dataesr/scientific-paragraphs-categorization huggingface dataset:
Measurement error and 3D scan reliability Systematic error between the two scan devices is shown by consistent displacement patterns in the PCA. Indeed, across all three PC axes, the scans differ in how they measure concavity around the braincase, frontal, and ventral surface. This systematic pattern could suggest that the 3D scanner technology errs by adding volume to the digital specimen relative to the µCT scan but it could also be the other way around with the µCT scan distorting the images to reduce volume. Furthermore, even when using the symmetric component of shape, the percent of variation contributed by scan device is quite substantial at about 14. 5%. Because scan device contributes this much to variation and because systematic error between scan device exists, researchers expecting very small variation due to biological sources would be advised not to combine 3D scan and µCT scan datasets.
In our test on the ability of different scan devices to classify according to sexual dimorphism, we showed that while variation contributed by each source was similar (and that from scan device slightly lower), both scan datasets presented a small sexually dimorphic pattern and supported the same classification performance. This suggests that 3D scans may even be acceptable for detecting some intra-specific patterns. However, this was a small sample (n = 11) and further studies with larger datasets would improve confidence for using 3D scans for intra-specific studies. Studies based on larger datasets might also be able to better highlight differences in classification performance between devices, if any. Nevertheless, it is promising that 3D scans and µCT scans performed equally even at such a small sample size for such a subtle intra-specific signal.
Finally, specimens need to be loaned from their collections for µCT acquisition, which requires specimen transport and curator permission and is particularly difficult when large numbers of specimens from distant locations need to be scanned. This study did not investigate photogrammetry, which is another and increasingly popular method for digitizing 3D shape. This method uses software to align 2D photographs taken from many different views into a 3D file. Photogrammetry is much cheaper and more portable than 3D surface scanning since it only requires a camera of suitable resolution and very affordable photo-alignment software like Agisoft PhotoScan (Agisoft LLC, St. Petersburg, Russia; http://www. agisoft. com). The trade-offs are that in our experience, photogrammetry takes at least three times longer to acquire the photos, it involves higher risk of human error or inconsistency during photography, and it requires an order of magnitude more time to align the photos into a 3D digital file.
CONCLUSIONS Here, we have shown that a 3D surface scanner can provide an acceptable alternative to a µCT scanner for assessing biological signal of 3D shape even in small specimens that are at the limits of 3D scanner resolution. Our analyses specifically showed that first, error contributes to a higher percentage of variation in 3D scan datasets than in µCT scan datasets of the same small specimens. As a result, we conclude that 3D scans are usually not appropriate for studies on very small sources of variation like fluctuating asymmetry. Second, we show that 3D scan datasets have a lower repeatability of landmark placement, especially for fixed landmarks, as compared to µCT scans. Relatedly, our comparisons of repeatability on data with asymmetry to the same data without asymmetry-i. e. , having bilateral symmetry-support analyzing the bilaterally symmetrical data of landmarks from low resolution scans.
Finally, we use a preliminary study of sexual dimorphism to suggest that despite elevated error and shape variance, bilaterally symmetrical datasets from 3D scans can support male versus female classification based on small biological differences as well as µCT datasets can. In summary, while 3D scans are a promising alternative, exploratory pilot studies of measurement error like this one are advisable when practically possible (see also  Fruciano, 2016). Furthermore, the best 3D capture method will vary based on the study's design, expected effect size for the biological variation of interest, and the researcher's limitations on time, money, and travel. In addition to image resolution requirements, it is wise to assess the time it will take to capture and process each specimen as well as portability needs.
Gold particle nearest neighbor analysis was performed using ImageJ. For TIRF microscopy, SNAP-GLP-1R-expressing cells were transfected with µ2-HA-WT (a gift from Prof. Alexander Sorkin, Addgene plasmid #32752), plated in glass bottom MatTek dishes, labeled with SNAP-Surface 488 probe as above, stimulated or not for 2 minutes with the indicated agonist, and fixed as above before immunofluorescence with a mouse monoclonal anti-HA (HA-7) antibody, catalogue no. H3663 (Sigma), followed by a secondary AlexaFluor 546 antibody. Un-mounted samples in PBS were imaged using a Nikon Eclipse Ti microscope equipped with a ×100/1. 49 numerical aperture TIRF objective, a TIRF iLas2 module and a Quad Band TIRF filter cube (TRF89902, Chroma). Images were acquired with an ORCA-Flash 4. 0 camera (Hamamatsu) and Metamorph software (Molecular Devices), and analyzed in Fiji.
