dataesr/scientific-paragraphs-categorization huggingface dataset:
For dynamic tracking a simple algorithm of nearest objects was used. For each cell identified at frame t, a circular region with a radius equal to the maximum displacement step was exanimated in frame t + 1; the nearest cell in this region was linked to the same track. Results were validated manually. Velocities were calculated from the tracking results (Δdisplacement/Δtime). The points of the trajectory (x0, x1, … , xn; y0, y1, … , yn) were used to calculate the mean square displacement (MSD) at time t = d 2 t = (x t -x t-1 ) 2 + (y t -y t-1 ) 2 : This was calculated with an R software script. Colocalization ratios were calculated by generating a mask of the fluorescent areas occupied by each fluorescent protein and reporting the colocalized area to the total area of AglZ-mCh or AglQ-mCh: (YFP-MglA∩AglZ-mCh)/AglZ-mCh; (YFP-MglA∩AglQ-mCh)/AglQ-mCh.
Acknowledgments Dr. Alan Hall  served as an editor of this manuscript in its early stages and he provided critical, constructive, and encouraging comments that were invaluable to us. We dedicate this manuscript in his memory. We thank  Nassos Typas  for helpful discussions. We thank  Emilia Mauriello  and  Frédéric Luton  for comments on the manuscript. We thank  Steffi Lindow  for excellent technical assistance and  Carole Baron-Gaillard  for advice and for MglA and MglB purification. We thank  Benedikt von der Heyde  for constructing strain SA6508. L. Søgaard-Andersen  is supported by the  German Research Council  within the framework of the  Graduate School "Intra-and Intercellular Transport and Communication ," the Landes-Offensive zur Entwicklung wissenschaftlich-ökonomischer  Exzellenz Research Center for Synthetic Microbiology , and the  Max Planck Society.
Sample preparation [TbPc 2 ] 0 was synthesized according to a procedure established earlier  17  and analytical data confirmed its intact structure (see Fig. S1  and S2 of the ESI †). The Si(111) substrates are covered by a native oxide layer of approximately 1. 6 nm. They were cleaned by dipping in acetone, ethanol, and deionized water for 5 minutes each in an ultrasonic bath. For the film preparation a vacuum chamber with a base pressure of 3 Â 10 À7 mbar was used. The material was evaporated from a Knudsen cell kept at a temperature of about 400 1C and the maximum pressure during the deposition did not exceed 3 Â 10 À6 mbar. The substrates were kept at room temperature. The preservation of the molecular structure in the films was verified ex situ by means of Raman spectroscopy.
Optical and magneto-optical characterization Spectroscopic ellipsometry and magneto-optical Kerr effect spectroscopy were used to characterize the optical and magnetooptical properties. The ellipsometry data were recorded using a Woollam T-solar ellipsometer in the spectral range of 0. 7 eV to 5. 0 eV. The WVASE32 software from Woollam 18 was used to analyze the ellipsometric spectra and to extract the optical constants and the diagonal elements of the dielectric tensor. The MOKE investigations were performed in polar geometry with a homemade setup under ambient conditions. 19 The samples were illuminated using s-polarized light at an incidence angle of about 1. 31. A magnetic field of 1. 7 T was applied perpendicular to the sample surface. Our modulated detection technique makes use of a photoelastic modulator and a lock-in amplifier and allows both the Kerr rotation angle and the Kerr ellipticity to be measured as a function of photon energy in the spectral range of 1.
Morphology studies Atomic force microscopy (AFM) measurements were performed in intermittent contact mode using a 5500 AFM from Agilent Technologies. Pure Si AFM probes with a typical tip radius of 10 nm and a nominal resonance frequency and spring constant of 60 kHz and 3 N m À1 , respectively, were used to investigate the samples under exactly the same conditions so that a direct comparison is possible and trustworthy. The recorded data were analyzed using Gwyddion, an open source software. 20.
For the determination of the optical constants and the diagonal elements of the dielectric tensor, we employed spectroscopic ellipsometry measurements performed at three angles of incidence (F = 501, 601, 701). A first attempt to fit the experimental data using isotropic optical constants was not successful. 3] [24]  The roughness was taken into account by introducing an additional top layer with optical constants described by the effective media approximation (EMA). Please refer to the ESI † for more details on the fitting model. The resulting optical constants, i. e. the complex refractive indices n ˜= n + ik, both in the sample plane (ip) and perpendicular to it (out of plane, oop) are shown in Fig. 2. On the left and right hand side of the figure the refractive index (real part of n ˜) and the extinction coefficient k (imaginary part of n ˜), respectively, are displayed.
