dataesr/scientific-paragraphs-categorization huggingface dataset:
Based on this approach, selected component Gaussians are interpreted to reflect the timing of moraine deposition or initial stabilisation. In contrast, younger component Gaussians may reflect post-depositional processes (e. g. moraine degradation, boulder exhumation or instability) while older component Gaussians likely incorporate pre-depositional processes (e. g. reworking of glacial deposits). was significantly different from a random distribution. For datasets that are non-random (p < 0. 05), local Moran's I was used to identify the location of statistically significant boulder clusters (Fig. 6 ). Current sampling approaches are based on the qualitatively-sound but quantitatively-untested assumptions that (i) the distribution of "good" boulders is non-random, and that (ii) "good" clusters are more likely on moraine crests.
Sensitivity analysis The above analyses provide important information on the relative occurrence and spatial clustering of "good" and "bad" boulders for moraines of varying age and morphology. However, this logistic classification is ultimately dependent on the calculated landform age, which will vary depending on the choice of numeric bandwidth estimator and the size and clustering of the input dataset  (Dortch et al. , 2021). To evaluate the reproducibility of our results, sensitivity testing was performed to evaluate the number of samples required to reproduce the estimated landform age based on 1σ and 2σ thresholds. The full analytical approach is described in the Supplementary Information and the results are presented in Fig. 7B.
SH-calibrated exposure ages There is a strong correlation between recalculated 10 Be ages and their corresponding SH R-values (Fig. 4 ; n = 54). Moreover, of the 15 10 Be and 36 Cl dated boulders re-sampled with the SH, the majority closely match the existing calibration dataset (n = 13). These observations indicate that when lithological variation is minimised, the relative degree of rock surface weathering can be used as a proxy for surface exposure age. Exceptions to this correlation are samples ECH03 (17. 2 ± 3. 5 ka) and ECH04 (16. 8 ± 3. 3 ka) from the Soum d'Ech moraines  (Rodés, 2008)  which are significantly more weathered (∼38 R) than their corresponding 10 Be ages would predict (∼47 R). This difference likely reflects sub-surface weathering prior to boulder exhumation. However, the scale of this influence is unlikely to be universal given the close correspondence between sample ECH01 (19.
7 ± 3. 6 ka) and the existing calibration dataset (see Fig. 4 ). While sub-surface weathering of boulders under thin soil cover (∼25 cm) can occur  (Darmody et al. , 2005) , boulders are often protected from weathering by sediment burial, as evidenced by the emergence of unweathered boulders from glacial tills and alluvium  (Ehlmann et al. , 2008). In turn, as SH-calibrated exposure ages from the Soum d'Ech moraines may well incorporate the effects of both sub-aerial and sub-surface weathering, and could also be influenced by weathering rate variability (e. g. differences between the Atlantic-(wet) and Mediterranean-influenced (dry) Pyrenees), it is possible that the estimated depositional age is an overestimate.
As the temporal distribution of SH-calibrated exposure ages is near identical (Table  2 ), we assign these deposits a landform age of 27. 3 ± 1. 8 ka based on P-CAAT (n = 100; STD / IQR bandwidth estimator; Numeric bandwidth = 0. 9877, R 2 = 0. 9989, p < 0. 01), and perform subsequent analyses on the combined dataset for computational ease. While this estimate is significantly older than the corresponding 10 Be ages (16. 8 ± 3. 3 ka, 17. 2 ± 3. 5 ka, 19. 7 ± 3. 6 ka;  Rodés, 2008) , it is consistent with limiting 14 C ages obtained from a proximal palaeolake sediment sequence at Lac de Lourdes  (Reille and Andrieu, 1995). While the oldest radiometric 14 C ages from this over-deepened glacial basin are now considered suspect due to contamination from mineral carbon  (Pallàs et al. , 2006) , a younger AMS 14 C age from glaciolacustrine clays suggests initial ice-free conditions by 24.
This interpretation is supported by sensitivity analysis (see Section 3. 5), as the number of TCN ages (n = 3) is below the threshold required to consistently reproduce the landform age derived from the full dataset at 2σ (n = 11) and 1σ (n = 41). In the Val de Molières catchment of the Noguera Rigaborçana, recalculated 10 Be ages on the Outer Pleta Naua moraine range from 12. 6 ± 1. 5 ka to 13. 2 ± 1. 6 ka (n = 3;  Pallàs et al. , 2006). These estimates are consistent with the SH-calibrated exposure ages, which range from 11. 8 ± 2. 0 ka to 13. 1 ± 2. 0 ka (n = 60). As the SHcalibrated exposure ages conform to a normal distribution (Fig. 5B ; Shapiro-Wilk test, W = 0. 96, p = 0. 07), are well-clustered (IQR = 0. 6 ka), and return an excellent P-CAAT model fit with a single com-  In turn, the Outer Pleta Naua moraine was likely deposited at 12.
