dataesr/scientific-paragraphs-categorization huggingface dataset:
ATRX and H3. 3 Are Enriched at Heterochromatic Repeats throughout the Genome Previously published data of validated ATRX chromatin immunoprecipitation sequencing (ChIP-seq) in mouse embryonic stem cells (ESCs) were obtained from the GEO database (GSM551138)  (Law et al. , 2010) , and reads were aligned to genomic repeats using Repeats Enrichment Estimator  (Day et al. , 2010). Consistent with previous studies, ATRX was enriched at pericentric and telomeric repeats relative to input (Figure  1A ;  Goldberg et al. , 2010; Law et al. , 2010). In addition, ATRX was enriched at a number of other genomic repeats (Figure  S1 ). As genomic repeats are known to be enriched for H3K9me3 and H4K20me3, chromatin modifications that are associated with heterochromatin, all ATRX-binding sites across the genome also were tested for association with these two repressive modifications.
Typical of ATRX targets, but unusual for CGIs, ATRX-bound CGIs were simultaneously enriched for H3K9me3, H4K20me3, and histone H3. 3 (Figures  2A  and 2C ). In addition, half of the ATRX-bound CGIs were DNA methylated (GSE28254)  (Brinkman et al. , 2012) , a modification that is present at only 5% of CGIs genome wide (Figure  2B ). Sequential filters applied to CGIs across the genome isolated several factors associated with ATRX binding. Selecting for CGIs enriched for modifications associated with heterochromatin (DNA methylation, H3K9me3, and H4K20me3) increased the proportion of CGIs bound by ATRX  (Figure 2D, left). Further selection for CGIs associated with H3. 3 enrichment and active transcription (determined by RNA sequencing [RNA-seq]) increased the ATRX-bound fraction to 90% (Figure  2D , left).
The SV129 3 Cast ATRX Flox ESCs were transduced with an adenoviral Cre-GFP cassette (AdCre) to generate ATRX KO cells. An adenoviral GFP cassette (AdGFP) was used as a negative control. Cells were sorted for GFP expression and RNA was extracted for sequencing. ATRX KO was confirmed by genotyping PCR (Figure  S6A ), real-time PCR for ATRX expression (Figure  S6B ), and RNA-seq data (Figure  S6C ). RNA was separated into PolyA+ and PolyAÀ fractions to capture both stable and unstable transcripts, and both fractions were subjected to strand-specific RNA-seq. Reads were aligned to the genome, and reads spanning an informative SNP were extracted and assigned to parental genomes using customized Perl scripts (Table  S4 ). Only genes with more than 50 informative SNP reads were considered for analysis.
A control dataset of 1,500 random genes were assessed for parental skewing (Table  S4 ), and the average parental skew was found to be 50% in the PolyA+ fraction and 54% in the PolyAÀ fraction toward SV129. The 150 imprinted genes (http://www. har. mrc. ac. uk/research/genomic_imprinting/) were assessed for allelic skewing (Table  S4 ). Within the PolyAÀ fraction, 11 genes showed >66% skewing toward the expected parental allele and one gene was skewed >60% toward the expected parental allele (Figure  5A ). Within the PolyA+ fraction, a total of 12 genes showed >66% skewing toward the expected parental allele, while an additional four genes were skewed >60% toward the expected parental allele (Figure  5B ). Focusing on 18 non-redundant genes that demonstrated allelic biases consistent with the literature, we examined their parental read distribution in the ATRX KO (AdCre) dataset.
H3. 3 is generally considered a mark of active chromatin, and, consistent with this, we found co-localization of H3. 3 with H3K4me3-modified promoters within our dataset. The deposition of H3. 3 into gene-rich regions is thought to be predominantly dependent on HIRA  (Pchelintsev et al. , 2013; Ray-Gallet et al. , 2002)  rather than the ATRX/Daxx complex, a finding that is also generally consistent with our data. We found that H3. 3 was moderately enriched at CGI promoters across the genome and this enrichment was unaffected in ATRX KO cells, confirming that H3. 3 deposition at these regions was generally independent of ATRX. However, the ATRX-bound CGIs identified in this study represent distinct exceptions to this general pattern. At these particular heterochromatin-modified regions, ATRX binding coincided with unusually high enrichment for H3.
