dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors thank the medical doctors  Pierre Anthony ,  Benjamin Cretin ,  Catherine Demuynck ,  Catherine Martin-Hunyadi , and  Nathalie Philippi  for the essential collection of the clinical data. They also thank  Jean-Paul Armspach ,  Julien Lamy ,  Paulo Loureiro de Sousa ,  Corrine Marrer , and  Vincent Noblet  from the  ICube laboratory (IMIS team)  and the Innovative imaging of the health MRI platform (Plateforme Imagines). They also thank  Mélanie Stackfleth ,  Timothée Albasser , and  Laetitia Berly  for organizational help.
Funding This study was funded by  Projet Hospitalier de Recherche Clinique (PHRC) inter-régional  ( IDRCB 2012-A00992-41 ), the  Fondation Université de Strasbourg  and famille  Jean Amrhein ,  AG2R   La Mondiale ,  Appel à Projet Interne (API) of the University Hospital of Strasbourg , and  Alsace Alzheimer 67.
New insights into B-Raf phosphorylation and ubiquitination Next, we used our experimental systems to identify ubiquitination and phosphorylation sites on B-Raf. To this end, we applied a multi-protease approach  [52] , which has not been applied to B-Raf complexes so far and delivered a very good sequence coverage. In case of phospho-peptide analyses, we used TiO 2 -based enrichment protocols. In addition to K88 as novel ubiquitination site (Supplementary Table  S5 ), we identified in total 36 phosphorylation sites of which 10 have not been listed in the Phosphosite database (http://www. phosphosite. org/ proteinAction. action?id=577&showAllSites=true) and only eight have been subject to functional characterization so far (Supplementary Tables  S6-S9 ). Importantly, several phosphorylation sites were observed in the three cell lines from chicken, mouse and man, indicating a conserved function in B-Raf regulation.
The presented data and work by the Morrison group  [26]  indicate that phosphorylation of S151 by a proline-directed kinase, or a negative charge at this position, impairs the signaling potential of B-Raf. This   concept is further supported by an entry in the COSMIC database listing a BRAF P152S mutation (Supplementary Table  S6 ). This alteration destroys the consensus motif required for S151 phosphorylation by proline-directed kinases of the ERK and CDK subfamilies  [65]. These findings raise the question which kinases control S151 phosphorylation. Using metabolic labelling with 32 P, the Morrison laboratory showed that growth factors induce the phosphorylation of S151 by a process that is sensitive to the MEK inhibitor UO126  [26, 66]. Similarly, UO126 and PD98059, another MEK inhibitor, blocked S151 phosphorylation in melanoma cells  [63].
Both mechanisms would explain the loss of B-Raf activity. Another site that was phosphorylated in B-Raf complexes from all three cell lines was S605, located in the activation loop. This evolutionary conserved residue is closely located to the T 599 VKS 602 -motif and is required for maximum B-Raf activity  [22, 24] , suggesting a potential role in Raf regulation. Furthermore, substitutions of S605 by other residues are listed in the Catalogue of Somatic Mutations in Cancer (COSMIC; http://www. cancer. sanger. ac. uk/cancergenome/projects/cosmic/), albeit at very low frequency compared to other activation segment mutations such as V600E/K. In order to analyze the role of this phosphorylation site and the so far unknown relevance of its mutation for the signaling potential of B-Raf, we replaced S605 by an alanine residue to prevent phosphorylation, by a phospho-mimetic glutamate residue or by a glycine residue, as the latter substitution represents the most frequently observed S605 alteration in the COSMIC database.
In addition to the multiple and dynamic proteinprotein interactions, the high number of phosphorylation sites as well as the emerging ubiquitination (this study,  [97, 98] , acetylation (Phosphosite database) and methylation events  [99]  further demonstrate the role of B-Raf as a signaling hub integrating multiple signaling pathways. In this study, we have focused on establishing a catalog of phosphorylation sites identified on B-Raf proteins purified from various cell types and conditions. In addition to well-established phosphorylation sites linked to B-Raf activation (S446;  [100] ), 14-3-3 binding (S365/S729;  [25, 27, 28] ) and feedback regulation (S751/T753;  [29, 30] ), we have confirmed and further characterized more recently identified sites such as S151 and discovered several novel ones.
