T 0 ) 2 .
× 10 -8 °C-2 and α 2 = 8.2 ± 2.3 × 10 -12 °C-3 .
After integration of the approximated function of α l (T ) between T and T 0 = 24 °C, f (T ) simplies to :

f (T ) ≈ α 0 (T -T 0 ) + α 1 2 (T -T 0 ) 2 + α 2 3 (T -T 0 ) 3 (5)
and nally, E p (T ) can be computed for any temperature between 24 and 600 °C according to equation ( 4). The insert of Fig. 1 shows the energy shift of Al volume plasmon with temperature within the free electron model over the considered temperature range. The reference volume plasmon energy at T 0 = 24 °C, taken from [START_REF] Abe | Temperature-dependence of the volume-plasmon energy in aluminum[END_REF], is equal to E p = 15.230 ± 0.019 eV. According to the free electron model, the volume plasmon redshifts non-linearly with temperature. The redshift is equal to about 0.45 eV when the temperature of solid Al is raised from room temperature to 600 °C. It should be noted that due to the uncertainty on the reference Al volume plasmon energy used, a linear t of the Al linear expansion coecient would have been sucient

to establish the temperature dependence of Al volume plasmon.

Experimental details

The gas nanoreactors used in this work are provided by Protochips Inc [START_REF] Allard | A new mems-based system for ultra-high-resolution imaging at elevated temperatures[END_REF][START_REF] Allard | Novel mems-based gas-cell/heating specimen holder provides advanced imaging capabilities for in situ reaction studies[END_REF]. experiments. The energy of the volume plasmon of the nanospheres for local temperature determination using Eq.( 4) were measured at single particle level using spatially and time-resolved low-loss electron energy spectroscopy (STEM-EELS). STEM-EELS analyses were performed with a JEOL ARM 200F TEM equipped with an aberration corrector of the objective lens and a cold FEG electron source operated here at 200 kV [START_REF] Ricolleau | Performances of an 80-200 kv microscope employing a cold-feg and an aberration-corrected objective lens[END_REF]. EELS data were acquired with sub-nm resolution (probe size setting 6C) and collected using a GIF Quantum ER spectrometer. The convergence and collection semi-angles were equal to 12 and 25 mrad respectively. On a JEOL ARM 200F TEM without probe correction operated at 200 kV (C s = 0.5 mm), the lens setting 6C with a convergence semi-angle of 12 mrad corresponds to a probe size of about 1.5 Å(based on STEM probe simulation with QSTEM [START_REF] Koch | Determination of core structure periodicity and density along dislocations[END_REF]). This probe size enables EEL data to be acquired with enough signal to noise ratio at aluminium volume plasmon while at the same time, substantially limiting beam-induced damage to the Al nanospheres while acquiring the EELS time series at their center. For precise tracking of the shifts in Al volume plasmon energy as a function of temperature and gas conditions, the energy dispersion of the spectrometer was set to 0.01 eV/pixel and we collected time series of EELS data spectra at each point of analysis. Typically, 100 spectra were acquired per serie with an acquisition time of 5 ms per spectrum. After acquisition, the EEL spectra in each time serie were aligned with each other to correct any residual instabilities of the spectrometer and its environment which would otherwise compromise the determination of Al plasmon energies.

Results and Discussion

Fig. 2a shows a temperature serie of EEL spectra at the volume plasmon peak of a Al NP heated in vacuum at dierent setpoint temperatures between 100 and 500 °C. with thickness about 4 nm and that while the core lattice expanded under tension at room temperature, it undergoes a free thermal expansion with no constraint above 300 °C [START_REF] Firmansyah | Microstructural behavior of the alumina shell and aluminum core before and after melting of aluminum nanoparticles[END_REF]. More generally, for Al NPs in the micrometer and nanometer range, the thermal expansion of the metallic core is not affected by the alumina shell as long as the thiskness of the latter is 3 ± 2 nm [START_REF] Runo | Structural changes and thermal properties of aluminium microand nano-powders[END_REF]. Consequently, the discrepancies observed between the expected plasmon energies and the measured ones in vacuum (and under H 2 hereafter) are not attributable to the presence of oxide layers at the surface of the probed NPs. We strongly believe that these discrepancies directly result from the dierences between setpoint and local temperatures of the heating E-chips. In order to determine the temperature stability of the SiN support, we have probed the stability of the volume plasmon energy of a single NP heated in vacuum at 200 °C setpoint temperature (where the average local and setpoint temperatures match as shown in Fig. 3). Fig. 5 shows EEL spectra acquired at a reference time t 0 (which is a few seconds after reaching the setpoint temperature) and 10 and 30 minutes later. At t 0 , the volume plasmon is equal to 15.13 ± 0.01 eV. This energy corresponds to a local temperature equal to 200 °C, i.e. the setpoint temperature. Subsequent EELS measurements of the particle plasmon energy after 10 and 30 minutes clearly shows that the local temperature is perfectly stable during the time period considered as no detectable plasmon shifts among the spectra are observed. [START_REF] Winterstein | Temperature calibration for in situ environmental transmission electron microscopy experiments[END_REF]. Indeed, in this case, due to important convective heat transfers between the heater and owing gas, a gas pressure of the order of 150 Pa is enough to produce a drop in sample temperature of the order of a few hundred degrees Celsius. In the present work, the temperature drop upon H 2 gas ow has not been assessed but it is expected to be higher than the ones measured with static gas environments.