Identification of SNPs in ChIP-Seq Reads ChIP-seq data of H3K4me3 (GSM307605) and H3. 3 (GSM487542) in female SV129 3 Castaneus male F1 ESCs were obtained from GEO. SNPs that differed between these two strains were obtained from The Mouse Genomes Project at the Wellcome Trust Sanger Institute webpage (https://www. sanger. ac. uk/resources/mouse/genomes/). ChIP-seq reads were mapped using Bowtie and reads that overlapped DMRs were extracted. As the IG-DMR was significantly broader than the ATRX-binding site, only reads underlying the ATRX peak were extracted from this region. DMR reads were overlapped with SNPs and parental SNPs were identified using custom Perl scripts (available upon request). Allelic Discrimination Assays ATRX-bound DMRs were assessed for differential SNPs that created a differential restriction site.
RNA Extraction and Sequencing RNA was extracted at 5 days post-transduction with Tri-Reagent (Sigma-Aldrich T9424). Total RNA was separated using a PolyATtract mRNA Isolation System (Promega Z5310). The rRNA was removed from PolyAÀ fractions with a Ribo-Zero Magnetic Kit (EpiCentre MRZH11124). Libraries were created using NEBNext Ultra Directional RNA Library Prep Kit for Illumina (New England Biolabs E7420L) with minor modifications  (Lamble et al. , 2013) , and sequenced on a HiSeq2500 in rapid mode to generate 50 bp pairedend reads. Allelic RNA-Seq Analyses RNA-seq data were aligned to the mm9 genome with TopHat v2. 0. 10  (Trapnell et al. , 2009)  using default parameters. Parental SNPs across genes were identified as previously described for ChIP-seq datasets, except that reads were analyzed in a strand-specific manner.
ACKNOWLEDGMENTS This work was supported by the  Medical Research Council (UK). We thank the  High-Throughput Genomics Group at the Wellcome Trust Centre for Human Genetics  (funded by  Wellcome Trust  grant reference  090532/Z/09/Z  and  MRC Hub  grant  G0900747 91070 ) for the generation of the sequencing data. We wish to acknowledge the  Computational Biology Research Group, Weatherall Institute of Molecular Medicine, Oxford , for use of their services in this project.
Data collection Patients were examined preoperatively and at regular intervals postoperatively and called back for the purpose of this study at an average follow-up of 4. 2 years (2-8. 2 years). Overall parent satisfaction as well as the impact of surgery on the child's daily activity were recorded. Assessment of shoulder function improvement was based on documentation of postoperative modification of active abduction/forward flexion, active external and internal rotations separately as well as the improvement in Mallet score. In younger children, evaluation of active range of motion was based on stimulation of the upper limb in the desired direction. The disappearance of the trumpet sign in abduction was considered an improvement in active external rotation. Evaluation of internal rotation was modified from the one present in the Mallet score to make it more representative of the real impairment of daily function.
Data analysis Immediate preoperative values were compared to 6-month and last follow-up values using a paired Student t-test with a 95 % confidence interval. Children younger than 3 years at surgery were compared to older ones and those with C5-C6 injury were compared to those with more extensive involvement. Statistical analysis was performed using SPSS 16. 0 software (SPSS Inc. , Chicago, IL, USA). p \ 0. 05 was considered statistically significant.
However, all of the chemotherapy regimens were based on 5-FU. IBM SPSS (Armonk, NY, USA) v. 20 was used for statistical analyses. Comparisons between groups were performed with w 2 , Fisher or Student's t-tests when appropriate. Correlations between qualitative variables were assessed by calculating Cramer's V. The prognostic influence of variables on survival was assessed with a log-rank test and Cox proportional hazard models, whereas a binary stepwise logistic regression was performed to evaluate influence of variables on recurrence after thoracic surgery. A twoway ANOVA was conducted that examined interaction between discontinuous variables. All tests were two sided, and the variables were considered significant for P-values o0. 05. All variables with P-values o0. 2 were tested in multivariate analysis.
