Justyna Gadzińska

Ernest Kuchar

Michał Matysiak

Monika Wanke-Rytt

Malgorzata Kloc

Jacek Z Kubiak

A 10-year-old girl with late Acute Lymphoblastic Leukemia recurrence diagnosed with COVID-19 and treated with remdesivir

Keywords: Acute Lymphoblastic Leukemia, COVID-18, SARS-CoV-2, remdesivir, pediatrics

Patients with haemato-oncological diseases are particularly vulnerable to severe infections. Adult patients with blood cancers infected with SARS-CoV-2 had poorer treatment outcomes and higher mortality than patients with COVID-19 without burden. However, in pediatric patients with haemato-oncological diseases the course of COVID-19 is milder than in adults in the same group of patients. In this report, we describe the case of our patient with Acute Lymphoblastic Leukemia (ALL) infected with SARS-CoV-2 and treated with remdesivir. We also review the existing literature of pediatric patients who have been diagnosed with both with haematooncological diseases and COVID-19.

Case Report

A nearly 10-year-old girl was admitted to the hospital for a scheduled cycle of consecutive chemotherapies due to the late isolated recurrence of Acute Lymphoblastic Leukemia (ALL) in the bone marrow. On admission, the child's general condition was described as good, with no signs of infection, and the girl did not report any complaints. The screening smears for SARS-CoV-2 taken from the nose of the girl and her mother and analyzed by RT-PCR gave negative results. These results of the laboratory tests made it possible to continue the treatment of the underlying disease. Between the 2nd and the 7th day of hospitalization, the girl received the SCA5 block without complications, but then she developed stomatitis requiring treatment, initially with micafungin and then with amphotericin B. In the following days, bone marrow suppression occurred, and morphology showed pancytopenia with agranulocytosis, which required multiple transfusions of concentrated red blood cells and concentrated platelets. On the 12th day of hospitalization, the girl began to cough. The next day, inflammatory markers increased (CRP to 7.9 mg/dl from 0.7 mg/dl on 08.10., PCT 0.65 ng/ml). Due to the persistent agranulocytosis, after the blood cultures were performed, standard therapy with cefepime was applied. On the 14th day of hospitalization, the patient was positive for Clostridioides difficile in stool culture; the metronidazole treatment was applied for ten days. On day 16th, the tests for fungal antigens in the blood (Candida, Aspergillus, Cryptococcus neoformans) were performed, which yielded negative results. On the 17th and 18th day of hospitalization, i.e., on the 5th and 6th day of antibiotic therapy, the girl had an episode of fever followed by a 4-day fever-free period. On the day 21st of the hospital stay, the girl began to fever again, this time up to 39 ° C.

After the blood cultures and swabs for alarm pathogens identified E. coli ESBL+, the antibiotic therapy was modified to meropenem. In the following days, despite the use of broad-spectrum antibiotic therapy, the fever continued, and the inflammation markers increased. On the 23rd day of hospitalization, the girl showed a decrease in oxygen saturation to 86%, thus requiring passive oxygen therapy. On the same day, to look for the infection source, an ultrasound examination of the abdominal cavity was performed. No significant abnormalities were detected. Also, a CT scan of the chest was performed, in which massive, scattered areas of the ground glass-type were described, merging into parenchymal consolidations with bronchial distribution including all lobes. Treatment with amphotericin B was replaced by the caspofungin and voriconazole due to the suspicion of fungal invasive lung disease. The next smear for SARS-CoV-2 was taken. On Accepted Manuscript day 25, after receiving a positive SARS-CoV-2 result by RT-PCR test, the girl was transferred to the Isolation Unit for further treatment. In laboratory tests collected on the 26th day of hospitalization, the highest CRP and PCT level was recorded, 17 mg/dl and 35.98 ng/ml, respectively.

The girl was consulted by two specialists in pulmonology and infectious diseases, who concluded that the presented symptoms and the changes described in the chest CT, were with a high probability, the effect of COVID-19. They decided that the compassionate use of remdesivir should be applied. The Local Bioethics Committee was informed about the proposed treatment.

Written consent was obtained from the girl's mother to administer the drug off-label. On the hospitalization day 26, remdesivir was introduced at a dose of 5 mg/kg/dose on the first treatment day, and then 2.5 mg/kg/dose for four days. During the treatment with remdesivir, the hepatic and renal parameters did not show any adverse effects. During the Isolation Ward hospitalization, the girl's general condition improved, and the fever subsided. Starting from the 27th day of hospital stay, the parameters of inflammation gradually decreased. From the 28th day onward there was no decrease in oxygen saturation, and the girl did not require passive oxygen therapy. All blood cultures collected at the hospital were negative. On the 38th day of hospitalization, the girl had been discharged home in good general condition with the follow-up recommendations.

Discussion

Severe course of SARS-CoV-2 infection occurs in <3% of children and critical in <1% of cases [1][START_REF] Alsohime | COVID-19 infection prevalence in pediatric population: Etiology, clinical presentation, and outcome[END_REF][START_REF] Galhotra | COVID-19 Pandemic and Children: A Review Vinay Rathore[END_REF]. In these patients, as in the adults, the multi-organ failure with a shock may develop, requiring treatment in an intensive care unit. In children, risk factors are not as well characterized as in adults. The following risk factors were described in a multicentre cohort study conducted in Europe, which enrolled 582 patients <18 years of age with PCR-confirmed SARS-CoV-2 infection: age <1 month, male gender, presence of lower respiratory tract symptoms, and comorbidities [START_REF] Galhotra | COVID-19 Pandemic and Children: A Review Vinay Rathore[END_REF]. Comorbidities at risk of severe COVID-19 include cancer, developmental delay / genetic defects, immunosuppressive states, obesity, diabetes, congenital heart disease, chronic lung diseases, including asthma, chronic kidney disease, and intussusception [1-3].

Patients with haemato-oncological diseases constitute a group of patients particularly vulnerable

Accepted Manuscript to severe infections. Several studies in adults have shown that patients with blood cancers infected with SARS-CoV-2 had poorer treatment outcomes and higher mortality than patients with COVID-19 without burden [START_REF] Passamonti | Clinical characteristics and risk factors associated with COVID-19 severity in patients with haematological malignancies in Italy: a retrospective, multicentre, cohort study[END_REF][START_REF] Lee | COVID-19 prevalence and mortality in patients with cancer and the effect of primary tumour subtype and patient demographics: a prospective cohort study[END_REF]. In pediatric patients with haemato-oncological diseases, including those treated with chemotherapeutic agents, the course of COVID-19 is milder than in adults in the same group of patients. Similarly to the general population, in pediatric patients group, the course of SARS-CoV-2 infection is usually mild or asymptomatic [START_REF] Götzinger | COVID-19 in children and adolescents in Europe: a multinational, multicentre cohort study[END_REF][START_REF] Sanchez-Pina | Clinical course and risk factors for mortality from COVID-19 in patients with haematological malignancies[END_REF][START_REF] Hrusak | Flash survey on severe acute respiratory syndrome coronavirus-2 infections in paediatric patients on anticancer treatment[END_REF]. However, the specialists emphasize the need to exercise particular caution in managing haemato-oncological diseases group of patients due to the existing risk of a severe course of COVID-19 [START_REF] Bisogno | Clinical Characteristics and Outcome of Severe Acute Respiratory Syndrome Coronavirus 2 Infection in Italian Pediatric Oncology Patients: A Study From the Infectious Diseases Working Group of the Associazione Italiana di Oncologia e Ematologia Pediatrica[END_REF].

Remdesivir is an antiviral drug with proven efficacy against SARS-CoV-2 in vitro [START_REF] Wang | Remdesivir and chloroquine effectively inhibit the recently emerged novel coronavirus (2019-nCoV) in vitro[END_REF]. Recently published results of a large randomized trial have shown that remdesivir is superior to placebo in treating adults with COVID-19 [START_REF] Beigel | Remdesivir for the Treatment of Covid-19 -Final Report[END_REF]. To date, we have less data on the outcomes of remdesivir treatment in children. For this reason, the case described here may be of special importance. So far, only a few case reports of children with COVID-19 treated with remdesivir have been found in the literature. The youngest was a 19-day-old newborn who received treatment on the 4th day of hospitalization [START_REF] Wardell | Dixit Severe Acute Respiratory Syndrome Coronavirus 2 Infection in Febrile Neonates[END_REF]. In all described cases, the children treated with remdesivir achieved clinical improvement, and no serious side effects were reported. A group of specialists led by K.

Chiotos published in the Journal of the Pediatric Infectious Diseases Society in September 2020 the guidelines for the treatment of children with COVID-19, approved by this organization. [START_REF] Chiotos | Multicenter Interim Guidance on Use of Antivirals for Children With Coronavirus Disease 2019/Severe Acute Respiratory Syndrome Coronavirus 2[END_REF].

These guidelines were also important for us when making therapeutic decisions during the treatment of the patient described in this article. We want to notice that the the FDA approved remdesivir for use in adult and pediatric patients 12 years of age and older and weighing at least 40 kilograms and in hospitalized pediatric patients weighing 3.5 kg to less than 40 kg or hospitalized patients younger than 12 years weighing at least 3.5 kg. Our 10-year old patient fulfilled the last criterion: 30,5 kg, 138 cm, BSA 1.09m2.

