Margaux Fresnais

Pascale Richardin

Marcela Sepúlveda

Emmanuelle Leize-Wagner

PhD Armelle Charrié-Duhaut

Omics for Precious Rare Biosamples: Characterization of Ancient Human Hair by a Proteomic Approach

Keywords: Proteomics, hair characterization, keratins, mass spectrometry, mummy's hair

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Introduction

Omics technologies have been applied in a variety of settings such as medicine and ecology.

Moreover, during the last decade, interest in hair analysis has strongly grown, particularly of ancient samples, with a view to deciphering the impact of aging and environment on the human or the animal host. Hair, as a biomaterial, is indeed a powerful biological indicator, whose composition is known to represent the blood composition to a certain extent [START_REF] Kempson | Hair analysis as a biomonitor for toxicology, disease and health status[END_REF][START_REF] Tobin | Hair in Toxicology[END_REF]. During fiber growth, compounds from blood interact with hair molecules, thus enabling time-resolved monitoring along the fiber after the exposition to a specific compound [START_REF] Kamata | Time-Course Mass Spectrometry Imaging for Depicting Drug Incorporation into Hair[END_REF][START_REF] Kumtabtim | Biomonitoring for arsenic, toxic and essential metals in single hair strands by laser ablation inductively coupled plasma mass spectrometry[END_REF].

Hair shaft can be of 15 to 150 µm in diameter, and exhibits three tubular morphological layers: the medulla, the cortex and the cuticle. The medulla is the innermost part of the fiber and is not always observable in human hair, according to the hair type. It consists of a narrow tube filled with air and loose cells that are mostly empty. The cortex enfolds the medulla and represents 80 to 90 % of the hair mass. It is mainly composed of 18 hydrophobic fibrous proteins called keratins, which can be of two types: type I, the acidic forms and type II, the basic forms. They are α-helical proteins that associate one with another into tetrameric cylinder-like filaments called Keratin Intermediate Filaments (KIF).

Assemblies of these KIFs are called macrofibrils and their cohesion is ensured by an amorphous Sulphur-rich matrix made up of Keratin Associated Proteins (KAP). The network of KIFs and KAPs is characterized by the presence of an important number of cysteine residues (Sulphur based amino acid), which contributes to the stability of hair fibers and their high resistance to degradations through the existence of inter-filament, inter-filament-matrix or even inter-matrix disulfide bridges. On the surface, the cuticle constitutes the robust protective outer layer of the hair fiber. It is about 3 to 5 µm thick and made up of several layers of overlaying scales joined one to another by an intercellular lipid-rich substance [START_REF] Barthélémy | Protéomique qualitative et quantitative, une passerelle pour relier l'expression génomique à la construction des édifices biologiques -Application à la compréhension de la structure moléculaire du cheveu humain[END_REF][START_REF] Swift | The chemistry of human hair cuticle -I: A new method for the physical isolation of cuticle[END_REF][START_REF] Wolfram | Human hair: A unique physicochemical composite[END_REF]).

In the last several years, hair has become a major medium for toxicology [START_REF] Kales | Hair in Toxicology -An Important Bio-Monitor[END_REF][START_REF] Kamata | Time-Course Mass Spectrometry Imaging for Depicting Drug Incorporation into Hair[END_REF], environmental [START_REF] Bencko | Hair and Exposure to Environmental Pollutants[END_REF][START_REF] Kintz | Application of Hair Biology to Environmental Assessments. Hair in Forensic Toxicology with a Special Focus on Drug-Facilitated Crimes[END_REF] and forensic studies [START_REF] Haglund | Forensic Taphonomy: The Postmortem Fate of Human Remains[END_REF][START_REF] Kintz | Arsenic speciation of two specimens of Napoleon's hair[END_REF][START_REF] Wilson | Selective biodegradation in hair shafts derived from archaeological, forensic and experimental contexts[END_REF], providing complementary information to urine [START_REF] Hardy | Multi-residue analysis of organic pollutants in hair and urine for matrices comparison[END_REF] and blood [START_REF] Goodrich | Exposure of dental professionnals to elemental mercury and methylmercury[END_REF] analyses, and enabling monitoring on a longer time period than that offered by urine and blood which can be of only a few hours or days [START_REF] Hardy | Multi-residue analysis of organic pollutants in hair and urine for matrices comparison[END_REF].

When it is well preserved thanks to extreme environmental conditions (aridity, extreme coldness or anoxia), ancient hair also represents a material of choice in archaeology [START_REF] Wilson | Hair as a Bioresource in Archaeological Study[END_REF] for dating [START_REF] Richardin | A new protocol for radiocarbon dating of hair and keratin type samples-application to an Andean mummy from the National Museum of Natural History in Paris[END_REF][START_REF] Richardin | Radiocarbon dating of mummified human remains, Application to a series of Coptic mummies from the Louvre Museum[END_REF], dietary [START_REF] Aufderheide | Contributions of Chemical Dietary Reconstruction to the Assessment of Adaptation by Ancient Highland Immigrants (Alto Ramirez) to Coastal Conditions at Pisagua, North Chile[END_REF], health [START_REF] Arriaza | Exploring chronic arsenic poisoning in pre-Columbian Chilean mummies[END_REF][START_REF] Boston | Arseniasis and teratogenic anomalies in the Atacama Desert coast of ancient Chile[END_REF], environmental [START_REF] Arriaza | Arseniasis as an environmental hypothetical explanation for the origin of the oldest artificial mummification practice in the world[END_REF][START_REF] Bartkus | Exploring lead exposure in ancient Chilean mummies using a single strand of hair by laser ablationinductively coupled plasma-mass spectrometry (LA-ICP-MS)[END_REF][START_REF] Byrne | Were Chinchorros exposed to arsenic? Arsenic determination in Chinchorro mummies' hair by laser ablation inductively coupled plasma-mass spectrometry (LA-ICP-MS)[END_REF][START_REF] Fresnais | Recent advances in the characterization of hair of mummies from the Chilean Andean coast[END_REF][START_REF] Hallégot | Accumulations of Ca/P in the core of hairs from Taklamakan desert mummies[END_REF] and cultural [START_REF] Arriaza | Arseniasis as an environmental hypothetical explanation for the origin of the oldest artificial mummification practice in the world[END_REF] studies. In archaeometry, many research works focus on the study of ancient proteins from collagen-based materials, such as bones [START_REF] Aufderheide | Contributions of Chemical Dietary Reconstruction to the Assessment of Adaptation by Ancient Highland Immigrants (Alto Ramirez) to Coastal Conditions at Pisagua, North Chile[END_REF][START_REF] Buckley | Proteome degradation in ancient bone: Diagenesis and phylogenetic potential[END_REF][START_REF] Collins | Analytical methods to detect ancient proteins. In Bio-and Material Cultures at Qumran[END_REF] or skin [START_REF] Bianucci | A Pre-Hispanic Head[END_REF], and from teeth [START_REF] Buckley | Collagen fingerprinting of archaeological bone and teeth remains from Domuztepe, South Eastern Turkey[END_REF][START_REF] Farell | Bioimaging of trace metals in ancient Chilean mummies and contemporary Egyptian teeth by laser ablationinductively coupled plasma-mass spectrometry (LA-ICP-MS)[END_REF], but hair also represents a good alternative or a complement to these other biological remnants that can be rare or whose sampling can be much more invasive. In that regard, obtaining a maximum of information from the minimum of raw archaeomaterial is a crucial issue.

In archaeometrical studies, characterization of ancient hair is mainly based on elemental (Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry -LA-ICP-MS, X-Ray Fluorescence spectroscopy -XRFand Particle Induced X-ray Emission imaging -PIXE imaging), structural (Fourier Transformed InfraRed spectroscopy -FTIRand X-Ray Diffraction -XRD) and biological analyses [START_REF] Wilson | Selective biodegradation in hair shafts derived from archaeological, forensic and experimental contexts[END_REF][START_REF] Lubec | Structural stability of hair over three thousand years[END_REF][START_REF] Bertrand | The fate of archaeological keratin fibres in a temperate burial context: microtaphonomy study of hairs from Marie de Bretagne (15th c., Orléans, France)[END_REF][START_REF] Bertrand | Microbeam synchrotron imaging of hairs from ancient Egyptian mummies[END_REF] but only few research focus on the molecular study of ancient hair proteins and their conservation state [START_REF] Lubec | Protein Oxidation of a Hair Sample Kept in Alaskan Ice for 800-1000 Years[END_REF] and in such cases, they often apply extraction protocols requiring important amounts of raw hair. Current research focus on the optimization of protocols for sample preparation, but also on developing new analytical techniques in order to reduce the initial amounts of raw archaeomaterial without losing any important information.

Proteomic analysis of modern hair is well described in literature [START_REF] Barthélémy | Protéomique qualitative et quantitative, une passerelle pour relier l'expression génomique à la construction des édifices biologiques -Application à la compréhension de la structure moléculaire du cheveu humain[END_REF][START_REF] Barthélémy | Proteomic tools for the investigation of human hair structural proteins and evidence of weakness sites on hair keratin coil segments[END_REF][START_REF] Lee | Proteome Analysis of Human Hair Shaft From Protein Identification to Posttranslational Modification[END_REF] and has led to the molecular characterization of cortical proteins (mainly keratins and KAPs). Our proteomic approach for the characterization of hair in archaeological context will, of course, be based on these studies but will also use specific protocols, adapted to be robust, reproducible and applicable to small amounts of samples. The aim is to ascertain whether keratins and KAPs can still be identified in ancient hair and in that case, to verify if it is possible to go further in their characterization and to detect the post-translational modifications (PTMs). These aspects are complex and challenging since, to the best of our knowledge, there are only few studies on degradation of ancient hair proteins, and only few references for the search of PTMs specific to ageing in archaeological context.