Moreover, the molecular data were obtained from the primary CRC and not from the metastatic tumours. Whether there is a concordance between the primary and the metastatic tumours remains a doubt, although  Cejas et al (2009)  reported a concordance of 94%. In addition, some data that may partially impact the OS are lacking. We were not able to determine the interval that separated the liver or lung metastases from the CRC or the hepatic metastases. This may not be important because, according to  Zabaleta et al (2011) , the time between the hepatic resection and the lung metastasectomy does not affect survival. Nevertheless, patients with synchronous liver metastases and CRC have poorer survival. Our study also covers a 13-year period, during which the management of patients could have changed, especially with the introduction of targeted therapies that might impact OS.
Decision making for the conservation and management of coral reef biodiversity requires an understanding of spatial variability and distribution of reef habitat types. Despite the existence of very high-resolution remote sensing technology for nearly two decades, comprehensive assessment of coral reef habitats at national to regional spatial scales and at very high spatial resolution is still scarce. Here, we develop benthic habitat maps at a sub-national scale by analyzing large multispectral QuickBird imagery dataset covering ~686 km 2 of the main shallow coral fringing reef along the southern border with Tanzania (4. 68 ˝S, 39. 18 ˝E) to the reef end at Malindi, Kenya (3. 2 ˝S, 40. 1 ˝E). Mapping was conducted with a user approach constrained by ground-truth data, with detailed transect lines from the shore to the fore reef.
Satellite Image Analysis Cloud free QuickBird imagery over the Kenyan coastline was acquired during the period 2008-2010. These included 17 scenes and 9 swaths/footprints. QuickBird is a multispectral sensor comprising three visible and one near-infrared band at 2. 4-m spatial resolution. We employed a hybrid classification system, by integrating expert knowledge with reflectance analyses in a hierarchical process  [31, 32]. Using ENVI software, we preprocessed the data by first masking out the land using the infrared band, and by outlining the reef break strip in order to establish the boundary of the reef crest and the slope limit. Broad geomorphological zones between the reef crest and the shore, such as rubble banks and patch reefs were manually outlined using digitization modules. During the outlining process, spectrally enhanced combinations of visible and infrared bands were used in order to delineate clearly the various habitat categories, while referring to field data where available.
Caveats Despite the novelty of this study, it was not without challenges and limitations and we identify six caveats. First, the time lag between the image overpass and ground-truthing data collection can be up to two years. This can result in a mismatch between field observations and image classification and subsequent scenario modeling. We suspect, however, that many of these habitats are stable on this time scale  [49]. Second, we lack a formal accuracy assessment of the habitat maps. Indeed, we lacked resources to re-survey the entire Kenya coastline for an independent accuracy assessment, and the training stage was already a big task. We reached an overall accuracy of 62% when using the training ground-truth data collection. This is a low value, furthermore theoretically optimistically biased because training quadrats were also used for accuracy assessment.
Conclusions This study demonstrated the use of very high-resolution space borne data in producing large-scale coral reef habitat maps and subsequently how these maps can support spatial prioritization decisions on the management of small-scale coral reefs fisheries. Our study analyzed multispectral imagery covering ~686 km 2 of reefs along the Kenyan coast, representing one of the few national-scale, consistently processed, and very high-resolution remote sensing benthic habitat mapping applications. We then combined habitat map data with information on the existing and planned multi-stakeholder use of coastal marine areas, population density, existing fishery managed areas, and climate change related threats to identify and prioritize areas that either minimize lost fishing opportunity, redistribute fisheries away from currently heavily fished areas, or prevent potential resource use conflicts.
Acknowledgments: The  Western Indian Ocean Marine Science Association Marine Science for Management Program (WIOMSA-MASMA)  supported the procurement of the satellite imagery. The  Wildlife Conservation Society  through grants from the  John D. and Catherine T. MacArthur Foundation  supported the mapping and data analyses aspects of this study. This research was conducted with support from the  Australian Research Council Centre of Excellence for Environmental Decisions. JEMW were also supported by a  Discovery Grant  from the  Australian Research Council  ( DP140100733 ). We thank  Nyawira Muthiga ,  James Mariara  and  Joan Kawaka  of  WCS , and  Pascal Thoya  and  Jelvas Mwaura  of  KMFRI  for their help with field data collection and logistics. This is an ENTROPIE contribution # 79.