This concept, which will be addressed in future studies, is further supported by the observation that B-Raf becomes ubiquitinated and interacts with Skp1 as well (this study;  [98] ). Indeed, while our manuscript was under revision, Hernandez et al. reported that alanine substitution of T401 and S405, a site that was also highlighted by the MASCOT software in our data sets but which was not included in Supplementary Table  S6  due to its low score, increased the half-life of B-Raf V600E  [105]. Moreover, based on the dynamics of HR phosphorylation revealed by our SILAC approach, we posit that the HR cluster phosphorylation involves a kinetic proofreading mechanism in which only the processive integration of multiple phosphorylation sites can execute a decision such as the disruption of a protein-protein or -lipid interaction or tagging the protein for its degradation  [106].
Immunoprecipitation, in vitro kinase assays and western blotting These procedures were conducted as described in detail previously  [13]. In brief, cells were lysed in normal lysis buffer (NLB: 50 mM Tris/HCl, pH 7. 5; 1% Triton X-100; 137 mM sodium chloride; 1% glycerin; 1 mM sodium orthovanadate; 0. 5 mM EDTA; 0. 01 μg/ μl leupeptin, 0. 1 μg/μl aprotinin, 1 mM AEBSF). Blotted proteins were visualized with horseradish peroxidaseconjugated secondary antibodies (Roche) using the SuperSignal West Femto Maximum Sensitivity Substrate (Thermo Scientific) and either a LAS-4000 reader (FujiFilm) or a Fusion Solo chemiluminescence reader. Densitometry was performed using MultiGauge software (FujiFilm) or FusionCapt software (Vilber Lourmat). MS analysis of immunoprecipitated B-Raf complexes is described in detail in the expanded view.
ACKNOWLEDGMENTS This work was supported by the  Emmy-Noether-Program of the Deutsche Forschungsgemeinschaft (DFG)  to TB. MR and MK were supported by the  Spemann-Graduate School for Biology and Medicine (German Excellence Initiative ,  GSC-4 )). TB, RB, ES and JD are also supported by  DFG -funded  Collaborative Research Centre 850  (projects  A6 ,  B4 ,  B8 ) and 746 (RB) as well as the  Centre for Biological Signalling Studies BIOSS (German Excellence Initiative ,  EXC294 ). TB and JD also acknowledge funding by the  Deutsche José Carreras Leukämie Stiftung e. V. (project  13/12 ).
This is crucial, since the shape of these two bones largely brackets the array of possible movements of the foot. In several taxa, comparable characters are described qualitatively, so that their variation can be functionally interpreted. This allowed us to define character states that best characterize the motion range of the ankle, and the favoured locomotor mode as a result. Legs were dissected in a few species in order to clarify the functional link between bones and muscles. Finally, we employed multivariate analyses on a larger dataset to infer the locomotory behaviours of some fossil species.
, 2010). This type of approach has already been applied successfully in primates (e. g. ,  Dagosto, 1986; Dagosto, 1988; Dagosto, 1993; Gebo & Simons, 1987; Gebo, 1988; Gebo et al. , 2001; Marivaux et al. , 2010; Marivaux et al. , 2012). Although quantitative analyses have the potential to make inferences based on a large dataset, the results of this study suggest that these inferences should be made carefully, and with the largest possible sample size. Comparative anatomy, if possible accompanied by some functional interpretations, should still be used as a referential (rather than directly interpreting quantitative analyses), or as a mean to compensate for lack of morphological representativeness of the extant dataset. Some important caveats are to be acknowledged when making inferences with this approach.
ACKNOWLEDGEMENTS We thank the  MNHN  and UM collections for lending several specimens, and for allowing SG to visit their rodent collections ;  François Catzeflis  for lending the specimen of Proechimys cuvieri ;  Thierry Noël  and  Marc Vianey-Liaud  for lending some dissection tools;  Camille Martinand-Mari  and  Emilie Liabeuf-Le Goff  who offered access to the dissection room. We are also grateful to  JX Samuels  and one anonymous reviewer for their helpful comments on the manuscript.
Microscopic analysis Images were acquired using a Zeiss Axio Imager M2 microscope (Zeiss, Le Pecq, France) with a 100 Â objective and Zeiss filters 45 and 46 for fluorescence microscopy. Axiovision 4. 8 software (Zeiss, Le Pecq, France) was used for image acquisition. Lipid body visualization was performed by the addition of Bodipy s Lipid Probe (2. 5 mg/mL in ethanol; Invitrogen) to the cell suspension (A 600 of 5) and after incubation for 10 min at room temperature.
Acknowledgments This work has benefited from aid managed by the  Agence Nationale de la Recherche  under the " Investissements d'avenir" program  with the reference code  ANR-11-BTBR-0003. R. Ledesma-Amaro  received financial support from the  European Union  through the  Marie-Curie   FP7 COFUND People Program  ( FP7-267196 ) in the form of an  AgreenSkills Fellowship. We thank  G. Kunze  for providing strain LS3. We would also like to thank  Jessica Pearce  and  Lindsay Higgins  for their language editing services.