Laser lines were switched on at least 1 hour prior to imaging to allow stabilization of the system in order to ensure minimal sample drift throughout the imaging. Each TIRF-PALM time-series was acquired using a cooled electron multiplying charged coupled device camera (EM-CCD; iXon DU 897, Andor Technology) and LMS Zen operating software with an exposure time of 30 ms. Localization of receptors was determined using QuickPALM Fiji plugin: fluorescent images of cropped non-overlapping areas of 7 × 7 µm within cell borders were analyzed using a full-width half-maximum value of 4, and signal to noise ratio of 8. Analyzed areas did not span cell membranes to exclude any potential biasing resulting from edge effects. Data tables containing particle localization 2-D coordinates were generated. The number of associated receptor molecules from these coordinates was determined with PD-Interpreter as in  (28)  with a 50 nm radius from each fluorophore.
After an initial blank subtraction, data were analyzed by ratiometrically normalizing the spectrum from each read to the signal measured at 490 nm; the normalized Lumi4-Tb-only read was subtracted from subsequent normalized reads to obtain the NR12S-specific TR-FRET signal in the presence and absence of agonist. Where indicated, the area under the curve (AUC) was calculated below and above the λEm maximum of 590 nm as a marker of relative contributions from liquid-ordered (530 -590 nm) and liquid-disordered (590 -650 nm) -associated SNAP-GLP-1Rs, and expressed relative to the total NR12S TR-FRET AUC (530 -650 nm). Alternatively, liquid-ordered and liquid-disordered FRET were assigned values of 570 and 610 nm respectively, with the NR12S-associated increase at each wavelength ratiometrically-expressed to indicate differences in SNAP-GLP-1R localization in each nanodomain.
Binding kinetics measurements Cells expressing SNAP-GLP-1R were first labeled with SNAP-Lumi4-Tb (40 nM) for 60 minutes at room temperature. Where relevant, MβCD treatments were performed after Lumi4-Tb labeling. After washing, cells were placed in HBSS + 0. 1% BSA, supplemented with metabolic inhibitors (20 mM 2-deoxyglucose and 10 mM NaN 3 ) to prevent GLP-1R endocytosis  (74) , and baseline TR-FRET was measured at over 10 minutes at 37°C using a Flexstation 3 plate reader (λEx = 335 nm, λEm = 520 and 620 nm, delay 50 µs, integration time 300 µs). FITC-conjugated agonists were then added and TR-FRET sequentially measured to monitor agonist association. Binding was quantified ratiometrically as 520 / 620 signal, and data were analyzed using the GraphPad Prism model "association kinetics: two or more concentrations of hot" to obtain association (k on ) and dissociation (k off ) rate constants, from which the equilibrium binding constant K d was determined.
Acknowledgements This work was funded by  MRC  project grant  MR/R010676/1. MIN6B1 cells were kindly provided by  Prof. Philippe Halban  ( University of Geneva, Switzerland ) with permission from  Prof. Jun-ichi Miyazaki  ( University of Osaka, Japan ) who produced the maternal MIN6 cell line. INS-1 832/3 cells were kindly provided by  Prof. Christopher Newgard  ( Duke University, USA ), and  INS -1 832/3 cells with endogenous GLP-1R or GIPR deleted by CRISPR/Cas9 were kindly provided by  Dr Jacqueline Naylor at MedImmune (AstraZeneca).
Phylogenetic tree and the four trans-Saharan clades For the phylogenetic tree reconstruction and time estimates, we used 150 Y chromosomes. The samples were analysed for ~3. 3 Mb of the X-degenerated portion of the MSY (Fig. 1 ) and belonged to different datasets: 104 Y chromosomes from our lab collection (77 of them belonging to the four trans-Saharan haplogroups) and 46 publicly available high-coverage sequences, including four precisely radiocarbon-dated ancient specimens as calibration points  [45] [46] [47] [48] [49]  (Additional file 1: Table  S1 ). In the set of 104 samples from our lab collection, we identified 5966 SNPs. Interestingly, 3044 variants (51 %) out of the 5966 were not reported in previous studies  [30, 48, 50, 51]  and this figure is significantly greater than that reported by Hallast et al.
