dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding The study was supported by the  Recherche Hospitalo-Universitaire RHU-COVIFERON  project under the program " Investissement d'Avenir " launched by the  French Government  and implemented by the  Agence Nationale de la Recherche (ANR)  with the reference  ANR-21-RHUS-08  and by the  EU Horizon 101057100   UNDINE  project (JLC and GG). The  Laboratory of Human Genetics of Infectious Diseases  is supported by the  Howard Hughes Medical Institute , the  Rockefeller University , the  St. Giles Foundation , the  National Institutes of Health (NIH)  ( R01AI088364  and  R01AI163029 ), the  National Center for Advancing Translational Sciences (NCATS) , the  NIH Clinical and Translational Science Award (CTSA) program  ( UL1 TR001866 ), a  Fast Grant  from  Emergent Ventures ,  Mercatus Center at George Mason University , the  Yale Center for Mendelian Genomics  and the  GSP Coordinating Center  funded by the  National Human Genome Research Institute (NHGRI)  ( UM1HG006504  and  U24HG008956 ), the  Yale High-Performance Computing Center  ( S10OD018521 ), the  Fisher Center for Alzheimer's Research Foundation , the  Meyer Foundation , the  French National Research Agency (ANR)  under the  "Investments for the Future" program  ( ANR-10-IAHU-01 ), the  Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence  ( ANR-10-LABX-62-IBEID ), the  French Foundation for Medical Research (FRM)  ( EQU201903007798 ), the  FRM  and  ANR   GENCOVID  project, the  ANRS-COV05, ANR GENVIR  ( ANR-20-CE93-003 ) and  ANR AABIFNCOV  ( ANR-20-CO11-0001 ) projects, the  European Union 's  Horizon 2020 research and innovation program  under grant agreement on.
Physical Measurements Melting points were recorded on a Kofler hot block Heizbank type 7841 and were uncorrected. The structures of the products were checked by comparison of Nuclear Magnetic Resonance (NMR), Infrared (IR) and Mass spectrometry (MS) data and by the TLC Colorectal cancer (CRC) is the third most common cancer-related death after prostate and lung cancer in western societies, with more than half a million annual deaths worldwide. The incidence is high and is steadily increasing. In France, CRC is the second leading cause of death (17,117 deaths/year)  [26, 27]. The current therapies used in the clinic of CRC still have several side effects and resistance issues. Therefore, there is an urgent need to find novel therapeutical alternatives to reduce these drawbacks. We described previously the synthesis of ferrocenyl DAM using McMurry cross coupling and the antibacterial activity evaluation  [2, 28].
Physical Measurements Melting points were recorded on a Kofler hot block Heizbank type 7841 and were uncorrected. The structures of the products were checked by comparison of Nuclear Magnetic Resonance (NMR), Infrared (IR) and Mass spectrometry (MS) data and by the TLC behavior. 1 H-and 13 C-NMR spectra were recorded on a Bruker BioSpin GmbH spectrometer 400 MHz, at room temperature. Chemical shifts are reported in δ units, parts per million (ppm). Coupling constants (J) are measured in hertz (Hz). Splitting patterns are designed as follows: s, singlet; d, doublet; dd, doublet of doublets; m, multiplet; t, triplet; td, triplet of doublet; ddd: doublet of doublet of doublet. Distortionless enhancement with polarization transfer (DEPT) experiments and various 2D techniques such as COr-relation SpectroscopY (COSY), Heteronuclear Single Quantum Coherence (HSQC) and Heteronuclear Multiple Bond Correlation (HMBC) were used to establish the structures and to assign the signals.
Molecular Modeling 2. 7. 1. Protein and Compounds Preparation The protein structures of the five potential targets were extracted from the Protein Data Bank (PDB):  [31]  AKT (PDB ID: 6S9W), ERK-1 (PDB ID: 4QTB), ERK-2 (PDB ID: 6SLG), PARP (PDB ID: 4ZZZ) and caspase-3 (PDB ID: 6CKZ). The structure of ERK-2 was superimposed on the structure of ERK-1 to allow comparison of the binding modes obtained on these two isoforms. All the structures were prepared using the DockPrep tool from UCSF Chimera  [32]  and MGL tools  [33]. Three dimensional structures of compounds 12a and 10a were generated using iCon  [34] , the LigandScout v. 4. 3. conformer generator (defaults settings of the BEST option were used, except for the maximum number of conformations that was setted to 2000). Compounds were protonated at pH 7.