Material and methods

Material and chemicals

Sinapic acid (SA), ammonium bicarbonate (NH4HCO3) and sodium dodecyl sulfate (SDS) were purchased from Fluka (Buchs, Switzerland). Iodoacetamide (IAM), DLdithiothreitol (DTT), Trizma® hydrochloride (Tris-HCl, buffer solution for proteins), thiourea, sodium deoxycholate (DCO), proteomics grade trypsin, trichloroacetic acid (TCA), trifluoroacetic acid (TFA), formic acid (FA), dichloromethane (DCM), acetonitrile (ACN) and acetone were from Sigma Aldrich (St. Louis, MO, USA). Urea was bought from Acros Organic (Geel, Belgium). Methanol (MeOH) and propan-2-ol (iPrOH) were supplied by Hipersolv CHROMANORM® (VWR Chemical Prolabo®, Radnor, PA, USA). Ethanol was from AnalaR Normapur® (VWR Chemical Prolabo®, Radnor, PA, USA). Ultrapure water was produced with a water purification Purelab® UHQ system (ELGA LabWater VEOLIA Water, Anthony, France). Dialysis cartridges (Slide-A-Lyzer 3.5 kDa, 3-12 mL) were from Thermo Scientific Pierce (Thermo Fisher Scientific Inc., Waltham, MA, USA) and SeP-Pak C18 1cc Solid Phase Extraction cartridges (SPE, 100 mg sorbent, particle size 55-105 µm, pore size 125 Å) were from Waters (Milford, MA, USA).

Modern and archaeological samples

Modern hair samples were taken from a man of European descent (country of origin France) who had not been exposed to any cosmetic or chemical treatment, such as perming, bleaching or dyeing. This sample was used as control for the first steps of the protocol optimization. We focused in the present study on one hair sample belonging to a larger corpus studied in the framework of a project in collaboration between the Laboratory of The mummies of the corpus were recovered from excavations conducted in the 1960's in the north of the Atacama Desert, on the coast of Arica and Parinacota region in Chile, and are conserved at the Museum San Miguel de Azapa of the University of Tarapacá (Arica, northern Chile). Moreover, the mummy bundle studied in this work, PLM7_T119, was found in the Playa Miller 7 archaeological site (Arica valley) by Guillermo Focacci in 1974[START_REF] Focacci | Excavaciones en el cementerio Playa Miller 7, Arica (Chile)[END_REF]. Thanks to radiocarbon dating of its hair [START_REF] Richardin | A new protocol for radiocarbon dating of hair and keratin type samples-application to an Andean mummy from the National Museum of Natural History in Paris[END_REF][START_REF] Richardin | Radiocarbon dating of mummified human remains, Application to a series of Coptic mummies from the Louvre Museum[END_REF], this mummy bundle was dated to 3880 ± 30 years BP -2123-1777 cal. years BCat the transition between Archaic (6500 -1700 years BC) and Formative (1700 years BC -500 years AD) periods.

As stated in the R.1211-49 article of the French Public Health Code, hair is not subject to the CODECOH regulation, which frames the preparation, the conservation, the importation or the exportation of human biological materials. No ethics committee approval was then required for this work in a French laboratory.

Hair cleaning workflow

Hair samples were cut into small sections, immersed in ultrapure water and placed in an ultrasonic bath for a few seconds to remove the solids and mineral surface contaminations (Fig. 1). After water removal, hair samples were then washed with a mixture of DCM/MeOH 1:1 (v/v) to remove organic coating materials, decomposition fluids and eventual varnishes or synthetic resins. The samples were then thoroughly rinsed once with ultrapure water, once with acetone, and three times with ultrapure water.

Protocol 1 for extraction, precipitation and digestion of hair proteins (P1)

Cortical proteins from 15 mg of cleaned hair were extracted following the protocol described by Richardin and co-workers [START_REF] Richardin | A new protocol for radiocarbon dating of hair and keratin type samples-application to an Andean mummy from the National Museum of Natural History in Paris[END_REF][START_REF] Richardin | Radiocarbon dating of mummified human remains, Application to a series of Coptic mummies from the Louvre Museum[END_REF] and this was completed by a digestion protocol adapted from the work of Barthélémy [START_REF] Barthélémy | Protéomique qualitative et quantitative, une passerelle pour relier l'expression génomique à la construction des édifices biologiques -Application à la compréhension de la structure moléculaire du cheveu humain[END_REF] (Fig. 1).

Samples were immersed in 6 mL of a 0.13 M DTT, 0.03 M SDS and 25 mM Tris-HCl solution and left for 2 days at 50°C without agitation. Under these conditions, the only residue was the cuticle emptied of the cortex and medulla. After extraction, the soluble and insoluble materials were separated, and the proteins present in the soluble fraction were purified by precipitation with 50 μL of a 2 % DCO solution and 500 μL of a 100 % TCA solution, forming a pellet. After centrifugation, the pellets were purified using acetone in ice bath, abundantly rinsed with ultrapure water, so that there was no trace of acetone left, and the pad was finally lyophilized overnight. 1 mg of protein extract was then resuspended and reduced at 60°C for 1 hour in a solution at 25 mM NH4HCO3 and 20 mM DTT.

The sample was alkylated with 40 mM IAM during 1 hour at 25°C in the dark and precipitated with 2.5 volumes of EtOH, rinsed with 70 % EtOH and dried with N2. Alkylated proteins were resuspended in 1 mL of 100mM NH4HC03 and 2M urea solution, and digested at 37°C overnight with 1 µg of trypsin. Digestion was quenched with 0.2 % FA and samples were finally desalted using Sep-Pak C18 1 cc SPE cartridges. Digests were rinsed with H2O 0.1 % FA and a mixture of ACN/H2O 0.1 % FA (1:1, v/v) was used for the elution of the peptides. Desalted digests were concentrated to dryness by vacuum centrifugation and stored at -20°C or resuspended in 50 µL ACN/H2O 0.1 % FA (1:1, v/v) for further analyses.

Protocol 2 for extraction of hair proteins (P2)

The extraction protocol was adapted from the experimental procedures given by Lee & al. and Barthélémy (Barthélémy, 2011;[START_REF] Lee | Proteome Analysis of Human Hair Shaft From Protein Identification to Posttranslational Modification[END_REF] dedicated to proteomic studies of modern hair (Fig. 1). After the cleaning step, 15 mg of cleaned hair samples were immersed in 6 mL of the extraction solution containing 7 M urea, 2M thiourea, 50 mM DTT, 50 mM Tris-HCl (buffer solution, pH 7.5) and 0.1 % Triton X-100. Hair was incubated without agitation for 18 hours at 37 °C. After decantation, the soluble (protein extract) and insoluble materials (mostly cuticle) were separated and the protein extract was alkylated with 1 M IAM and 3 M Tris-HCl for 10 minutes in the dark at room temperature. The alkylated extract was then dialyzed in 3500 MWCO Slide-A-Lyzer dialysis cartridges against water (two changes) over 2 days and the extract was lyophilized overnight. Finally, the extract was reduced and digested following the same workflow as for Protocol 1.

Protocol 3 for extraction and digestion of hair proteins (P3)

Initial experiment was led on 1 mg of cleaned hair using a protocol adapted from the work of Solazzo and coworkers dedicated to the study of archaeological keratinized fibers [START_REF] Solazzo | Species identification by peptide mass fingerprinting (PMF) in fibre products preserved by association with copper-alloy artefacts[END_REF] (Fig. 1). Samples were immersed in 1 mL of a solution of 8 M urea and 50 mM DTT in a 100 mM NH4HCO3 pH 9.1 buffer, and incubated with gentle agitation for 4 hours at room temperature. The soluble (protein extract) and insoluble (mostly cuticle) materials were separated and the protein extract was alkylated with 40 mM IAM for 45 minutes in the dark at room temperature. The alkylated extract was then digested at 37°C overnight by adding an equivalent volume of 50 mM NH4HCO3 pH 8.3 buffer and 10 µg of trypsin. Digestion was quenched with 0.2 % FA and samples were finally reduced to 1 mL by vacuum centrifugation and desalted using Sep-Pak C18 1 cc SPE cartridges. Digests were rinsed with water 0.1 % FA and ACN/H2O 0.1 % FA (1:1, v/v) was used for the elution of the peptides. Desalted digests were concentrated to dryness by vacuum centrifugation and stored at -20°C or resuspended in 50 µL ACN/H2O 0.1 % FA (1:1, v/v) for further analyses.

Peptide mass fingerprinting by Matrix Assisted Laser desorption/Ionization -Time of Flight -Mass Spectrometry

The matrix assisted laser desorption-ionization mass spectrometry (MALDI-MS) analyses were conducted on an Autoflex II MALDI -Time of Flight (TOF) -mass spectrometer (Bruker Daltonics, Bremen, Germany). The device was featured with a pulsed nitrogen laser emitting at 337 nm and operated at an extraction voltage of 20 kV. The SPE desalted hair digests were resuspended in 50 µL ACN/H2O 0.1 % FA and deposited on a stainless steel target as dry droplets with a saturated solution of SA, in ACN/H2O 0.1 % TFA (1:1, v/v). Gated suppression was applied in order to prevent any saturation of the detector by matrix ions. 1000 laser shots were averaged for each spectrum and acquisitions were realized in positive linear mode, adapted to compounds between 2 and 30 kDa. Laser power was optimized and kept constant for the different samples. Bruker Protein Calibration Standard I (4000 to 20,000 Da) was used for calibration and spectra were processed with the Flex Analysis 3.4 software (Bruker Daltonics).

Peptide analysis and protein identification by nano Liquid Chromatography -Electrospray Ionization -Tandem Mass Spectrometry

Desalted tryptic digests, resuspended in 50 µL ACN/H2O 0.1 % FA, were separated on an Ultimate 3000 nano-flow liquid chromatography device (Dionex, Thermo Scientific). The solvent system consisted of water with 0.2 % FA (Solvent A) and acetonitrile with 0.2 % FA (Solvent B). Samples were first loaded onto a reversed phase C18 pre-column (PepMap100, 5 µm particles, 100 Å pore size, 300 µm i.d.) at a flow rate of 20 µL/min with 2 % B. They were then transferred on-line to the analytical reversed phase C18 column (75 µm i.d. x 15 cm, 3 µm particle, 100 Å pore size, Acclaim PepMap100 NanoViper from Thermo Fisher) and peptides were separated at a flow rate of 300 nL/min, using a gradient from 2 % B to 55 % B. The column outlet was connected to a microTOF-Q II mass spectrometer (Bruker Daltonics) through a stainless steel nano-spray needle. Acquisition for the detection of precursor ions (MS) and fragmented product ions (MS/MS) was made in positive mode.