Conclusions

More research is needed to find optimal treatment regimens for children with severe COVID-19, including the groups of patients with high-risk factors for severe COVID-19. Until appropriate therapeutic standards are developed, and an effective vaccine is introduced to the market, preventive actions aimed at reducing the risk of SARS-CoV-2 infection are of the highest priority.

Accepted Manuscript

Acknowledgemenets

During the writing of this article, JZK was supported by "Kościuszko" grant # 508/2017/DA from the Polish Ministry of National Defense.


Ayman Akhdar

Jean-Michel Andanson

Sophie Faure

Mounir Traïkia

Arnaud Gautier
email: arnaud.gautier@uca.fr

Application of Quantitative 1H and 19F NMR to Organometallics

Keywords: Purity, NMR, Analytic Chemistry, Imidazolium, Organometallics

come   L'archive ouverte pluridisciplinaire

Introduction.

The structure determination of ligands and organometallic complexes is usually performed by NMR, infrared (IR), Ultraviolet-Visible (UV-vis) and X-ray spectroscopies. The purity is determined accurately by HPLC for ligands if analytical pure samples of the constituent(s) of a mixture are available. However, this technique cannot be applied routinely to organometallic complexes. Elemental analysis is useful to establish elemental composition and molecular formula. However, it does not provide a direct measurement of the molar purity. Quantitative NMR (qNMR) presents a valuable alternative because the measured signals represent a direct measurement of the composition. [1] Therefore, in presence of an internal reference of known purity, the composition of a synthetic sample can be determined accurately using a single spectrum/ integration sequence (so-called absolute quantification). Equation 1 affords the purity value after accurate weight and NMR measurements. 𝑃 = . . . . 𝑃 Eq. 1

(where s and std stand for the sample and the standard respectively; I being the sum of integrals, N the total number of nucleus ( 1 H, 19 F for example), M the molecular mass, m the measured masses, and P the purities of the standard). This method gives a direct access to the sample purity regardless the content of water or residual solvents, inorganic or organic chemicals, and sorbents, such as silica gel or celite. qNMR, especially using proton measurement is now a technique of choice routinely used in natural product and pharmaceutical chemistry with a reported accuracy of 1-2%. [1a] Several reviews are available, [1b] reporting the acquisition parameters and this protocol is recently entered into the guideline for authors of the Journal of Medicinal Chemistry. [START_REF] Cushman | Absolute Quantitative 1 H NMR Spectroscopy for Compound Purity Determination[END_REF] Chemical suppliers are commercializing 'gold standard' which are used as internal or external standards. Expensive 'gold standards' can also be used to determine the purity of a cheaper or more adequate secondary standard, especially if the commercial standard is not compatible with the chemical stability of the organometallic complexes. The purity is of importance for the synthesis of the desired complexes. For example, the starting material could contain residual water, a metathesis could afford a mixture of counter ions or a transmetallation leading to a contamination. In addition, in the case of kinetic studies the determination of the concentrations in stock solutions (reactants and catalyst) should be taken into consideration to perform accurate measurements. In this communication, the application of 1 H and 19 F qNMR for ligands and organometallics is reported (ESI for NMR parameters and protocols). In brief, after proper weight measurements, choice of the adequate solvent, recording of 1 H and 19 F spectrum takes 30 to 60 minutes making this technic attractive for routine determination.

2. Results and discussion.

Choice of the standard.

To make this method straightforward and broadly applicable to a wide variety of ligands and organometallics, the standard should be cheap and inert in presence of the organometallics. This prohibits standards containing some functional groups that could chelate or react such as carbonyls, reducing agents (formic salts), double bonds, acidic or redox compounds (phenol, quinones). We find that 1,4-dimethoxy benzene (1,4-DMB) is a good secondary standard for 1 H qNMR as it is cheap, easily purified by recrystallization from hexane or sublimation [START_REF]thEds[END_REF] and presents a good solubility in all common NMR solvents. Using maleic acid as primary gold standard (99.995% traceable certified reference materials, TraceCERT®) we find a purity of 99.5 % for our recrystallized sample of 1,4-DMB (Karl Fisher analysis demonstrates the absence of residual water -115.3 -117.0 -117.7 -117.3 -115.8 3.3 [a] 6.5 [b] 4.8 [a] 4.7 [a] 4.4 [a] BF4 - dmso-d6 Acetone-d6 MeOH-d4 -151.9

10 B: 6.8 3.8 [e] 3.4 [e] 3.1 [e] MeCN-d3 CDCl3 -72.8 -72.3

3.5 [e] 2.0 [f] [a]: taken from Aldrich, TraceCERT® CRMs for quantitative NMR. [b]: this work. [c]: using SIPr.HBF4. [d]:

using N(Pr)4, BF4.

[e]: using SIPr.HPF6. [f]: using N(Bu)4, PF6.

2.2.Ligands.

As illustration of this methodology due to their actual increasing interest in catalysis, we decided to focus on imidazolium and imidazolinium that are used for N-heterocyclic carbenes (NHCs) and metal-NHCs synthesis.

[4] In his seminal communications, Arduengo reports a convenient synthesis of these azoliums that consists of refluxing diamines in triethyl orthoformate (Scheme 1).

[4b] After filtration, salt/triethyl orthoformate adduct are obtained and simple purification can be achieved by repeated recrystallizations from acetonitrile/ether.

Scheme 1: Imidazolium and imidazolinium synthesis.

At first, we followed the Arduengo's procedure for SIMes.HCl and find, using 1,4-DMB, a purity of 72% for the crude product after filtration (Table 2, entry 1). Simple recrystallization [4b] and determination of the water content by Karl-Fisher analysis (0.5 molecule of water, Table 2, entry 2) affords a purity of 91%. For the azolium series, the amount of water varies from 0 to 1 molecule. This first screening of classical imidazolium and imidazolinium highlights the importance of the purity determination that should be corroborated with Karl-Fisher analysis results.

Table 2: purity of azolium chlorides.

Entries (a) Azolium Purity (b) 1 SIMes.HCl (c) 72% The same procedure was followed for the non-coordinating anions PF6 and BF4, commonly used for metal carbene complexes. Importantly, both 1 H qNMR and 19 F qNMR should give comparable results. We simply synthesize the PF6 and BF4 salts by anion metathesis from the corresponding azolium chloride as previously described (Table 3). [START_REF] Bantreil | Synthesis of N-heterocyclic carbene ligands and derived ruthenium olefin metathesis catalysts[END_REF] We obtained high yields and in contrast to the chloride series, Karl-Fisher analysis demonstrates the absence of residual water (less than 0.1%). The results show a good correspondence between both technics with a maximum deviation of 3% (entries 6) and an averaged deviation of 1.8%. In all cases, good purity was observed.

Entry (a) Azolium Yield Purity by 1 H qNMR (b) Purity by 19 F qNMR (b) 1 SIMes.HPF6 90% 98% (± 2%) 97% (± 1%) IMesAgCl, SIMesAgCl, IPrAgCl and SIPrAgCl were synthesized by the classical silver oxide route [4a], [START_REF] De Frémont | Synthesis of Well-Defined N-Heterocyclic Carbene Silver(I) Complexes[END_REF] and recrystallized from dichloromethane/pentane affording good purities (Table 4, entries 1-4) in CDCl3.

At this stage, we noticed that a decomposition of the complex in presence of the maleic acid standard takes place after few hours; therefore 1,4-DMB as secondary standard was selected. The copper complexes SIMesCuCl and IPrCuCl (entry 5-6) were obtained by a soft, direct metalation of the azolium using the ammonia protocol. [START_REF] Gibard | Simplified Preparation of Copper(I) NHCs Using Aqueous Ammonia[END_REF] Both complexes are obtained with good purity, 93 and 98% respectively.

IPrAuCl was synthetized by transmetallation from IPrAgCl affording 93% purity (entry 7). The cationic complex [IPrAu(MeCN)], BF4 was obtained from Strem chemical (95%) and analyzed by 1 H and 19 F qNMR to give an average purity of 95% in perfect agreement with the supplier's specifications (entry 8).

Pd(PEPPSI)IPr was synthetized from the well-established procedure from the Organ's group. [12a] We find small amounts of indefinite materials in the crude mixture that were removed by washing with dichloromethane/pentane (1:5). The NMR spectrum shows the presence of 0.8 molecules of CH2Cl2 that cannot be removed under reduced pressure (2mbar) for several hours. [12b] The Pd(PEPPSI)IPr's purity in this mixture is found to be 87% and, taking the dichloromethane amount into account, a 96% purity is obtained (entry 9).

Conclusion

In conclusion, we have applied 1 H and 19 F quantitative NMR to organometallics complexes and their corresponding ligands. According to previous reports, this technique is easy to conduct, affording purity of compounds in approximately 30 minutes to 1h experiment. Care should be taken regarding the choice of the solvent and if possible, Karl Fisher analysis should be considered, especially regarding the ligands.

Acknowledgment

Financial support from the Centre National de la Recherche Scientifique (CNRS) and the Université Clermont-Auvergne are acknowledged.

Appendix.

Supplementary material: procedure, NMR sequence and spectra can be found, in the online version, at ... S3. Determination of the purity of 1,4 DMB standard. Purity was determined as the average of 6 independent measurements in dmso_d6 from maleic acid gold standard trace certified (Aldrich) as primary standard.

