dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis of gene expression by quantitative RT-PCR This was performed as described recently  (Lichius et al. , 2014). Briefly, Dnase-treated (DNase I, RNase free; Fermentas) RNA (5 μg) was reverse transcribed with the RevertAid™ First Strand cDNA Kit (Fermentas) according to the manufacturer's protocol with a combination of oligo-dT and random hexamer primers, and all qPCR assays were performed on a Bio-Rad (Hercules, CA) iCycler IQ. Relative gene expression ratios were calculated using REST © Software  (Pfaffl et al. , 2002). All samples were analyzed in at least two independent experiments with three replicates in each run. The significance of differences in gene expression between different knockout mutants and the remaining strains at a given time point was evaluated by the Student's t-test (http://studentsttest.
Acknowledgements We would like to thank  M. Leakey  who kindly offered us to excavate and study the locality she discovered in the Lokone area in 1994. We thank the  Ministry of Science and Technology of Kenya  for permission to conduct research in the Turkana District,  Dr I. O. Farah  (Director General of the NMK),  Dr E. Mbua  (head of the  Geoscience Department)  and the staff of the  NMK  for their assistance with collections. We are indebted to people who participated in field missions for their logistic and field assistance ( M. Macharwas , people of Loperot and Lomeloku,  F. Emekui  and  Mary ). Many thanks to  J. -J. Tiercelin  for his help on the field and the geological survey of the area. We gratefully thank the heads and people of institutions who graciously provided access to their collections (see Supplementary Methods).
We also thank  M. Orliac  for help with the morphology of basal Cetartiodactyla and  B. Nabholz ,  F. Delsuc  and  P. Gaubert  for help with ML analysis. This work was supported by the  Agence Nationale pour la Recherche programs Palasiafrica  ( ANR-08-JCJC-0017-01 ) and  EVAH  ( ANR-09-BLAN-0238 ).
The tree 2 and the apomorphic list are given in Supplementary Data 2 and Supplementary Fig. 16. A maximum-likelihood analysis was performed with RaxML v. 8. 0. 2 (ref. 53) on the same data matrix, but as RaxML does not accept polymorphism for morphological data, these were changed to the most common state in the systematic group (Supplementary Data 3). Homacodon vagans was set as the outgroup. We performed 1,000 bootstraps to assess node supports (Supplementary Fig. 17 ). The command used was: raxmlHPC-SSE3 -s EpirigenysML. txt -n EpirigenysML. out -f a -m MULTIGAMMA -K MK -#1000 -p $RANDOM -x $RANDOM. (Shell command to generate a random number). Nomenclatural Acts. This published work and the nomenclatural acts it contains have been registered in ZooBank, the proposed online registration system for the International Code of Zoological Nomenclature (ICZN).
Conclusions: We show that co-reference resolution substantially improves over a baseline system which does not use co-reference information: about 3. 5 F-measure points on the test corpus for the end-to-end system (5. 5 points on the development corpus) and 7 F-measure points on both development and test corpora when gold mentions are used. While this outperforms the best published system on the BioNLP-ST 2013 Bacteria Biotope dataset, we consider that it provides mostly a stronger baseline from which more work can be started. We also emphasize the importance and difficulty of designing a comprehensive gold standard co-reference annotation, which we explain is a key point to further progress on the task.
We noticed that 78. 0% of the tokens from both training and development corpora found in the ontology correspond to a habitat name in the reference. • Presence of the token in the NCBI taxonomy. The NCBI taxonomy (http://www. ncbi. nlm. nih. gov/taxonomy/) [34]  describes a small part (about 10%) of the living species on Earth, based on public sequence databases. We extracted from this taxonomy all names belonging to the Bacteria category (24. 3% of the content), resulting in a list of 357,387 bacteria taxa, including a few variants of bacteria names. We noticed that 95. 1% of the tokens from both training and development corpora found in this subset of the NCBI taxonomy correspond to a Bacteria name in the reference annotations. • We used the Cocoa (Compact cover annotator for biological noun phrases http://npjoint.