2. 3 The pp → ppX 0 reaction Figure  3 : Cross sections of the pp → ppX 0 reaction Saturne (SPES3)  [1]. The pp → ppX 0 reaction was studied at Saturne (SPES3 beam line)  [1]. Missing mass spectra M X were obtained at fifteen different experimental conditions, namely three incident proton energies: T p = 1520, 1805, and 2100 MeV, and several spectrometer angles at each energy. Both protons were simultaneously detected in the same detection, thanks to its large momentum range 600≤pc≤1400 MeV/c. Peaks were extracted at the following masses: M = 62, 80,  100, 181, 198, 215, 228, 310, 350, 426, 470, 555, 588, 647, 681, 700, and 750  MeV. A selection of data is shown in Fig. 3 ; the S. D. of the peaks from left (l) to right (r) are in (a)l: 2. 9, 2. 1, and 3. 9; (b)l: 5. 2, 2. 9, and 6. 1; (c)l: 13.
Y-chromosome haplotyping and nomenclature In the 461 Central Asian samples up to 31 binary markers were typed hierarchically by using the SNaPshot protocol on an ABI3100 capillary electrophoresis apparatus (Applied Biosystems Inc. , Foster City, CA, USA). Primers were based on ones published previously  [22] [23] [24]  with additional primers based on published sequences. 25 The binary markers define haplogroups that are represented in a maximum parsimony tree  25  (Supplementary Figure  1 ). For comparison among data sets, some simplification of resolution was required, but the original haplogroup information was also retained and correspondence between previous nomenclatures was established. Eight Y-specific microsatellites (DYS19, DYS389I, DYS389b (equivalent to DYS389II-DYS389I), DYS390, DYS391, DYS392, DYS393 and DYS439) were typed in two previously described multiplexes  26, 27  by using an ABI3100 apparatus and GeneMapper v 4.
DC definition and display Having identified frequent haplotypes, we applied a rule to define a DC centred upon each, using a script ('Cluster Generator'; Supplementary Information) to extract these DCs from the database. This selects individuals from the data set, starting from a given haplotype and including all haplotypes that are continuously traceable to a core haplotype via a pathway of increasing frequency, a topology consistent with a Poisson-distributed single-step mutational model. 8 An additional 'single-step+10% two-steps' model was tested, but had the effect of incorporating unrelated chromosomes belonging to distinct haplogroups. We therefore applied only the most conservative single-step model. For each population, we reported the proportion of each DC detected, and, as a measure of DC diversity, its average within-population microsatellite variance.
Frequent haplotypes are expected to be accompanied by neighbouring haplotypes within the same Y-SNP haplogroup that arise from them via mutation, thereby forming DCs. 6 These DCs were uniformly and objectively extracted from the database using 'Cluster Generator'. When applied to the 15 most frequent haplotypes, this led to some common haplotypes merging into clusters centred on other common haplotypes, and thus to a total of 11 DCs, whose characteristics are summarised in Table  1.
Characteristics of DCs A total of 2000 (37. 8%) males carry Y-chromosomes belonging to these DCs, and they are remarkably widespread, with only three of the 127 populations analysed lacking any DC chromosomes; DC2 is also detected in one of the two ancient DNA population samples in our database (Krasnoyarsk_Kurgan  17  ). Overall DC proportions in each population vary greatly from 5 to 96. 7% (Supplementary Table  3 ). As DC4 is restricted to Korea,  18  it was not considered for interpopulation analysis. For the remaining 10 DCs, we explored three characteristics for each: its geographical frequency distribution, the geographical distribution of its mean microsatellite variance to indicate its likely expansion source and its TMRCA to suggest the age of expansion. Frequency and variance are represented on maps (Figure  3  and Supplementary Figure  2 ), and are summarised in Table  2  and Figure  4 , which also gives TMRCA estimates.
ACKNOWLEDGEMENTS We thank all DNA donors;  Matt Hurles ,  Emma Parkin  and  Denise Carvalho-Silva  for primers; and  Philippe Mennencier  for help with language classification. This work was supported by the  Centre National de la Recherche Scientifique (CNRS) ATIP programme  (to EH), by the  CNRS interdisciplinary programme 'Origines de l'Homme du Language et des Langues' , and by the  European Regional Development Fund , the  Conseil Régional Midi-Pyrénées  and the  PRES (Pôle de Recherche et d'Enseignement Supérieur) Toulouse  (to PB). MAJ was supported by a  Wellcome Trust Senior Research Fellowship in Basic Biomedical Science  (grant nos. 057559  and  087576 ), and PB by the  Wellcome Trust  and the  CNRS.