Targeted NGS Library preparation, targeting, sequencing and alignment were performed by BGI-Tech (Hong Kong). The targeted unique regions of the MSY were captured using a Roche Nimblegen custom capture array, composed of a set of 200-bp probes. The probes excluded almost all the repetitive elements from the 22 X-degenerated blocks, capturing a total of about 4. 4 Mb. The captured regions were loaded onto an Illumina HiSeq 2500 platform to produce a > 50× mean depth for the targeted 4. 4 Mb. The low quality reads, contamination with adapters and repeated reads were discarded and the sequences of each subject aligned to the human Y chromosome reference sequence (GRCh37/hg19) by means of the BWA (Burrows-Wheeler Aligner) software  [70] , generating an alignment file (. bam format)  [71, 72].
Tree reconstruction and validation The maximum parsimony phylogenetic tree was reconstructed using the MEGA software  [74]. Because we did not assign univocally to A00 or A0-T the mutational events on branch 1, the tree root was positioned by default to the midpoint (Additional file 3: Supplementary Text). We identified 25 recurring mutations, 11 triallelic variants and two variant positions whose direction cannot be assigned on the basis of the phylogeny (Additional file 1: Tables  S2  and S8  and Additional file 3: Supplementary Text). All these positions were accurately checked in the alignment files. The presence in our list of already identified variants in published papers  [35, 48, [50] [51] [52] [53] 57]  and in the ISOGG dataset  [75]  made it possible for us to check the efficiency of all the steps from the SNP calling to the tree reconstruction.
Mutation rate, dating and star-like index The estimate of the mutation rate was obtained using the BEAST software  [76]. The input file (nexus format) was loaded onto the BEAUTY suite and we assigned to the four ancient samples the calibrated radiocarbon dates, expressed in years before present (BP): 1) Loschbour  [46] , 8055 years BP; 2) Kotias  [49] , 9712 years BP; 3) Bichon  [49] , 13,665 years BP; 4) Ust'-Ishim  [47] , 44,890 years BP. We used a GTR nucleotide substitution model under a strict clock or a lognormal relaxed clock and an expansion growth model for the population size, using parameters set as in Trombetta et al. [52]. The output was checked with the Tree Annotator and Tracer platforms. The mutation rate for the ~3. 3 Mb analysed here was 0. 735 ± 0. 03 × 10 -9 /site/year, corresponding to about one new mutational event every 408 years.
Population analysis We selected a total of 142 informative polymorphisms to be genotyped in the whole set of 6065 men from the 128 populations of our lab collection (Fig. 4 ; Additional file 1: Table  S5 ). The 142 SNPs (including 21 known variants which did not fall within our NGS target region) were chosen on the basis of their phylogenetic position and ethno-geographic distribution, also considering other datasets  [35, 48, [50] [51] [52] [53] 57]  and in the ISOGG  [75]  (Additional file 1: Table  S4 ). The chosen SNPs have been analysed by PCR and Sanger sequencing or RFLP. Moreover, we extracted the frequency distribution of the selected variants from the NGS data of one Sardinian population  [53]  and 16 populations from phase 3 of the 1000 Genomes Project  [51]  (Fig. 4 ; Additional file 1: Table  S5 ).
Acknowledgments The authors are grateful to all the donors for providing DNA samples and to the people that contributed to the sample collection. In particular, they thank  Farha El Chennawi ,  Anne Cambon-Thomsen, M. S. Issad ,  Abdellatif Baali ,  Mohammed Cherkaoui ,  Mohammed Melhaoui  and  Eliane Beraud Colomb  for their help in the collection of the Moroccan, Algerian and  Egyptian Berbers  samples; and the  National Laboratory for the Genetics of Israeli Populations.
Availability of data and materials The alignment files (. bam) of the targeted sequences of the 104 Y chromosomes analysed here are available through the European Nucleotide Archive (ENA, https://www. ebi. ac. uk/ena) under the study accession number PRJEB24071 (sample accession numbers from ERS2065703 to ERS2065806)  [72]. The final set of bases used for the SNP calling are included in this published article (and its supplementary information file; Additional file 1: Table  S7 ). Variant positions analysed during the current study are available in the dbSNP repository (https://www. ncbi. nlm. nih. gov/projects/SNP/) under accession numbers ss2137541472 and from ss3029944616 to ss3029952147 (handle, HUMGEN, ID Y_DEEPSEQ_2017) and are included in this published article (Additional file 1: Table  S2 ).
perplexa F. White x D. yaouhensis  (Guillaumin 9233, 9326, Hequet et al. 3699, 3700, Veillon 7134); D. pustulata x D. revolutissima (Hequet 3639, Veillon 6664); and D. pustulata x D. yaouhensis (Chambery 12, 21). In all cases, the two presumed parental species of the putative hybrids are known to occur in close proximity. All of the identifications made during our review are reflected in the  Sonnerat (2018)  and Tropicos (2018) databases, which contain comprehensive records of the material in the respective herbaria. For collections lacking geo-coordinates, these were assigned post facto, when possible, and are indicated in square brackets in the specimens cited. A series of recent papers  (Duangjai et al. , 2009; Turner et al. , 2013a Turner et al. , , 2013b Turner et al. , , 2016;; Paun et al.