Example of spectrum

Purity is calculated as the ratio of the number of mol of 1,4-DMB determined by qNMR divided by the number of mol of 1,4-DMB weighted, taking into account of the primary standard purity (99.98 +/-0.13 %). The linearity of the response in the range 10 to 25 mg (0.08 to 0.17 mmol) was verified:

11 B

 11

  (a): in dmso-d6. (b): uncertainty from 3 experiments. (c): before recrystallization. (d): after recrystallization.

  (a): in dmso-d6.(b) Uncertainty from 2 experiments.2.3.Metal-NHCs.The purity of a selection of useful metal-NHCs was determined. We used well-established literature procedures to synthetize silver, copper, gold and palladium complexes. Indeed, Ag(I)-NHCs and Cu(I)-NHCs are important synthons for transmetallation. [6] Silver(I)-NHCs is used in A3 coupling reactions and oxazole synthesis, copper(I)-NHCs are used in CuAAC, reduction, conjugated additions, allylic substitutions, alkene and alkyne transformations, nitrene transfert, allene synthesis, hydrocarboxylation, A3 coupling, oxidative coupling of phenol. [7] Regarding gold(I)-NHCs, they are used in a wide range of catalysis including enyne cycloisomerization, alkyne hydration, C-C coupling reactions;[START_REF] Nolan | The Development and Catalytic Uses of N-Heterocyclic Carbene Gold Complexes[END_REF] and palladium(II)-NHCs has gained high importance in homogeneous cross-coupling.[START_REF] Fortman | N-Heterocyclic carbene (NHC) ligands and palladium in homogeneous crosscoupling catalysis: a perfect union[END_REF]

  (a): average of two measurements. (b): by 1 H qNMR. (c): by 19 F qNMR. (d): Pd(PEPPSI)IPr: Dichloro[1,3bis(2,6-diisopropylphenyl)imidazol-2-ylidene)](3-chloropyridyl)palladium(II).

  ). 1 H NMR of 1,4-DMB shows two well defined, sharp singlets at 6.7 and 3.7 ppm. Regarding 19 F qNMR, we used the commercially available 'gold standard' 2-chloro-4-

	close signals, a singlet ( 10 B) and a quadruplet ( 11 B, S=3/2, J=1.1Hz) in a ratio 2:8. Chemical shifts () and
	relaxation times (T1) are collected in Table 1. The relaxation times show that 30 seconds of inter-pulse
	delay (>5 T1) could be used for 19 F qNMR.
	Table 1: Chemical shifts () and relaxation times (T1)
	compound	Solvent	 (ppm)	T1 (s)
		dmso-d6
	2-chloro-4-fluoro	Acetone-d6
	Toluene	MeOH-d4
		MeCN-d3
		CDCl3

fluoro toluene that is found inert in presence of the compounds described below. It is also interesting to note that the chemical shift of this standard is well positioned between the signals displayed by BF4 and PF6 anions. PF6 appears as a doublet due to the coupling with phosphorus whereas BF4 appears as two

Table 4 :

 4 Purity of metal-NHCs.

	Entries	M-NHC	Solvent	Purity
	1	IMesAgCl	CDCl3	97% (± 1%)
	2	SIMesAgCl	CDCl3	95% (± 2%)
	3	IPrAgCl	CDCl3	97% (± 1%)
	4	SIPrAgCl	CDCl3	98% (± 1%)
	5	SIMesCuCl	CDCl3	93% (± 2%)
	6	IPrCuCl	CDCl3	98% (± 1%)
	7	IPrAuCl	dmso-d6	93% (± 1%)
	8	[IPrAu,MeCN], BF4	dmso-d6	94% (± 1%) (b)
				96% (± 1%) (c)
	9	Pd(PEPPSI)IPr, (d) 0.8CH2Cl2	CDCl3	96% (± 2%)

  S4. 19 F NMR spectra of SIPr.HPF6 S5. 19 F NMR spectra of SIPr.HBF4 S6. SIMes.HCl 0.5H2O Verifié!! Somme of the integral: sample, 709.2; standard, 290.9. S32. IPrAuCl, 1 H qNMR Vérifié !! Somme of the integral: sample, 714.4; standard, 285.7. S33. [IPrAu,MeCN], BF4 , 1 H qNMR, Vérifié!! Somme of the integral: sample, 772.71; standard, 289.5.

