dataesr/scientific-paragraphs-categorization huggingface dataset:
Materials and Methods IUCN Red List Database. The IUCN Red List of Threatened Species is widely regarded as the world's most comprehensive information source on the global extinction risk of species  42, 43. It is used in a varied way: setting priorities in the compilation of species action plans, reserve selection, and management, or defining indicators for the state of the environment  [50] [51] [52]. The IUCN Red List  23  and BirdLife International  53  have assessed the conservation status of more than 12,680 insular terrestrial and freshwater species. In this study, we focused our analyses on the 15 most documented insular regions worldwide (i. e. , containing >50 assessed species) (Supplementary Figure  S2 ; Africa Atlantic, Asian Coast, East Indies, Indo-Burma, Japan, Madagascar, Mediterranean Basin, New Caledonia, New Zealand, North America Pacific, Papua New Guinea, Philippines, Polynesia and Micronesia, South America Pacific, and West Indies), which together encompass 12,533 insular species.
Networks were created with R software (version 3. 3. 1)  55 using igraph and rgexf packages  56, 57. Then, network structures were explored and visualized with the interactive platform Gephi 58 using a ForceAtlas 2 layout. A layout is an algorithm that positions nodes in a 2-D or 3-D space. ForceAtlas 2 layout 59 allows the network to be visualized based on the forces of attraction and repulsion, where the nodes repulse each other based on their degree (i. e. , the number of incoming and outgoing edges), while the edges attract the connected nodes. As a result, high-degree nodes tend to separate from other high-degree nodes and attract low-degree nodes to which they are connected. In our case, species that tend to be associated with the same threat or group of threats will be clustered and repulsed from other threats and/or species that are associated with different threats.
Networks between taxa and threats. Second, we built weighted bipartite networks to analyze the associations to threats at the taxa level. Each taxa was connected to threats with an associated weight depending on the number of their species threatened by each threat. Networks were created and visualized using the plotweb function implemented in the R package bipartite  60. Networks between insular regions and threats. Third, we built weighted bipartite networks to analyze insular regions-threats associations. Each of the 15 insular regions was connected to threats with an associated weight depending on the number of insular species threatened by each threat in that region. As above, networks were created using the R packages igraph and rgexf, and network structures were explored and visualized with Gephi software using a Geo Layout, which uses latitude and longitude coordinates to set the node position in the graphic space.
Acknowledgements This work is supported by the  Fondation BNP Paribas (INVACOST)  and the  ANR  grant ( 14-CE02-0021-01 ). We gratefully acknowledge  Janet Scott  of the  IUCN Red List Unit  for sending us the assessment database. We also thank  D. Zarzoso-Lacoste  and  J-Y. Meyer  for our fruitful discussions. We are grateful to  Victoria Grace  for English corrections on the last version of manuscript. We thank the assistant editor and the two anonymous reviewers whose comments have contributed to improving the previous version of this manuscript.
Results With increasing passage number, endogenous miR-32 expression and WWP2 expression are negatively associated in HuAECs. The results of the RT-qPCR analysis suggested that the mRNA expression levels of WWP2 were gradually increased in the HuAECs with continued passage (Fig. 2 ). Northern blot analysis confirmed that with continued passage of HuAECs, the hybridization signal of endogenous miR-32 became increasingly weaker (Fig. 2 ). These results indicated that endogenous miR-32 expression and WWP2 expression in HuAECs were negatively associated. Targeted binding of miR-32 to the 3'UTR of WWP2 mRNA silences its expression. Sequence alignment, using bioinformatics analysis software (microRNA. org -Targets and Expression tools, http://34. 236. 212. 39/microrna/home. do), identified a complementary binding site for mature miR-32 in the 3'UTR (between 1777 and 1783 bp) of WWP2 mRNA (Fig.
Acknowledgements The present study was supported by grants from the  National Natural Science Foundation of China  (grant nos. 81471461  and  81202811 ), the  China Postdoctoral Science Foundation  (grant nos. 2014M550250  and  2015T80455 ), the  Shanghai Municipal Health Bureau Fund  (grant nos. 14XD1403200 , SH D  C12 013114, 2 0142 0 0 6, PW Z xq2 014 -02  a nd 20124320), the  Shanghai Natural Science Foundation  (grant no. 16ZR1434000 ), the projects sponsored by the  Development Fund for Shanghai Talents  (grant no. 2017054 ) and the  Fund for Xinglin Talents of Shanghai University of TCM  (grant no. 201707081 ).
