dataesr/scientific-paragraphs-categorization huggingface dataset:
The arrays were washed with TBST, probed with Alexa647-Streptavidin (Molecular Probes, Eugene, OR, USA) diluted in TBST-BSA for 1 h at RT and washed again in TBST. For staining of total protein, arrays were incubated 15 min in 7% acetic acid and 10% methanol, rinsed twice in water, incubated 10 min in Sypro Ruby (Invitrogen, Carlsbad, CA, USA) and rinsed again. The processed slides were dried by centrifugation and scanned using a GenePix 4000B microarray scanner (Molecular Devices, Sunnyvale, CA, USA). Spot intensity was determined with MicroVigene software (VigeneTech Inc. , Carlisle, MA, USA). All primary antibodies used in RPPA have been previously tested by Western Blotting to assess their specificity for the protein of interest. Raw data were normalised using Normacurve  (Troncale et al, 2012) , which normalises for fluorescent background per spot, a total protein stain and potential spatial bias on the slide.
Differences were considered significant at confidence levels 495% (Po0. 05). To visualise the efficacy of a molecular marker (MALAT1 level) to discriminate two populations (patients that developed/did not develop metastases) in the absence of an arbitrary cut-off value, data were summarised in an receiver operating characteristic curve  (Hanley and McNeil, 1982). The AUC (area under curve) was calculated as a single measure for discriminate efficacy. Metastasisfree survival (MFS) was determined as the interval between initial diagnosis and detection of the first metastasis. Survival distributions were estimated by the Kaplan-Meier method, and the significance of differences between survival rates were ascertained with the log-rank test. The Cox-proportional hazards regression model was used to assess prognostic significance and the results are presented as hazard ratios and 95% confidence intervals.
23; data not shown). Results of MALAT1 mRNA levels shown in Supplementary Table  3  were obtained by using a primer pair (U13/L13) that encompass region 4891-4975 of published MALAT1 cDNA sequence (GenBank #NR_002819. 2; Figure  1 ). Similar results (frequency of MALAT1 overexpression links with classical clinicopathological parameters and patient outcome) were obtained with a second primer pair (U9/L9) localised in MALAT1 gene at region 6565-6658 (Figure  1 ). Relationship between MALAT1 mRNA level and HOTAIR and ANRIL mRNA levels. We tested possible relation between MALAT1 and HOTAIR and ANRIL (the two most documented lncRNAs that also interact with Polycomb repressive complexes) mRNA levels. We did not observe any association between MALAT1 and these two Polycomb complexes associated lncRNAs (r ¼ þ 0.
Identification of a major alternatively spliced MALAT1 transcript in breast tumours, and relationships with classical clinicopathological parameters and patient outcome. Screening of the dbEST database with the MALAT1 cDNA led to identification of two major groups of alternatively spliced MALAT1 ESTs. The first major alternatively spliced MALAT1 transcript (named D1sv-MALAT1) had a 119-bp deletion (from 6446 to 6564, from the NR_002819. 2 sequence), resulting from alternative splicing of MALAT1 mRNA, whereas the second alternatively spliced MALAT1 transcript (named D2sv-MALAT1) had a 243-bp deletion (from 4633 to 4875, from the NR_002819. 2 sequence). The deleted nucleotide sequences show consensus sequences of donor/acceptor splice sites. To verify presence and quantify mRNA levels of these alternative splicing variants in our breast cancer series, we carried out non-quantitative (classical) RT-PCR using primer pairs with one of the two primers placed at the junction of the two spliced regions: U2/L2 for the 243-bp alternatively spliced MALAT1 transcript (D2sv-MALAT1) and U18/L18 for the 119-bp alternatively spliced MALAT1 transcript (D1sv-MALAT1; Figure  1 ).