	S7. IMes.HCl, 1H2O 1 H qNMR Verifié!! S8. SIPr.HCl, 0H2O 1 H qNMR Verifié!!! S9. IPr.HCl, 0.5H2O 1 H qNMR Verifié!! S10. SIMes.HPF6 1 H qNMR Verifié !!!! S11. SIMes.HPF6 19 F qNMR Vérifié S12. IMes.HPF6 1 H qNMR verifié !!! S13. IMes.HPF6 19 F qNMR Verifié! S14. SIPr.HPF6 1 H qNMR Vérifié !!! S18. SIMes.HBF4 1 H qNMR Verifié !!! S20. IMes.HBF4 1 H qNMR Verifié !! S30. SIMesCuCl, 1 H qNMR Vérifié!! S31. IPrCuCl, 1 H qNMR VERIFIER !
	Zero filling 256K Zero filling 256K Zero filling 256K Zero filling 256K Zero filling 256K Zero filling 256K Zero filling 256K Zero filling 256K S19. SIMes.HBF4 19 F qNMR Verifié LB 0.1 LB 0.1 LB 0.1 LB 0.1 LB 0.1 LB 0.1 LB 0.1 LB 0.1	Phase Manual Phase Manual Phase Manual Phase Manual Phase Manual Phase Manual Phase Manual Phase Manual	Baseline 6 Th order Baseline 6 Th order Baseline 6 Th order Baseline 6 Th order Baseline 6 Th order Baseline 6 Th order Baseline 6 Th order Baseline 6 Th order
	Zero filling 256K Zero filling 256K Zero filling 256K Zero filling Zero filling Zero filling Zero filling Zero filling Zero filling Zero filling Zero filling Zero filling 256K Zero filling 256K 256K 256K 256K 256K 256K 256K 256K	LB 0.1 LB 0.1 LB 0.1 LB LB LB LB LB LB LB LB LB 0.1 LB 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1	Phase Manual Phase Manual Phase Manual Phase Phase Phase Phase Phase Phase Phase Phase Phase Manual Phase Manual Manual Manual Manual Manual Manual Manual Manual	Baseline 6 Th order Baseline 6 Th order Baseline 6 Th order Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline 6 Th order 6 Th order 6 Th order 6 Th order 6 Th order 6 Th order 6 Th order 6 Th order 6 Th order
	256K	0.1	Manual	6 Th order
	Masse sample (mg) 36.61 Signals standard (ppm) Integration Signals sample (ppm) Integration Somme of the integral: sample, 732.8; standard, 267. Masse Standard (mg) 12.52 6.86 104.8 7.1 115.2 Number of protons: sample, 26; standard, 10. Molecular weight: sample, 351.9; standard, 138.2. Mass of: sample, 36.61; standard: 12.52. 𝑃 = 𝐼 𝐼 . 𝑁 𝑁 . 𝑀 𝑀 . 𝑚 𝑚 . 𝑃 = 732.8 267 . 10 26 . 351.9 138.2 . Purity: 91% Masse sample (mg) Masse Standard (mg) 13.48 13.96 Signals standard (ppm) 6.86 Integration 211.3 Signals sample (ppm) 8.3 Integration 38.5 Somme of the integral: sample, 468.5; standard, 531.5. Number of protons: sample, 24; standard, 10. Molecular weight: sample, 358.9; standard, 138.2. Mass of: sample, 13.48; standard: 13.96. 𝑃 = 𝐼 𝐼 . 𝑁 𝑁 . 𝑀 𝑀 . 𝑚 𝑚 . 𝑃 = 468.5 12.52 36.61 531.5 . 10 24 . Purity: 98% Masse sample (mg) Masse Standard (mg) 31.98 15.53 Signals standard (ppm) 6.86 Integration 119.6 Signals sample (ppm) 7.5 Integration 110 Somme of the integral: sample, 699.6; standard, 300.5. Number of protons: sample, 40; standard, 10. Molecular weight: sample, 445.1; standard, 138.2. Mass of: sample, 31.98; standard: 15.53. 𝑃 = 𝐼 𝐼 . 𝑁 𝑁 . 𝑀 𝑀 . 𝑚 𝑚 . 𝑃 = 699.6 300.5 . 10 40 . Purity: 91% Masse sample (mg) Masse Standard (mg) 31.75 11.27 Signals standard (ppm) 6.86 Integration 226 Signals sample (ppm) 8.6 Integration 105.7 Somme of the integral: sample, 437.3; standard, 562.7. Number of protons: sample, 8; standard, 10. Molecular weight: sample, 434.1; standard, 138.2. Mass of: sample, 31.75; standard: 11.27. 𝑃 = 𝐼 𝐼 . 𝑁 𝑁 . 𝑀 𝑀 . 𝑚 𝑚 . 𝑃 = 552.1 562.7 . 10 8 . . 0.995 = 0.914 Solvent: dmso-d6 3.7 162.2 4.45 2.35 112.7 337.5 Solvent: dmso-d6 3.7 320.2 7.2 2.4 77.7 116.7 358.9 138.2 . 13.96 13.48 . 0.995 = 0.983 Solvent: dmso-d6 3.7 180.9 4.5 3.1 75 74.2 445.1 138.2 . 15.53 31.98 . 0.995 = 0.906 Solvent: dmso-d6 3.7 336.7 7.7 109.7 434.1 138.2 . 11.27 31.75 . 0.995 = 0.96 Purity: 96% Masse sample (mg) Masse Standard (mg) Solvent: dmso-d6 31.00 12.98 Signals standard (ppm) 6.86 3.7 Integration 138.7 208 Signals sample (ppm) 7.1 4.43 2.34 Integration 100.1 102.7 299.1 Somme of the integral: sample, 653.9; standard, 346.7. Number of protons: sample, 26; standard, 10. Molecular weight: sample, 452.4; standard, 138.2. Mass of: sample, 30.00; standard: 12.98. 𝑃 = 𝐼 𝐼 . 𝑁 𝑁 . 𝑀 𝑀 . 𝑚 𝑚 . 𝑃 = 653.9 346.7 . 10 26 . 452.4 138.2 . 12.98 31 . 0.995 = 0.98 Purity: 98% Masse sample (mg) Masse Standard (mg) Solvent: dmso-d6 10.64 28.14 Signals standard (ppm) -115 Integration 587.3 Signals sample (ppm) -70 Integration 412.8 Somme of the integral: sample, 412.8; standard, 587.3. Number of fluorine: sample, 6; standard, 1. Molecular weight: sample, 453.4; standard, 144.6. Mass of: sample, 10.64; standard: 28.4. 𝑃 = 𝐼 𝐼 . 𝑁 𝑁 . 𝑀 𝑀 . 𝑚 𝑚 . 𝑃 = 412.8 587.3 . 1 6 . 453.4 144.6 . 28.4 10.64 . 0.996 = 0.96 Purity: 96% Masse sample (mg) Masse Standard (mg) Solvent: dmso-d6 31.51 15.25 Signals standard (ppm) 6.86 3.7 Integration 161.1 243.8 Signals sample (ppm) 8.28 7.2 2.34 2.12 Integration 49.35 100.2 151.1 294.5 Somme of the integral: sample, 595.2; standard, 404.9. Number of protons: sample, 24; standard, 10. Molecular weight: sample, 450.4; standard, 138.2. Mass of: sample, 31.51; standard: 15.25. 𝑃 = 𝐼 𝐼 . 𝑁 𝑁 . 𝑀 𝑀 . 𝑚 𝑚 . 𝑃 = 595.2 404.9 . 10 24 . 450.4 138.2 . 15.25 31.51 . 0.995 = 0.96 Purity: 96% Masse sample (mg) Masse Standard (mg) Solvent: dmso-d6 11.27 25.82 Signals standard (ppm) -115 Integration 544.2 Signals sample (ppm) -70 Integration 455.8 Somme of the integral: sample, 455.8; standard, 544.2. Number of fluorine: sample, 6; standard, 1. Molecular weight: sample, 450.4; standard, 144.6. Mass of: sample, 11.27; standard: 25.82. 𝑃 = 𝐼 𝐼 . 𝑁 𝑁 . 𝑀 𝑀 . 𝑚 𝑚 . 𝑃 = 455.8 544.2 . 1 6 . 450.4 144.6 . 25.82 11.27 . 0.996 = 0.99 Purity: 99% Masse sample (mg) Masse Standard (mg) Solvent: dmso-d6 32.09 11.51 Signals standard (ppm) 6.86 3.7 Integration 108.4 166 Signals sample (ppm) 7.49 4.54 3.1 11.35 1.19 2.3 167.4 2.1 235.6 2.3 440.4 3.7 221.9 2.29 152 Integration 114.6 78.6 77.3 231 224.1 Somme of the integral: sample, 725.6; standard, 274.4. Number of protons: sample, 38; standard, 10. Molecular weight: sample, 536.6; standard, 138.2. Mass of: sample, 32.09; standard: 11.51. 𝑃 = 𝐼 𝐼 . 𝑁 𝑁 . 𝑀 𝑀 . 𝑚 𝑚 . 𝑃 = 725.6 274.4 . 10 38 . 536.6 138.2 . 11.51 32.09 . 0.995 = 0.96 Purity: 96% Masse sample (mg) Masse Standard (mg) Solvent: dmso-d6 41.33 20.79 Signals standard (ppm) 6.86 3.7 Integration 154.0 234.9 Signals sample (ppm) 8.56 7.61 1.21 Integration 37.8 116.2 457.1 Somme of the integral: sample, 611.1; standard, 388.9. Number of protons: sample, 32; standard, 10. Molecular weight: sample, 536.6; standard, 138.2. Mass of: sample, 41.33; standard: 20.79. 𝑃 = 𝐼 𝐼 . 𝑁 𝑁 . 𝑀 𝑀 . 𝑚 𝑚 . 𝑃 = 611.1 388.9 . 10 32 . 536.6 138.2 . 20.79 41.33 . 0.995 = 0.95 Purity: 95% Masse sample (mg) Masse Standard (mg) Masse sample (mg) Masse Standard (mg) Solvent: CDCl3 Solvent: dmso-d6 12.80 33.25 13.18 14.98 Signals standard (ppm) -115 Integration 427 Signals sample (ppm) -70 -70 Integration 287 285.9 Somme of the integral: sample, 572.9; standard, 427. Number of fluorine: sample, 6; standard, 1. Molecular weight: sample, 534.6; standard, 144.6. Mass of: sample, 12.8; standard: 14.98. 𝑃 = 𝐼 𝐼 . 𝑁 𝑁 . 𝑀 𝑀 . 𝑚 𝑚 . 𝑃 = 572.9 427 . 1 6 . 534.6 144.6 . 14.98 12.8 . 0.996 = 0.96 Purity : 96% Masse sample (mg) Masse Standard (mg) Solvent: dmso-d6 31.18 11.75 Signals standard (ppm) 6.86 3.7 Integration 130.8 199 Signals sample (ppm) 7.09 2.32 Integration 121.6 548.7 Somme of the integral: sample, 670.3; standard, 329.8. Number of protons: sample, 22; standard, 10. Molecular weight: sample, 394.3; standard, 138.2. Mass of: sample, 31.18; standard: 11.75. 𝑃 = 𝐼 𝐼 . 𝑁 𝑁 . 𝑀 𝑀 . 𝑚 𝑚 . 𝑃 = 670.3 329.8 . 10 22 . 394.3 138.2 . 11.75 31.18 . 0.995 = 0.98 Purity: 98% Masse sample (mg) Masse Standard (mg) Solvent: dmso-d6 10.62 14.58 Signals standard (ppm) -115 Integration 484 Signals sample (ppm) -148 Integration 515 Somme of the integral: sample, 515; standard, 484. Number of fluorine: sample, 4; standard, 1. Molecular weight: sample, 394.2; standard, 144.6. Mass of: sample, 10.62; standard: 14.58. 𝑃 = 𝐼 𝐼 . 𝑁 𝑁 . 𝑀 𝑀 . 𝑚 𝑚 . 𝑃 = 515 484 . 1 4 . 394.2 144.6 . 14.58 10.62 . 0.996 = 0.99 Purity: 99% Masse sample (mg) Masse Standard (mg) Solvent: dmso-d6 35.55 12.02 Signals standard (ppm) 6.86 3.7 Integration 117.8 178.3 Signals sample (ppm) 8.3 7.2 2.4 2.1 Integration 58 116.9 178.9 350.2 Somme of the integral: sample, 704; standard, 296.1. Number of protons: sample, 24; standard, 10. Molecular weight: sample, 392.2; standard, 138.2. Mass of: sample, 35.55; standard: 12.02. 𝑃 = 𝐼 𝐼 . 𝑁 𝑁 . 𝑀 𝑀 . 𝑚 𝑚 . 𝑃 = 704 296.1 . 10 24 . 392.2 138.2 . Masse sample (mg) Masse Standard (mg) Masse sample (mg) Masse Standard (mg) Solvent: dmso-d6 Masse sample (mg) Masse Standard (mg) Masse sample (mg) Masse Standard (mg) Solvent: dmso-d6 Solvent: dmso-d6 Solvent: dmso-d6 30.8 31.58 10.96 Signals standard (ppm) 6.86 50.83 13.52 49.3 12.21 3.7 12.92 Signals standard (ppm) 6.86 3.7 Integration 112.2 172.2 Signals sample (ppm) 7.5 4.6 3.1 1.29 Integration 112.8 78.3 76.2 448.4 Somme of the integral: sample, 715.7; standard, 284.4. Number of protons: sample, 38; standard, 10. Molecular weight: sample, 478.4; standard, 138.2. Mass of: sample, 30.8; standard: 12.92. Signals standard (ppm) 6.86 3.7 Integration 113.2 171.8 Signals sample (ppm) 8.7 7.8 1.4 Integration 45.53 134.3 537.2 Somme of the integral: sample, 717.3; standard, 285. Number of protons: sample, 32; standard, 10. Molecular weight: sample, 476.4; standard, 138.2. Mass of: sample, 31.58; standard: 10.96. Masse sample (mg) Masse Standard (mg) Integration 139.7 220.7 Masse sample (mg) Masse Standard (mg) Solvent: Acetone-d6 Solvent: CDCl3 11.86 12.83 Signals standard (ppm) 6.86 Signals sample (ppm) 7.0 31.38 13.00 2.3 Integration 120.1 Signals standard (ppm) 6.86 3.7 Signals standard (ppm) 6.86 3.7 519.5 Integration 107.7 178.0 Integration 119.2 179.3 Somme of the integral: sample, 639.6; standard, 360.4. Signals standard (ppm) 6.86 Signals sample (ppm) 8.0 7.6 7.4 1.2 Signals sample (ppm) 8.2 7.6 7.5 2.0 1.3 3.7 3.7 Integration 193.6 294.5 Signals sample (ppm) 7.43 4.1 3.1 Integration 115.1 175.8 Integration 47 48.2 83.5 535.7 Integration 41.7 44.1 87.4 71.2 528.3 Number of protons: sample, 22; standard, 10. Signals sample (ppm) 7.9 7.6 7.4 2.7 1.3 1.4 Integration 23.0 46.7 46.4 275.9 Integration 39.3 39.7 78.1 78.9 473.2 Molecular weight: sample, 405.4; standard, 138.2. Number of protons: sample, 35; standard, 10. Number of protons: sample, 32; standard, 10. Somme of the integral: sample, 392; standard, 488.1. Mass of: sample, 33.25; standard: 13.18. Number of protons: sample, 36; standard, 10. Molecular weight: sample, 713.4; standard, 138.2. Molecular weight: sample, 621; standard, 138.2. Number of protons: sample, 34; standard, 10. 𝑃 = 𝐼 𝐼 . 𝑁 𝑁 . 𝑀 𝑀 . 𝑚 𝑚 . 𝑃 = 639.6 360.4 . 10 22 . 405.4 138.2 . 13.18 33.25 . 0.995 = 0.93 Molecular weight: sample, 489.6; standard, 138.2. Mass of: sample, 49.3; standard: 12.21. Mass of: sample, 50.83; standard: 13.52. Molecular weight: sample, 533.9; standard, 138.2. 12.02 35.55 . 0.995 = 0.94 𝑃 = 𝐼 𝐼 . 𝑁 𝑁 . 𝑀 𝑀 . 𝑚 𝑚 . 𝑃 = 715.7 284.4 . 10 38 . 478.4 138.2 . 12.92 . 0.995 = 0.95 𝑃 = 𝐼 𝐼 . 𝑁 𝑁 . 𝑀 𝑀 . 𝑚 𝑚 . 𝑃 = 717.3 285 . 10 32 . 476.4 138.2 . 10.96 31.58 . 0.995 = 0.93 Mass of: sample, 11.86; standard: 12.83. Mass of: sample, 31.38; standard: 13.00. 𝑃 = 𝐼 𝐼 . 𝑁 𝑁 . 𝑀 𝑀 . 𝑚 𝑚 . 𝑃 = 714.4 285.7 . 10 32 . 621.0 138.2 . 13.52 50.83 . 0.995 = 0.92 𝑃 = 𝐼 𝐼 . 𝑁 𝑁 . 𝑀 𝑀 . 𝑚 𝑚 . 𝑃 = 772.7 289.5 . 10 35 . 713.4 138.2 . 12.21 . 0.995 = 0.93 49.3 30.8 𝑃 = 𝐼 𝐼 . 𝑁 𝑁 . 𝑀 𝑀 . 𝑚 𝑚 . 𝑃 = 392 488.1 . 10 34 . 533.9 138.2 . 12.83 . 0.995 = 0.98 Purity: 93% 𝑃 = 𝐼 𝐼 . 𝑁 𝑁 . 𝑀 𝑀 . 𝑚 𝑚 . 𝑃 = 709.2 290.9 . 10 36 . 489.6 138.2 . 13.00 . 0.995 = 0.98 31.38 11.86 Purity: 94% Purity: 95% Purity: 93% Purity: 98% Purity: 98% Purity: 92% Purity: 93%