Acknowledgments We are very grateful to our friend and colleague  Dominique Fleurot , who has extensively explored northern New Caledonia, locating populations of many species of Diospyros, and with whom we had the pleasure of seeing ten species and one putative hybrid during just three days in the field. We also thank  Roger lala Andriamiarisoa  for the excellent line drawings,  Jérôme Munzinger  for preparing the map,  Ehoarn Bidault  for assistance with data entry, and an anonymous reviewer for helpful suggestions. We are grateful to the Curators of the following herbaria for providing access to their collections:  BM, K, NOU, P, US  and  WU. The Direction de l' environnement,  Province Sud , and the Direction du développement économique et de l'environnement,  Province Nord , kindly provided permission to conduct field work and to collect specimens.
Materials and Methods Molecular Dynamics simulation details and protocols. All MD simulations were carried out on dimers of the transporter because of their physiological relevance  26. We studied isothermal-isobaric ensembles at 300 K with the program NAMD2. 9 software  44  as described previously  23. The CHARMM27 force field  45  with CMAP corrections  46  was used to describe protein, water, and ion atoms. A united atom force field described the lipids  47. Binding of Arg + , Arg 2+ , and Agm  2+  to the OF open unbound state was simulated by targeted MD (tMD). tMD simulations were performed because a previous study has shown that conventional simulations failed to produce migration of Arg + to the binding site  23. 5-ns long tMD simulations during which the arginine substrate was guided to the binding site (using the position of Arg in the OF open arginine-bound structure (PDB ID: 3OB6  19  ) as a target) were carried out following a protocol described elsewhere in detail for Arg + binding  23  under two different pH conditions: a transmembrane pH gradient (pH 2:5) and a uniform pH 6.
The interaction probability was calculated as the number of snapshots featuring a given interaction and having the center of mass of the ligand heavy atoms located in a given slice divided by the total number of snapshots having the center of mass of the ligand heavy atoms located in the same slice. In the interaction plots the interactions formed with a probability higher than or equal to 20% over all MD trajectories in at least one slice along the main axis are shown. Anion-π interactions potentially formed by the Glu208 carboxylate group with surrounding residues were computed with an in-house modified version of the program EUCB. A distance smaller than 6 (Tyr, Phe) or 7 Å (Trp) between the centers of mass of the Glu carboxylate sidechain group and of the ring atoms of the aromatic residues in addition to an angle <60° between the plane formed by the Glu carboxylate sidechain group and the ring atoms of the aromatic residues was used.
The dielectric constants used to evaluate ΔG solv,ligand, ΔG solv,p and ΔG solv,complex were set to 80, 4 and 4 respectively. The latter two values were chosen because the solvation process in this case consists in transferring the subset of residues lining the binding site with/without the ligand from vacuum to the surrounding rest of the protein and membrane. We first assumed the entropy terms to be similar and to cancel out when comparing the potential ligands. With this assumption the difference in ΔΔG binding between different ligands thus reduces to: Secondly, the change in configurational entropy ΔS config (vibrational, rotational and translational) of the ligands upon binding was estimated in the rigid rotor, harmonic oscillator (RRHO) approximation to the minimized free and bound systems using the MacroModel Embrace module of Schrödinger software with the OPLS-2005 force-field.
Acknowledgements M. P. is a senior research associate and E. M. K. is a postdoctoral researcher of the  Fonds de la Recherche Scientifique de Belgique  (F. R. S. -F. R. N. S. ),  Belgium. This work was supported by an  ARC grant  ( AUWB 2010-15-2 ) from the  Fédération Wallonie-Bruxelles  and by the  Fonds National de La Recherche Scientifique  ( PDR T. 1107. 15 ). Computational resources were provided by the  Consortium des Équipements de Calcul Intensif (CÉCI)  and the F. R. S. -F. N. R. S. under convention 2. 5020. 11,together with the supercomputing facilities of the  Université catholique de Louvain (CISM/UCL) , the  Université de Liège (ULg) ,  ULB , and the Tier-1 supercomputer of the  Fédération Wallonie-Bruxelles  under Grant Agreement No. 1117545.