However, we expect such temperature drops in the presence of a gas environment will be mitigated when closed loop temperature control of the SiC heater is activated. the second highest thermal conductivity (after helium) among the common gases [START_REF]Thermal conductivity of gases[END_REF], these measurements enable to quantify the temperature drop upon introduction of a static gas environment in the nanoreactor in the worst case scenario.

Conclusions

In summary, we have used Al plasmon nanothermometry for local temperature measurement up to 550 °C in in situ temperature controlled TEM. Accessing these discrepancies at the nanoscale is of paramount importance for the reliable interpretation of in situ TEM experiments with temperature as a parameter.

Figure 1 :

 1 Figure 1: Polynomial t of the Al linear expansion coecient α l between 24 and 600 °C.The experimental data points [•] are extracted from[START_REF] Wilson | The thermal expansion of aluminium : Further experiments[END_REF]. The best-t polynomial of degree two is shown in plain red line. The temperature dependence of the Al volume plasmon within the free electron model using the polynomial t coecients of α l is shown in insert (plain blue lines). The two red dashed lines correspond to the upper and lower predictions limits taking into account the uncertainties on the tting coecients of Al linear expansion coecient and the reference Al volume plasmon energy at 24 °C (from[START_REF] Abe | Temperature-dependence of the volume-plasmon energy in aluminum[END_REF]).

Figure 2 :

 2 Figure 2: (a) Redshift of Al volume plasmon with increasing temperature measured by EELS on a single Al probe. Each spectrum is a sum of 100 spectra acquired sequentially in time with an acquisition time of 5 ms per spectrum. The time series were acquired at the center of a single Al nanoparticle using time-resolved spectrum imaging available in Gatam Microscopy Suite 3. Before summing the individual spectra in each spectrum image, their shifts in the spectral position were corrected by applying the Align SI by peak Gatan 3 SI plugin at the zero loss peak(b) Comparison of the temperature-induced Al volume plasmon shift on three NPs and the free-electron model. The diameters of three nanospheres are between 100 and 150 nm. The Al plasmon shift according to the free electron model is shown in blue lines with its upper and lower prediction limits in dashed red lines. The experimentally-measured plasmon energies as well as the average plasmon energy at each setpoint temperature are shown alongside (coloured markers). The uncertainty on the plasmon energy was considered equal to the width of one energy channel in the EEL spectra, i.e. 0.01 eV while the one on the setpoint temperature is ± 5 % of the nominal value as per Protochips Inc specication. A low magnication HAADF-STEM image showing the localization of the three NPs is presented in insert.

Figure 3 :Fig. 3

 33 Figure 3: Comparison between local (green line) and setpoint temperatures (blue lines) in a MEMS-based nanoreactor from Protochips Inc. operating under vacuum between 24 and 550 °C. The red dashed lines represent the upper and lower temperature limits constructed by considering the 95 % accuracy on the setpoint temperatures as per Protochips specications.

3. 1 .

 1 Spatial and temporal stability of heater in vacuum Besides the dierence between local and setpoint temperatures, other factors that need to be taken into account during in situ heating TEM observations are the spatial and temporal uniformities of the local sample temperature. Indeed, a temperature gradient along the electron-transparent SiN windows renders comparison among TEM observations made at dierent locations impossible. Similarly, if the temperature at a given location on the SiN lm is not stable over time, TEM observations of dynamic processes during in situ heating experiments is compromised. In this work, we have assessed the spatial and temporal uniformities of the temperature of the SiN support by tracking, at given setpoint temperature, the Al plasmon peak shifts at dierent locations and the Al plasmon peak shifts at a given location as a function of time, respectively.3.1.1. Measurement of local temperature at dierent points in the sample

Figure 4 :

 4 Figure 4: Spatial stability of the local temperature as shown by EELS tracking of the volume plasmon peaks of three Al NPs supported on three dierent SiN windows heated at 300 °C in vacuum.

Fig. 4 Figure 5 :

 45 Fig.4shows the superposition of the EEL spectra at the volume plasmon energy of three Al NPs located at three dierent windows (numbered 1, 2 and 3 in insert of Fig.4) and heated at 300 °C in vacuum. The perfect plasmon peak coincidence among the three EEL spectra is a clear indication that the local temperatures of the NPs considered here are similar, i.e. the temperature is uniform over the three chosen locations at 300 °C. However, if we consider the local temperature measurements in Fig.2bwhich were performed over a given window, it is also clear that temperature is not always uniform over the heated region.