Capillary voltage was set to -4.5 kV, nebulizer gas to 0.4 bar, dry gas to 4.0 L/min, source temperature to 180°C and data acquisition for spectra collection was set to 2 Hz. For each MS scan, 3 precursor ions were selected for fragmentation and the total duty cycle was then 2 sec. Mass range for both MS and MS/MS scans was m/z 100 to m/z 3000. MS/MS data were then processed through SwissProt (Sp_20130109_decoy) and Human_Up11-6_decoy databases using the Mascot 2.2.06 search algorithm developed by Matrix Science in Biotools 3.2 and ProteinScape 3.1 softwares (Bruker Daltonics). Trypsin cleavage rules were applied and the mass tolerance for precursor ions was set to ± 30 ppm and to ± 0.5 Da for fragments, allowing a maximum of 3 missed cleavages. N-terminal glutamine cyclization (-17.03 Da), N-terminal acetylation (+42.01 Da), carbamidomethylation of cysteine (+52.02 Da), N-terminal carbamylation (+43.01 Da), deamidation of asparagine and glutamine (+0.98 Da) and methionine and cysteine oxidation (+15.99 Da) were selected as variable modifications.

Research was processed using decoy databases in order to obtain the peptide false discovery rates (FDR) for each sample. A manual validation was made on the identification results by fixing the protein FDR to a maximum of 1 %. Only proteins respecting this fixed FDR value and yielding a Mascot score above 80, as well as at least 2 unique peptides, were accepted as identified. Moreover, peptides were accepted only when they yielded a Mascot score above 20 and p value under 0.05. In order to verify the reproducibility of the workflows and the analysis while preserving the samples, preparation of hair with each protocol was duplicated and each digested sample was analyzed twice by nanoLC-MS/MS. To check the absence of contaminants from the laboratory environment, such as modern keratins, control samples were realized for each set of samples with ultrapure water, using the same protocol and at the same time as hair samples.

Results and Discussion

Three protocols adapted from the literature were applied to cleaned hair samples.

Protocol 1 (P1) was adapted from the workflow for sample preparation for accelerator mass spectrometry (AMS) measurements [START_REF] Richardin | A new protocol for radiocarbon dating of hair and keratin type samples-application to an Andean mummy from the National Museum of Natural History in Paris[END_REF][START_REF] Richardin | Radiocarbon dating of mummified human remains, Application to a series of Coptic mummies from the Louvre Museum[END_REF] on ancient hair. It was designed to selectively extract proteins from archaeological keratinized materials, while removing all traces of exogenous compounds in order to prevent any misleading results for the dating of ancient hair. We also added several steps for protein digestion and peptide digest desalting in order to complete the sample preparation for nanoLC-MS/MS analyses. This workflow presents some key steps with a high risk of material losses, such as protein precipitation and purification, and thus requires important material amounts. Richardin and coworkers indeed mentioned that they recovered about 20 % in mass of proteins with this workflow and this, from more than 30 mg of raw ancient hair. Protocol 2 (P2) was adapted from the work of Lee et al. and Barthélémy (Barthélémy, 2011;[START_REF] Lee | Proteome Analysis of Human Hair Shaft From Protein Identification to Posttranslational Modification[END_REF]. These works were dedicated to the study of important amounts of modern hairtypically between 40 and 100 mgin order to identify a maximum of hair proteins along with their location in the fiber and their molecular modifications. Protocol 3 (P3) was adapted from the work of Solazzo [START_REF] Solazzo | Species identification by peptide mass fingerprinting (PMF) in fibre products preserved by association with copper-alloy artefacts[END_REF] for the protein extraction and digestion, and aimed to analyze ancient proteins in small amounts of keratinized archaeomaterials, typically less than 1 mg.

In the case of P1 and P2, working on too small hair amounts could lead to technical complications and significantly reduce the extraction rate, but due to the preciousness of our samples it is impossible to work on quantities as large as those described in the literature for these two first workflows.

In the scope of this work, the initial amounts of hair were then set to 15 mg for both P1 and P2, while the first experiments with P3 were conducted on 1 mg. This last quantity was not similar to the two others in order to stay in the mass range of the original study but this difference will be taken into account for the result interpretations. Preserving the archaeological samples is a crucial issue regarding their preciousness and thus, optimizations of extraction and digestion protocols, of nanoLC-MS/MS parameters and of MALDI-TOF-MS parameters were done on modern hair. For proper comparison of modern and ancient samples, each type of hair was cleaned and prepared for analyses using the same protocols described in the experimental section. Desalted digests were analyzed by MALDI-TOF-MS in order to visualize the peptide mass fingerprint of the different samples.

Desalted digests were also processed through nanoLC-MS/MS to separate and to analyze the hair peptides. This enabled us to identify the proteins present in the different samples and to study their molecular modifications.

First observations on the three chosen protocols with modern hair

After the cleaning step common to the protocols, P1 and P2 were first applied to 15 mg of modern hair and P3 to 1 mg of hair. During the extraction step of proteins from modern hair, no clear change was visible with any of the three applied protocols. Hair seems to remain almost intact, hair color is quite similar and fibers seem to be a little more flexible but keep the same shape (Supplementary information Fig. S-1). The protein extractsoluble partshows a very light brown coloration almost not visible at first sight. The more obvious observation is the complexity of P1 and P2 in comparison to P3. They require many stages, are time-consumingmore than five daysand poorly reproducible due to their multiple steps of precipitation-rinsing or of dialysis. Important initial amounts of raw hair are thus necessary in order to ensure the recovery of enough digested material to enable protein identification.

Moreover, the use of TCA and acetone can induce protein modifications, such as proteolysis due to the high acidity of TCA or glycine modification due to acetone washing [START_REF] Simpson | Acetone Precipitation of Proteins and the Modification of Peptides[END_REF], and thus leads to misinterpretations on the conservation state of the samples. On the contrary, P3 is much easier, requires little timeless than two days -and shows a good reproducibility. The molecules of interestproteins or peptidesremain solubilized throughout the entire process, from protein extraction to vacuum drying of SPE desalted digests. This highly improves reproducibility, minimizes sample losses and enables to work on small amounts of raw material, such as 1 mg. Given these observations, P3 is very promising in the scope of our project since it is crucial to preserve the integrity of the studied archaeomaterials. However, the compatibility of P3 with nanoLC-MS/MS analysis and the quality of mass results of P3 versus P1 and P2 remain to be verified.

MALDI-TOF-MS and protocol efficiency on modern hair

As mentioned before, MALDI-MS analyses were performed on SPE (C18 cartridges) desalted hair digests to visualize the peptide fingerprint of modern hair and to verify the efficiency of the digestion step for the three different protocols. A first experiment was realized on 1mg of protein extracts from P1 and P2, which were digested as described in Protocol 1 (data not shown). Regarding peptide distributions and intensities, MALDI-MS spectra show highly similar profiles for both hair digests and also for the control sample (ultrapure water processed through the same workflows than hair samples) which exhibits peaks from trypsin autolysis. The most significant differences compared to the control sample are two main peak groups at mass to charge ratios (m/z) 6500 and 8400 and three smaller ones at m/z 6900, 7200 and 9200, but for both protocols, the results are not particularly conclusive.

Digestion in Protocol 1 requires a complex workflow whereas digestion in Protocol 3 is much easier to execute. In addition, it is more relevant to compare MS results from the digests of the three protocols if the digestion workflow is the same for the three protein extracts. 1 mg of P1 and P2 extracts were then prepared using the Protocol 3 digestion workflow as for the entire protein extract recovered from P3. The three peptide profiles are well resolved but quite different one from another and also from the control sample (Fig. 2).

The difference of the hair samples with the control sample comes mostly from the high number of different peptides and from the lower relative intensities of peaks from trypsin autolysis in hair digests. These two points seem to point out the successful and efficient digestion of hair proteins.

NanoLC-MS/MS for identification of modern proteins and molecular degradation

SPE desalted peptide digests from the three protocols were processed through nanoLC-MS/MS analysis in order to identify modern hair proteins and to study the molecular degradation of modern hair, coming either from natural occurrence or induced by the treatment (Fig. 3, Supplementary information Table S-1). With the applied analysis technique -microToF-Q II mass spectrometerno peptide or protein is identified in the control samples, even when searching for other taxonomies than "Homo Sapiens", thus showing the absence of detectable modern contaminants in our hair samples.

In P1 digests from the Protocol 1 digestion, more than 600 spectra were detected and the Mascot algorithm was able to match 15 % of the spectra, from which it was possible to identify and accept 19 different proteins. Among them, 4 hair keratins were found out of the 18 described in literature [START_REF] Szeverenyi | The Human Intermediate Filament Database: comprehensive information on a gene family involved in many human diseases[END_REF]. On the other hand, 1001 spectra were recovered from P1 samples after the Protocol 3 digestion and 41 % of spectra corresponded to database spectra. This enabled to accept 19 proteins, including 9 described human hair keratins. With P2 after the Protocol 1 digestion, 395 spectra were detected and only 9 proteins were identified from 6 % of matched spectra, including only 2 hair keratins. As for P1, 1001 spectra were recovered from P2 samples after the Protocol 3 digestion, but Mascot research yielded only 5 % of matched spectra and 14 identified proteins with 5 described hair keratins. P3 enables to detect about 750 spectra in the analyzed digests and to identify 23 different proteins from 33 % of matched spectra, including 8 keratins out of the 18 expected for human hair.

With this technique, it was also possible to study the molecular modifications of the keratins detected in modern hair and more particularly in digested samples from P1 and P3.

The main modifications observed are induced by the different treatments, such as carbamylation or carbamidomethylation that come from extraction with urea [START_REF] Lippincott | Carbamylation of Cysteine: A Potential Artifact in Peptide Mapping of Hemoglobins in the Presence of Urea[END_REF][START_REF] Preedy | Adhesion Molecules[END_REF] and alkylation with IAM [START_REF] Rombouts | Improved identification of wheat gluten proteins through alkylation of cysteine residues and peptidebased mass spectrometry[END_REF]. It should be noted here that the use of NH4HCO3 buffer is known to inhibit protein carbamylation induced by urea [START_REF] Sun | Inhibition of protein carbamylation in urea solution using ammonium-containing buffers[END_REF], however the applied NH4HCO3 concentrations are too low in this work to completely prevent these modifications that are then still detectable in the different digests. When carbamylations and carbamidomethylations are not taken into account, an average of 15 to 35 % of matched spectra exhibit different post-translational modifications (PTMs) such as deamidation of glutamines and asparaginesless than 30 % of deamidated sites on average, and oxidation of cysteinesless than 35 % of oxidized sites (Fig. 3). Occasional methylation of aspartic and glutamic acids, oxidation of methionines, Nterm cyclization of glutamines and N-term acetylation were also identified.