Sensitivity results Based on the sensitivity analysis described in Section 2. 6, there are clear differences in the number of SH samples required to reproduce the landform ages obtained from the full datasets (n = 60 -275; Fig. 7B ). The Outer Pleta Naua landform age requires only three samples at both 1σ and 2σ. Landform ages for both the Arànser left and right moraines can be reproduced with relatively few samples at both 1σ (n ≤ 26) and 2σ (n ≤ 16), while both the Soum d'Ech and Tallada moraines require ≥ 40 samples to reproduce the landform age at 1σ. These trends are largely explained by the degree of overlap between component Gaussian distributions (see Fig. 5 ). Both the Tallada (Fig. 5A ) and Soum d'Ech moraines (Fig. 5E ) feature lower probability component Gaussians, centred on 4.
7 ± 0. 9 ka and 24. 4 ± 1. 7 ka respectively, which overlap with the highest probability component Gaussian. In contrast, there is minimal overlap between component Gaussians for the Arànser left moraine (Fig. 5C ), despite the high degree of dataset skew and the large number of "bad" boulders (44%). The Arànser right moraine is intermediate in character (Fig. 5D ), with clear unidirectional skew but a greater degree of overlap between the highest probability Gaussian (22. 3 ± 0. 9 ka) and younger lower probability component Gaussians (17. 6 ± 2. 9 ka; 20. 9 ± 0. 9 ka). This distribution explains the larger number of samples required at both 1σ and 2σ relative to the Arànser left moraine. Ultimately, as the degree of overlap between component Gaussians increases, more samples are required to isolate the highest probability component Gaussian and eliminate PDE skew.
Despite this, all landform ages could be reproduced with relatively few samples at both 1σ (n ≤ 40) and 2σ (n ≤ 26). While these values exceed typical sample size recommendations for TCN dating  (Putkonen and Swanson, 2003) , they are based upon strict thresholds (≥ 95% of simulated landform ages within 1σ or 2σ of the full dataset landform age) and should be utilised by researchers when pre-screening a larger population of boulders prior to targeted TCN sampling.
Discussion Efforts to minimise sampling bias of moraine TCN datasets may significantly improve the utility of moraine chronologies in determining glacial history and the climatic drivers of glacial cycles. However, while careful geomorphological assessment of individual boulders is necessary to isolate those influenced by pre-or post-depositional processes, many criteria for TCN sample selection have not been tested quantitatively. Of these, boulder location is traditionally thought to be critical, as moraine crest boulders are prioritised due to perceived stability (e. g. Gosse et al. , 1995; Hallet and Putkonen, 1994) , while those deposited on ice-proximal or -distal slopes are typically rejected. This study is the first to quantitatively assess this approach. Based on 10 Be (n = 10) and Schmidt hammer sampling (n = 635) of ice-marginal moraines in the Pyrenees, it is clear that the spatial distribution of SH-calibrated exposure ages is both complex and site-specific.
9 ka; Skew = -1. 02) and Arànser right moraines (IQR = 6. 9 ka; Skew = -1. 13) and this trend may be partially explained by forest growth and boulder toppling  (Ivy-Ochs et al. , 2007) , as well as the effects of fluvial incision (Fig. 6 ), while historic land use may also play a role  (Pallàs et al. , 2010). In contrast, the boulder-rich, matrix-poor Outer Pleta Naua moraine stabilised rapidly after glacial retreat, as evidenced by the distribution and clustering of both its SH-calibrated exposure ages (IQR = 0. 6 ka; Shapiro Wilk W = 0. 96, p = 0. 07) and the corresponding 10 Be dataset  (Pallàs et al. , 2006). The sedimentology of the Outer Pleta Naua moraine is likely a function of catchment size and glacier area, and the short transport distance from the bedrock source area (Fig. 3 ; ≤ 300 m).