ADME Profile and Drug-Likeness Predictions of ADME properties and drug-likeness for the most promising compounds were conducted in SCHRÖDINGER Maestro v11. 9 software, using the Molecular Properties Panel from QikProp v5. 9 platform  [37]. Structure Minimization was performed using Force field OPLS3e,  [38]  the Polak-Ribier method of Conjugate Gradient (PRCG), with a convergence threshold of 0. 05 and a maximum of 2500 iterations.
Normal Cell Line Viability Cell viability of L929 cell line (murine fibroblasts) was evaluated in presence of synthesized compounds using the MTT colorimetric assay. At the concentrations evaluated (between 0. 78 and 100 µ M), all compounds have no effect on the cell viability of these normal cells. The figures and graphs concerning these results are reported in the supplementary data section (SI-2). On human CRC HT-29 cells, compounds 10a and 10b showed a slight DNA fragmentation compared to the control. However, significant fragmentation was observed with compound 12a at 24 and 48 h, 2. 90-and 3. 25-fold, respectively compared to the control (Figure  7A ). On human CRC HCT116 cells, no significant DNA fragmentation was observed after 24 h showing only 1. 46-fold with compound 12a compared to the control.
Molecular Modeling In order to study the potential binding mode of compounds 10a and 12a in the biological targets previously identified, a docking approach was used. For AKT (PDB ID: 6S9W), the predicted binding modes obtained for compounds 10a and 12a were quite identical and also very similar to a part of the co-crystallized ligand (Figure  8A ). Indeed, our compounds were predicted to share similar hydrophobic interactions with the residues of the AKT binding site (with W80, L264, V270 and Y272) and πstacking (W80), than the co-crystallized ligand (Figure  8B ). Additionally, to these shared interactions, our compounds 10a and 12a are predicted to establish a hydrogen bond (HB) with K268. Biomolecules 2022, 12, x FOR PEER REVIEW 25 of 32.
Molecular Modeling In order to study the potential binding mode of compounds 10a and 12a in the biological targets previously identified, a docking approach was used. For AKT (PDB ID: 6S9W), the predicted binding modes obtained for compounds 10a and 12a were quite identical and also very similar to a part of the co-crystallized ligand (Figure  8A ). Indeed, our compounds were predicted to share similar hydrophobic interactions with the residues of the AKT binding site (with W80, L264, V270 and Y272) and πstacking (W80), than the co-crystallized ligand (Figure  8B ). Additionally, to these shared interactions, our compounds 10a and 12a are predicted to establish a hydrogen bond (HB) with K268. We also evaluated the potential binding mode of our compounds 10a and 12a in the two isoforms of the ERK proteins, namely ERK-1 and ERK-2.
The binding site of these two proteins are highly similar, however the predicted binding mode in these proteins were dissimilar (Figure  9 ). In the ERK-1 binding site (PDB ID: 4QTB), our compounds are predicted to present different binding modes (Figure  10A ) but with some similarities. Indeed, compounds 10a and 12a are both predicted to be able to establish hydrophobic interactions with ERK-1 residues, among which Y53, K71 and L173, are also involved in hydrophobic interactions with the 4QTB co-crystallized ligand. Additionally, compound 12a predicted binding mode includes a halogen bond with the ERK-1 binding site residue E88, which is engaged in a salt bridge with the 4QTB co-crystallized ligand (Figure  10B ). For compound 10a, We also evaluated the potential binding mode of our compounds 10a and 12a in the two isoforms of the ERK proteins, namely ERK-1 and ERK-2.
The binding site of these two proteins are highly similar, however the predicted binding mode in these proteins were dissimilar (Figure  9 ). In the ERK-1 binding site (PDB ID: 4QTB), our compounds are predicted to present different binding modes (Figure  10A ) but with some similarities. Indeed, compounds 10a and 12a are both predicted to be able to establish hydrophobic interactions with ERK-1 residues, among which Y53, K71 and L173, are also involved in hydrophobic interactions with the 4QTB co-crystallized ligand. Additionally, compound 12a predicted binding mode includes a halogen bond with the ERK-1 binding site residue E88, which is engaged in a salt bridge with the 4QTB co-crystallized ligand (Figure  10B ). For compound 10a, In the ERK-1 binding site (PDB ID: 4QTB), our compounds are predicted to present different binding modes (Figure  10A ) but with some similarities.