Percentages of peptides with no, low (1) or high (2 and 3) number of miscleavages were also calculated in order to check the efficiency of the two digestion protocols. Results showed that digestion of Protocol 1 leads to higher number of miscleavages for P1 and P2 protein extracts with less than 60 % of peptides with no miscleavages, 25 to 40 % with only 1, and 19 to 30 % with 2 or 3. On the other hand, P1 and P2 digests from digestion of Protocol 3 exhibit more than 90 % of peptides with no miscleavage, less than 10 % with 1 miscleavage and less than 2 % with 2 or 3 miscleavages. P3 yields also good percentages of peptides with 0 or only 1 miscleavagerespectively 77 % and 19 % -and a low percentage of peptides with 2 or 3 miscleavages -5 % (Fig. 4).

These results once again highlight the very promising behavior of P3 for hair protein analysis. Indeed, regarding the better identification results for expected hair keratins, the higher percentages of matched spectra and the higher rate of low miscleavages (0 or 1 miscleavage), digestion of Protocol 3 yields a much better efficiency than Protocol 1 digestion (Fig. 3, Supplementary Information Table S-1). In addition, 1001 spectra were recovered from both P1 and P2 samples digested as described in Protocol 3, while less than 600 and 400 spectra were respectively obtained for P1 and P2 samples digested as described in Protocol 1. This difference comes very likely from the low reproducibility of the protein precipitation-rinsing step in Protocol 1 digestion. Moreover, despite the small initial amount of raw hair, and in comparison with P1 and P2, P3 enables to easily obtain with a good reproducibility, a quite satisfying identification of the major hair proteins with high sequence coverages between 40 and 80 % (Supplementary information Table S-1). P1 yields better identification results than P3 with higher percentages of matched spectra, more identified proteins with similar or higher sequence coverages but from a 15 times higher amount of raw hair than P3. P2 however shows much poorer results than the two other protocols since less than 6 % of detected spectra could match with the databases. In addition, proteins are not well identified and sequence coverages are lower than 30 %, despite the same amount of raw hair as P1 and a 15 times higher initial amount of hair than P3. In the scope of this project, and regarding the preciousness of archaeological samples, P3 was then selected to be further tested on the archaeological hair samples, while P1 and P2 were set aside for the following of the study.

Analysis of hair from an Andean mummy

After optimization on modern hair, the most promising workflow -P3was applied to 1 mg of hair from a 3500-year-old Andean mummy. Early global observations pointed out the first indicators of the molecular degradation of ancient hair. While no clear change was detectable on modern hair during protein extraction, archaeological fibers were almost completely solubilized during the same extraction time, giving a yellow solution mostly made of cortical proteins, melanins and internal lipids, and a tubular colorless insoluble residue composed by the outer layers of the cuticle (Supplementary information Fig. S-1). Further testing on P3 showed that archaeological sample was almost solubilized after about 1 hour while modern hair could be extracted during 48 hours without any significant change. This difference of behavior between modern and archaeological samples could be explained by molecular modifications, such as the probable alteration of inter-and intramolecular interactions inside hair, thus weakening the cohesion of archaeological fibers and fostering the protein extraction process. In particular, disulfide bridges are an abundant molecular interaction in hair that gives its great robustness to the fiber. They are however very sensitive to oxidation, a frequently occurring modification in archaeological context, that could dramatically reduce the hair resistance to the protein extraction step. MALDI-MS spectra of archaeological hair digests exhibit similar masses in the low mass range to that of modern hair digests. However, peptides from ancient hair seem less abundant and distributed on a narrower mass range at low m/z ratios (Supplementary information Fig. S-2). These observations altogether seem to point out the molecular degradation of ancient samples and the probable fragmentation of protein assemblies.

Finally, as with modern samples, hair from the mummy was analyzed by nanoLC-MS/MS (Fig. 3, Supplementary information Table S-2). 1001 spectra were recovered from P3 and enabled to identify 11 archaeological proteins from 25 % of matched spectra, including 8 described hair keratins. Major hair keratins are still well identified in ancient hair but only few KAPs and other minor hair proteins were detected thus pointing out a probable more advanced degradation (Fig. 3). Among these major identified keratins, 7 were also found in modern hair: KRT31, KRT33a, KRT33b, KRT 34 (Type I), KRT83, KRT85 and KRT86 (Type II). The study of PTMs was then applied to these 7 specific proteins, so that the interpretations and discussions could be more accurate.

As for modern hair, treatment-induced modifications are still present on an important part of peptides from both protocols, but in the case of archaeological samples, they are not the major modifications. When these are not taken into account, more than 75 % of matched spectra on average show the presence of PTMs. More specifically, P3 digest from ancient hair exhibits more than 85 % of modified deamidation sites against less than 30 % in modern hair, and more than 35 % of the identified cysteines are oxidized with 25 % of trioxidated cysteines on average against less than 10 % of trioxidation for modern cysteines (Fig. 3). Despite a good identification of ancient proteins in the hair from the studied mummy, with high sequence coverages for the 6 main keratins in the range 35-75 % (Supplementary information Table S-2), these results seem to highlight an advanced molecular degradation of ancient hair proteins that have now to be studied further.

Minimization of raw hair amounts

In archaeometrical study, preserving the integrity of studied artefact is a crucial concern. It is then primordial to minimize as much as possible the amounts of archaeological samples required for the different analyses without creating new technical difficulties or risks of sample losses. P3, which was first applied to 1 mg of raw ancient hair, was then tested on 500 µg and 200 µg of archaeological hair (Supplementary information -Table S-2). The 500 µg sample gave globally similar MALDI-MS and nanoLC-MS/MS results than for 1 mg of hair without changing the dilution factor. 790 spectra were indeed detected and 22 % of spectra could be matched with database spectra, enabling to identify 7 proteins. Among them, 6 were expected hair keratins, all common to digest from 1 mg of ancient hair and to modern hair digest. On the other hand, workflow was more difficult to apply to 200 µg of hair because of the weighing and cleaning steps on such small samples, and quality of MS and MS/MS results was also poorer due to the lack of material. 700 spectra were detected and 6 hair keratins were identified but only 15 % of matched spectra were obtained and identification results showed lower sequence coverages and Mascot identification scores.

Further considerations

The results presented here gave new insights into the understanding of hair protein alteration processes and could be extended to the study of molecular modifications induced by heavy metals, which were an important environmental pollutant already in ancient times.

This work could also be helpful for the study of modern phenomena, such as the degradation and ageing processes of modern capillary materials or the impact of our environment on our organism. For further validation of this optimized workflow dedicated to the proteomic analysis of archaeological hair, our method can be evaluated further by independent laboratories internationally.

Conclusions

In this report, we described the development of a proteomic approach for a new insight in the characterization of hair from Andean mummies. First, tests and optimizations of the three chosen protocols were done on modern hair and the most promising protocol -P3was selected for further optimization on archaeological hair. With this workflow, it was indeed possible to lower the initial hair amounts to 500 µg while maintaining a good quality of MS/MS data, especially for the 6 main keratins with high sequence coverages and high identification scores in the range 1100-4000. P3 was then selected for the following of this work on hair from Andean mummies with initial hair amounts of 500 µg. This workflow helps not only to successfully identify main proteins from very small amounts of a quite complex material with high identification scores and high sequence coverages, but also to go further in the characterization of this material with the study of PTMs. We were then able to identify main ancient keratins from hair of a prehispanic mummy and to assess for the first time the molecular preservation of ancient hair by a proteomic approach. In terms of methodology, our work enables to apply a proteomic approach to precious and rare samples not only in the context of archaeometrical studies but also for any applications that would require the use of very small amounts of sample.

The results presented here collectively open the way for new insights into the understanding of hair protein alteration processes such as those due to ageing and ecological exposures.

PhD thesis scholarship and the Proyecto UTA 2014, 3741-14. We also thank Convenio de Desempeño UTA-MINEDUC as well as the National Network of Interdisciplinary Archaeometrical Competencies (CAI-RN) and their ITC program "Fostering the competency transfer" for their financial help. Finally, we thank Jean-Claude Dran for the improvements of the English writing.

Disclosures

Archaeometrical

  Analyses and Research (LAIA) from the University of Tarapacá (UTA, Chile), the Research and Restoration Centre for French Museums (C2RMF, Paris, France) and the Laboratory of Mass Spectrometry of Interactions and Systems (LSMIS, Strasbourg, France).

  AbbreviationsBP -Before present BC -Before Christ AD -Anno domini KIF -Keratin intermediate filaments KAP -Keratin associated proteins PTM -Post-translational modification SA -Sinapic acid SDS -Sodium dodecyl sulfate IAM -Iodoacetamide DTT -DL-dithiothreitol DCO -Sodium deoxycholate TCA -Trichloroacetic acid TFA -Trifluoroacetic acid FA -Formic acid DCM -Dichloromethane ACN -Acetonitrile

Acknowledgments

The authors would like to thank Pr. Vivien Standen, Head of Physical Anthropology Laboratory from Museum San Miguel de Azapa of the University of Tarapacá; Veronica Silva, PhD student at the Max Planck Institute for Evolutionary Anthropology (Leipzig, Germany) and at the University of Tarapacá and the Catholic University of Northern Chile (Arica) for her collaboration on the archaeological samples; Laura Bom and the Laboratory of Magnetic Resonance and Membrane Biophysics from the University of Strasbourg for their help with the technical materials. Project was financed by the University of Strasbourg for the


PhD Frank W Pfrieger
email: fw-pfrieger@gmx.de

Insight into the workforce advancing fields of science and technology

Keywords: Pfrieger, Workforce Analysis 2 Authorship, team science, science of science, citation, scientometrics, bibliometrics, metaanalysis, data science, genealogy, collaborations

Advances in biomedicine and other fields of science and technology depend on research teams and their peer-reviewed publications. The scientific literature represents an invaluable socioeconomic resource guiding future research. Typically, this growing body of information is explored by queries in bibliographic databases concerning topics of interest and by subsequent scrutiny of matching publications. This approach informs readily about content, but leaves the workforce driving the field largely unexplored. The hurdle can be overcome by a transparent team-centered analysis that visualizes the teams working in a field of interest and that delineates their genealogic and collaborative relations. Context-specific, but citation-independent metrics gauge team impact and reveal key contributors valuing publication output, mentorship and collaboration. The new insight into the structure, dynamics and performance of the workforce driving research in distinct disciplines complements ongoing efforts to mine the scientific literature, foster collaboration, evaluate research and guide future policies and investments.