However, given that it is not possible to ascertain the underlying distribution a priori, a relatively large sample size is ultimately required. For most landforms, sampling a minimum of ∼30 boulders would be a reasonable approach to estimate a depositional age within 2σ (n ≥ 23), but more would be required (n ≥ 40) to improve precision to 1σ for complex datasets or if Schmidt hammer R-values were being used as a basis for cosmogenic nuclide sample selection  (Tylmann et al. , 2018). Collecting a minimum of 30 -40 samples is necessary to ensure a full understanding of the underlying age distribution, even for complex datasets. Based on this preliminary sampling, statistical approaches could be used to isolate component Gaussian distributions  (Dortch et al. , 2013 (Dortch et al. , , 2021) )  and to identify individual boulders which are consistent with the age of the landform and to reject those which are "young" or "old"  (Heyman et al.
Dortch et al. , 2021)he sampled moraines. Method used for kernel density estimation (seeDortch et al. , 2021)and its associated numeric bandwidth. c All model p values < 0. 01. d Reported uncertainty (±) is the 1σ bounds (68%) of the highest probability component Gaussian, unless stated otherwise. e Interquartile range. f Shapiro-Wilk test for normality p values. g Based on the landform age ± 2σ. h Arithmetic mean of 60 samples ± total uncertainty. i Calculation based on a reduced dataset of 274 samples. Sample ARL-192 (1. 97 ± 2. 06 ka) is more than three standard deviations from the mean of the remaining samples and was removed for program stability.
Acknowledgements MT was funded by a  University of Manchester President 's  Doctoral Scholar Award. The fieldwork for this paper was supported by a  British Society   for Geomorphology Postgraduate Research grant  awarded to MT. 10 Be analysis was performed at the  ASTER AMS national facility (CEREGE) , which is supported by the  CNRS/INSU , by the  ANR , through the  "projets thématiques d'excellence" programme for the "Equipements d'excellence" ASTER-CEREGE action , and by the  Institut de Recherche pour le Développement (IRD). MT, JD, PH and RP designed the project, with refinements from IB and CD. MT, JA and AS conducted Schmidt hammer sampling. 10Be sampling and analysis was undertaken by RP, AR, DB, VR, VJ, LRR and RC. MT and JH conducted spatial analysis. MT, JH and TB improved error propagation.
5 44. 53 ± 0. 74 SAL-07 10 Be 42. 4229 1. 6415 1896 16. 7 0. 5 1. 4 47. 43 ± 0. 96 SAL-08 10 Be 42. 4223 1. 6447 1863 17. 1 0. 6 1. 4 47. 7 ± 0. 9 SAL-09 10 Be 42. 4215 1. 6481 1820 20. 7 0. 9 1. 7 44. 77 ± 0. 8 SAL-10 10 Be 42. 4213 1. 6489 1808 22. 4 0. 7 1. 8 43. 03 ± 0. 95 PIR-11-13 36 Cl 42. 4213 1. 6495 1809 18. 2 1. 6 2. 1 - PIR-11-14 36 Cl 42. 4209 1. 6499 1805 17. 3 1. 7 2. 2 47. 6 ± 0. 83 Soum d'Ech e ECH01 10 Be 43. 0863 -0. 0870 776 19. 7 3. 2 3. 6 42. 43 ± 0. 98 ECH02 10 Be 43. 0858 -0. 0880 778 59. 0 43. 2 f 43. 0 - ECH03 10 Be 43. 0862 -0. 0873 779 17. 2 3. 3 3. 5 38. 86 ± 1. 11 ECH04 10 Be 43. 0865 -0. 0867 781 16. 8 3. 0 3. 3 38. 77 ± 1. 05 a Full sample information used for exposure age calculation is provided in the Supplementary Information or is available on GitHub: https://github.
Sequencing of candidate genes. DNA was extracted from peripheral blood leukocytes using an automated procedure (Tecan EVO-ReliaPrep, Promega). Next Generation Sequencing (NGS) was performed using the ionTorrent technology on a S5XL instrument (Life Technologies, ThermoFischer Scientific, U. S. A. ) (AmpliSeq panel) and bioinformatics analysis of variants were as previously described  5. DCT variants (NM_001129889. 2) were confirmed by Sanger sequencing. See Supplementary Data for more details.
Between 5 and 9 animals were studied, depending on the strain. Eye histology: Mice were sacrificed by cervical dislocation and eyes enucleated and processed as detailed in Supplementary Materials and Methods. Randomization was not required, since comparison was between genotypes not treatments. Spectrometric measurements were carried out blind to genotype. The zebrafish homolog of DCT was identified by searching in the genome sequence database at http://www. ensembl. org/Danio_rerio taking into account the synteny with the human genome. Morpholino (MO) were designed by and obtained from GeneTools, LLC and embryo injection is detailed in Supplementary Materials and Methods. Melanin assay was based on already described methods  15  and detailed in Supplementary Materials and Methods. Histological analysis was performed as described in Supplementary Materials and Methods.