com/annotate. php, annotations were provided by the organizers on the three sub-corpora as part of the supporting resources of the BioNLP 2013 Bacteria Biotopes shared task) annotation categories. Indeed, we observed that a few annotation categories are often tied with one of the three kinds of entities we have to process: Cell, Chemical, Mutant Organism, Organism, Protein, Unknown with Bacteria mentions, Body part, Cell, Cellular component, Chemical, Disease, Food, Geometrical part, Habitat, Location, Multi-tissue structure, Organism, Organism subdivision, Pathological formation, Tissue with Habitat names and Company, Habitat, Technique, Unknown with Geographical names. • Finally, we also created unsupervised word clusters using Brown's algorithm  [35]  with Liang's code (https://github. com/percyliang/brown-cluster) [36]  on a total amount of 2,015 scientific documents: 131 documents from the training, development and test sub-corpora, and 1,884 new documents from the same sources.
• optimal configuration: the baseline and all four features sets; • optimal configuration minus one set of features: we removed either the Brown clusters, the NCBI taxonomy, the OntoBiotope ontology, or the Cocoa annotations. The evaluation of the mention identification component was performed using the conlleval. pl script (http:// www. clips. ua. ac. be/conll2000/chunking/)  [41]  created to evaluate results in the CoNLL-2000 Shared Task. We achieved a global F-measure ranging from 0. 642 with our baseline features to 0. 834 using the optimal configuration. The feature sets that increase the results are the following ones, in ascending order of the obtained gain: the NCBI taxonomy (+8. 3 points), the Brown clusters (+8. 9 points), the Cocoa annotations (+11 points) and the OntoBiotope ontology (+12.
We have used version 7 of the scorer initially designed for the CoNLL-2011 co-reference resolution task (dated Dec 2013 on the web site but 2013-10-30 on the file. Last news and code available at http://conll. cemantix. org/ 2011/software. html and http://conll. github. io/referencecoreference-scorers/). It implements what is presented as consensual versions of MUC, B-CUBED and CEAF (entity based and mention based). BLANC is currently being revised and will be included in a next release. This scorer compares predicted co-reference chains to gold co-reference chains. It implements four methods (MUC, B 3 , and CEAF using mention-based similarity or using entity-based similarity), each of which produces recall, precision and F-measure scores. For details on  these co-reference evaluation measures, please see the previous links.
Acknowledgements Part of this work was performed within the  Quaero program , funded by  Oseo, French State Agency for Innovation. This work was partly funded through project  Accordys (Agrégation de Contenus et de COnnaissances pour Raisonner à partir de cas de DYSmorphologie foetale , Content and  Knowledge Aggregation for Case-based Reasoning in the field of Fetal Dysmorphology  ( ANR 2012-2015 )) funded by  ANR  under grant number  ANR-12-CORD-0007-03.
PCR products were separated by electrophoresis on 6% polyacrylamide gels run on a LI-COR 4000 L sequencer, and allele sizes were determined by comparison with a 50-to 350-bp IRDye 800 TM standard (Li-Cor Inc. , Lincoln, NE, USA). Alleles were scored using the program GENEPROFILER, v. 4. 03 (Scanalytics, Inc. , Fairfax, VA, USA).
1. 4. 2 and ADE4 v. 1. 6-2 packages in R v. 3. 0. 3. A classical IBD pattern should result in a continuous cline of genetic differentiation and a correlation between the two distance parameters. However, distant and differentiated populations would also exhibit such a pattern. To disentangle these different possible scenarios, we visually assessed local point density using a colored scatterplot (two-dimensional kernel density estimation, kde2d function in the MASS v. 7. 3-29 package and the ADEGENET v. 1. 4. 2 package), which showed either a single consistent cloud of points (continuous cline) or several patches (distant populations)  (Jombart 2008).
Analyses of genetic structure using Bayesian clustering models To complement the latter frequency-based methods, we used two Bayesian clustering algorithms,  STRUCTURE (v. 2. 2. 3, Pritchard et al. 2000)  and  GENELAND (v. 4. 0. 4, Guillot et al. 2005) , to infer population structure (i. e. the number of clusters, K) and to (probabilistically) assign colonies to clusters based on the multilocus genotype data. Both of the algorithms assume that clusters are panmictic units with distinct allele frequencies. In order to circumvent possible problems that might have arisen from the non-independence of genotypes within colonies, all the analyses were performed using data sets in which each colony was represented by a single, randomly chosen individual (see description above). In the STRUCTURE analyses, five runs, each using a different resampled data set, were conducted to look for consistency in the predicted K values.