Alternative mRNA splicing is a common mechanism for regulating gene expression in higher eukaryotes, and there are many examples of development-specific, tissue-specific and tumour-specific differences in splicing events. In the GENCODE v7 catalogue of human lncRNAs, 425% of lncRNA genes show evidence of alternative splicing with at least two different transcript isoforms per gene locus  (Derrien et al, 2012). The vast majority of alternatively spliced lncRNA introns are flanked by canonical splice sites (GT/AG), with no differences in splicing signal compared with the protein-coding genes  (Derrien et al, 2012). In the present study, by screening the dbEST database with the FL-MALAT1 cDNA (named FL-MALAT1), we identified a major alternatively spliced MALAT1 transcript (named Dsv-MALAT1) with two concomitant deleted regions of 119 bp and 243 bp.
ACKNOWLEDGEMENTS We thank the staff of  Institut Curie-Hospital ReneHuguenin  for their assistance in specimen collection and patient care. We thank  Aurelie Barbet ,  Floriane Bard  and  Caroline Lecerf  for performing RPPA experiments. The RPPA platform is supported by  Canceropole Ile-de-France. This work was supported by the  Comite departemental des Hauts-de-Seine de la Ligue Nationale Contre le Cancer , the  Association pour la recherche en cancerologie de Saint-Cloud (ARCS) , and by  Grant INCa-DGOS-4654.
5×10 8 plaque-forming units (PFU) to 18 participants and at a dose of 3×10 8 PFU to 12 participants, with stratification according to priming-dose group. We then recruited and immunized two additional groups of 8 participants each to assess the effect of reducing the interval between priming and boosting to either 1 week (group 4) or 2 weeks (group 5). In this analysis, all the participants received a priming dose of 2. 5×10 10 viral particles of ChAd3 and a boosting dose of 1. 5×10 8 PFU of MVA. Details regarding the study design and participants are provided in Figure  S1  in the Supplementary Appendix, available with the full text of this article at NEJM. org. Additional data on vaccines, safety-assessment techniques, and study design are provided in the study protocol, also available at NEJM.
(Summary data are provided in Tables S9 through S12 in the Supplementary Appendix. ) Neutralizing antibody titers to live ZEBOV (Mayinga strain) from all participants who received the MVA booster were measured at 28 days after the ChAd3 dose and at 14 days after the MVA dose (Fig. 1D ). Low levels of neutralizing antibodies were detected in participants at 28 days (geometric mean titer, 14. 9; 95% CI, 12 to 18. 5) -levels that were similar to those reported after the rVSV-ZEBOV vaccine4 (geometric mean titer, 22. 2; 95% CI, 15. 7 to 31. 4); by 14 days after the MVA vaccine, the levels had increased by a factor of 9 (geometric mean titer, 139; 95% CI, 90 to 215) and all participants were seropositive (geometric mean titer, >8). Boosting with the high dose of MVA elicited neutralizing antibody titers that were higher than those with the low dose (geometric mean titer in the high-dose group, 243.
Acknowledgments Supported by the  Wellcome Trust , the  United Kingdom Medical Research Council , the  United Kingdom Department for International Development , and the  United Kingdom National Institute for Health Research Oxford Biomedical Research Centre. The  National Health Service Blood and Transplant and Public Health England  provided funding for the competition ELISA. The ChAd3 vaccine was provided by the  Vaccine Research Center of the National Institute of Allergy and Infectious Diseases (NIAID)  and  GlaxoSmithKline. MVA-BN Filo  was produced under a contract ( FBS-004-009 ) between the  NIAID  and  Fisher BioServices  and a contract ( HHSN272200800044C ) between the  National Institutes of Health and Fisher Bioservices. Katharina Fehling, Ph. D. ,  Thomas Strecker, Ph. D. ,  Nadine Biedenkopf, Ph.