Introduction

Progress in biomedicine and other fields of science and technology (S&T) depends on research teams working in specific fields and on the publication of their results by peer-reviewed articles.

The rapidly growing body of scientific information reflects past and current states of the art and represents an invaluable socio-economic resource guiding future research activity, policies and . CC-BY-ND 4.0 International license was not certified by peer review) is the author/funder. It is made available under a

The copyright holder for this preprint (which this version posted June 2, 2020. . https://doi.org/10.1101/2020.06.01.128355 doi: bioRxiv preprint Pfrieger, Workforce Analysis 3 investments [START_REF] Mukherjee | The nearly universal link between the age of past knowledge and tomorrow's breakthroughs in science and technology: The hotspot[END_REF][START_REF] Fischhoff | The Science of Science Communication III[END_REF]. Moreover, scientific publications are explored by the "Science of Science" aiming to understand the inner workings of science from global points of view [START_REF] Clauset | Data-driven predictions in the science of science[END_REF][START_REF] Zeng | The science of science: From the perspective of complex systems[END_REF]Fortunato et al., 2018;[START_REF] Fischhoff | The Science of Science Communication III[END_REF][START_REF] Hardwicke | Calibrating the Scientific Ecosystem Through Meta-Research[END_REF]. The utility of this information relies on bibliographic databases, on refined methods to search and analyse content [START_REF] Lu | PubMed and beyond: a survey of web tools for searching biomedical literature[END_REF][START_REF] Mclevey | Introducing metaknowledge: Software for computational research in information science, network analysis, and science of science[END_REF] and on efficient science communication [START_REF] Fischhoff | The Science of Science Communication III[END_REF]. For global analyses complex algorithms process large data sets [START_REF] Muller | Textpresso: An ontology-based information retrieval and extraction system for biological literature[END_REF][START_REF] Zeng | The science of science: From the perspective of complex systems[END_REF][START_REF] Miotto | Deep learning for healthcare: review, opportunities and challenges[END_REF][START_REF] Kastrin | Disentangling the evolution of MEDLINE bibliographic database: A complex network perspective[END_REF], whereas a typical user queries a bibliographic database on a specific topic using relevant keywords. From the resulting list of publications, scientific content can be extracted, but the workforce driving the field, its size, dynamics and key contributors remain largely inaccessible. This hurdle can be overcome by a team-centered approach, named TeamTree Analysis (TTA). Based on scientific articles related to a specific topic, this approach reveals instantly the teams working in a research field, visualizes workforce growth, delineates family and collaborative connections and gauges team performance in a citation-independent manner.

Results

To explore TTA, the workforce of an exemplary field in biomedical science was analyzed. A PubMed query using the term "circadian clock" (Clock) yielded a list of articles published between 1960 and 2020, from which TTA identified principal investigators (PIs)/teams working in the field based on last author names (Table 1; Supplementary Data 1).

Visualization and quantitative analysis of team publications, genealogy and collaborations

. CC-BY-ND 4.0 International license was not certified by peer review) is the author/funder. It is made available under a

The copyright holder for this preprint (which this version posted June 2, 2020. . https://doi.org/10.1101/2020.06.01.128355 doi: bioRxiv preprint Pfrieger, Workforce Analysis 4 Plotting publication years of each PI/team against a chronologic team index with alternating sign (TI) creates a tree-like visual revealing each team's entry into the field and its publication count (PC) per year (Fig. 1A). The Clock field expanded steadily in terms of workforce and of publication output as indicated by annual counts of newly entering teams and of published articles, respectively (Fig. 1A). Individual PIs/teams published up to 113 articles (PC, publication count) as last authors (Last) with a maximum annual output of nearly 7 papers per year. The majority of teams (69%) contributed single articles (Fig. 1B). Ranking teams by numbers of publications revealed the top ten players in the Clock field with respect to publication record (Fig. 1C).

TTA exposes ancestor -offspring relations based on last -first authors of articles, respectively. A quarter of PIs/teams with last author articles published previously as first authors (PC First) thus qualifying as offspring in this field (Fig. 1B). About 11% of the teams qualified as ancestors that generated up to 24 offspring teams (OC, offspring count) and published up to 70 articles with their offspring (PCoff). Offspring teams and their articles represented a relatively constant fraction of the annual workforce and of the publication output (Fig. 1D). Overall, the Clock field comprised 543 families with up to 43 members (FS, family size) spanning 5 generations (TG, team generation; Fig. 1E). The genealogic analysis indicated the most prolific players in the field and their family relations (Fig. 1D,F).

Collaborations among teams in the Clock field were delineated from co-authorship of PIs/teams on PubMed articles. Figure 2A shows connections between teams (left; last authors) and their collaborators (right; coauthors) and the numbers of collaborators per team (CC, collaboration count). The analysis separates out-and in-degree connections, where teams are listed as last authors or co-authors, respectively. Collaborating teams represented a substantial Pfrieger, Workforce Analysis 5 fraction of the workforce and contributed more than half of all publications (Fig. 2A). The increasing importance of collaborations was indicated by the steadily increasing mean number of authors per article published annually (Fig. 2B). About 60% of the PIs/teams working in the Clock field established up to 61 and 58 out-and in-degree collaborations, respectively.

Collaborative articles represented 81% of the total publication output with individual PIs/teams publishing up to 68 and 95 collaborative papers as last and coauthor, respectively (Fig. 2C).

Ranking teams based on CC values revealed the most strongly connected players in the field.

They form a large network with a diameter of 10, a mean distance of 4.4 and an edge density of 0.0095 (Fig. 2D).

Team ranking based on publication output, mentorship and collaborations

A frequent goal in S&T is to identify key contributors to a field, who can serve as referees, experts, collaborators or awardees. TTA delivered three parameters (PC, OC and CC) allowing to estimate team performance (Figs. 1,2). Intersection of the top 100 teams for each parameter revealed some overlap between PC-, OC-and CC-based rankings and a core of 35 teams that figured among the top in all three categories (Fig. 2E). Plotting individual teams in the threedimensional parameter space showed that the top teams occupied distinct volumes (Fig. 2F).

This suggested that the product of the three parameters, further referred to as POC, enables differentiated team ranking that values scientific production, offspring generation and cooperativity (Fig. 2G). Do teams with high POC values have a strong impact on the field? This was supported by the high ranks of distinguished scientists (Fig. 2G) including Nobel prize winners (M. Rosbash: 7th, M.W. Young: 17th, J.C. Hall: 30st out of 6,506;

Analysis of distinct scientific disciplines and of highly productive teams

S&T comprise a wide range of disciplines raising the question, whether the field-specific workforce can be evaluated in disciplines other than biomedicine. To address this, exemplary fields in geoscience, computer science, chemistry, astronomy and physics were analyzed (Table 1; Fig. 3A; Supplementary Data 1). Synoptic graphs summarized the workforce expansion, genealogic relations and collaborative connections of each field and revealed field-specific differences. Notably, the teams with top ten POC values comprised winners of field-specific scientific awards (Table 1; Fig. 3A) corroborating that this parameter can identify high impact teams in different scientific disciplines.

Frequently, specific teams are of interest, for example those with exceptionally high publication output [START_REF] Ioannidis | Thousands of scientists publish a paper every five days[END_REF]. TTA allows to trace their publication activity over time while separating articles originating from offspring and collaborators. To illustrate this point, PubMed articles authored by selected PIs working in distinct areas of biomedicine were analyzed (Table 1; Fig. 3B-D). This revealed their distinct publication histories and the contributions from offspring and collaborators. For example, two PIs/teams, Lip and Raoult, showed strong increases of annual publication rates 6 and 8 years ago, respectively. Breakdown of contributions revealed that these changes were driven by increased numbers of in-degree collaborations, where the PIs are listed as co-authors (Fig. 3B,C).

Workforce impact on field development

An important question is how research fields develop over time. The dynamics can determine priorities for public funding, private investments and workforce allocation. To explore whether 1; Fig. 4A-C). Separation of "Newcomers" entering a field per year from "Established" teams working already in the field per year revealed their respective impact on the field's development (Fig. 4A). Many newcomers published only one article (SATs, single article teams; Fig. 4A) excluding a sustained contribution to the workforce.

Their annual fraction was consistently lower in expanding compared to non-expanding fields (Fig. 4B). On the other hand, teams with collaborative and family connections showed consistently longer publication periods indicating that they influence the development of each field (Fig. 4C).

The visuals and quantitative measures introduced here provide a comprehensive view on the workforce behind a field of research. The choice of field subjected to TTA can be userdependent (Figs. 1-3, Table 1) or it may be driven by public interest. An example for the latter is the Covid-19 pandemic, where TTA reveals the major contributors, their connections and the remarkable dynamics of the field during coronavirus-induced disease outbreaks (Table 1, Fig. 5).