Sample Preparation: The sample and the standard are weighed using a Mettler Toledo balance (0.01 mg accuracy) into a single Eppendorf, then solvent was added and the recipient was gently shaken to ensure a total dissolution. The solution was transferred into a 3 mm standard NMR tube for analysis.

Pulse Program: Sample Temperature: 19 °C (regulated ± 0.1 K), single pulse, without carbon decoupling ('zg' with 90° pulse). Data Points (acquired): 64 K. NS=64. Relaxation delay: D1=60s. Acquisition Time: 4s. Spectral window for proton: SW=30 ppm and O1: 7.5 ppm; for fluorine, PF6: SW=120 ppm and O1=90 ppm, BF4: SW=-130 ppm and O1=100 ppm.

Post-Acquisition Processing was performed with ACDLABS software (1.2, academic version). Zero Filling: to 256K. Line Broadening: LB = 0.1 Hz. Phasing: manually. Baseline Correction, for 1 H NMR: 6 th order polynomial. For each signal measured a ratio signal/noise>100 is verified.


Fadi Badra

Rokia Bendaoud

Rim Bentebitel

Pierre-Antoine Champin

Julien Cojan

Amélie Cordier

Sylvie Després

Stéphanie Jean-Daubias

Jean Lieber

Thomas Meilender

Alain Mille

Emmanuel Nauer

Amedeo Napoli

Yannick Toussaint

TAAABLE: Text Mining, Ontology Engineering, and Hierarchical Classification for Textual Case-Based Cooking

Keywords: Textual case-based reasoning, case-based cooking, text mining, ontology engineering, hierarchical classification

This paper presents how the TAAABLE project addresses the textual case-based reasoning challenge of the CCC, thanks to a combination of principles, methods, and technologies of various fields of knowledge-based system technologies, namely CBR, ontology engineering (manual and semi-automatic), data and text-mining using textual resources of the Web, text annotation (used as an indexing technique), knowledge representation, and hierarchical classification. Indeed, to be able to reason on textual cases, indexing them by a formal representation language using a formal vocabulary has proven to be useful.

Introduction

The participants in the Computer Cooking Contest (CCC 1 are invited to propose a knowledge-based system-e.g., a case-based reasoning (CBR) system-for solving cooking problems using a given set of recipes in a textual form, with a shallow XML structure. The goal of the TAAABLE project is to propose such a system, based on a textual CBR approach and using the recipe book as a case base. 2 The challenges that the project wishes to address are the "compulsory challenge" and the "negation challenge".

Section 2 presents the general notions and notations about CBR used in TAAABLE. The TAAABLE system is composed on an on-line part: a CBR system presented in section 3. This CBR system involves some knowledge requirements; section 4 explains how this requirements are fufilled thanks to knowledge acquisition and extraction. Section 5 presents some preliminary results. Section 6 concludes the paper.

CBR: basic notions and notations

CBR aims at answering queries Q with the help of a case base, a case being an answer to a previous query. In TAAABLE, a case is a recipe, denoted by R. Retrieval aims at selecting in the case base (i.e., the recipe book) cases similar to the query Q. "Similar" corresponds here either to an exact matching (the retrieved recipe answers the query) or to an approximate matching, leading to an adaptation of the retrieved case for answering the query.

Structure of the on-line part of TAAABLE

The on-line part of TAAABLE aims at solving cooking problems. It uses an ontology, an indexed case base, and adaptation knowledge. It is based on retrieval and adaptation.

Modeling and Representation issues

The language used for representing knowledge units in this section is propositional logic. In practice, description logics have also been used, but they are not presented in this paper, for the sake of simplicity.

A cooking ontology. An ontology O has been built that defines the main concepts and relations about cooking. The way it has been elaborated is presented in section 4.1. The focus has been put on ingredients and recipe types (not on, e.g. kitchen ustensils) that are crucial for this project: the queries are based on these notions and the inferences are not based on other cooking notions (for the current version of the system). Therefore, Recipe is the main concept of this cooking ontology. Recipe relates to all other main concepts at a higher level in order to describe a cooking procedure. As shown on Figure 1, a recipe has some ingredients and produces a dish. Both ingredients and dishes are food components (Ingredient and Dish are two subclasses of FoodComponent). A food component consists in a given quantity of food, qualified by a given state: an instance of FoodComponent uses an instance of Quantity to fill its quantity attribute, an instance of Food to which the measurement refers, and an instance of Qualifier to make precise the state of the food. An instance of Food can be replaced in a recipe with an equivalent instance of Food.

The main part of the ontology consists in a hierarchy of classes, which corresponds, in propositional logic, to a set of implication axioms a =⇒ b. For example, the axiom apple =⇒ fruit of O states that apples are fruits.

Representation of queries and of recipe indexes. A query in TAAABLE specifies recipe types (salad, vegetarian, italian, etc.), desired ingredients, and undesirable ingredients. It is represented by a conjunction of literals based on a vocabulary V, which is a finite set of propositional variables. For example, searching a Chinese dessert recipe with fruits but without ginger corresponds to the query Chinese, dessert, fruit, ginger ∈ V standing for "Chinese recipe", "recipe of a dessert", "recipe with fruit", and "recipe with ginger". In these examples, the propositional variables of the domain represents recipe classes (e.g., ginger is used as an abbreviation of RecipeWithGinger).