Standard population genetic analyses of the full sample Sequencing of 684 bp of the mtDNA COII locus revealed four polymorphic nucleotide sites that, taken together, resulted in three haplotypes: HT1, HT2, and HT3 (Table  S2 ; accession numbers AY510581, AY510577, and AY510576, respectively;  Kutnik et al. 2004 ). HT1 and HT2 were found throughout the study area and were present in 31 colonies (60%) and 14 colonies (27%), respectively (Fig. 1  and Table  S1 ). In contrast, HT3 was found in only 7 colonies in the south (13%). The mean number of alleles per locus across all colonies was 4. 67; it ranged from 2 to 13 (see Table  1  for information on the individual loci). There were no significant linkage disequilibria between loci. As a group, the loci deviated significantly from HWE; furthermore, deviations were detected for over 50% of the 20 resampled data sets for most of the loci.
Acknowledgments This work was partly funded by  Dow AgroSciences  and the  CNRS , who financed  M. Kutnik 's PhD work (2001-2004). We wish to thank  D. Andrieu  ( MSH of Tours ) for his help in editing Fig. 1 andJ. Pearce for her English editing services. We also wish to thank the four anonymous reviewers who provided useful comments on a previous version of this manuscript.
NPY We used anti-human/rat NPY antiserum (T-4070; Bachem) at a dilution of 1 : 10 000 in 0Á3% PBT. We used normal goat serum (Vector Laboratories; 1 : 30 in 0Á3% PBT) to block non-specific sites and biotinylated rabbit IgG (Vector Laboratories; 1 : 100 in 0Á3% PBT) as a secondary antibody. © 2015 The Authors. Functional Ecology published by John Wiley & Sons on behalf of British Ecological Society. , 29, 1421-1434 I M M U N O C Y T O C H E M I S T R Y D A T A C O L L E C T I O N All data were collected without knowledge of the treatment group. For each bird, we counted the number of cells immunoreactive for GnRH-I, GnIH and NPY present in each immunostained section. We quantified the area and optical density of GnRH and GnIH immunolabelled perikarya using digital photographs taken at 4009 magnification with an Olympus DEI-750D digital camera mounted on an Olympus BX60 light microscope (Olympus Optical Co.
Photoinduced testicular development of adult male Abert's Towhees Melozone aberti was not affected by food availability. Towhees were exposed to either ad libitum food availability for 4 weeks ('ad libitum'), 2 weeks of food restriction (70% of ad libitum consumption) followed by 2 weeks of ad libitum food ('reinstated ad libitum') or restricted food availability for 4 weeks ('food-restricted') before we collected testes. Data presented are means (AE SEM).
Acknowledgements This research was funded by a research grant to SD from  Arizona State University's Central Arizona -Phoenix Long-Term Ecological Research  programme (CAP LTER; under  National Science Foundation  grant no. DEB-0423704 ) and  Roslin Institute Strategic  Grant funding from the  BBSRC (SLM). We thank  Kyle Waites  and  Kirsten Heller  for help with bird husbandry, and two anonymous reviewers who's insightful comments greatly improved the manuscript. We have no conflict of interest to declare.
Background: Atypical hemolytic uremic syndrome (aHUS) is a rare, genetically-mediated systemic disease most often caused by chronic, uncontrolled complement activation that leads to systemic thrombotic microangiopathy (TMA) and renal and other end-organ damage. Methods: The global aHUS Registry, initiated in April 2012, is an observational, noninterventional, multicenter registry designed to collect demographic characteristics, medical and disease history, treatment effectiveness and safety outcomes data for aHUS patients. The global aHUS Registry will operate for a minimum of 5 years of follow-up. Enrollment is open to all patients with a clinical diagnosis of aHUS, with no requirement for identified complement gene mutations, polymorphisms or autoantibodies or particular type of therapy/management.