Effects of subtopics, journals and affiliations

Teams focus on specific subtopics within a field, they publish in different journals and they are affiliated with distinct organizations. How do these factors impact research within a field? To address these points, TTA was applied to teams a) focusing on distinct subtopics related to Alzheimer's disease (Abeta, tau and ApoE; Fig. 6A-C), b) publishing in multidisciplinary journals in the climate field (J1: Science; J2: PNAS; J3: Nature; J4: PLoS One; J5: Nature Communications; J6: Scientific Reports; Fig. 6D-F) and c) working on artificial intelligence and machine learning at selected institutions (Affiliation 1: Harvard; A2: Stanford; A3: IBM; A4: 1; Fig. 6G-I). The graphics reveal the factor-dependent development of each field, notably the strong expansion of the Abeta-related workforce and publication output, the strong increase of climate-related teams publishing in recently founded journals and the successive and rather parallel growth of the AI/machine learning field at academic and private institutions (Fig. 6A,D,G). Most teams worked on single topics (Fig. 6B), published in single journals (Fig. 6E) and worked at single institutions (Fig. 6H). Increasing the numbers of these factors potentiated POC values (Fig. 6B,E,H) and thus probably the team impact. Teams with top ten POC values formed large networks connecting topics (Fig. 6C) and affiliations (Fig. 6I). Co-publishing and collaborating teams in the Climate field established selective connections between journals and affiliations, respectively. These connections differed in strength and depended in part on the total workforce and on the publication period (Fig. 6F,I).

Discussion

The approach introduced here provides new insight into the workforce that advances a field of interest in S&T based on peer-reviewed articles. The field-and team-related visuals and measures can be scrutinized ad-hoc following a database query. Thereby, users gain an accessible and transparent tool to mine the ever-growing scientific literature. Learning about the workforce of an important, but unfamiliar field facilitates the identification of experts and the establishment of collaborations crossing disciplinary boundaries [START_REF] Trujillo | Document co-citation analysis to enhance transdisciplinary research[END_REF]. The field-specific approach complements global analyses focusing on team impact [START_REF] Sekara | The chaperone effect in scientific publishing[END_REF][START_REF] Ahmadpoor | Decoding team and individual impact in science and invention[END_REF], evolution [START_REF] Milojevic | Principles of scientific research team formation and evolution[END_REF] and affiliation [START_REF] Jones | Multi-University Research Teams: Shifting Impact, Geography, and Stratification in Science[END_REF][START_REF] Way | Productivity, prominence, and the effects of academic environment[END_REF]. Similar to other approaches, TTA faces the name ambiguity challenge, whose solution requires more refined approaches [START_REF] Zeng | The science of science: From the perspective of complex systems[END_REF]. The measures introduced Pfrieger, Workforce Analysis 9 here can help to evaluate context-specific team performance and to reveal the impact of the workforce on the development of a research area. Notably, the POC value takes into account three key activities in research, namely scientific production, mentorship and cooperativity, and reliably identifies key contributors. Thereby, this metric complements measures such as citations [START_REF] Hirsch | An index to quantify an individual's scientific research output[END_REF][START_REF] Ioannidis | Multiple Citation Indicators and Their Composite across Scientific Disciplines[END_REF][START_REF] Zeng | The science of science: From the perspective of complex systems[END_REF] without requiring proprietary databases.

On the other hand, POC values are field-specific and depend on the period of activity therefore precluding absolute ranking of teams or evaluation of junior scientists. The delineation of family relations and of collaborative connections allows to categorize the publication output of individual PIs/teams and to identify the different types of contributors (e.g. offspring, collaborators). The automatic delineation of family connections based on first author-last author pairs provides an alternative to previous efforts requiring user input [START_REF] David | Neurotree: a collaborative, graphical database of the academic genealogy of neuroscience[END_REF][START_REF] Hirshman | Impact of medical academic genealogy on publication patterns: An analysis of the literature for surgical resection in brain tumor patients[END_REF][START_REF] Lienard | Intellectual synthesis in mentorship determines success in academic careers[END_REF] (see also Mathematics Genealogy Project: https://www.genealogy.math.ndsu.nodak.edu/). However, this approach may underestimate offspring counts in the case of first or last co-authorship, when first authors change the field of interest and in the case of alphabetical author lists or of field-specific author ranking [START_REF] Waltman | An empirical analysis of the use of alphabetical authorship in scientific publishing[END_REF]. Genealogic and collaborative connections seem to enhance the impact of teams and to prolong their life-span within a field. This underlines the relevance of training and mentorship ensuring the continuity of research in S&T [START_REF] Sauermann | Authorship and contribution disclosures[END_REF] and supports the importance of cooperations regardless of the field [START_REF] Lu | PubMed and beyond: a survey of web tools for searching biomedical literature[END_REF][START_REF] Mukherjee | The nearly universal link between the age of past knowledge and tomorrow's breakthroughs in science and technology: The hotspot[END_REF][START_REF] Wuchty | The increasing dominance of teams in production of knowledge[END_REF][START_REF] Stallings | Determining scientific impact using a collaboration index[END_REF][START_REF] Coccia | Evolution and convergence of the patterns of international scientific collaboration[END_REF][START_REF] Parish | Dynamics of co-authorship and productivity across different fields of scientific research[END_REF].

Considering these connections and the resulting publications within a research area adds an important component to evaluate team performance, inform about underlying networks and forecast the dynamics of the field. [START_REF] Milojevic | Accuracy of simple, initials-based methods for author name disambiguation[END_REF]. Articles with an arbitrary limit of 1000 authors were omitted from the analysis. TTA assigned a chronologic index (TI) according to the year of the first publication with alternating sign enabling a tree-like display of the workforce. Further, TTA attributed a color to each team and calculated parameters summarizing publication record, genealogy and collaborations. Genealogic relations were based on offspring -ancestor pairs, where offspring was defined as PIs that appeared initially as first authors on a publication of an ancestor team and that published subsequently as last (team) authors. Each offspring (first author; generation i+1) was assigned to the ancestor (last author; generation i) with the earliest common publication. Families were defined as progeny of first ancestors (i = 1) encompassing all subsequent generations. TTA derived collaborations based on co-authorship [START_REF] Newman | Coauthorship networks and patterns of scientific collaboration[END_REF]. Out-and in-degree connections specified how often a team listed other teams as co-authors and how often the same team was listed as co-author, respectively. The research fields selected for analyses are summarized in Table 1. Statistical analyses were performed using the indicated tests.

Figure 1 .Figure 2 .Figure 3 .

 123 Figure 1. Publication record and genealogic relations of the workforce driving a field of

Figure 4 .

 4 Figure 4. Workforce dynamics in selected fields of biomedicine

Figure 5 .

 5 Figure 5. TeamTree analysis of PubMed articles related to "coronavirus" and associated

.Figure 6 .

 6 Figure 6. Segregation of the workforce by specific factors.

.

  CC-BY-ND 4.0 International license was not certified by peer review) is the author/funder. It is made available under a The copyright holder for this preprint (which this version posted June 2, 2020. . https://doi.org/10.1101/2020.06.01.128355 doi: bioRxiv preprint Pfrieger, Workforce Analysis. Supplementary Information

  . CC-BY-ND 4.0 International license was not certified by peer review) is the author/funder. It is made available under a

	bioRxiv preprint	doi:	. https://doi.org/10.1101/2020.06.01.128355	this version posted June 2, 2020.	The copyright holder for this preprint (which

Table 1

 1

	; Fig. 2G;

  . CC-BY-ND 4.0 International license was not certified by peer review) is the author/funder. It is made available under a The copyright holder for this preprint (which this version posted June 2, 2020. . https://doi.org/10.1101/2020.06.01.128355 doi: bioRxiv preprint Pfrieger, Workforce Analysis 7 and how the workforce impacts field development, exemplary fields in biomedicine showing distinct dynamics were analyzed (Table

  . CC-BY-ND 4.0 International license was not certified by peer review) is the author/funder. It is made available under a

	bioRxiv preprint	doi:	. https://doi.org/10.1101/2020.06.01.128355	this version posted June 2, 2020.	The copyright holder for this preprint (which
					Pfrieger, Workforce Analysis
	MIT; A5: Microsoft: A6: Google; Table
				8

  . CC-BY-ND 4.0 International license was not certified by peer review) is the author/funder. It is made available under a

	bioRxiv preprint	doi:	. https://doi.org/10.1101/2020.06.01.128355	this version posted June 2, 2020.	The copyright holder for this preprint (which

  . CC-BY-ND 4.0 International license was not certified by peer review) is the author/funder. It is made available under a /cran.r-project.org/package=plot3D]. Code and additional data are available upon written request to the author. TTA was applied to lists of publications resulting from queries of bibliographic databases concerning topics of interest. PIs listed as last authors defined research teams. Last author names included initials to reduce author ambiguity

	bioRxiv preprint	doi:	. https://doi.org/10.1101/2020.06.01.128355	this version posted June 2, 2020.	The copyright holder for this preprint (which
					Pfrieger, Workforce Analysis
	Materials and Methods
	TTA was implemented by custom-written software with additional data analysis and
	visualization accomplished using the open source software R (R Core Team, 2019) and selected
	R packages [data.tree: Hammil et al., 2018 https://cran.r-project.org/package=data.tree;
	data.table: Dowle, 2019 https://cran.r-project.org/package=data.table; dunn.test: Dinno, 2017
	https://cran.r-project.org/package=dunn.test; eulerr: Larsson, 2019 https://cran.r-
	project.org/package=eulerr; ggplot2: Wickham, 2016; ggrepel: Slowikowski et al., 2018
	https://cran.r-project.org/package=ggrepel; igraph: Csardi and Nepusz (2006); plot3D: Soetaert,
	2017 https:/
				10

Table 1 . Summary of research areas analyzed.