Inclusion criteria and statistical analysis Each egg was given a number, which identified it to its treatment group. At the end of the experiment, the anatomical and histological observations of the embryos were made blindly without knowledge of the treatment received by the embryos. Only embryos alive at the time of observation were included. Data are represented in contingency table indicating the percentage of embryos presenting malformations. Corresponding number of malformed embryos per total number of embryos are also shown in parentheses. Statistical differences were assessed by x 2 analysis.
Results During December 2013, 121 samples from patients with suspected CHIK from Saint-Martin Island were used to evaluate the performance of Chikungunya infection diagnosis from serum spotted onto filter paper, by molecular and/or serological techniques. The patients had presented at the biological laboratory of Saint-Martin with fever, headache, myalgia and severe joint pain. The Marseille-Lab used thawed frozen serum samples and Cayenne-Lab used dried serum spots on filter paper from the same corresponding patient and collection time, for all PCR and serology testing performed. Each laboratory used its own techniques for molecular and serological diagnosis. The participation of the two laboratories at a European quality control of CHIKV has previously shown the excellent concordant results between the two laboratories (data not shown).
The kappa coefficient was 0. 97 (95% CI: 0. 93-1. 00). The sensitivity and specificity of Chikungunya MAC-ELISA performed on SFP were 100% (15/15) and 97. 6% (42/43; 95% CI: 0. 93-1. 00), respectively. Due to recent cases of Dengue in Saint-Martin, the two FNRCA laboratories also tested for the possible presence of dengue IgM antibodies in the 60 patients from whom samples were collected after day 5 of the disease. These investigations led to the detection of dengue IgM in 15 samples for which CHIKV infection had been ruled out (data not shown). These results were concordant for the two FNCR laboratories, regardless of the type of biological support and technique used.
APOE ε4, the most significant genetic risk factor for Alzheimer disease (AD), may mask effects of other loci. We re-analyzed genome-wide association study (GWAS) data from the International Genomics of Alzheimer's Project (IGAP) Consortium in APOE ε4+ (10,352 cases and 9,207 controls) and APOE ε4-(7,184 cases and 26,968 controls) subgroups as well as in the total sample testing for interaction between a SNP and APOE ε4 status. Suggestive associations (P<1x10 -4 ) in stage 1 were evaluated in an independent sample (stage 2) containing 4,203 subjects (APOE ε4+: 1,250 cases and 536 controls; APOE ε4-: 718 cases and 1,699 controls). Among APOE ε4subjects, novel genome-wide significant (GWS) association was observed with 17 SNPs (all between KANSL1 and LRRC37A on chromosome 17 near MAPT) in a meta-analysis of the stage 1 and stage 2 datasets (best SNP, rs2732703, P=5•8x10 -9 ).
Study Population Details of the stage 1 sample from the International Genomics of Alzheimer's Project (IGAP) Consortium including subject recruitment, genotyping, imputation, quality control, population substructure, and statistical methods for association analyses were previously described. 4 In brief, phenotype and genotype data, including APOE genotypes, for a total of 53,711 subjects were assembled by IGAP from the Alzheimer's Disease Genetic Consortium (ADGC), the Cohorts for Heart and Ageing Research in Genomic Epidemiology (CHARGE) consortium, the European Alzheimer's Disease Initiative (EADI), and the Genetic and Environmental Risk in Alzheimer's Disease (GERAD) consortium. Characteristics of this sample are in Supplementary Table  S1. The stage 2 dataset included GWAS and APOE genotype data for 4,203 subjects of European ancestry from the ADC4, ADC5, ADC6, MTV, Pfizer, and TARCC datasets in the ADGC.
Procedures QC, Imputation, and Population Substructure in Stage 2 Datasets-Quality control of the clinical and genotype data in these cohorts was performed using procedures described elsewhere. 4 SNP genotypes in each stage 2 dataset were imputed with IMPUTE2 using reference haplotypes from the March 2012 release of 1000 Genomes. We compared imputation results for selected variants in the stage 1 datasets using the March 2012 release of 1000 Genomes and prior imputation on the December 2010 release, and found no significant difference in the distribution of genotype probabilities between old and new imputations for the same samples among the original ADGC datasets. We used actual APOE genotypes when available because previously we observed that imputation in this region using the 1000 Genomes reference panel is unreliable.