Indeed, compounds 10a and 12a are both predicted to be able to establish hydrophobic interactions with ERK-1 residues, among which Y53, K71 and L173, are also involved in hydrophobic interactions with the 4QTB co-crystallized ligand. Additionally, compound 12a predicted binding mode includes a halogen bond with the ERK-1 binding site residue E88, which is engaged in a salt bridge with the 4QTB co-crystallized ligand (Figure  10B ). For compound 10a, additional interactions are π-stacking with Y53 and π-cation interaction with K71 (Figure  10C ). additional interactions are π-stacking with Y53 and π-cation interaction with K71 (Figure  10C ). In the ERK-2 binding site (PDB ID: 6SLG), our compounds are predicted to interact in a similar way, but differently from both the co-crystallized ligand (Figure  11A ) and as previously mentioned, the predicted ERK-1 binding modes.
In the ERK-2 binding site (PDB ID: 6SLG), our compounds are predicted to interact in a similar way, but differently from both the co-crystallized ligand (Figure  11A ) and as previously mentioned, the predicted ERK-1 binding modes. Our compounds are predicted to interact with the ERK-2 binding sites (Figure  11B ) through numerous hydrophobic interactions (with residues A18, Y19, V22, K37, I39 and D150) and hydrogen bonds (with residues Y19, K37 and R50). additional interactions are π-stacking with Y53 and π-cation interaction with K71 (Figure  10C ). In the ERK-2 binding site (PDB ID: 6SLG), our compounds are predicted to interact in a similar way, but differently from both the co-crystallized ligand (Figure  11A ) and as previously mentioned, the predicted ERK-1 binding modes. Our compounds are predicted to interact with the ERK-2 binding sites (Figure  11B ) through numerous hydrophobic interactions (with residues A18, Y19, V22, K37, I39 and D150) and hydrogen bonds (with residues Y19, K37 and R50).
Figure13. Predicted binding modes of compounds 10a (in salmon) and 12a (in orange) in the caspase-3 binding site (PDB ID: 6CKZ, 1GFW) (A) in comparison with the 6CKZ co-crystallized ligand (in blue) and the 1GFW co-crystallized ligand (in cyan) and (B) with detailed information about the interactions established between our compounds and the caspase-3 binding site obtained using PLIP [52]  (green dotted lines: πstacking; grey dotted lines: hydrophobic).
Acknowledgments: Authors gratefully thank  CAMPUS France  ( PHC Program ,  UTIQUE 2017 37094W ) and the  Ministère de l'Enseignement Supérieur et de la Recherche Scientifique of Tunisia  for the financial support. A. Hadj Mohamed  gratefully thanks excellence Eiffel program 2019-2020 for her PhD scholarship 964896H. The authors also thank the  UTIQUE team  for followed this work. The authors would also like to thank  Jean François Zagury , for allowing the cell viability studies to be carried out in the Peptinov start-up.
1 Figure 8. 18 Figure 8. Predicted binding modes of compounds 10a (in salmon) and 12a (in orange) in the AKT binding site (PDB ID: 6S9W) (A) in comparison with the 6S9W co-crystallized ligand (in blue) and(B) with detailed information about the interactions established between our compounds and the AKT binding site obtained using PLIP [52]  (plain line blue: HB; green dotted lines: πstacking; grey dotted lines: hydrophobic.
1 Figure 10. 110 Figure 10. Predicted binding modes of compounds 12a (in orange) and 10a (in salmon) in the ERK-1 binding site (PDB ID: 4QTB) (A) in comparison with the 4QTB co-crystallized ligand (in cyan) and with detailed information about the interactions obtained using PLIP [52] (green dotted lines: πstacking; orange dotted lines: πcation interactions; grey dotted lines: hydrophobic; green line: halogen bond) and established between the ERK-1 binding site and (B) compounds 12a or (C) compound 10a.
1 Figure 11. 111 Figure 11. Predicted binding modes of compounds 10a (in salmon) and 12a (in orange) in the ERK-2 binding site (PDB ID: 6SLG) (A) in comparison with the 6SLG co-crystallized ligand (in blue) and (B) with detailed information about the interactions established between our compounds and the ERK-2 binding site obtained using PLIP [52] (grey dotted lines: hydrophobic, blue lines: HB).
1 Figure 12. 112 Figure 12. Predicted binding modes of compounds 12a (in orange) and 10a (in salmon) in the PARP binding site (PDB ID: 4ZZZ) (A) in comparison with the 4ZZZ co-crystallized ligand (in blue) and(B) with detailed information about the interactions established between our compounds and the PARP binding site obtained using PLIP [52]  (plain line blue: HB; green dotted lines: πstacking; grey dotted lines: hydrophobic.