Study Design This ongoing, prospective, randomized, 2-arm, active-controlled study is being conducted at 12 sites in the United Kingdom and France. Subjects were enrolled and treated in 2012; this report covers data collected through 3 months. The study (NCT01579305) was approved by independent ethics committees, and all subjects provided written informed consent. Subjects were randomized (1:1) to receive Juvéderm Volbella with Lidocaine or Restylane-L based on a central randomization schedule. An automated interactive voice and Web response system was used to manage the randomization and treatment assignment. Sites dispensed treatment according to the instructions provided by the automated system. Investigators at each site determined the appropriate treatment injection volume based on clinical experience and subject lip treatment goals.
In the present study, investigators enrolled subjects who had minimal or mild baseline LFS scores, and the primary endpoint was based on LFS assessments made by the ICR from 3D images. The ICR and investigator assessments of LFS at baseline differed considerably; the ICR assessment found a higher mean LFS score and rated >50% of the subjects as having moderate or marked lip fullness at baseline. Accordingly, these subjects had little room to show the 1-point improvement in overall lip fullness necessary for classification as a responder. Differences were also observed between assessors for oral commissures and perioral lines. Together, these data suggest that photographs may present less detail and therefore are less sensitive for measuring observable changes compared with in-person assessment.
ACKNOWLEDGMENTS Juvéderm Volbella Study Group Principal Investigators:  Raj Acquilla, MD  ( Cheshire, United Kingdom ) has no financial relationships to disclose;  Lakhdar Belhaouari, MD  ( Toulouse, France ) has received research grant support from  Allergan  for conducting the study;  Jonquille Chantrey, MD  ( Cheshire, United Kingdom ) has received research grant support from  Allergan  for conducting the study;  Simon Connolly, MD  ( Glasgow, United Kingdom ) has received a study fee schedule from  Allergan, Inc. ;  David Eccleston, MD  ( Sutton Coldfield, United Kingdom ) has received speaker honoraria from Allergan in the past;  Véronique Gassia, MD  ( Toulouse, France ) has received speaker honoraria from  Allergan, Inc. ;  Annelize Meyer, MD  ( West Sussex, United Kingdom ) received funding through  Allergan, Inc.
In vivo cardiac function (cine-CMR) The assessment of myocardial mass and function by cine-CMR was performed according to the hemisphere cylinder model using one short axis area measurement and one long axis length for volume approximation. This model has recently been validated as one of the best methods to assess cardiac function in a reduced scan-and post-processing time  [24]. A FLASH cine-CMR sequence (37 phases, slice thickness 1 mm, in-plane resolution 234 x 234 μm 2 , TR = 5. 1 ms, TE = 1. 4 ms, two averages, duration 3 min per slice) was used to acquire three perpendicular views (2-chamber, 4-chamber and mid-LV short axis). Image segmentation and data analysis were performed using an in-house developed program running under IDL environment (ITT, Boulder, CO, USA). Epicardium, endocardium, and left ventricular lengths were manually delimited in both systole and diastole to obtain needed data for volume approximation as described previously  [33].
In vivo myocardial perfusion (Arterial spin labeling CMR) Myocardial blood flow was quantified using a modified version of the cine-ASL technique, as described previously  [23]. Cine-ASL parameters were: flip angle α = 8°, TE/ TR =1. 64/8 ms, field of view = 25 mm x 25 mm, matrix size = 128 x 64, resolution = 0. 195 mm x 0. 391 mm, excitation pulse duration = 0. 5 ms, inversion pulse duration = 6 ms, imaging slice thickness = 1. 5 mm, labeling slice thickness = 2. 5 mm, N echoes = 10, N cine = 30. Images analyses were performed using a home-made program running in an IDL environment (ITT, Boulder, CO, USA) which generates perfusion maps. Using these maps, we quantified myocardial perfusion in mL/g/min.