1406_1419del p. (Phe469*) (ClinVar submission number SUB7603850), inherited from her heterozygous mother. Patient 2 (age at diagnosis not known) is a woman aged 36 at the time of consultation who was born to consanguineous parents originating from Northern Africa. She had cream-coloured skin, light brown hair, nystagmus, photophobia, iris transillumination, hypopigmentation of the retina and reduced visual acuity (4/10 and 5/10). Morphology of the fovea was not investigated. Patient 2 was homozygous for variant c. 183C>G p. (Cys61Trp) (ClinVar submission number SUB7603854) in DCT exon 1 (Figure  1 ). Segregation analysis could not be completed and no further information on her phenotype could be gathered, as she was lost from contact after DNA sampling. All three variants were predicted to be deleterious by several prediction software (see Materials and Methods) taking into account parameters such as allele frequency in public genome databases, conservation, as well as effect on protein structure and physicochemical properties.
Note that C3 to C8 (involved in disulfide bonds 2, 3, 4) belong to the highly conserved EGF-like signature. According to the Human Gene Mutation Database, amongst 354 missense and nonsense mutations in TYR, substitutions in all of the C1 to C10 cysteine residues are found associated with OCA1. Of particular interest, C2 [patient 1 variant p. (Cys40Ser)] is mutated in two unrelated OCA1 Pakistani patients  22  , and C5 [patient 2 variant p. (Cys61Trp)] is also mutated in OCA1 patients with 3 identified variants  [23] [24] [25]. Interestingly one of the OCA1 patients with a C2 substitution described by Jaworek et al. 22  is homozygote for a Cys to Arg change and has a somewhat mild phenotype (fair blond hair and pinkish skin) compared with known complete loss of function. Moreover, this mutated TYR has a temperature-sensitive behaviour likely due to slight protein misfolding.
in the Cys-rich domain are renumbered C1 to C10 to facilitate correspondence between the 3 enzymes. C3 to C8 are part of the EGF-like signature. The DCT/TYRP2 patients' mutations are therefore in C2 (Cys40Ser) involved in Disulfide bond (DiS) 1 (light gray) for patient 1, and C5 (Cys61Trp) involved in DiS 2 (dark grey) for patient 2. Cys residues that have been seen mutated in OCA1 (TYR) or OCA3 (TYRP1) patients are indicated in bold and the number of different allelic variants described in the Human Gene Mutation Database (HGMD) are indicated in parentheses. Mutations in TYR and TYRP1 that affect the same DiS bond and are therefore likely to result in a same functional consequence as DCT Cys40Ser or Cys61Trp are shaded in light gray or dark gray respectively.
ACKNOWLEDGEMENTS The authors are grateful to the  French Albinism Association Genespoir  for financial support and for timeless action in favour of patients with albinism. IG, LM, MK and IJJ were funded by the  MRC University Unit  award to the  MRC Human Genetics Unit , grant number  MC_UU_00007/4. They also wish to thank the patients for participating in this study and  Angus Reid  for assisting with colorimetric measurements. Cédric Delevoye: Conceptualization Claudio Plaisant:  Next Generation Sequencing Vincent Michaud: Patients cohort characterization Didier Lacombe: Supervision Eulalie Lasseaux: Patients cohort characterization Sophie Javerzat: Conceptualization, Eye histology interpretation  (mouse and zebrafish), Visualization, Writing -original draft, review and editing Ian Jackson: Conceptualization, Visualization, Writing -original draft, review and editing, Funding acquisition Benoit Arveiler: Conceptualization, Project administration, Supervision, Visualization, Writingoriginal draft, review and editing, Funding acquisition.
High-throughput experimental and theoretical approaches are being used to build protein-protein interaction (PPI) networks. The data covering PPIs are increasing exponentially. Indeed, in the year 2012 more than 235,000 binary interactions were reported  [13]. Most protein interaction databases (DBs; nonexhaustive list)  offer general information about experimentally validated PPIs of all types. The IMEx Consortium and the PSICQUIC service (http://www. ebi. ac. uk) group all data dealing with non-redundant protein interactions at one interface  [33]. Nevertheless, the DBs are mostly concerned with water-soluble globular proteins and the juxtamembrane interactions of MPs. However, unlike globular proteins, MPs are water-insoluble, their exterior is much more hydrophobic than the interior because of allosteric interactions with the lipid environment, and they lose their native structure when removed from their natural membrane environment.