Results: As of September 30, 2014, 516 patients from 16 countries were enrolled. At enrollment, 315 (61. 0 %) were adults (≥18 years) and 201 (39. 0 %) were <18 years of age. Mean (standard deviation [SD]) age at diagnosis was 22. 7 (20. 5) years. Nineteen percent of patients had a family history of aHUS, 60. 3 % had received plasma exchange/plasma infusion, 59. 5 % had a history of dialysis, and 19. 6 % had received ≥1 kidney transplant. Overall, 305 patients (59. 1 %) have received eculizumab. Conclusions: As enrollment and follow-up proceed, the global aHUS Registry is expected to yield valuable baseline, natural history, medical outcomes, treatment effectiveness and safety data from a diverse population of patients with aHUS.
A single, global registry with multiple participating centers worldwide can optimize patient enrollment, which is particularly important for ultra-rare disease states like aHUS. Data generated from an aHUS registry with maximal patient and physician participation can advance epidemiological characterization and inform scientific inquiry and discourse on important issues, such as natural history, systemic manifestations, genotype/phenotype correlations, optimal patient management and long-term treatment outcomes. Also, a global registry provides opportunity for successful partnership between worldwide academia and industry, driven by the shared goal of improving outcomes for patients with aHUS. Initiated in April 2012, the observational, noninterventional, multicenter, global aHUS Registry has been designed to prospectively collect demographic, disease characteristic and treatment information for all eligible patients with aHUS, regardless of treatment received.
Methods The global aHUS Registry (US National Institutes of Health www. ClinicalTrials. gov Identifier NCT01522183) was initiated in April 2012 with the support of Alexion Pharmaceuticals, Inc. The objectives of the aHUS Registry are as follows: 1) to assess the long-term effects of aHUS, including clinical outcomes such as TMA complications, and morbidity and mortality in aHUS patients receiving eculizumab treatment or other disease management; and 2) to collect and evaluate safety and effectiveness data specific to the use of eculizumab in aHUS patients. Scientific oversight, governance and coordination are provided by an independent scientific advisory board (SAB), whose members offer expertise in key specialties related to management of aHUS (e. g. , adult and pediatric nephrologists, hematologists, and/or transplant nephrologists/surgeons) and also include representation from Alexion Pharmaceuticals, Inc.
The term of SAB membership is 2 years. A member may serve more than one term and also may terminate their membership at any time by notifying the SAB Chairperson. The SAB coordinates development of scientific publications, including advising on analyses and scientific questions of interest, providing feedback on publication goals and logistics, contributing to the publication plan, establishing criteria for review/approval of external requests for analyses and publications, reviewing publication drafts and counseling individuals who publish data collected from the aHUS Registry. Registry participants and nonparticipating physicians may request data access or specific analyses by submitting a concept sheet or contacting the SAB through the Registry website (when available) for evaluation. The protocol was approved by the institutional review board at each participating center or by an independent ethics committee, where required, and was conducted in accordance with International Conference on Harmonisation Good Clinical Practice Guidelines and the Declaration of Helsinki.
All patients provided written informed consent before study participation. The forthcoming global aHUS Registry website will be programmed and maintained by a vendor specializing in the development of web-based electronic data collection systems. Investigators will have access to the secure website of the aHUS Registry for entering and accessing patient data online, which will then be stored at a secure and confidential location. Physicians will be able to access the aHUS Registry website at any time for the following purposes: entering and editing data, accessing important documents (frequently asked questions, reminders, etc. ), responding to online queries, accessing posted news items, sending/responding to messages, posing questions and accessing standard reports, including enrollment statistics and site-specific patient data reports.
During enrollment and every 6 months thereafter, the following data are collected as available: patient demographics, medical and disease history, symptomatology, appropriate laboratory results (including those from genetic tests), TMA complications, associated treatments and concomitant medications, clinical and patient-reported outcomes, safety of eculizumab and information regarding treatment or disease management. During each assessment period, physicians will note changes in a patient's clinical status since the last assessment period. Symptoms and signs listed on the case report forms are renal (edema, hypertension, proteinuria), gastrointestinal (liver necrosis, hepatitis, pancreatitis, diabetes mellitus), cardiovascular (cardiac insufficiency/failure, vasculopathy/atherosclerosis, tachycardia), central nervous system (confusion, focal neurological deficit, ocular defects, headache) and pulmonary (hemorrhage, edema, shortness of breath).