We show that the optimal rigid motions associated to our motifs leverage the combined lRMSD, a notion of flexible lRMSD mixing local (motif based) lRMSD into a global score assessing a global deformation  [22]. 4. Motifs to seed iterative alignments. Iterative aligners consist in iteratively finding the alignment for fixed positions of the conformations and finding optimal superimposition given the alignment  [14, 12]. We show that our motifs can be used to seed iterative structural alignments, yielding alignments outperforming those of the seed aligners (Apurva, Kpax). 5. Software. All our methods are made available from the SBL (http://sbl. inria. fr). Validation: test-set. We use two datasets. The first one is a dataset of remote homologous class II fusion proteins. The second one is a dataset of ten challenging cases for flexible aligners.
exe, sbl-structural-motifs-chains-kpax. exe, sbl-structural-motifs-conformations. exe. Methods: Iterative aligners. Seeding Kpax with our motif yields four different iterative aligners, namely Align-Kpax-SFD/iter, Align-Kpax-CD/iter, Align-Apurva-SFD/iter, Align-Apurva-CD/iter. Packages from the SBL and main parameters. Methods and executables are summarized in SI Table  2. The programs computing motifs are provided in the package Structural_motifs package (SBL package: https://sbl. inria. fr/doc/Structural_motifs-user-manual. html), and are used in particular to compute flexible distance measures in the package Molecular_distances_flexible (https://sbl. inria. fr/doc/ Molecular_distances_flexible-user-manual. html). Iterative aligners are provided in the package Iter-ative_alignment (https://sbl.
Datasets Structural comparisons and motifs. To understand properties of motifs, we use a dataset of eight class II fusion proteins (SI Fig. 11 ), namely proteins used by enveloped viruses to trigger the fusion between their membrane and that of the target cell  [30]. These 8 structures (sizes varying from 380 to 461 a. a. , SI Fig. 11 ) yield 8 × 7/2 = 28 pairwise comparisons. The rationale for this dataset is twofold. Structurally, class II fusion proteins are elongated molecules with three domains (DI, DII, DIII) composed primarily of β-sheets. The central DI domain connects via a flexible hinge to the longer DII. Typically, DII contains several conserved disulfide bonds as well as the so-called fusion loop at its tip. Additionally, a linker region connects DI to the DIII domain, which has an Immunoglobulin (Ig)-like fold.
On the biological side, (class II) fusion proteins are challenging structures, as they harbor a low sequence identity (typically less than 20%), and overall loose structural homology (typically of the order of 10Å). Yet, they have the same function, for a reason which has remained elusive. Thus, in investigating structurally conserved motifs of such proteins, we provide insights on fusion mechanisms  [31]. On the computational side, such a dataset remains tractable. To see why, recall that we use two aligners, namely Apurva (with a focus on contacts) and Kpax (with a focus on backbone geometry), at two stages: first, to obtain the seed alignment; second, to obtain the motifs by performing a local alignment when processing pairs of points from the persistence diagrams (Def. 6). This latter operation is the computational bottleneck.
Method illustration: investigating a conformational change We illustrate each step of our method to study the conformational change undergone a prototypical class II fusion protein, from the tick-borne encephalitis virus. The ectodomain of this protein was crystallized both in soluble form (PDB: 1SVB,  [32] , 395 residues) and in postfusion conformation (PDB: 1URZ,  [33] , 400 residues). For these structures, the identity alignment identifies 376 a. a. Step 1 -scores and C α ranks. We compute C α ranks as described in Sec. 2. 3 and analyze the C α distance plot, the sequence shift plot, and the score plot. (Fig. 3 ). No correlation is observed between C α ranks and (i) the proximity along the sequence, and (ii) the location on SSE (inferred from C α distances). That is, C α ranks identify rigid pairs throughout the structures.
5 ). These motifs are characterized by:  Method. We challenge our four methods (Align-Kpax-CD, Align-Kpax-SFD, Align-Apurva-CD, Align-Apurva-SFD) and also compare them to the seed aligners (Apurva, Kpax), using all pairwise comparisons of the class II fusion dataset. In a first step, we analyze statistics describing the motifs yielded by the contenders. In a second step, we exploit motifs to perform an overall comparison of whole structures based on motifs. For the seed aligners Apurva and Kpax, we report the lRMSD associated with the seed alignment, together with the alignment length. For our four aligners, we report the combined RMSD Comb. associated with our motifs and the number of a. a. involved in this calculation  [22]. Seed aligners. The alignments returned by our base aligners Apurva and Kpax for the 28 comparisons call for two comments (Fig.