Thus, the absence of His residue in the motif II of PolpTN2-like, PrimQH and PrimHYQ family enzymes might be compensated by the conserved Arg in motif IV. To test this hypothesis, we analyzed covariation between the corresponding residues from 1427 sequences of PolpTN2 primase domain homologs using PSICOV (see Materials and Methods). It turned out that covariation between His (motif II) and Arg (motif IV) residue positions (PolpTN2, 179, and 231, respectively) is very low (covariation score of 0. 03; Fig. 2C ). However, each of the two positions showed a strong link to another nucleotide-binding residue within motif IV (PolpTN2, Asn237). Covariation scores of pairs 179 | 237 and 231 | 237 were among the top four results reported by PSICOV (Supplementary data file 2; Fig. 2 ). Furthermore, to substantiate these results, analysis was repeated using PLMC script from the EVmutation package.
Taxonomic distribution of the new AEP families Almost all PrimPol-PV1 members (91%) are encoded by highly diverse bacteria belonging to 12 bacterial phyla and their viruses (Fig. 4 ; Supplementary data file 1). The PolpTN2-like family is the only AEP family that exclusively includes archaeal members. It contains proteins encoded by plasmids and integrated mobile genetic elements (MGEs) from six archaeal species belonging to the order Thermococcales (phylum Euryarchaeota)  [21, 22] , although euryarchaeal proteins (from orders Methanococcales and Archaeoglobales) are also found in the PrimQH family (Supplementary data file 1). In addition, the Anabaena all3500-like family contains a single protein sequence from an uncharacterized archaeon GW2011_AR11. Similarly, the PrimPol proper family is characterized by a mixed membership of proteins encoded by pRN1-like plasmids from the archaeal phylum Crenarchaeota and homologs from diverse bacteria.
PriQH, PriHYQ, all3500, and RepB′ families are predominantly found in Proteobacteria, whereas most of the InversePrim members are encoded by Firmicutes (Fig. 4 ). It should be noted that these results are likely to be biased by the current composition of the bacterial genomic databases which are dominated by proteobacteria and firmicutes. This potential bias notwithstanding, some observations are noteworthy. Of particular interest is the apparent specificity of the RepE/RepS family to the gut-associated microbiota. More than half of the members represent sequences extracted from the gut metagenomes of various mammals (human, rat, pig, mouse) (Fig. 4 ). Consistently, additional quarter is derived from Chlamydiae, Bacteroidetes, Fusobacteria, and Firmicutes, all of which are prominent components of the gut microbiome.
PolpTN2-like family Previous analysis has suggested that PolpTN2 is a fusion of two domains: an N-terminal AEP domain and a C-terminal domain homologous to the large subunit (PriL) of archaeal and eukaryotic primases  [20]. Using more sensitive sequence analysis methods, we found that PolpTN2-like proteins in addition possess a PriCT-1 domain located between the AEP and PriL-like domains, whereas the C-terminal region following the PriL-like domain can be confidently (94% HHsearch probability) predicted to adopt a winged-helix (WH) domain (Fig. 6 ). To see how well sequence of PolpTN2 from Thermococcus nautili plasmid pTN2 would fit into a structure of a WH domain fold, we submitted it to two structure prediction servers, I-TASSER  [33]  and RaptorX  [34]. The C-terminal WH domain of Salmonella enterica Typhi protein STY4665 (PDB: 2ipq), a relaxase belonging to the PFL_4751 family widespread in various bacterial integrative and conjugative elements  [35] , was identified as the best template for modeling.
, toxin; HNH, HNH family nuclease. C-terminal region is occupied by the WH domain, which might be involved in origin recognition, like in the case of PolpTN2-like enzymes discussed above. Between the PriCT-1/2/ZnBD and the WH domains, the majority of proteins in this family contain diverse P-loop ATPase domains. Whereas some of them are related to known helicases, including RecA-type helicases of the RepA and DnaB families as well as the MCM-family helicase, many representatives contain experimentally uncharacterized AAA+ superfamily ATPase domains of the VirE family (PF05272) or two different domains of unknown function (DUF), namely, DUF927 and DUF3987 (Fig. 6 ). Given that these ATPase domains are associated with primases and substitute for known helicase domains (MCM, RepA, DnaB), we predict that they represent novel families of helicases involved in MGE replication (Fig.