Clinicians have the option to list additional symptoms for these organ systems. Occurrences of multisystemic symptoms (i. e. , symptoms/signs categorized under more than one organ system) also are recorded. Baseline values were defined as those collected at time of Registry enrollment or before the first dose of eculizumab (for patients who received eculizumab). Safety evaluations included adverse events and/or side effects associated with eculizumab treatment and other management strategies and measurement of human anti-human antibodies. Adverse events of interest included meningococcal and other serious infections, malignancy, renal impairment, hepatic impairment, sepsis and infusion reactions. The data cutoff for this analysis was September 30, 2014. Patients with all of the following data were included in the current analysis of enrollment characteristics: 1) date of birth, gender, Registry enrollment date; 2) knowledge of treatment with eculizumab or no previous eculizumab treatment; and 3) for eculizumabtreated patients, date of first eculizumab dose.
Discussion This report describes the initial enrollment, pattern of global participation and types of data gathered in the industry-sponsored global aHUS Registry, and outlines baseline demographic and clinical characteristics of current participants. While aHUS and TMA registries have been established previously  [4, 20, 21] , the global aHUS Registry is the largest and the only one to include aHUS patients both treated and not treated with eculizumab. Overall, patients were diagnosed with aHUS soon after presenting with initial symptoms. While historically, aHUS was thought to manifest primarily in children  [1] , 53 % of current Registry participants were adults at diagnosis. This is similar to findings from a recent aHUS case series, which demonstrated that 58 % of cases in France occur in patients older than 18 years  [2].
Limitations of the Registry include possible underreporting of outcomes, missing data, and/or inadequate follow-up. Robustness of data gathered depends on the quality of data entry, number of aHUS patients who enroll, the diversity of their demographic and disease characteristics, including age and disease duration, and retention of recruited patients. Thus far, enrolled patients have had a mean of 5-year histories of aHUS before Registry enrollment. Future enrollment of patients who are newly diagnosed with aHUS will allow for analysis of more contemporary treatment and management approaches. Other limitations include the potential for varying interpretation of baseline disease characteristics by enrolling physicians. Increased clinician participation and collection of complete information will optimize the quality of Registry results and inform forthcoming analyses.
Future analyses from the global aHUS Registry will be conducted to increase understanding of the natural history and progression of the disease. Data gathered during the minimum 5-year follow-up period are collected from clinicians worldwide and will be valuable to help describe aHUS presentation, progression with or without eculizumab therapy, and patient outcomes. Thus far, observational studies in aHUS patients have led to varying conclusions regarding the effect of genetic background on prognosis  [2, 4]. Although complement gene mutations, polymorphisms or factor H autoantibodies typically are identified in up to 70 % of patients  [2, 4, 26] , they are not required for a diagnosis of aHUS  [6, 7]. As part of ongoing research efforts, future analyses of the global aHUS Registry may also help to determine the relative degree to which genetic and other patient factors (e.
Acknowledgments The authors would like to thank the patients who have enrolled in the aHUS Registry and their families. The authors acknowledge the enrolling clinicians, data coordinators at individual enrolling centers, including  Ilaria Possenti, MD  and the national coordinators of the aHUS Registry:  Miquel Blasco  (Spain),  Donata Cresseri  (Italy),  Anatolievna Galina  (Russia),  Tim Goodship  ( United Kingdom ),  Patricia Hirt-Minkowski (Switzerland) ,  Nicole Isbel (Australia) ,  Natalya Lvovna Kozlovskaya (Russia), Danny Landau (Israel), Anne-Laure Lapeyraque (Canada) ,  Chantal Loirat (France) ,  Christoph Mache (Austria) ,  Michal Malina  ( Czech Republic ),  Leena Martola  (Finland),  Annick Massart (Belgium) ,  Eric Rondeau (France) ,  Lisa Sartz  (Sweden) and  Franz Schaefer  (Germany).
The comparison of RIG-I hel-CTD/RNA (PDB ID 4AY2) and MDA5 hel-CTD/RNA (PDB ID 4GL2)  [16]  structures suggested to us that hel2 residues 666-671 of hRIG-I, a partially ordered loop in most crystal structures, may contact the 5′ppp end of dsRNA. Additional file 4 shows that this is not the case since substitution of this 6 aa long loop by the corresponding 7 aa long loop from MDA5 or LGP2, or by an unrelated sequence SGSGSS, hardly affected the stimulation by 5′ppp dsRNA or poly(I:C).