6 ). First, Apurva alignments have a lRMSD which can be up to four times larger than Kpax alignments; second, Kpax alignments are smaller -the median is 100 residues smaller. This is in line with the goals of these aligners, as Apurva tries to maximize the numbers of contacts, while Kpax favors the coherence of backbone geometry. Motifs per se. The motifs returned by our four methods provide insights on the merits of the seed aligners and the filtrations (see also SI Sec. 10. 1 for the statistical significance of motifs. ) Three facts emerge from the general statistics obtained on the whole dataset (Table  1 ). First, the CD filtration yields longer motifs (Table  1 ; median size 38 and 31. 5, versus 18 and 18). This is expected, as defining connected components from a space filling diagram is more stringent than from the MST exploiting conserved distances.
Using this approach, we successfully resolved DA projections in wild-type and Parkin S65A/S65A mice; however, we observed no differences between groups (figure  4b ). Dysfunction in non-neuronal cells is also a reported feature of PD mouse models  [35]. However, immunohistochemical labelling of microglia and astroglia by Iba1 and GFAP, respectively, revealed no overt differences between wild-type and Parkin S65A/S65A mice (data not shown). To further investigate nigrostriatal function, we   and wild-type mice at 12 months and 18 months of age. Animals were assessed by their ability to reach a platform by competently traversing a raised and tapered beam. The performance was recorded by the number of errors scored as slips (forelimb, hindlimb and combined). Parkin S65A/S65A mice made significantly more forelimb errors and hindlimb errors at 12 and 18 months ( p , 0.
Targeted next-generation sequencing (NGS) revealed that the patient carried a novel homozygous c. 194G. A variant in exon 3 of the PARK2 gene causing an amino acid change p. Ser65Asn (S65N) in the PARKIN protein (figure  5b ). The variant is very rare: only two heterozygous carriers among 122 271 subjects were found in the Genome Aggregation Database (gnomAD) with an allelic frequency of 8. 2 Â 10 26. In the Exome Aggregation Consortium (ExAC) database, two heterozygotes were found among 60 691 subjects (allele frequency 1. 6 Â 10 25 ). Furthermore, the variant site is highly conserved in vertebrates (electronic supplementary material, figure  S6a ), and in silico analysis of the variant by the method of Combined Annotation Dependent Depletion (CADD)  [42]  predicted that the mutation is deleterious with a CADD C-score of 25.
DaT SPECT imaging was performed three hours after an intravenous injection of 186 MBq 123 I-FP-CIT using a double head gamma camera with a fan beam collimator. Thyroidal uptake was blocked by 300 mg of oral potassium perchlorate before the tracer injection. Occipital lobe was used as a reference region of non-specific binding. The Z-scores in the top right of the figure indicate significant deviation from the population mean values for both the left and right caudate and putamen. In the putamen, the DAT density was slightly more reduced, which is typical of the rostro-caudal pattern of uptake loss in PD. (b) Electropherogram detailing S65N mutation data (Exon 3 G. A). (c) Parkin S65A and S65N mutants display reduced Parkin activation in response to PINK1 activation. Flp-In T-Rex-HeLa cells stably expressing wild-type (WT), S65A and S65N Parkin were induced with 0.
Mitochondrial depolarization does not activate the PINK1 -Parkin pathway in Parkin S65N patient cells (CCCP treatment (3 h, 10 mM)). (e) Composite schematic illustrating the X-ray crystal structure of the Parkin S65N disease mutant, superimposed over the wild-type structure (PDB ID: 5C1Z, Grey). ParkinS65N structure is depicted in colour: UBL (teal), RING0 (magenta), RING1 (orange), IBR (blue), REP (yellow) and RING2/Rcat (red). rsob. royalsocietypublishing. org Open Biol. 8: 180108 Understanding the mechanistic basis of DA neuronal loss in mammalian models of PINK1 and Parkin has proved challenging, because it has been difficult to recapitulate a PD-like neurodegenerative phenotype in rodents, with the notable exceptions of mutant Parkin over-expression in mice and rats  [59, 60] ; the Pink1-null rat model  [61] ; and exposure of Parkin-null mice to chronic mitotoxic stress  [21].
Each mouse provided data (fore and hind limb foot-slips, latency to turn and cross the beam) from two runs for analysis. Gait analysis was run using a footprint test whereby the feet of the mice were painted with red (front feet) and blue (rear feet) non-toxic water-based paint, and the mice were permitted to run a 1-m corridor that was fitted with paper on the floor such that they left coloured paw prints. The paw print patterns permit the measurement of variation in gait: stride length; base width (distance between left and right front/hind limbs); front/hind paw overlap (in normal locomotion the hind paw placement overlaps closely with the location where the fore paw had previously landed; gait impairment is manifest by an increase in the magnitude and variability of the separation. For the motor activity testing using an automated system (Med Associates Inc, Vermont, USA), mice were placed in an open field arena for 32 h with free access to food and water throughout.