 1

	Field / Discipline	Database Pubs / Teams / Year
	Alzheimer's Disease + Subtopics / Biomedicine	PubMed	37453 / 13837 / 1984
	Aplysia (APL) / Biomedicine	PubMed	4657 / 1577 / 1898
	Artificial Intelligence, Machine Learning +
		WOS	17410 / 9557 / 1962
	Affiliations / Computer Science
	Chirped Laser Pulses 1 / Physics	WOS	9437 / 4695 / 1969
	Circadian Clock 2 / Biomedicine	PubMed	17,023 / 6506 / 1960
	Climate + Journals / Multiple Disciplines	WOS	24125 / 16776 / 1883
	Coronavirus OR corona-virus OR "corona virus"
	OR covid-19 OR sars-cov OR "severe acute
		PubMed	19414 / 9346 / 1949
	respiratory syndrome" OR "Middle East
	respiratory syndrome"
	Cosmic Inflation 3 / Astronomy	WOS	9018 / 3510 / 1981
	Creutzfeldt Jacob Disease (CJD) / Biomedicine	PubMed	7950 / 4317 / 1946
	CRISPR-CAS (CRI) / Biomedicine	PubMed	14255 / 7738 / 2002
	Extracellular Vesicles (EVS) / Biomedicine	PubMed	13621 / 7034 / 1970
	Freeze-Fracture (FRE) / Biomedicine	PubMed	8981 / 4665 / 1961
	Ice core climate 4 / Geoscience	WOS	9727 / 5910 / 1956
	Ind. Pluripotent Stem Cells (IPS) / Biomedicine	PubMed	16286 / 7504 / 1967
	Quantum Computer 5 / Computer Science	WOS	29938 / 10575 / 1967
	Supramolecular Chemistry 6 / Chemistry	WOS	19409 / 7820 / 1985
	Gregory Y.H. Lip / Selected Team / Biomedicine PubMed	1802 / 335 / 1994

  Table 1). B, PC per year of indicated PIs and their associated teams detected by TTA of PubMed publications. C, Publication rates per year separating articles by contributions (First, first author; Col in, indegree collaborations; Col, out-degree collaborations without offspring; Off+Col, offspring with collaborators; Off, offspring without collaborators; Rest, all other publications). Insets, contributions of indicated categories to total publication output. D, Mean number of authors (AC; Q75 values) per article published each year.

	bioRxiv preprint	doi:	. https://doi.org/10.1101/2020.06.01.128355	this version posted June 2, 2020.	The copyright holder for this preprint (which
	was not certified by peer review) is the author/funder. It is made available under a	. CC-BY-ND 4.0 International license
						Pfrieger, Workforce Analysis
				23

Acknowledgments

The author thanks Drs. V. Demais, M. Muzet, V. Pallottini, H. Runz, and M. Slezak for helpful comments on previous versions of the manuscript.

Supplementary Information

Insight into the workforce advancing fields of science and technology Frank W. Pfrieger Email: fw-pfrieger@gmx.de or frank.pfrieger@unistra.fr Supplementary Data 1. Excel file with TeamTree data for selected fields of research.


M A Gaveau

M Mons

L Bruel

A Potapov
email: alexey.potapov@uni-jena.dee

A C Turner
email: acturner@berkeley.eduf

V Boudon

P Asselin
email: pierre.asselin@upmc.fr

Conformational Landscape of the SF 6 Dimer as Revealed by Various Free Jet Conditions

The experiment SPIRALES coupling a tunable infrared laser to a pulsed supersonic free jet has allowed us to reinvestigate the high-resolution absorption spectroscopy of the SF 6 dimer in the ν3 mode region under various experimental conditions. In addition to the SF 6 dimer spectrum so far reported, we demonstrate the existence of two new spectral features: i) one assigned to a second conformer of this species, characterized by a larger interatomic distance as well as ii) a complex (SF 6 ) 2 -He.

INTRODUCTION

Supersonic free jets are an ideal way to form and study van der Waals complexes [1], through the very fast cooling and rarefaction of the gas during the expansion leading to the stabilization of these isolated species. Of course, the formation of these complexes depends dramatically on the expansion conditions, i.e. the nozzle geometry, the nature of the gas mixture and the stagnation conditions (P 0 , T 0 ). Taking advantage of this sensitivity, we have used a series of different expansion conditions to explore the potential energy landscape of the SF 6 dimer with high resolution spectroscopy.

Low resolution spectra of the SF 6 dimer were first studied by photodissociation spectroscopy in the groups of Reuss [2] and coworkers and of Scoles et al. [3]. They used line-tunable cw CO 2 or N 2 O lasers to excite the dimers: In the first group, they monitored the depletion of the dimer while in the latter one they measured the dissociation fragments ejected off the beam axis. The absorption spectra of the dimer were not rotationally resolved and featured two broad bands respectively blue-shifted and red-shifted from the monomer band at 948 cm -1 .

The first high resolution absorption spectrum of the SF 6 dimer was obtained by Urban and Takami in a jet-cooled IR diode laser spectroscopy study [4]. The rotationally resolved spectrum was recorded in an axisymmetric free jet of 5% SF 6 in helium and stagnation pressure 7-8 bars at a distance of 20 mm from the nozzle (diameter D = 0.5 mm) with a rotational temperature of 1 K. In the SF 6 dimer, the triply degenerate ν 3 band of the monomer splits into two bands, the parallel and perpendicular bands respectively, centered at 934.01 and 956.10 cm -1 . A single conformer of (SF 6 ) 2 was observed. The rovibrational analysis of the // band allowed to determine ν//, the rotational constants B'' and B' and a vibrationally averaged S-S distance equal to 475.4 pm. Only one perpendicular band was observed and found to be consistent with a D 2d structure of the SF 6 dimer. This was in agreement with the work of van Bladel et al. who predicted that the most stable (SF 6 ) 2 had a D 2d symmetry in a simple atom-atom potential model [5]. Beu and Takeuchi used a more sophisticated intermolecular potential and obtained similar conclusions [6]. However, a second predicted structure of C 2h symmetry was not observed experimentally by Urban and Takami.

The high resolution absorption spectrum of small SF 6 clusters embedded in helium clusters has also been observed in the 10 micron region [7]. It displays a partially rotationally resolved structure at the very low temperature (0.37 K) of the helium clusters. The perturbation due to the helium environment is weak and the shift of the solvated dimer bands with respect to those of the free dimer is expected to remain small. However, even if the rotation of the small SF 6 clusters is nearly free in the helium droplet, the proximity of helium atoms leads to an increase of the moments of inertia, and therefore to a decrease of the rotational constants, which prevents determining structural information about the free dimer.

More recently, 2 or 3 conformers of the SF 6 dimer have been predicted to be nearly isoenergetic within 0.5 kJ/mole by quantum chemistry calculations [8].

As only one conformer had been detected experimentally, the spectrum of the SF 6 dimer has been reinvestigated at high resolution in the 10.5 micron region thanks to the SPIRALES setup [9].

EXPERIMENTAL SETUP

The SPIRALES experimental setup couples a tunable infrared laser and a pulsed supersonic free jet through multipass optics in order to record the absorption spectrum of rotationally cold species and weakly bound complexes that can be formed in the jet [10] [9]. The light source is a continuous wave external cavity quantum cascade laser (EC-QCL) with a spectral width of 10 MHz. It covers the 930-990 cm -1 range and its power is about 120 mW around 950 cm -1 . It can be scanned on a frequency range of 0.8 cm -1 at a repetition rate of 100 Hz allowing data acquisition with a very good detectivity. 8% of the laser light is sent through two laser channels for relative and absolute frequency calibration with an accuracy around 0.0001 cm -1 . The remaining light is sent through an astigmatic optical cavity aligned according to a 182-pass configuration mounted in the jet chamber (Fig. 1). The light intensity transmitted through the jet is measured by an HgCdTe detector in order to record the absorption spectrum. The SF 6 clusters were generated in pulsed supersonic expansions of He/SF 6 or He/SF 6 /Rg mixtures. The gas mixtures were prepared in a 10 l tank fitted to the gas lines. Their compositions were determined by the ratio of the partial pressures of different gases forming the mixture. In order to reinvestigate the conformational landscape of (SF 6 ) 2 , it was necessary to change the conditions of formation and stabilization of these clusters, and therefore the conditions of the expansion. This was achieved in three different ways:

• The supersonic free jet was generated from a pulsed nozzle (General Valve) with different geometries, either a 0.35 mm circular nozzle or a slit nozzle (30 mm length × l= 50 µm width). A good balance between efficient rovibrational cooling and high molecular density was found even at longer distances up to z = 18 mm for slit jet expansions (T R = 3 K) and at shortest distances (z = 12 mm) for pin hole jet expansions (T = 0.8 K). • The SF 6 dilution was varied from 0.25 to 4 % in He and also in ternary mixtures He/Rg/SF 6 .

• The stagnation pressure was varied up to 10 bars. For all the experiments, the reservoir and the nozzle were at room temperature. The stagnation pressure was measured by two sensors connected to the gas lines: a Baratron capacitance manometer for the pressure ranges up to 1 bar (precision: 0.1 mbar), and a transducer based on a piezo-resistive ceramic (precision: 0.1 bar) for the pressures between 1 and 10 bar. The absorption spectra have also been recorded at different distances from the nozzle between z = 9 and 18 mm, in order to probe different regions of the expansion. However, the region probed with the multipass cavity is relatively broad (about 10 mm) and corresponds to a large range of reduced distances z/D (z/l).

EXPERIMENTAL RESULTS

Slit Nozzle: Evolution of the Parallel Band Region of (SF 6 ) 2 with the SF 6 Concentration and Evidence for Three Different Features in He-SF 6 Expansions

We started our study by investigating the parallel band region of (SF 6 ) 2 (933-935 cm -1 ) in order to detect other spectroscopic signatures than the band detected by Urban and Takami [4]. Figure 2 presents the evolution of the parallel band region of the SF 6 dimer as a function of the SF 6 concentration (from 0.25 % for (a) to 4 % for (e)) in He for the slit expansion. The backing pressure is 6 bars, the distance from the nozzle 9 mm.