Biomolecules 2022 , 32 Figure 12. 20223212 Figure 12. Predicted binding modes of compounds 12a (in orange) and 10a (in salmon) in the PARP binding site (PDB ID: 4ZZZ) (A) in comparison with the 4ZZZ co-crystallized ligand (in blue) and (B) with detailed information about the interactions established between our compounds and the PARP binding site obtained using PLIP [53] (plain line blue: HB; green dotted lines: π-stacking; grey dotted lines: hydrophobic.
1 Figure 13. 113 Figure13. Predicted binding modes of compounds 10a (in salmon) and 12a (in orange) in the caspase-3 binding site (PDB ID: 6CKZ, 1GFW) (A) in comparison with the 6CKZ co-crystallized ligand (in blue) and the 1GFW co-crystallized ligand (in cyan) and (B) with detailed information about the interactions established between our compounds and the caspase-3 binding site obtained using PLIP [52]  (green dotted lines: πstacking; grey dotted lines: hydrophobic).
Acknowledgments The authors are grateful to  T. Aubert ,  W. Chen ,  B. Dierre ,  F. Dorson ,  M. Dubernet ,  K. Harada ,  O. Makrygenni ,  C. Neaime ,  N. Nerambourg ,  N. Saito ,  F. Sciortino  and  G. Truong , who made a part of their internship, PhD or postdoc work on the "nanocomposites based on MC" research project. The authors deeply thank  Dr. Y. Matsui ,  T. Takei  from  NIMS  and  M. C. Derouet ,  M. V. Dorcet , M. F. Gouttefangeas,  Ms V. Le Cam  and  M. S. Paofai  from  Rennes Univ. ( UR1 ) for their technical assistance support. The authors thank  Prof. Y. Bando ,  Prof. H. Haneda ,  Prof. K. Kimoto ,  Dr. Y. Matsushita ,  Prof. T. Mori ,  Dr. N. Shirahata  from  NIMS  and  MANA-NIMS ,  Prof. S. Ravaine  from  Bordeaux Univ. ,  Prof. K. Lang  and  Dr. K. Kiracki  from the  Institute of Inorganic Chemistry of the Czech Academy of Sciences ,  Dr M.
ACKNOWLEDGMENTS The authors thank the experts involved with the  European Medicines Agency (EMA) Committee for Medicinal Products for Human Use (CHMP)  who participated in the Broad Scientific Advice request. The published information is based on EMA feedback received during the Broad Scientific Advice request. EMA/CHMP have not been involved in the drafting or review of the manuscript to be published. This publication does not constitute a formal EMA/ CHMP endorsement of the manuscript. Medical writing support was provided by  Linda Edmondson , independent medical writer, funded by  ESOT.
Recommendation 2: skin ultrasound should be performed at the standardised areas used in the mRSS In general, relevant publications followed the 17-point dermal ultrasound scoring system proposed by Moore et al  21  in 2003, with a complete or reduced list of skin sites assessed. This approach, including exactly adherence to the examination sites, in endorsed by the task force. In fact, ultrasound evaluations of different skin sites cannot be combined into larger databases for detailed analysis, comparison with mRSS or longitudinal evaluation. However, authors are free to analyse other sites deemed preferable for some reason. Examining all 17 sites is time consuming and perhaps unnecessary, but the best trade-off of validity and This was prioritised on the research agenda (table  2 ).
Bins are created by partitioning the frequency range into ten intervals of equal log frequency width. The abscissa represents occurrences of a word per one million words (log scale). Data is based on the SUBTLEX-US database  (Brysbaert and New, 2009). Musca  and Chemero 10. 3389/fpsyg. 2022. 940950  of experimental associations formed during the study. Because HF words have stronger associations than LF words, pure HF lists are more easily recalled than pure LF lists. Moreover, as LF words in mixed lists are cued by HF words, there is a better recall of LF words in mixed lists as compared to pure LF lists (which would explain why often no WF effect is found in mixed lists). Finally, because LF words are less effective cues than HF words, HF words are easier to recall in pure HF lists than in mixed lists.