Data were collected automatically with activity being measured as the number of infrared beam breaks. Activity was measured as the initial 30 min period (transfer activity) and a 24 h period (15. 00 to 15. 00) to capture a circadian cycle. In addition, several simple behavioural assessments were run: clasping and reaching reflexes where the mice is suspended by the tail and lowered to the benchtop with clasping and reaching measured through observation and recorded as present or absent; negative geotaxis, measuring the natural inclination of the mouse to face upwards when placed head down on a vertical grid, with the mouse being allowed 30 s to correct its orientation to the upright position; and horizontal grid grip strength, whereby the mouse is placed on a flat grid surface which is then rotated 1808 along the horizontal axis such that the mouse is clinging to the underside of the grid, the maximum time for the mouse to remain upturned being 60 s without falling.
Quantification and statistical analysis Semi-automated quantitation images were processed with VOLOCITY 6. 3 image analysis software (PerkinElmer) using algorithms developed to analyse object overlap and count individual structures. For all analyses, we obtained images using uniform random sampling by an experimenter blind to all conditions. All images in each experimental group were processed as a batch using identical protocols. All images were prefiltered to suppress noise (3 Â 3 median filter). The same strategy was used to quantify all non-immunolabelled mito-QC-labelled images using auto-thresholding to identify objects (mean intensity þ 3 standard deviations). Objects were filtered using a minimum size cutoff (0. 16 mm 2 ) and touching objects were separated using a guide size (0. 4 mm 2 ).
Virus was harvested 48 h after transfection and applied to cells in the presence of 10 mg ml 21 polybrene. Cells were selected with either 500 mg ml 21 hygromycin B or 2 mg ml 21 puromycin. Selected cells were seeded and incubated for 24 h into glass bottom dishes (Ibidi m-Dish 35 mm) with media free of sodium pyruvate and non-essential amino acids. Mitophagy was stimulated for 24 h with 10 mM antimycin A (Sigma #A8674) and 5 mM oligomycin A (Sigma #75251), and controls were treated at equal volumes of DMSO. Cells were fixed for 20 min with 3. 7% PFA (Sigma #P6148) pH 7. 0, and subsequently washed twice with 2 ml of DMEM þ 10 mM HEPES pH 7. 0 and incubated for at least 10 min. Images were acquired using a ZEISS LSM 710 confocal microscope and quantified using VOLOCITY 6. 3 image analysis software as previous described  [39].
5, 200 mM LiSO 4 and 20% PEG3350 by using a vapour diffusion method under sitting drop. Crystals were flash frozen in 20% glycerol, the native dataset was collected at ID30A-1 beamline, European Synchrotron Radiation Facility (ESRF, Grenoble). Data were processed using Mosflm and Aimless in CCP4 (CCP4, 1994). Parkin S65N structure was determined by molecular replacement using Phaser in CCP4, WT-Parkin (pdb id: 5C1Z) was used as the search model. The initial model obtained from Phaser was further constructed and refined using Coot [75] and REFMAC5 in CCP4  [76] , respectively. The final model of Parkin S65N shows excellent geometry (electronic supplementary material, table  S4 ). 4. 17. Human genetic analysis 4. 17. 1. Case 1 (S65N-M70) Patient DNA samples were screened for variants using HaloPlex targeted sequencing of 82 selected PD-associated loci (Agilent Technologies, Santa Clara, CA, USA).
For detection of ubiquitylation (marked by open circles), lysates were subjected to ubiquitylated-protein capture by His-Halo-Ubi-quilin1 UBA-domain tetramer (HALO-UBA UBQLN1 ), prior to immunoblot with anti-CISD1 (Proteintech Europe) and anti-Miro1/2 (DSTT, S531D, 5th bleed). (d ) Activitybased profiling of Parkinson's disease associated Parkin S65N patient-derived fibroblasts. Primary fibroblasts derived from skin biopsies from a healthy subject (WT) and a PD patient harbouring a Parkin S65N homozygous mutation were profiled with E2-ubiquitin-based probe to monitor Parkin E3 transthiolation activity as described previously [41]. Mitochondrial depolarization does not activate the PINK1 -Parkin pathway in Parkin S65N patient cells (CCCP treatment (3 h, 10 mM)). (e) Composite schematic illustrating the X-ray crystal structure of the Parkin S65N disease mutant, superimposed over the wild-type structure (PDB ID: 5C1Z, Grey).