Statistical Analysis Genome-wide Association Study-Within each stage 1 dataset, genome-wide association analyses were conducted separately in subgroups of subjects with and without the APOE ε4 allele using a logistic generalized linear model (GLM) in case-control datasets and a logistic generalized estimating equation (GEE) in family-based datasets. The potential independent effect of the APOE ε2 allele was not examined because of the paucity of carriers of this allele, thus rendering very small cell sizes particularly among AD cases and in smaller datasets. Cox-proportional hazards models were used to evaluate association with incident AD in three CHARGE cohorts. A quantitative estimate between 0 and 2 for the dose of the reference allele for a SNP was used to incorporate the uncertainty of the imputation estimates.
Interaction between a SNP and APOE genotype was evaluated in the APOE genotype subgroups combined within each dataset using regression models including age, sex, the first three PCs, and terms for the SNP, APOE ε4 status, and interaction between the SNP and APOE ε4 status. Results for each model across datasets were combined by meta-analysis using the inverse variance method implemented in the software package METAL (http://www. sph. umich. edu/csg/abecasis/Metal/). Effect sizes were weighted by their inverse variance and a combined estimate was calculated by summing the weighted estimates and dividing by the summed weights. SNPs with a minor allele frequency >5% that were available in at least 50% of the datasets were included in the meta-analysis. The meta-analysis P-value for association was estimated by the summarized test statistic, after applying genomic control within each individual study.
Gene Expression Analysis The effect of top-ranked SNPs on gene expression was evaluated using an open access database of control brain microarray data (BRAINEAC) made publically available by the UK Human Brain Expression Consortium (http://caprica. genetics. kcl. ac. uk/BRAINEAC). This dataset contains information generated by analysis of tissue samples obtained from 12 different central nervous system regions in 134 individuals. Details of the expression quantitative trait locus (eQTL) analysis are reported elsewhere. 6 In this study, the experiment-wise significance threshold for association of a genetic marker with expression was determined to be 1. 6x10  -7  at the gene level and 1. 8x10 -6 for individual exons. Potential for functionality of the top-ranked SNPs was assessed using the Regulome database (http:// www.
RESULTS We conducted a genome-wide association study for AD using datasets stratified by APOE genotype assembled by IGAP which were from the ADGC, CHARGE consortium, EADI, and GERAD consortium. Meta-analyses were performed separately in APOE ε4+ (10,246 cases and 11,924 controls) and APOE ε4-(7,231 cases and 19,603 controls) subgroups, as well as the total sample using a model including a term for the interaction of the SNP with the APOE ε4 status. There was limited genomic inflation in the GWAS results in the APOE ε4+ (λ=1. 05) and APOE ε4-(λ=1. 06) groups, but not in the total sample (λ=0. 98) testing the ε4 * SNP interaction (Supplementary Figure  S1 ). Genome-wide significant (GWS) association (P<5x10 -8 ) for AD was found in five distinct regions (CR1, BIN1, CLU, PICALM and APOE) in the APOE ε4+ subgroup (Supplementary Figure  S2A , Supplementary Table  S2 ) and four distinct regions (BIN1, HBEGF, MS4A6A/MS4A4A, SLC24A4, and APOE) in the APOE ε4subgroup (Supplementary Figure  S2B , Supplementary Table  S2 ).
8x10  -9  ). The association was consistent in nearly all datasets which contained rs2732703 information (Figure  1B ). To verify the reliability of the association with rs2732703, an imputed SNP, we compared rs2732703 allele dosages obtained directly by genotyping using a Taqman assay with those derived from imputation among 1,010 subjects from the ACT, ADC4, ADC5 and ADC6 datasets. The correlation of these values, 0. 813 in the entire sample and 0. 834 among APOE ε4subjects, as well as a genotype misclassification rate of only 3. 5% among subjects with imputed probability scores > 0. 8 for a particular genotype, suggest that our association findings were not influenced substantially by imputation quality. Further examination of this region in the total sample revealed an association peak spanning more than 1.
, 8 To explore the possibility that the association observed in the present analysis is explained by previously identified haplotypes H1/H2 in the MAPT region,  8  we evaluated six models in the entire dataset conditioning on rs8070723 (an H1/H2 tagging SNP), rs2732703, or rs199533. Rs2732703 remained significant in models conditioning on rs8070723 (P=0. 013) or rs199533 (P=0. 0020), and rs8070723 was marginally significant in the model conditioning on rs199533 (P=0. 043) (Supplementary Table  S5 , Supplementary Figure  S3 ). These results suggest that KANSL1/ LRRC37A is the only AD risk locus in this region. We also examined the effect of APOE ε4 status on previously established AD loci (Supplementary Table  S2 ). Four of these loci attained genome-wide significance in at least one of the APOE subgroups (Table  2 ), and the association signal in the MS4A cluster region was evident primarily in the APOE ε4subgroup (Supplementary Figure  S4 ).