Daily Diary tags (DD, Wildlife Computers) recording tri-axial acceleration and tri-axial magnetic field at 16Hz, and depth, light level, and water temperature at 1 Hz were glued as close as possible to the projection of the center of mass on the back of the animal (roughly between the scapulae). Fastloc ® GPS MK10 loggers (Wildlife Computers) were glued lower down the back from the DD tags. They recorded GPS coordinates along the track of the animal at sea, as well as depth and water temperature at 1 Hz. Once the devices were securely attached and the measure of energy expenditure via DLW were completed, the females were released upon full recovery from the anaesthesia and allowed to rejoin the colony. Individuals were recaptured after a single foraging trip at sea and anaesthetized as previously described, and all the data loggers were.
Diving and foraging behaviours We used depth data recorded by the DD or MK10 tags to determine diving behaviours using a custom-made R program previously developed for Antarctic fur seals. Dives were defined as periods of time that animals spent under water below a minimum depth of 3m and for a minimum of 4 seconds until they went back to the surface. Any drift in the pressure sensors or error spikes were corrected prior to analyses. Distances traveled at the surface of the ocean (horizontal distances) were calculated by measuring the linear distance between two successive GPS locations taking into account the curvature of the Earth using the Haversine formula  (Sinnott 1984). GPS locations have a high spatial and temporal resolution (they were set to record a location every 5 min), so GPS tracks did not require interpolation or filtering  (Tremblay et al.
2006). Part of the distance traveled under water while diving is inherently taken into account in the measured horizontal distance traveled. We calculated vertical distance traveled while diving by doubling the maximum dive depth of each dive. Fur seal behaviours were separated into 4 categories to determine time-activity budgets: 1) diving; 2) resting and sleeping; 3) surface activities, grooming, slow travel; and 4) fast transiting. These 4 behaviours were identified using a custom-made classification-tree algorithm in R detailed in Jeanniard du Dot et al. (In review). In short, Diving and foraging time was defined as the period when animals were actively diving and included the post-dive intervals calculated using the package diveMove in R (Author, S. Luque), validated for diving fur seals  (Luque & Guinet 2007).
Results Three DD tags failed to record any data and 4 stopped recording before the end of the foraging trip. Seven females also came back on land with blood H and O isotopic levels too close to initial background levels to yield accurate metabolic rate measurements and were removed from further analyses. Consequently, sample size for analyses that only required acceleration data or that only required energy expenditure data was n = 16 for NFS and n = 17 for AFS. However, females missing acceleration data were usually not the ones also missing metabolic rate measurements. Consequently, sample size for analyses in which energy expenditure and acceleration data were combined was n = 12 for northern and n = 13 for Antarctic fur seals.
Discussion We collected data on more than 25 animals in free-ranging conditions and used acceleration and other foraging-related parameters paired with measures of field energy expenditure to test whether acceleration metrics are accurate predictors of metabolic rates at sea in wild top marine predators. Our acceleration data allowed us to analyse time-activity budgets of individual fur seals in the wild at a much finer scale than usual methods based on location and dive data only. Our results showed that VeDBA calculated independently of foraging behaviors or time-activity budgets could not accurately estimate energy expenditure of full foraging trips. However, activity-specific acceleration metrics could accurately predict energy spent during specific types of behaviours at sea, and could be summed by types of activity (i.
The other contributing factor that likely impacted the relationship in Fig. 3  is the previously mentioned error associated with the measured total energy expenditure from the DLW method. The individual error was determined for measured DLW values, but was likely buffered for the predicted values because it was calculated using 'population' estimates from the models. Earlier methods to estimate energy expenditure at sea were based solely on dive profiles form TDR records  (Arnould, Boyd & Speakman 1996). Interestingly, we did not find the same negative relationship between metabolic rate and dive rate in our animals (R 2 = 0. 03). This might be either to their low sample size (n = 9) or to the crudeness of their behavioural data (depth recorded every 10s only, while average dive duration can be as short at 18s depending on animals -average for AFS 50 ± 23s).