Q ex = Chinese ∧ dessert ∧ fruit ∧ ¬ginger
A recipe R is also indexed by a conjunction of literals Idx(R). More precisely, the index Idx(R) of a recipe R is the most specific expression in the query language that describes R. For example, if R ex is a recipe of a Chinese dessert with pear, cherry, ananas, sugar, and no other ingredients, then it is indexed by:

Idx(R ex ) = Chinese ∧ dessert ∧ pear ∧ cherry ∧ ananas ∧ sugar ∧ Nothing else
where Nothing else is a conjunction of the negative litterals ¬a, for all the a ∈ V such that Chinese ∧ dessert ∧ pear ∧ cherry ∧ ananas ∧ sugar O a. This kind of "closed world assumption" means that every propositional variable that is not stated in the index recipe, either explicitely or through deduction modulo the ontology, is forced to false. For example, if no ingredient a stated in the recipe is meat (a O meat), then Idx(R) contains the literals ¬meat, ¬chicken, ¬beef, etc.

A recipe R exactly matches a query Q if

Idx(R) O Q (1)
i.e., given the knowledge O, every literal stated in Q is satisfied by the index Idx(R).

If, e.g., ananas =⇒ fruit ∈ O and no positive literal a of the query is under ginger, then R ex exactly matches Q ex .

With each recipe R is associated an index Idx(R) and, conversely, given an index Idx(R), the corresponding recipe R is accessed through a pointer.

Adaptation knowledge: reformulations and substitutions. The adaptation knowledge is constituted by a set of reformulations. A reformulation is a pair (r, A r ) where r is a binary relation between queries and A r is an adaptation function associated with r [START_REF] Melis | Reformulation in Case-Based Reasoning[END_REF]. It has the following semantics: if Q 1 r Q then, for every recipe R 1 matching the query

Q 1 , A r (Q 1 , R 1 , Q) = R, a recipe matching the query Q.
In the current version of TAAABLE, a reformulation (r, A r ) is given by a substitution σ such that

Q 1 r Q if Q 1 = σ(Q) and A r (Q 1 , R 1 , Q) = σ -1 (R 1 )
, where σ -1 is the inverse of the substitution σ. Thus, the adaptation knowledge is given by a set of substitutions.

For example, let Q = dessert ∧ lemon be a query and σ = lemon orange be a substitution. Then,

Q 1 = σ(Q) = dessert ∧ orange. If a recipe R 1 exactly matches Q 1 , -Idx(R 1 ) O Q 1 -then R = σ -1 (R 1 ), obtained by substitution of orange by lemon in R 1 , matches Q -Idx(R) O Q.
A substitution σ = α β is given by two conjunctions of literals α and β. σ is applicable to a query Q if Q O α and, when it is the case, σ(Q) is obtained from Q by first removing the literals of α and then adding the literals of β: if Q, α, and β are considered as sets of literals closed modulo the ontology O, then σ(Q) = (Q\α) ∪ β.

For example, to express that, for Thai cuisine, basil can be substituted by mint, the substitution σ = Thai ∧ basil Thai ∧ mint can be used. A large part of the substitutions used in the system is simply based on the ontology: if a is a subclass of b in O (i.e., (a =⇒ b) ∈ O), then the substitution by generalization a b may be considered. If a b has been accepted as adaptation knowledge, this means that a food of type a may be substituted by any food of type b. For example, (x =⇒ citrus) ∈ O, for any x ∈ {grapefruit, lemon, orange}. Thus, if orange citrus is in the adaptation knowledge, then substituting orange by lemon or by grapefruit is likely to produce a "correct recipe".

A numerical value cost(σ) > 0 is associated with each substitution, that measures the "adaptation effort". This cost represents a preference between adapted recipes: if cost(σ) < cost(τ ) then, a recipe σ -1 (R 1 ) is prefered to a recipe τ -1 (R ′ 1 ), where R 1 and R ′ 1 are two recipes from the recipe book that respectively match σ(Q) and τ (Q).

Case-based problem-solving process: retrieval and adaptation

Recipe retrieval algorithm is based on the algorithms of strong and smooth classification in an index hierarchy [START_REF] Lieber | Using Classification in Case-Based Planning[END_REF].

Strong classification points out the indexes Idx(R) that exactly matches the query Q, as in equation ( 1). The algorithm is based on a depth-first search of the index hierarchy. If no such index exists, then smooth classification is executed.

Smooth classification aims at finding a modification Q ′ of the query Q such that there exists at least one index matching exactly

Q ′ . The modification of Q into Q ′ is based on a similarity path, i.e., a composition SP = σ p • σ p-1 • . . . • σ 1 of substitutions: Q p = σ p (σ p-1 (. . . (σ 1 (Q)) . . .)). Let cost(SP) = p i=1 cost(σ i ).
The smooth classification algorithm combines an A* search for finding the similarity path SP minimizing cost(SP) such that Q p = SP(Q) exactly matches at least one of the indexes of the recipe.

Complexity remark. The use of propositional logic for case retrieval, with a large case base and hundreds of propositional variables, may be computationally costly. Actually, it is not the case in our application: only a fragment of propositional logic leading to polynomial problems is used. In practice, strong classification is very efficient and smooth classification depends on the cost of the first similarity path giving a non-empty set of indexes: the higher is this cost, the more smooth classification requires time and memory, and the more adaptation effort is needed.

Recipe adaptation. If strong classification is successful, then there is no need to adapt the retrieved recipe. If smooth classification is successful, it returns a recipe R p and a similarity path

SP = σ p • σ p-1 • . . . • σ 1 such that Idx(R) O SP(Q).
Then, adaptation consists simply in applying every σ -1

i successively: R = SP -1 (R p ) = σ -1 1 (σ -1 2 (. . . σ -1 p (R p ) . . .))
is the adapted recipe.

Off-line part of TAAABLE

The previous section has highlighted three ingredients: the ontology, the indexed case base, and the adaptation knowledge. All of them have required a large amount of work that is presented in this section.

Ontology building

Principles for building manually the general part of the ontology. The ontology O has been built in order to help the conception of the retrieval and adaptation processes of the TAAABLE system. Therefore, the conceptual choice for the ontology development has been strongly driven by the goal of this particular CBR system. The ontology design process is achieved through the use of the standard methodology that distinguishes the following phases: feasibility study, requirement analysis, conceptualization, and deployment [START_REF] Cimiano | Ontologies on Demand? A Description of the State-ofthe-Art, Applications, Challenges and Trends for Ontology Learning from Text[END_REF]. In this presentation, only the conceptualization phase is considered. Three subtasks have been carried on: development of the cooking conceptual model, formalization of the domain, and implementation in OWL language. The reuse of existing ontologies has been carefully examined but no more considered as the examined ontologies did not cover what was intended to be reached in this project. During the elaboration of the cooking conceptual model, several main classes were identified: Recipe, Ingredient, FoodComponent, Food, Action, Amount, and Utensil. These classes were sufficiently independent to decide to build a modular ontology. This conceptual choice should facilitate the enrichment of the different modules (because the concepts included in these modules are disjoint) without changing the content of the others. The main relations linking these modules have been manually defined. Once the choice of a modular ontology was adopted, the building process has been shared out among the different partners in order to take advantage of their skills. The general part of the ontology has been built manually and formalized in OWL. This step has been essential to obtain the skeleton of the conceptual model. It appeared that some modules play a central role in the retrieval and adaptation processes. Therefore, it was necessary to refine further the design in order to reach a finest granularity level. The parts of the ontology about the dish types, the dish origins, and the food types are finely grained according to existing terminological resources and manual expertise. How these concepts are structured is detailed below.

DishType and DishOrigin hierarchies. These two hierarchies have been built manually. Starting from the organisation of dish types and dish origins in the Recipe Source database 3 , a list of general dish types and dish origins has been collected, and hierarchically organised following the specialisation relation.

Concretely, the dish origin hierarchy has only two levels. The first level classifies dishes following their regions, such as Africa, MiddleEast, Asia, Europe, etc. Each first level concept is specialised, on the second level, by the country, origin of the dishes. For example, British, French, German, etc. are subconcepts of Europe.

In the same way, the DishType hierarchy is mainly organised into two levels. At the first level, there are concepts like BakedGood, Burger, Dessert, MainDish, etc. The second level details, if necessary, the first level concepts. For example, BakedGood is divided into Bagel, Biscuit, Bread, Muffin, Brownie, Cookie, etc. However, these concepts are no more deeply detailed even if more specific categories exist in Recipe Source, as it is for example the case for Cookie which is subdivided into Apple Cookie, Chocolate Chip Cookie, Diabetic Cookie, etc. These concepts could indeed be defined by conjunction of being a Cookie and by containing, or not, some specific food.

Food hierarchy. The Food hierarchy was also built manually starting from the Cook's Thesaurus 4 and from the recipe book. The Cook's Thesaurus is a cooking encyclopedia that covers thousands of ingredients, including synonyms and suggested substitutions. More than 250 HTML files were examined in order to extract an initial Food hierarchy. At the same time, a terminological database is built in order to associate to each food subconcept a linguistically preferred form (e.g., bok choy) as well as a set of morphological variants or synonyms (e.g., pak choy, pak choi, Chinese cabbage, Chinese mustard cabbage, etc.). The food hierarchy and the terminological database are then manually enriched by adding new concepts and new lexical forms that occur in the recipe book but do not occur in the Cook's Thesaurus. This extension process is iterative and stops when each food component of the recipe book can be linked to a food concept by the annotation process (see below). The current version of the food hierarchy contains 3936 concepts, organised upon 8 levels of specialisation. The terminological base contains 7824 lexical forms, without taking into account singular/plurial variations.