The association of AD with CR1, BIN1, and CLU was supported in both APOE subgroups. Next, we interrogated the BRAINEAC database to determine whether any of the 17 GWS SNPs located between KANSL1 and LRRC37A are cis-eQTLs. Data were available for only one of these SNPs (rs113986870) which is in high LD with and 2,461 base pairs away from rs2732703 (r 2 and D′>0. 9). Ten exon probes from four genes (KANSL1, LRRC37A4P, MAPT, and C17orf69) were significantly associated with rs113986870 when averaged across all brain regions (Table  3 ). Rs113986870 was significantly associated with gene-level expression (Figure  2A ) as well as with exon-level expression (Figure  2B ) in hippocampus, temporal cortex, and cerebellum. In these brain regions, rs113986870 was significantly associated with KANSL1 probes 3762011, 3762012 and 3762013 that measure expression of the first translated exon.
12 LRR motifs are important for intermolecular or intercellular interactions with exogenous factors in the immune system and/or with different cell types in the developing nervous system. 12 However, expression analysis of exon array data in control brain tissue revealed that rs113986870, which is in high LD with the top-ranked SNP (rs2732703) in the GWAS, is an eQTL for expression of the first translated exon in KANSL1 and the alternatively spliced exon 3 in MAPT. Previous studies suggest that splicing of MAPT may be a crucial regulatory mechanism in the brain and tauopathies in particular,  13  and that increased expression of exon 3 protects against neurodegeneration. 14 Although rs113986870 is apparently not an eQTL for its adjacent gene LRRC37A, it was significantly associated with a closely related gene, LRRC37A4P, in all three AD-related brain regions.
These results suggest that rs113986870 may have a potential function as a cis-acting regulatory element for multiple genes in this region. , 8 Thus, it is possible that the exon probes targeting the first translated in KANSL1 may be tagging this duplication. In addition, interrogation of a database curating information about DNA features and regulatory regions revealed that five of the GWS SNPs, including rs2732703 and rs113986870, may have strong regulatory potential. The association peak for AD on chromosome 17q21. 31 is located in a well-recognized and perplexing genomic region containing a 900 kb inversion. 8 Previous GWAS identified associations of variants within and at the edges of this inversion with Parkinson disease (PD)  15  and progressive supranuclear palsy (PSP),  16  but the most significant associations were not with SNPs between KANSL1 and LRRC37A (Supplementary Table  S6 ).
, 34 , 36 Recently, Allen et al. reported that the rs8070723-G allele was associated with reduced MAPT expression in the cerebellum and temporal cortex of AD subjects. 29 Robust genetic associations have also been identified for AD with several genes in cytoskeletal and axonal transport pathways including tau or leading to neurofibrillary tangles, most notably BIN1, EPHA1, RIN3, CASS4, and FERMT2. 4 Based on the observation that overexpression of human ApoE4 in transgenic mouse neurons results in hyperphosphorylation of tau,  37  it is possible that associations with AD-related loci in the chromosome 17q21. 31 region are obscured by the much stronger effect of APOE ε4 on MAPT expression or function. 38 This idea is consistent with lack of GWS association with 17q21. 31 SNPs in the same dataset without stratification by APOE genotype,  4  and no evidence for interaction between APOE and any SNPs in the MAPT-KANSL1-LRRC37A region in the current study.
Another possible explanation for the significant association of 17q21. 31 SNPs with AD only among subjects lacking APOE ε4 is genetic heterogeneity suggesting that variation at the chromosome 17q21. 31 locus is associated with a distinct etiological subtype of AD where tau is the primary disease activator. 39 Finally, the diagnosis of AD for most subjects in this dataset was established clinically suggesting the possibility of misdiagnosis or AD accompanied by other processes associated with other dementing illnesses. Further studies are needed to determine whether this subtype can be distinguished clinically or neuropathologically. Our study also showed that the previously established association with the MS4A gene cluster is derived almost completely from subjects lacking APOE ε4, suggesting the contribution of the MS4A locus to AD may be mechanistically different than AD-related processes that are associated with APOE ε4.