We retained those with at least six buried interacting residues in each monomer (eight buried residues is typical of biological interfaces,  [58] ). This amount is the minimum for presenting an accessible surface area leading to quaternary structure. To obtain a high-quality homogeneous dataset, we adopted stringent selection conditions. Thus, for the PDB structure to be considered valid, it had to have a high-resolution limit of 3. 5 Å or better if it was obtained by X-ray crystallography or cryoEM. We took in all different conformational states, regardless of the presence or absence of ligand(s), pH, symmetry group, apo or holo form, and allosteric effects, unless the differences modified the set of interface residues. We eliminated PDB MPs presenting engineered mutations, insertions, and deletions in the TM segment with respect to the wild-type or natural variant sequence in UniProtKB, just like chimeras in which the xenophobic part is TM.
However, in some cases, one of the monomers of a homodimer may reveal another interacting surface. In this case, the corresponding motif is different. In this manner, we queried our consensus motifs against the Pfam dataset (https://pfam. xfam. org/). We defined a COST parameter as the number of amino acid mutations allowed anywhere in the motif, depending on the number of contact residues it contains and the mutation rate for the run. We assigned a score of 0 to both insertions and deletions to ensure that no contact residue is lost. For instance, when generating new sites from a valid motif with eight contact residues, COST = 2 for a mutation rate of 20% (8 Â 0. 20 = 1. 6 rounded up to 2). The values of COST_A vary from 0 to 4 and those of COST_B from 0 to 4, 7-8, reflecting the fact that there are TMs whose sequences contain more than 30 contact residues in their interaction motifs and accommodating up to 20% mutations with respect to the consensus motifs.
We also checked which TMs showed the motifs in their PDB structures, if available. 2. 1. 4. Implementation 2. 1. 4. 1. The PPIMem database. Thereafter, we naturally built a heterogeneous relational DB named PPIMem, which contains all the found interactions. The DB is the result of the effective implementation of the automated template-based recognition site search pipeline. We used a MySQL DB to maintain all the information collected in a structured manner. To access the DB and search for our data, we built a web interface using PHP and HTML5, which allows the user to query the needed information. Users can query the DB for obtaining motifs by entering a UniProtKB AC, a gene name, a type of organism, a mutation percentage, or a motif of interest or part thereof using the Regex format. A link to the Uni-ProtKB site for each UniProtKB AC will be available shortly.
The user can choose more than one filter option when querying and will exclusively obtain interactions thought to occur in TM regions between plasma membrane proteins of the same species. The user can also adjust the values of the following parameters: Number of contact residues, Mutation rate, COST and Valid, as an interval or a fixed value. Homodimers of the TMs dealt with do not appear in the database, as all PPIMem TM proteins are expected to form them. We will update our DB following each release of the Uni-ProtKB and OPM datasets and then regenerate all statistics.
Neither were we concerned whether the docking program was trained on sets composed primarily of cytosolic proteins. Even though several tested docking programs were sufficiently precise, we decided to use GRAMM-X  [64, 65]  for creating the novel protein -protein 3D complexes. GRAMM-X has an option in which the user can submit those residues that might form the interface between the ''receptor " and the ''ligand. " The program also has options for listing the residues required to be in contact. To verify the performance of GRAMM-X for TMs, we benchmarked it against several TM complexes in the PDB. GRAMM-X was indeed able to reproduce many of the experimental TM complexes. For molecular docking simulation and identification of the PPIMem-predicted complexes, we chose examples in which the 3D PDB structures of proteins were already available or represented highly homologous templates.
The PPIMem pipeline results indicate there are 1504 unique TMs involved in the predicted complexes, going from UniProtKB accession codes A0PJK1 to Q9Y6W8. Of these, 417 are human (Table  S3 ). The combined number of motifs found after removing redundancies due to different chains of the same structures interacting more than once was 98 (Table  S4 ). In many complexes, the motifs in each subunit are identical, i. e. , motif A is coupled to an identical motif B. But in other instances, the two paired motifs are different (Table  S5 ). In this situation, the complexes are typically comprised by different monocatenary subunits, although if both subunits are the same protein, but contain more than one interacting surface, the complex is a homodimer in which the contact surfaces are not the same.
3 ). As expected, the latter show in average a larger number of non-bonded contacts. Finally, we looked at the Pfam-A set of protein families  [71]  to which the 63 different proteins in the template complexes of Table  S1  belong. We found that the most populated families were PF07714 (Pfam family PK_Tyr_Ser-Thr) and PF00520 (Pfam family Ion_trans), with occurrences of 10/110 and 6/110, respectively. Forty-two proteins out of the 63 had only one Pfam occurrence each (not shown).