Annotation and indexing process

The case base engine needs a formal representation of a recipe. The annotation process aims at formally representing the content of a recipe as well as defining its categories. This process is in between what is usually called controlled indexing [START_REF] Jo | Topic Spotting on News Articles with Topic Repository by Controlled Indexing[END_REF] where terms come from a predefined terminology and semantic annotation [START_REF] Uren | Semantic annotation for knowledge management: Requirements and a survey of the state of the art[END_REF] where terms (named entities, sequences of words) are explicitly associated with the respective and most specific classes in the ontology. The result of the annotation of a recipe is a set of concepts indexing the recipe. The current state of our prototype does not deal with the preparation part of recipes. Only, the list of ingredients is parsed. First, each ingredient entry in the recipe is parsed and split into the following 4-uple (<quantity>,<unit>,<food-component>,<modifiers>). For example, the entry <IN>1/3 cup milk</IN> is parsed as (1/3,cup,milk,_). The terminological database guides the parsing process. For instance, as light brown sugar is a lexical form associated to the food component concept light brown sugar in the food hierarchy, the entry <IN>1/2 c Packed light brown sugar</IN> is parsed as (1/2,cup,light brown sugar, packed). At present, only the list of food components is kept for indexing.

For example, the recipe entitled Cinnamon Rolls is indexed by the conjunction of the ingredients sweet dough mixture, brown sugar, pecans, dark seedless raisins, ground cinnamon, butter or margarine, and sugar glaze (butter or margarine is a concept in the ontology which is more general than both concepts butter and margarine).

Finally, the annotation process has to index recipes following the region and the country (e.g., Asia, Chinese) and following the dish type (e.g., main dish, dessert). As there is no indication concerning these types in the recipe book, Recipe Source is used again in order to build a corpus where recipes are assigned to types. The process to type a recipe book recipe types takes 3 steps. For a given recipe:

-If there exists a recipe in Recipe Source with the same title, then its types are assigned to the Recipe Book recipe; -If the title of the recipe (e.g., Chinese Vegetable Soup) contains keywords corresponding to subconcepts of DishType (e.g., soup) or DishOrigin (e.g., Chinese) then these types are assigned to the recipe; -A set of association rules has also been extracted from the Recipe Source corpora, using the data-mining toolkit CORON [START_REF] Szathmary | Coron: A framework for levelwise itemset mining algorithms[END_REF]. According to exact associations rules (with a 100% confidence) of the form <set of ingredients> -→ <origin or dish type> (e.g., vanilla bean, banana, chocolate -→ dessert), type assignations can be done as follows: if part of the recipe matches the left-hand side of the rule, then the type in the right-hand side is assigned to the recipe.

For example, the recipe entitled Cinnamon Rolls is indexed by some ingredients, as presented below, but also by the following recipe types: baked good, bread, pastry, and roll.

Adaptation knowledge acquisition

The substitution base is a set of equivalence classes of food. The knowledge about food substitution has been acquired from the recipe book and from the Cook's The-saurus. This process is automatic for the Cook's Thesaurus by extracting the suggested substitution according patterns based on the HTML structure. For the recipe book, the substitution are manually extracted from the food components containing a choice. For example, the possibility to substitute butter by margarin, or inversely, is acquired from the sequence butter or margarin which appears in the food components.

Preliminary Results

The core of the TAAABLE system has been implemented in Java.

The first step of the TAAABLE process is initialisation: it consists in parsing the knowledge containers, i.e., the vocabulary V, the ontology O, the set of substitutions, and, finally, the recipe book. The parsing of the recipe book is associated with indexing, that consists in organizing hierarchically the indexes. With the current knowledge containers, the time taken for this initialization is about 30 seconds (on a current PC computer).

The TAAABLE system retrieves cases for all the six exercice queries proposed in the contest (the queries 1 to 5 for the compulsory challenge and the query 6 for the negation challenge). For a given CBR session, let SP be the similarity path, p be the number of susbtitutions in SP (p = 0, means that the similarity path is empty and thus that strong classification succeeds), c = cost(SP), and let n be the number of retrieved recipes R (i.e., the R such that Idx(R) O SP(Q)). The queries are recalled below and associated with the information about SP, p, c, and n: Q1 "Cook a main dish with meat and cauliflower." (p = 0, c = 0, n = 2) Q2 "I would like to have a nut-free cake." (p = 0, c = 0, n = 31) Q3 "Prepare a Chinese dessert with fruit." (p = 1, c = 3.33, n = 15, SP = (dessert dish_type): the fact that a dessert is required is removed from Q3!) Q4 "Cook a main dish with turkey, pistachio, and pasta." (p = 3, c = 7.0, n = 1, SP = (turkey chicken)•(pasta grain_product)•(pistachio_nut fresh_beans)) Q5 "I would like to cook eggplant soup." (p = 0, c = 0, n = 1) Q6 "I want to have a salad with tomato but I hate garlic and cucumber." (p = 0, c = 0, n

Any of these requests has required less than 1 second for retrieval, except Q4, that has required less than 4 seconds.

The results of these tests are not fully satisfactory yet: some more work on knowledge acquisition and extraction remains to be done. Let us consider with more details the result for query Q4. In the form of a conjunction of literals, this query is Q4 = main-dish ∧ turkey ∧ pasta ∧ pistachio and thus, SP(Q4) = main-dish ∧ chicken ∧ grain_product ∧ fresh_beans It returns only one recipe, which is the only recipe R such that Idx(R) O SP(Q4). This is the recipe entitled "Hazelnut Chicken with Orange Thyme Cream". Indeed, this recipe is indexed by

Idx(R ex ) = main-dish ∧ chicken ∧ all-purpose_flour ∧ hazelnut ∧ etc.
where etc. is a conjunction of other literals, irrelevant for our example. The ontology contains the following axioms, that explain why the recipe has been retrieved: all-purpose_flour =⇒ wheat_flour wheat_flour =⇒ grain_product hazelnut =⇒ fresh_beans

The adaptation consists in following SP -1 on the recipe. Since

SP -1 = (fresh_beans pistachio_nut) • (grain_product pasta) • (chicken turkey)
Thus, the adaptation takes 3 steps:

-Substituting hazelnut (a subclass of fresh_beans) by pistachio_nut; -Substituting wheat_flour (a subclass of grain_product) by pasta; -Substituting chicken by turkey.

Here, substitution 2 is questionable. In fact, a more sophisticated adaptation than this simple substitution should be carried out in order to obtain an applicable recipe.

Conclusion and ongoing work

This paper presents how the TAAABLE project addresses the textual CBR challenge of the CCC, thanks to a combination of principles, methods, and technologies of various fields of knowledge-based system technologies, namely CBR, ontology engineering (manual and semi-automatic), data and text-mining using textual resources of the Web, text annotation (used as an indexing technique), knowledge representation, and hierarchical classification. Indeed, to be able to reason on textual cases, indexing them by a formal representation language using a formal vocabulary has proven to be useful. The first version of the system relies on simple choices: a propositional representation language (that is not enough expressive for representing, e.g., quantities), a vocabulary restricted to ingredients and recipe types (that does not handle, e.g., the actions of the preparation), and a CBR process relying on simple choices. The ongoing work (until the contest) and the future work (after the contest, if -what we hope-the TAAABLE project goes on) will concentrate on improving these features. In particular, case retrieval returns sometimes a set of more than 5 recipes, and further versions should be able to make a clever choice among them (e.g., thanks to more detailed indexes). Some studies on adaptation knowledge acquisition for TAAABLE will be carried on. In particular, the CABAMAKA system [START_REF] D'aquin | Case Base Mining for Adaptation Knowledge Acquisition[END_REF] will be used for this purpose. Moreover, the IAKA methodology [START_REF] Cordier | Opportunistic Acquisition of Adaptation Knowledge and Cases -The IakA approach[END_REF] has to be applied for acquiring adaptation knowledge and elements of the ontology by testing the system with various queries: the comparison of the system results and of what is expected should be useful to point out some missing or faulty pieces of knowledge.

Fig. 1 .

 1 Fig. 1. A cooking ontology (excerpt).

http://www.wi2.uni-trier.de/eccbr08/index.php?task=ccc

The TAAABLE system is on line at the following address: http://labotalc.loria.fr/TaaableCCC/.

http://www.recipesource.com/

http://www.foodsubs.

Acknowledgements

The participants of the TAAABLE project wish to thank the organisers of the CCC for providing this benchmark, that entails many interesting problems, and the need to collaborate with researchers in various domains on knowledge engineering.


S Lupone

S Damoy

A Husseen

N Briand

M Debiossac

S Tall

P Roncin

A large open ratio, time and position sensitive detector for time of flight measurements in UHV

We report on the construction of a UHV compatible 40 mm active diameter detector based on micro channel plates and assembled directly on the feed-throughs of a DN63CF flange. It is based on the charge division technique and uses a standard two inch Si wafer as a collector. The front end electronic is placed directly on the air side of the flange allowing excellent immunity to noise and a very good timing signal with reduced ringing. The important aberrations are corrected empirically providing an absolute positioning accuracy of 500 µm while a 150 µm resolution is measured in the center.