3. 2 .Figure 6 :

 26 Figure6: Temperature drop in a MEMS-based nanoreactor from Protochips Inc. operated at 100, 300 and 400 °C setpoint temperatures upon introduction of a static hydrogen gas environment at 230, 400 and 760 Torr respectively. The data points correspond to temperature determination done in the nanoreactor after it has been evacuated. The heating E-chip is dierent to the one used in the previous gures.

Fig. 6

 6 Fig.6shows the variation of the local temperature as a function of the H 2 pressure at each setpoint temperature measured at a given location. For com-

Figure 7 :Fig. 7

 77 Figure 7: Comparison between local and setpoint temperatures in a MEMS-based nanoreactor from Protochips Inc. with static hydrogen gas up to atmospheric pressure. The evacuated conditions corresponds to the presence of residual argon gas at 32 mTorr in the nanoreactor.

  It has been applied to assess the performances of the SiC heater of a MEMSbased gas nanoreactor in terms of the accuracy of the local temperature and the spatial and temporal uniformities of the latter. To establish these performances, we have compared the actual local temperatures deduced from plasmon-based nanothermometry to the setpoint temperatures provided by ex situ calibration of the heater using optical pyrometry. Temperature comparisons performed both under vacuum and in the presence of a static H 2 gas environment reveal that the local and setpoint temperature can be dierent.

  In this work, taking advantage of the absence of closed loop temperature control, we have assessed, both qualitatively and quantitatively, the eect of a static H 2 environment on the local sample temperature in the nanoreactor. Al nanospheres with an average diameter of about 50 nm were used as local temperature probes. The NPs were provided by US Research Nanomaterials Inc. and were synthesized by electrical explosion method. They were dry-deposited on the SiN lm of the heating E-chips prior to in situ

	Protochips Atmosphere gas holder. Among others, it is devoid of the closed
	loop temperature control of the SiC heater which is a standard feature of the
	latter. This temperature control ensures, independently of the gas environ-
	ment prevailing in the nanoreactor, a temperature accuracy of more than 95

They are composed of two disposable MEMS devices commonly known as environmental chips (or E-chips) and a dedicated sample holder with electrical and gas feedthroughs for in situ gas experiments at elevated temperature. One of the E-chips is heat conductive while the other is not. The non-conductive E-chip is a 6.0 mm × 4.5 mm, 300 µm-thick Si chip with a 300 µm × 300 µm hole drilled at its center. The hole is covered with a 50 nm-thick electron-transparent SiN lm. The conductive E-chip is a 2.0 mm × 2.5 mm, 300 µm-thick Si chip with a 300 µm × 300 µm SiC membrane suspended at its center. This membrane is patterned with six (2 × 3) 7 µm in diameter holes on which a 30 nm thick layer of SiN is overlaid. The six electron-transparent regions serve as observation windows during TEM experiments. For in situ heating and/or gas TEM, the E-chips are precisely aligned and xed into a closed-cell conguration at the tip of the dedicated TEM holder. In our experimental set-up, gas is fed to the nanoreactor using a manually-operated gas manifold also from Protochips. The sample is heated through resistive heating of the SiC membrane. The electrical current is delivered to the SiC membrane through gold connector pads via two electrical feedthroughs at the back of the sample holder connected to an electronic control unit (ECU). The heating E-chips are calibrated individually by Protochips using optical pyrometry performed in vacuum to obtain the corresponding current-temperature calibrations. For controlled sample heating, the ECU is interfaced with the custom Protochips Aduro 300 software which uses the calibration le provided with each heating E-chip as input. It should be noted that the gas nanoreactor used in this work is a prototype of % and a temperature uniformity of the order of 99.5 % as per Protochips specications.

  parison, the local temperatures determined by plasmon thermometry before introduction of H 2 gas, under residual argon gas at 32 mTorr, are included for each experiment (the three data points at zero Torr). At 100 and 300 °C, the local temperature remains, within experimental uncertainties, stable around the setpoint temperatures as gas pressure increases. In contrast, the local temperature decreases with H 2 gas pressure at 400 °C. The temperature of the evacuated nanoreactor at this setpoint temperature is 439.6 °C. As in the open conguration, the local temperature in the absence of gases is slightly higher that the set temperature. As H 2 gas pressure increases to 230 Torr, the local temperature instantly drops from 439.6 °C to 360.3 °C. Then, as gas pressure is further increased to atmospheric pressure, the local temperature drops to 322.7 °C, corresponding to a temperature drop of about 117 °C upon lling the nanoreactor with static H 2 gas at 760 Torr. This temperature drop is less than the ones measured by Winterstein et al. when H 2 gas at much lower pressure is own in furnace-based and MEMS-based holders at 300 and 500 °C setpoint temperatures

Acknowledgements

This work was supported by the French National Research Agency through the TOTEM project [Grant Number ANR-17-CE07-0031] and the Région Ilede-France through convention SESAME E1845 for the support of the JEOL ARM 200F electron microscope installed at Paris Diderot University.