The experimental linewidth (100 MHz) is rather large for a planar expansion. If the slit nozzle was perfect, at the distance z = 9mm (z/l = 180), the translational temperature would be equal to about 17 K, corresponding to a Doppler width of only 7 MHz at 934 cm -1 for SF 6 (142 amu). But our slit has a finite length and is fixed to the front valve very close to the circular hole exit to limit the size of the buffer volume. As a consequence, the gas is not very well distributed over all the length of the slit and an expansion not quite planar is expected. In this case, residual Doppler broadenings take place, particularly important when using helium as a carrier gas: It is due to the fact that a streamline can be at an angle to the jet axis, and therefore π/2-θ to the laser direction. This has the effect of inducing a Doppler shift ∆ν = ν ο U sin(θ)/c for the transitions at frequency ν ο of a SF molecule flowing at speed U along this streamline. SF 6 is seeded in helium, the speed of which is about 1700 m/s at z = 9mm. Of course U is smaller than this value due to the velocity slip. If we take U = 1200 m/s then ∆ν = 32 MHz for θ = 20°, and 47 MHz for θ = 30°. The distribution of molecules along diverging streamlines can therefore explain the observed linewidths which nevertheless remain much smaller than in the case of axisymmetric expansions [11]. The band denoted #1 is in agreement with the high resolution spectrum of Urban and Takami [4]: It is composed of a Q Q(J,K) branch peaking at 934 cm -1 as well as well J-resolved Q P(J,K) and Q R(J,K) branches. The experimental linewidth (100 MHz) does not allow resolving the K substructure. On the red side of band #1, another feature is observed (noted #2 hereafter) at 933.58 cm -1 . This new spectral feature that was not detected previously presents well identified P, Q and R branches. The intensities of #1 and #2 begin to increase with the SF 6 concentration before dropping significantly above 1%. However, the ratio #2 / #1 increases continuously as the SF 6 ratio is increased: It suggests that the formation of #2 is favored with respect to that of #1 in these expansion conditions, i.e. with more between SF molecules and a slightly higher translational and rotational temperature. One can notice also the presence of a small satellite band (noted #3) on the red side of the feature #2 at small concentration of SF 6

Parallel Band Region of (SF 6 ) 2 : Comparison between Slit and Circular Nozzle Spectra

The absorption spectrum has also been obtained with the circular nozzle free jet: It also shows the 3 spectral features detected with the slit nozzle but with very different intensities. Figure 3 presents a comparison of spectra recorded in the axisymmetric and slit expansion, with a stagnation pressure of 6 bars. Figure 3 shows that the feature #3 becomes intense in the circular nozzle free jet and at low concentrations of SF 6 . This band, not previously detected, is located at 933.56 cm -1 and presents P, Q and R branches clearly visible in this case (Fig. 3a andb). On the contrary, the intensity of the feature #3 becomes much smaller in the slit nozzle expansion, even if it increases a little bit at larger distances from the nozzle exit (Fig. 3c). So, depending on the experimental conditions, the new spectral features (#2 and #3) can reach intensities that are comparable to that of the dimer feature #1. However, it seems that #2 and #3 are not favoured by the same expansion conditions: #3 appears mainly at low SF 6 concentrations and low temperature expansion (circular nozzle), whereas #2 is more intense at higher SF 6 concentrations and higher temperature (slit expansion). Moreover, these features are very close (933.58 cm -1 and 933.56 cm -1 for # 2 and #3 respectively) and #2 and #3 have to be assigned while taking into account these puzzling properties.

Rovibrational Band Contour Analysis and Assignments

The conformer #1 is similar to (SF 6 ) 2 previously observed by Urban and Takami [4]. The corresponding experimental spectra of the parallel and perpendicular bands are presented in red on Fig. 4. The simulated spectra are plotted in blue assuming a D 2d structure and a rotational temperature of 3 K. It allows to determine the rovibrational constants and the distance S-S equal to 474.2 pm in agreement with that previously (475 pm) measured. The rotational temperature of 3 K is fully consistent with the translational temperature (3.1 K) that can be calculated in a slit expansion at z/l = 180 for this mixture from the formula of H. Murphy [START_REF] Murphy | The effects of source geometry on free jet expansions[END_REF] [13], if we neglect the energy release due to nucleation. We have then to assign bands #2 and #3 knowing that they have maximal intensities for different experimental conditions and that the two features are very close in frequency. Several possibilities have to be considered for these assignments: Another conformer of the SF 6 dimer, the SF 6 trimer or larger multimer, or a complex with He as (SF 6 ) 2 -He. Actually, the simulation shows that #2 corresponds to a new conformer of the SF 6 dimer: Figure 5 presents the comparison of the experimental spectrum recorded at z = 9 mm in a slit nozzle expansion of 0.5% SF 6 in He (P 0 = 6 bars), with a simulation spectrum. The latter is the sum of the simulation spectrum of the (SF 6 ) 2 conformer #1 with a symmetry D 2d and a rotational temperature of 3 K, and the simulation spectrum of the (SF 6 ) 2 conformer #2 with a symmetry C 2h and a rotational temperature of 8K. The distance S-S in this second conformer is equal to 480.1 pm. It is interesting to note that the two rotational temperatures (3 and 8 K) used in the simulations are substantially different: Presumably because, with the multipass cell, the laser beam is able to probe several conformers present in different regions of the jet.

Concerning the band #3, despite the contour features P, Q, R branches, some details of the contour, especially on the R branch side, could not be properly reproduced by a simulated symmetric top transition, suggesting that the species responsible could not be a symmetric top. The rotational constants derived being nevertheless very similar to bands 1 and 2, but inconsistent with a homotrimer molecule, we are left with an assignment to a (SF 6 ) 2 -He trimer. This is consistent with the fact that the presence of the He atom is not expected to strongly affect the SF 6 dimer features: Neither the rotational constants, nor the vibrational spectral shift (-0.02 cm -1 ). A further support to this assignment has been found in complementary experiments which were performed by recording spectra at z = 9 mm in slit nozzle expansions of ternary mixture 0.5%SF 6 /Rg/He and Rg = He, Ne, Ar, Kr, Xe. Except for pure helium spectra, a SF 6 /Rg equimolar concentration is used. The backing pressure was P 0 = 6 bars. These spectra are shown on Fig. 6 (a). Each spectrum presents a specific spectral feature red shifted with respect to #2. The corresponding Q Q branches are marked by an asterisk. The vibrational shift of these branches with respect to the central frequency of #2 is plotted as a function of the polarizability α of the rare gas Rg: The experimental data can be perfectly fitted by a straight line and this linear dependence suggests that these spectral features are composed of the #2 dimer complexed with a single rare gas atom and similar structures. The fact that the vibrational shift of feature #3 follows this linear dependence, supports its assignment to a helium complex of the #2 dimer. On the contrary, the #1 dimer does not show any additional bands in its vicinity that could be assigned to a complex of the #1 dimer complexed with a rare gas atom. This seems to indicate a specific behavior in terms of complexation.

DISCUSSION AND CONCLUSION

In this experiment, two different conformers #1 and #2 of the SF 6 dimer were observed including one which had not been detected previously. The rovibrational analysis of the parallel band contours of the ν3 transition allows determining two distinct S-S distances for these conformers. A complex of He with the conformer #2 was also identified. This was only possible thanks to the versatility of the experimental setup that enables us to vary the free jet conditions, by using either a circular nozzle or a slit nozzle, by changing the SF 6 concentration of the gas mixture with He, by varying P 0 and the distance z from the nozzle exit to the location where the jet was probed or by adding another rare gas to the mixture. Indeed, the dynamics of their formation seems specific to each conformer. In the circular nozzle expansion, at high pressure and low SF 6 concentration, the fast cooling in the early expansion leads to the stabilization of dimer #1 and of the heterotrimer #3 with a low rotational temperature (0.8 K). As the concentration of SF 6 increases, an increased number of SF 6 -SF 6 collisions leads to the formation of some population of the conformer #2.

The slit expansion is slower and the collision rates are larger at the same reduced distance (z/l) than in the circular expansion (z/D), which apparently favors the formation of the conformer #2. There is also a conformational relaxation as proved by the decrease of the ratio between populations of #2 and #1 at large z (see Fig. 3 c andd). This also indicates a larger stability of #1 and a low energy barrier to interconversion. The larger stability of the conformer #1 is confirmed by its presence in all the experiments where SF 6 dimers are detected. As the concentration of SF 6 increases, the conformer #2 is strongly favored with respect to #1 (Fig. 2) by SF 6 -SF 6 collisions and SF 6 -dimer collisions suggesting a mode of formation of #2 significantly different from that of #1. Population of #2 is consistent with hot complexes formed in the early stages of the expansion by these collisions. This is also consistent with the two different rotational temperatures measured for the two conformers in the slit expansion.

A third band has been assigned to (SF 6 ) 2 -He trimer on the ground of complementary experiments with SF 6 :Rg:He mixtures that has allowed the assignment of all (SF 6 ) 2 -Rg trimers. The hetero trimers are formed from the conformer #2. It also suggests an interconversion between #1 and #2 consistent with the picture of a conformer #2 entropically favored in presence of rare gas collision partners.

FIGURE 1 .

 1 FIGURE 1. Scheme of the multipass cell mounted in the jet chamber

FIGURE 2 .

 2 FIGURE 2. EC-QCL absorption spectra of the parallel band of SF 6 dimer at various concentrations: (a) 0.25%, (b) 0.5%, (c) 1%, (d) 2%, (e) 4% of SF 6 diluted in helium. The stagnation pressure is 6 bars and the axial distance from the nozzle 9 mm. Reproduced from Phys. Chem. Chem. Phys. 19 (26), 17224 (2017) with permission from the PCCP Owner Societies.

FIGURE 3 .

 3 FIGURE 3. Comparison of spectra of the parallel band region of (SF 6 ) 2 obtained in the pinhole expansion (in blue) and in the slit expansion (in red) with a stagnation pressure of 6 bars, at low concentration of SF 6 and various distances from the nozzle exit: (a) pin hole nozzle at z = 12 mm with 0.25% SF 6 ; (b) pin hole, z = 12 mm, 0.5% SF 6 ; (c) slit nozzle, z = 18 mm, 0.5% SF 6 and (d) slit nozzle, z = 9 mm, 0.5% SF 6 . Reproduced from Phys. Chem. Chem. Phys. 19 (26), 17224 (2017) with permission from the PCCP Owner Societies.

FIGURE 4 .

 4 FIGURE 4. Conformer #1: Experimental spectra (in red) of the parallel (a) and perpendicular bands (b) compared to the corresponding simulated spectra in blue. The parallel and perpendicular bands of (SF 6 ) 2 have been recorded with 0.5% SF 6 seeded in 6 bar He at z = 9 mm using a slit nozzle.

FIGURE 5 .FIGURE 6 .

 56 FIGURE 5. Slit nozzle spectrum (bottom in red) of the parallel band of (SF 6 ) 2 recorded at z = 9 mm with 0.5% SF 6 in 6 bar He. It is compared to the total simulation spectrum (in blue) which is the sum of the simulated spectrum of conformer #1 (D 2d symmetry, 3 K, in purple) and conformer #2 (C 2h symmetry, 8 K, in green). Reproduced from Phys. Chem. Chem. Phys. 19 (26), 17224 (2017) with permission from the PCCP Owner Societies .

ACKNOWLEDGMENTS

AP acknowledges COST ActionCM1401 ''Our Astro-Chemical History''.