Mice fed HFHSD underwent MR examination and IPGTT at 16 weeks, before treatment with exendin-4 or physiological serum during 14 days and then underwent once again MR examination and IPGTT after treatment at 1000 ms for heart and liver. To verify whether partial saturation has occurred with possible different TRs, we fitted triglyceride peak in both saturated spectra, and unsaturated reference spectra for heart an liver. Ratio of TG concentration in saturated spectra over TG concentration in unsaturated spectra is about 0. 96 ± 0. 15 in average in heart and liver. Therefore, underestimation of TG is inferior to 5 % and variation from one experiment to the other is weak. Data fitting and analysis were performed using AMARES time-domain fitting routines from the MRUI package (http://www. jmrui.
sWthD: Septum wall thickness, sFS: Septum fractional shortening, CI: Cardiac index. HFHSD mice displayed hypoperfusion at 4-month (ki) which is completely corrected after Ex-4 treatment (kiii) compared to placebo group (kii). Data are presented as means ± SEM. Treatment effect on AUC glucose during IPGTTT was assessed using Two-way ANOVA test. Other parameters were statistically assessed with paired t-test or Wilcoxon signed rank test when values were not normally distributed. *p <0,05; **p <0,005; ***p <0,0005 perfusion and function. Our longitudinal MRS analysis showed early TG accumulation in liver and heart. Moreover, TG accumulation in the liver preceded that of the heart suggesting pivotal involvement of hepatic insulin resistance in the development of metabolic complications of obesity.
Another relevant observation is that although we found a significant correlation between both hepatic and cardiac fat accumulation, linear regression showed an important disparity between both tissue TG accumulations. These combined data strengthen our hypothesis regarding a tissue specific mobilization of ectopic fat development based on data obtained in patients. Indeed, obese patients have different phenotype of ectopic fat accumulation. While some patients exhibit elevated hepatic triglyceride content and normal pancreatic triglyceride content level, others have increased cardiac triglyceride content with normal hepatic triglyceride content. This indicates tissue-specific distribution of TG in non-adipose tissue. Furthermore, in our previous studies, we assessed ectopic fat modulation after weight loss in obese patients who underwent bariatric surgery.
It is hard to make strong comparisons to the standard NER task, as KB tags can be considered partial, unaligned gold-standard supervision -so fully supervised models should perform better, the question is by how much and why. This paper explores effective ways to use KB tags for improving NER. We use the CoNLL 2003 English NER dataset  (Tjong Kim Sang and De Meulder, 2003) , annotated with Wikipedia links  (Hoffart et al. , 2011). This allows us to simulate a set of KB tags for each document in the TRAIN, TESTA and TESTB splits of the dataset. We use a document's KB tags to build a documentspecific gazetteers which we use in addition to standard features for a conditional random field (CRF) model  (Lafferty et al. , 2001). We compare against wide-coverage gazetteers, which score 89. 85% F-score on TESTA.
Baseline (CRF) We train a CRF model using CRFsuite  (Okazaki, 2007)  with a standard set of features that encode lexical context, token shape, but no external knowledge features such as gazetteers. All following configurations build on the CRF with standard features. KB tag repair (CRF +REPAIR ) We label the text using the baseline CRF, then find the longest full match from the document gazetteer and assign the known type. When a gazetteer match overlaps with a CRF match, we prefer the gazetteer and remove the latter. Although we do not consider partial matches, this may recognise longer names that can be difficult for CRF models.
Wide-coverage gazetteers (CRF +WIDE ) This uses gazetteers distributed with the Illinois NER system  (Ratinov and Roth, 2009). We encode each 1 We omit the O tag as all gazetteer tokens are inside. phrase using the BMEOW scheme described above, and use the filename of each gazetteer as its type. There are 33 gazetteers drawn from many sources with approximately 2 million entries. We fetch and cache KB information using a Wikipedia API client. 2  We assume the tag set of person (PER), organisation (ORG), location (LOC) and miscellaneous (MISC), and report precision, recall and F-score from the conlleval evaluation script. The median proportion of mentions in a document that are linked to the KB is 81% in TRAIN and TESTB, and 85% in TESTA. Augmenting the gazetteer with aliases produces, on average, 26 times the number of gazetteer entries than KB tags alone in TESTA, and 23 times in TESTB.
Second, with adjusted weights for eco-driving and third, with a "soft" eco-driving approach using a lower weight on the charge consumption and a speed reference increased by 10%. This "soft" eco-driving leads to the same average driving speed like the purely reference tracking controller to highlight that energy can be saved by changing the driving style without going slower. The results of the above described simulation scenario are computed within Matlab/Simulink and are depicted in Fig. 9. The parameter values of the controller model are given in Table  II  while the simulation parameters are the same as in Section III (Table  I ). The quadratic program  (24)  has been solved with the Matlab function quadprog() and the solver interior-point-convex which has proven to work best in the simulations.