In recent years, there has been a trend towards MRI guided radiotherapy. MR offers exquisite softtissue contrast and is highly versatile, capable of imaging a wide variety of structures. The ability to visualize lymph nodes, for example, enables the use of stereotactic boost to individual nodes. In the context of moving targets, MRI offers the ability to perform 4D imaging, implemented as a repeated acquisition of 3D image volumes. Currently achieved temporal resolutions are in the order of 2 s to 3 s. Further development is needed to obtain real time 4D data acquisition. However, there has been recent progress on retrospectively reconstructing 4D-MRI datasets from multi-slice acquisitions Another approach for non-ionizing, non-invasive imaging of soft-tissue motion is to use US guidance  [Fontanarosa 2015 , Western 2015].
The motion of the implanted markers can be easily determined using stereo x-ray images  [Zhang 2013 ]. This can be used to check the accuracy of the models during treatment, and to update or rebuild the models if required. There has been much research into correspondence models that can model the motion of an entire region of interest rather than just a few specific points  [McClelland 2013 ]. In this case the motion is usually determined by applying DIR to a 4D dataset, and then fitting the correspondence model relating the registration results to the surrogate signal(s). As such, the afore-mentioned issues in the context of DIR also apply to correspondence modelling and translate into model errors, respectively. In addition one of the main problems with fitting correspondence models to DIR results is acquiring appropriate 4D data.
Most currently available 4D imaging methods cannot acquire data over the entire region of interest fast enough to 'freeze' the respiratory motion. Instead they acquire data over several breath cycles, and then sort the data into coherent volumes after the acquisition  [von Siebenthal 2007 , Hugo 2012]. In essence this assumes a particular correspondence model during the image formation (although the model is usually not explicitly calculated), and therefore it is at least questionable to fit other correspondence models to the 4D dataset. One potential solution to this is to use several fast helical CT acquisitions and record the surrogate signal value for each individual slice [Thomas 2015]. Another potential solution is to combine the correspondence model fitting and DIR into a single optimisation  [McClelland 2014] , which allows the model to be fitted directly to the unsorted 'raw' or 'partial' data before it is sorted into coherent volumes.
The quality of GNSS ZTD estimates systematically improve with time, from RT to NRT, and further on to postprocessed data. Within operational meteorology this is a highly uncommon issue, and meteorological databases and DA systems are not built to handle quality updates of the same observation type. In E-GVAP, this is currently handled by naming the solutions differently, e. g. METR vs. METO (sub-hourly vs. hourly). Handling this issue in a more sys-tematic way, transparent to all users and data producers, will likely improve the impact of GNSS tropospheric products in NWP. The importance of this increases with the movement toward rapid-update NWP with short cut-off times, while still running global NWP with a 12 h DA time window. It is clear from the nature of GNSS processing that GNSS ZTDs will have correlated errors in space and time.
Acknowledgements. The authors would like to acknowledge the support provided by  COST -European Cooperation in Science and Technology  for providing financial assistance for publication of the paper. W. Soehne ,  Federal Agency for Cartography and Geodesy (BKG) ,  Germany , is acknowledged for the generation of the EUREF ZTD product. J. Douša  acknowledges the national support from the  Ministry of Education, Youth and Sports  ( LD14102 ). G. Guerova acknowledges support through  Marie Curie International Reintegration Grant  ( FP7-PEOPLE-2010-RG ) of the 7th European Community  Framework Programme. E. Pottiaux  acknowledges the support of the  Solar-Terrestrial Centre of Excellence (STCE) , funded by the  Belgian Federal Science Policy Office (BelSPO). Finally, the authors are grateful to the  EUREF  and the  IGS , and all colleagues and data providers who made GNSS meteorology and GNSS climate research possible.
Estimation of LOY from SNP-Array Data All participants were genotyped using different versions of Illumina SNP arrays and we applied a strict quality criterion as described in the Results. A continuous variable, the median of the log R ratio (i. e. , mLRRY), was used to estimate the degree of LOY for each subject and calculated as the median value of the SNP-array probes positioned within the male-specific region of The American Journal of Human Genetics 98, 1208-1219, June 2, 2016 1209 chromosome Y (i. e. , MSY, chrY: 2,694,521-59,034,049, hg19/ GRCh37) as described. 30 The ULSAM samples were genotyped with the Illumina HumanOmni2. 5M chip, the PIVUS samples with the Illumina HumanOmniExpress chip, and the EADI1 samples with the Illumina Human610Quad chip, containing 2,560, 1,690, and 2,153 SNP probes located within the MSY, respectively.