Acknowledgements. We wish to thank the  Finnish Institute for Molecular Medicine (FIMM)  for sequencing services. We are grateful to  Tuula Manninen  ( University of Helsinki, Finland ) for technical assistance with human fibroblasts cultures and Sanger sequencing. PPMI is a public -private partnership funded by the  Michael J. Fox Foundation for Parkinson's Research  and funding partners including:  Abbvie, Avid Radiopharmaceuticals, Biogen Idec, Bristol-Myers Squibb, Covance, Eli Lilly & Co. , F. Hoffman-La Roche, Ltd. , GE Healthcare ,  Genentech ,  GlaxoSmithKline ,  Lundbeck ,  Merck, MesoScale, Piramal ,  Pfizer  and  UCB. Informed consent was signed by the patients enrolled in the PPMI study. We thank the  PPMI Data & Publications (DPC)  for reviewing the manuscript prior to submission and to  Ms Alyssa Reimer  and  Dr Shalini Padmanabhan  of the  Michael J.
Fox Foundation  for advice on obtaining PPMI data. The authors would like to thank the Genome Aggregation Database (gnomAD) and the groups that provided exome and genome variant data to this resource. A full list of contributing groups can be found at http://gnomad. broadinstitute. org/about. The authors would like to thank the  Exome Aggregation Consortium. A full list of contributing groups can be found at http://exac. broadinstitute. org/about. We are grateful to  Dr Xiao Tian  and  Prof. Vera Gorbunova  ( University of Rochester, USA ) for guidance on establishing adult primary fibroblast cultures. We are grateful to  Dr Wolfdieter Springer  ( Mayo Clinic, USA ) for providing an initial aliquot of anti-phospho-ubiquitin antibody for optimization studies. We thank  Dr Shane Grealish  ( Lund University, Sweden ) and  Dr Fumi Suomi  ( University of Helsinki, Finland ) for critical reading of the manuscript.
After removal of improper pairs, reads mapped with a mapping quality below 20 and duplicated reads using SAMTOOLS  (v. 1. 1; Li et al. , 2009)  and PICARD Tools (v. 1. 104, http://broadinstitute. github. io/pica rd), the reads were realigned around short insertions and deletions and indels from the Drosophila Genetic Reference Panel  (Mackay et al. , 2012)  (DGRP freeze2: ftp://ftp. hgsc. bcm. edu/DGRP/freeze2_ Feb_2013/; options RealignerTargetCreator: -maxIntervalSize 400 -minReadsAtLocus 15; IndelRealigner: -entropy 0. 05 -LOD 3 -max-Consensuses 50 -greedy 250 -model USE_READS) using GATK (v. 3. 5. 0; McKenna et al. , 2010). The Italian flies were screened for D. simulans contamination before library preparation. Nevertheless, we applied the same filtering procedure as described in  Bastide et al.
| Association mapping and false detection rate estimation Association of variants with trident pigmentation was tested using a Cochran-Mantel-Haenszel (CMH) test implemented in POPOOLATION2 as previously described  (Bastide et al. , 2013; Kofler, Pandey et al. , 2011). The CMH test assesses the independence of counts in contingency tables over multiple strata, in our case replicates  (Kuritz, Landis, & Koch, 1988). For each position, a 2 × 2 contingency table is constructed based on the counts of reference and alternative alleles in the light and dark pools for each replicate. Odds ratios and confidence intervals were calculated using the mantelhaen. test function of the stats package of R (R Core Team, 2016) and RPY2 (rpy. sourceforge. net). Only polymorphic sites with a minimum coverage of 15 for each sample, and a minimum overall minor allele count of 8 in each population were considered for testing.
The 2% most highly covered sites were excluded from the calculations. All variants were further filtered for a minimum average distance from the ends of supporting reads (average distance >8) and strand bias (max(forward/reverse, reverse/forward) >0. 1 or Fisher's exact test on alternative vs. reference strand balance p-value > 10 -2 ). Some functions for plotting were adapted from scripts by Stephen Turner (http://www. get tinggeneticsdone. com). The p-value of the CMH test depends on the counts at each SNP and the sequencing depth of the autosomal chromosomes is on average twice that of the X chromosomes in males. To be able to compare p-values across all chromosomes, we downsampled SNP counts at each autosomal position to 50% for calculation of the p-values (random sampling without replacement), but used full sequencing depths for odds ratios and allele frequency estimates.