Firstly, we do not contend that FR performance is based on the computation of SP in the brain. Crucially, we assume no computation -of the kind we will detail below when constructing our SP variable -is carried out implicitly or explicitly by the participants. Secondly, the SP value for a to-be-remembered word on a list is not a transformation of that word's frequency value. This is to say, that while for a given WF database, the WF value associated with a word is a fixed number 4  , the very same word will have SP values that will be low or high depending on which other words are in (and which are absent from) that list, and also on its order of occurrence with respect to the other words of the list. This latter point is made more manifest in the following presentation of the computation of SP.
Considering an experiment in which the words in the experimental lists are French nouns in singular form, we would take as a reference all nouns in French in the singular form (hereafter called only 'words' , for simplicity's sake) of all frequencies 5. There are 24,530 French nouns in the singular form in the database we used, LEXIQUE  (New et al. , 2001 (New et al. , , 2004)) , the reference database for frequency of use of words in adults in French. Next, one must ensure the distribution of our reference population of words follows a Zipfian distribution, that is, the relation between the ranks of the words and their frequency of use follows a decaying exponential law. This was indeed the case, as a decaying exponential model explains 99. 84% of the variance in the data (p < 0. 0001). Importantly, the computation of SP takes into account the words so far presented to a participant in the experiment, which means a word's SP value depends on that word's position in the list.
If participants' cognitive systems are not sensitive to the discrepancies that exist between the WF distributions in the language and in the experimental lists, SP will not be a predictor of FR performance. This is a perfectly valid prediction despite being a null hypothesis prediction because we will carry out all statistical analyses in a Bayesian framework that allows for validating the null hypothesis if the model derived from it fits the data best. In this case, if WF is indeed a genuine predictor of FR performance, if it exerts its influence in a way different from what we propose here, the effect of WF should manifest itself. On the other hand, if SP explains FR performance, that is, there is no WF effect above and beyond that of SP, then one must conclude that the manipulation of WF does not have a direct effect on FR performance.
Considering as a predictor SP yields a model (M16) that is better than the null model (M0). The other word descriptor that seems to explain the performance is animacy (cf. M17), with a better recall for animate than for inanimate things (39. 56% vs. 24. 37% mean percent recall). A model that includes both predictors (i. e. , SP and Anim) under an interactive effect assumption (M18) does not account better for the data than the model that supposes only an effect of SP (i. e. , M16). The same is true for a model that includes both predictors under an additive effect assumption (M19) 8. To summarize, no word descriptor among those considered -including WF -other than SP explains the FR performance in this experiment. The best model involving adult WF as a predictor (although, again, not better than the null model), Resp∼(1| Subj)+log 10 WFA, has a poor fit to the data (this conclusion is confirmed by the inspection of Figure  3A ).
3,500 ms) effect, or an interactive effect of these. As can be seen from the comparison of models M0 to M4, the two lists are equivalent and FR performance does not differ between the two presentation times that were used. Indeed, the model includes List as a predictor (M1), the model including presentation time (PTime) as a predictor (M2), the additive model including List and presentation time as predictors (M3), and the interactive model including List and presentation time as predictors (M4) all have a less good fit to the data than the null model (M0). Therefore, all subsequent analyses are carried out on all data, irrespective of the list and word presentation time. The next analyses concern the existence of order effects. The fit of a linear order model (M5) is not better than that of the null model (M0).
The model including animacy as a predictor (M7) has a less good fit to the data than the reference model (M6), which means that animacy cannot explain the FR data. The same is true for conceptual familiarity (models M8 and M9) and for word length (number of phonemes; models M10 and M11). The models considering as a predictor adult WF, a linear version (M12) or its log-transformation (M13), are both better than the reference model, with the latter being the best of them (see Figure  4A ), BF 10 = 13. 67 ("positive evidence":  Jeffreys, 1961; Raftery, 1995). The model considering an effect of SP (M14) fits the data better than the model of reference (M6) too. As in Experiment 1, the relationship between SP and FR is one such as the higher the Bayesian surprise, the higher the probability of subsequent recall of a word (see Figure  4B ).
For words' age of acquisition and child WF, there are also models that fit better the data than the model of reference: a quadratic effect model of words' age of acquisition (M17) fits the data better than models considering a linear effect (M15 and M16); the same is true for child WF, with the best model being M21 -the other models being M18, M19, and M20. Log-transformed adult WF and SP are not the only word descriptors that explain (part of) FR performance when considered alone in a model (in addition to the quadratic order effect). The other candidates are i) a quadratic effect of the age of acquisition, and ii) a quadratic effect of the log-transformed child WF. In order to test which of these effects are genuine and which are not, we first created a model that includes them all and then pruned it, that is, took away one predictor at a time from that model.