I. INTRODUCTION

Micro channel plate (MCP) based position sensitive detectors are now widely used in atomic and molecular physics as well as in surface science. Such detectors can be placed in the focal plane of an electrostatic analyzer 1,2 to allow ion energy measurements or in direct view of the interaction zone to record neutral and charged particles scattered 3,4 or emitted from the surface 3,5 . Depending on the respective geometry of the target and primary beam, such detectors are well suited for a variety of surface science techniques such as direct recoil spectroscopy (DRS), low energy ion scattering (LEIS) 1 , grazing incidence ion scattering 4 as well as grazing incidence atom diffraction 6 .

II. DETECTOR OVERVIEW

The detector displayed in figure 1 has been designed to be mounted on a DN63CF port in an molecular beam epitaxy chamber 6 where space is limited and UHV compatibility stringent. When doing time of flight for DRS or LEIS, the required spatial resolution is needed only to prevent the broadening of the time of flight peaks due to the rapid variation of the binary collision kinematics with outgoing angle 5 . With a simple counting detector such as a channeltron the acceptance is usually below a degree opening angle and these aberrations are not considered as a problem 7,8 . Four such detectors were needed justifying a little investment in design. We have used the well-established charge division technique which already showed to be rather compact. To meet the tight space budget, the collector is a silicon wafer having the same size as the MCP and the whole detector is supported by the electrical feed-troughs. The noise sensitive electronic is mounted directly on the air side of the flange taking advantage of modern printed circuit boards facilities which allow a smart combination of electronic and mechanical properties up to the signal and high voltage plugs (figure 2,3). The distortions associated with this anode are measured and corrected by software with a polynomial equation (eq 1) allowing a 500 µm absolute position accuracy well enough for DRS and LEIS measurements but probably not for application such as diffraction where accurate line profiles are important.

III. THE RESISTIVE ANODE

Quite a number of position sensitive detectors have been developed with various aspect ratio and performance 9 . Delay line detectors 10,11 have the highest count rates and lower dead time but tend to be large whereas charge division techniques have shown to be highly adaptable 1,2,12 and robust limiting the need for maintenance. The distortion-free resistive anodes matching the Gear conditions 12 would have been a obvious choice but the 40 mm active diameter commercially available has a 72 mm overall diameter, too large to fit in a DN63CF flange. Since UHV compatibility is mandatory, we have decided to use high impedance 2 inch Si wafers (high purity single crystal un-doped float zone Si(111), 280 µm thick one side polished) available at almost no cost from the semi-conductor industry. The size perfectly matches the 40-44 mm active diameter of 50 mm MCP. A simple mask was designed to evaporate four sub micron thick copper collection stripes (fig. 1).

IV. THE VACUUM-SIDE ASSEMBLY

The eight high voltage feed-troughs that support the detector are welded directly on the on the DN63CF flange. Four are used to bias a two MCP stack and four are used to connect the four copper, charge collection stripes of the anode. The stems of the feed-throughs on the vacuum side were threaded for 1.4 nuts before welding allowing tight and short UHV connections. As shown on the inset on fig. 1 the detector structure is a 7 mm thick ring made of Macor or PEEK with the MCP on top and the collector on the bottom side. The detector is sitting on the four anode connectors and compressed by the four other ones connecting the MCP's with copper coated kapton rings. The MCP stack as well as the collector are compressed independently by four flat pieces of CuBe acting as springs so that the detector can be assembled either separately or directly onto the flange acting as a holder.

V. FRONT END ELECTRONIC, ADC

The detector is designed to operate indifferently for neutrals, positive or negative ions (or electrons), provided that neither HV + nor HV -exceed 5 kV while HV + -HV -2kV. The decoupling and the readout electronic are placed close to the detector. This is easily achieved with modern printed circuit board (PCB) facilities offering custom machining of the PCB so these can be part of the mechanical structure. The assembly is made of three circular PCB. The first one (fig. 2 ) holds the high voltage divider to bias the MCP's as well as the decoupling of the fast timing signal from the output of second MCP. This PCB is soldered to the flange by the four MCP feed-throughs. The four other ones, connected to the anode, are capped by the tip of a 1.55 mm ITT CANNON connector head and used as male connectors for the 2 nd and 3 rd PCB (see fig. 3). The 2 nd PCB imprisons the female connector in a plastic cylinder to allow flexibility. The PAC09 charge preamplifiers purchased at the Orsay institute of nuclear physics (IPNO) are connected to these pins via four 5 kV, 2 nF ceramic capacitor. These are soldered to 3 rd PCB and compressed between the 2 nd PCB and 3 rd PCB. The third PCB holds all the connectors; two MHV high voltage connectors to bias MCP's, four SMA connectors for preamplifier outputs, a test input common to all preamplifiers and a SMA output for the fast timing signal. Grounding and shielding are achieved by a simple metal tubing fixed directly onto the flange. The contact with the top PCB turned out to be more tricky than expected and CuBe springs pressing the top PCB have been needed. The rest of the electronics is rather standard with four triangular shape linear amplifiers with a 2 µs shaping constant (also from IPNO). The analog to digital conversion is achieved with low cost NI6143, eight channels, 250 kHz simultaneous sampling ADC. The maximum count rate should therefor lie in the 100 kHz range. Higher count rate would require faster digitizer but would rapidly hit the pileup limit of the amplifiers. The cards offers an embedded 50 ns counter which could allow stand alone coincidence operation of two PSD. For better timing resolution, an additional electronics is needed but could be as simple as a time to amplitude converter fed in one of the four unused channels. The fast signal decoupled from second MCP is probably in the 1 ns range but could not be measured accurately on our test system.

VI. CALIBRATION AND ABERRATION CORRECTION

To calibrate the detector, a copper mask with an array of 0.5 mm holes every 3 mm is mounted 5 mm above the 1st MCP which was then grounded.The large size of the holes is to allow efficient illumination by a 2 keV Ar + beam. At variance, the central hole is only 0.1 mm in diameter allowing resolution information. The image in fig-ure 4 is obtained with the standard formula (eq. 1) where Q i are the four charges. The image displays all ion impact illuminating 145 holes. It shows a good occupancy of the electrical space together with very important distortions. The measured size of the central spot imaging the 100 µm hole is 200 µm fwhm suggesting an intrinsic resolution of 150 µm fwhm which compares with that of aberration free anode operated with two MCP ref. 12 .

X = Q lef t -Q right 2 i Q i , Y = Q botom -Q top 2 i Q i (1)
To recover a correct image, we have used brut force; each observed spot is fitted by a 2D gaussian and is associated to integer indexes i,j, then a 4 th order polynomial (eq. 2) is optimized to adjust X, Y observed values to X', Y' values sitting on the mask position (fig. 4).

X = i j a mn X m Y n , Y = i j b mn X m Y n (2)
FIG. 4. Left is a raw image of all impact through the mask.

After correction by formula 2, the image on the right shows that most of the spots centers are within 500 µm of their ideal position as illustrated by the 1 mm circles sitting at mask locations. The 43 mm circle is only to guide the eyes.

VII. CRITICAL ANALYSIS

The outstanding quality of the mono-cristalline Si Wafer did not turn out to make a perfect collector. The measured level of distortions is larger that the one obtained in preliminary electrical tests using evaporated Ge or even painted carbon film suggesting that amorphous materials could give better results. Since kapton printed circuit are now easy to purchase from PCB industry, simple layers evaporated carbon or Ge could be a better choice and will be tested. The threaded feed-throughs are excellent to compress the detector but those connecting the anode and used in compression would probably be easier to adjust with, for instance, male twist pins crimped on the stems and inserted into perforated screw. When additional space is available, a fourth PCB layer hosting linear amplifiers could suppress the need of bulky NIM crate. Surface mount high voltage resistor was a success but the use high voltage multilayer ceramic capacitors (visible on fig. 3) should be avoided, indeed, the insulating resistance in the 10-100 MΩ range is too low so that, when operated at high voltage, the collector injects charges directly into the preamplifier which then display a significant noise. In the present form the first PCB holding the voltage divider is soldered on the flange. Applying the trick of capping the stem with a connector tip would allow easy removal of all PCB making air side final impedance check as well as bake-out more simple. The present design is rather conservative, the outer diameter is 64 mm which fits easily in the 70 mm of so called large bore 63CF flanges.

VIII. ACKNOWLEDMENT

FIG. 1 .

 1 FIG.1. designer view of the detector mounted on eight 5 kV UHV feed-throughs threaded on the vacuum side. The insets are photos of the resistive anode (lhs), a two inch silicon wafer with four collection anodes made of evaporated copper and of the assembled detector (rhs) the four large head screws press the assembly while contacts are made by the small nuts.

FIG. 2 .

 2 FIG. 2. Schematic view of the base PCB with the HV divider made of surface mount 1kV resistors on one side and the timing signal pickup (MCX connector) on the other side (blue). It includes MCP protection resistors, inter-plate acceleration and acceleration to the collector. The electrical connection is sketched on the right. The collector itself bias is biased only via one input with a 22 MΩ resistor.

FIG. 3 .

 3 FIG. 3. Air side view of the front end electronic with the three PCBs holding the voltage divider, the charge preamplifier and all the connectors

We are grateful to O. Jagutzki from Roentdek Gmbh for kindly giving us a circular glass anode with evaporated Ge to test our approach. M. Albaret is acknowledged for his bright ideas combining electronic and mechanic, A. Momeni for his continuous support and X. Fléchard for help in the correction algorithm. This work has been supported by Agence Nationale de la Recherche ANR-07-BLAN-0160.