Validation of LOY via Next-Generation Whole-Genome Sequencing In 100 ULSAM subjects, observations of LOY as well as other autosomal mosaic copy-number variants detected by SNP array were validated using low-coverage (~53) WGS data, as described previously. 30 A similar validation approach was applied for EADI1 study. Whole-genome sequences from 183 subjects were generated with the Illumina HiSeq platform of the McGill University and Genome Quebec Innovation Center with paired-end reads of 125 base pairs at a mean depth of 303. Sequencing reads were aligned to the GRCh37 human reference genome with BWA software  45  using the BWA-MEM algorithm (v. 0. 7. 7-r441). PCR duplicate reads were flagged with the Picard MarkDuplicates software (v. 1. 123). A local realignment step was then performed around known indels and base qualities were recalibrated with GATK software (v.
Statistical Analyses We used the statistical software R (v. 3. 2. 3)  47  for data mining and statistical analyses. Cox proportional hazards regressions were performed with the Survival package in R (Web Resources). Pooled analyses using data from the ULSAM and PIVUS cohorts were fitted with the strata-option in the Cox-model to define source of data. Study entry in the Cox regressions were the date of blood sampling and age was used as timeline. Confounding factors at baseline that were fitted in statistical analyses are summarized in Table  S1. LOY was modeled both as a continuous explanatory variable (i. e. , mLRRY) and as a binary variable after scoring participants based on defined thresholds of mLRRY, as described in the Results.
28 are considered high quality (see link in Web Resources to Illumina Tech Note). In total, 3,218 experiments passed this quality control and were included in further analyses (Figure  S3 ). Furthermore, validation of the SNP-array-based inferences of LOY was achieved by analysis of next-generation whole-genome sequencing data (WGS). This was performed by estimating ploidy values from WGS data using the FREEC software. 46 Using this approach in 100 ULSAM participants, all observations of LOY as well as other autosomal mosaic copy-number variants detected by SNP array could be validated using the low-coverage (~53) WGS data, as described previously. 30 A similar strategy was used to validate LOY observations in SNP-array data in the EADI1 study using WGS data with higher coverage (~303) from 183 participants among the 1,611 passing the genotyping quality assessment (Figure  S3 ).
Association between LOY in Blood Cells and Age Of the 3,218 participants included in our analyses, 546 (17. 0%) had a detectable level of LOY mosaicism, i. e. , an mLRRY value lower than the 99% confidence limit of the experimental variation. The frequency of LOY was similar in the ULSAM, PIVUS, and EADI1 cohorts with 17. 5%, 21. 1%, and 15. 4% of participants scored, respectively. A linear regression model performed on this dataset showed that LOY was more common in older participants (F (1, 3216) ¼ 26. 79, p < 0. 0001) (Figure  2A ), and grouping participants based on age (Figures  2E  and 2F ) further illustrates that the fraction of men affected with LOY is increasing with participant age. Hence, both the frequency of LOY and the incidence of AD are positively correlated with age. This covariation was handled by fitting the effect of age at baseline as a continuous explanatory variable in the statistical models evaluating the association between LOY in blood cells and AD.
LOY and Smoking We have previously showed that LOY in blood cells is associated with smoking. 31 Moreover, some studies suggest that smoking might play a role in dementia and Alzheimer disease. 48, 49 The present dataset included 1,565 subjects passing genotyping quality control and with smoking data available (n ¼ 1,097 and n ¼ 468 in ULSAM and PIVUS, respectively). However, in the current dataset there was no significant association between smoking status at 70 years of age and AD diagnosis (logistic regression; p ¼ 0. 2570). The lack of association between AD diagnosis and smoking status was replicated also after adjusting for the confounding effects of APOE genotype and sampling age (logistic regression; p ¼ 0. 2459). Nevertheless, the potential effect from smoking on AD status was fitted as a binary variable (current smoker at age 70 or not) in the subsequent models testing association between LOY and risk for AD.
Association between LOY and Risk for AD in Two Prospective Cohorts The association between LOY in blood cells and risk for AD diagnosis was also examined using Cox proportional hazards regression model performed with the R package Survival (Web Resources). These analyses were performed using a pooled dataset encompassing results from two prospective cohorts (i. e. , ULSAMþPIVUS) and no prevalent cases of AD were included, i. e. , cases diagnosed prior participants. This was performed by estimating ploidy values from about 303 NGS data using the FREEC software. 46 o study baseline (n ¼ 8). Among the 1,599 participants included in these analyses, 140 incident cases of AD were observed during follow-up time (123 in ULSAM and 17 in PIVUS). Study entry in the Cox regressions were the date of blood sampling and age was used as timeline.
We have previously shown that cigarette smoking is likely to induce LOY in blood in a dose-dependent and transient manner. 31 It has further been suggested that smoking is a risk factor for development of AD. 48, 49  Moreover, smoking is a well-known risk factor for cancer development, with lung cancer being the prime cause of cancer-associated death. Smoking is also a risk factor for tumors outside the respiratory tract and these are more common in males than females. 50 Considering the above, we have thoroughly tested the possible effects of smoking in the statistical models in the current paper. In order to understand the relationship between smoking, LOY, and risk for AD, we included smoking as a co-variate in our analyses. There was no significant association between smoking status at 70 years of age and AD diagnosis in our dataset.
Supplemental Data Supplemental Data include 5 figures, 12 tables, and a list of the members of The European Alzheimer's Disease Initiative Investigators and can be found with this article online at http://dx. doi. org/ 10. 1016/j. ajhg. 2016. 05. 014. S9  The ''cont. mLRRY'' is the continuous mLRRY estimate (i. e. , the median of the log R ratio values of SNP-array probes positioned within the male-specific region of chromosome Y) reflecting the degree of LOY mosaicism in each participant. We also scored participants as 1 or 0 based on their mLRRY value using two different thresholds, i. e. , mLRRY < À0. 4 and mLRRY < 99% CI, as further described in the text. Abbreviations are as follows: D, the Kolmogorov-Smirnov test statistic; HR, hazard ratio; OR, odds ratio. e Cox hazards regression model testing the effect from level of LOY in blood and risk for AD diagnosis during follow-up time, after adjusting survival only for the significant confounders (Table  S3 ).
Acknowledgments We thank  John Armour ,  Bradley Hyman ,  Alfredo Ramirez , and  Constanin Polychronachos  for critical evaluation of the manuscript. The study was sponsored by funding from the  Olle Enqvist Byggma ¨stare Foundation and European Research Council (ERC)   Starting Grant  to L. A. F. and by the  Swedish Cancer Society , the  Swedish Research Council , the  Swedish Heart-Lung Foundation , the  Torsten So ¨derberg Foundation , and  Sci-Life-Lab-Uppsala  to  J. P. D. Genotyping  and next-generation sequencing was performed by the  SNP&SEQ Technology Platform in Uppsala, Sweden , and supported by  Wellcome Trust  Grants  WT098017 ,  WT064890 ,  WT090532 ,  Uppsala University ,  Uppsala University Hospital , the  Swedish Research Council , and the  Swedish Heart-Lung Foundation. The SNP&SEQ Technology Platform is part of  Science for Life Laboratory at Uppsala University  and supported as a national infrastructure by the  Swedish Research Council.
C. M. L. is a  Wellcome Trust   Research Career Development Fellow  ( 086596/Z/ 08/Z ). The work on EADI1 dataset was funded by the  French National Fondation on Alzheimer 's disease and related disorders, The  Fondation pour la recherche sur le cerveau (FRC) , the  Lille Metropole Communauteurbaine council , and the  LABEX (laboratory of excellence program  investment for the future)  DISTALZ grant  ( Development of Innovative Strategies for a Transdisciplinary approach to ALZheimer's disease ). Received: February 19, 2016 Accepted: May 9, 2016 Published: May 23, 2016  Web Resources A Package  for Survival Analysis in S, http://www. mayo. edu/research/documents/tr53pdf/doc-10027379 Burrows-Wheeler Aligner, http://bio-bwa. sourceforge. net/Control FREEC, http://bioinfo-out. curie. fr/projects/freec/dbVar, http://www.
ncbi. nlm. nih. gov/dbvar/GATK, https://www. broadinstitute. org/gatk/ IGAP Summary Statistics , http://web. pasteur-lille. fr/en/recherche/u744/igap/igap_download. php ILLUMINA Tech Note, www. illumina. com/content/dam/illuminamarketing/documents/products/appnotes/appnote_cnv_loh. pdfOMIM, http://www. omim. org/Picard, http://broadinstitute. github. io/picard/PIVUS, http://www. medsci. uu. se/pivus/ULSAM, http://www2. pubcare. uu. se/ULSAM/index. htm.