Accepted Article This article is protected by copyright. All rights reserved. In a finer scale study, Skinner et al. (2014) also found that VeDBA multiplied by distance traveled, mass of the animal, and vertical distance swam were together the best metrics to assess energy expenditure of northern fur seals at sea. We applied the best model from Skinner et al. (2014) to our data and only obtained an R 2 = 0. 50. However, there are parallels in our two respective models in that both take into account effort and time spent diving versus other activities (see Table  2. 4 in Jeanniard du Dot 2015). Both models point out that knowing how much time an animal spends foraging, diving, transiting and being surface active is as important as knowing the intensity with which the seals perform these activities.
Tables Activities included active foraging (diving + post dive surfacing), resting at the surface, transiting at a speed greater than 1m/s, and slow surface movements (< 1m/s) / grooming. Gap refers to the proportion of time when data were missing and could not be allocated to either of the 4 activity types. Values are means ± SE and asterisks show the values significantly different between species. 28. 8 ± 1. 4* (19. 4 -36. 4)  36. 3 ± 2. 0* (24. 9 -47. 7) 0. 0119 ± 0. 0007* 0. 0198 ± 0. 0009* Resting 10. 9 ± 1. 3 (3. 9 -24. 6)  8. 2 ± 1. 7 (1-16. 9) 0. 0033 ± 0. 0001* 0. 0049 ± 0. 0002* Gap 1. 1 ± 0. 26 (0. 0 -3. 9) NA NA NA.
Acknowledgments We thank  Alistair Baylis ,  Rachel Orben ,  Michelle Barbieri ,  Nory El Ksabi  and  Jade Vacquie Garcia  for their help in collecting the data. We are also thankful to the  Institut Paul-Emile Victor  for their logistic and financial support to the Kerguelen field season, and to NPRB and  NSERC  for funding this project. This study was also supported in part by  NOAA  and the  North Pacific Universities Marine Mammal Research Consortium  through the  North Pacific Marine Science Foundation. All data were collected under the US NMFS permit #  14329-01 , the  University of British Columbia  animal care permit #  A10-0364  and the ethical regulations approval from the  French Polar Institute (IPEV).
This report summarizes the most important priority areas including (i) understanding the crucial role of podocyte protection and regeneration, (ii) targeting mutations by new molecular techniques for new animal models and potential gene therapy, (iii) creating optimal interaction between nephrologists and geneticists for early diagnosis, (iv) establishing standards for mutation screening and databases, (v) improving widespread accessibility to current standards of clinical care, (vi) improving collaboration with the pharmaceutical/ biotech industry to investigate new therapies, (vii) research in hearing loss as a huge unmet need in Alport patients and (viii) the need to evaluate the risk and benefit of novel (including 'repurposing') therapies on an international basis. Keywords: Alport syndrome, chronic kidney disease, guidelines, hereditary kidney disease, nephroprotection.
U N M E T N E E D S I N D I A G N O S I S : R E PO R T O F T H E G E N E T I C S & D I A G N O S I S B R E A KO U T G RO U P The Genetics & Diagnosis breakout group sessions (and a special pre-meeting) discussed late-breaking achievements and F U L L R E V I E W U n m e t n e e d s i n A l p o r t s y n d r o m e developments in the fast-growing field of molecular genetics extensively in the context of earlier and more accurate diagnosis. Why is this issue so important? It is highly likely that the current EARLY PRO-TECT Alport trial of early ACE inhibition (www. clinicaltrials. gov; identifier NCT01485978) with ramipril  [16]  will be followed by other trials as candidate therapies emerge from basic science research or from other clinical trials  [17]. It is thus essential that all patients who enrol in these trials have their diagnosis confirmed at the DNA level so that any possible genetic factors that may influence response to therapy are identified.
Renal panels that can be screened by whole-exome sequencing are being developed, and in some patients who were initially thought to have AS, mutations in other genes expressed in the kidney may be identified. There are clear European and American guidelines for determining the pathogenicity of variants, and most laboratories participate in external quality assessment schemes such as the European Molecular Quality Network. The value of submitting mutations to databases, together with relevant clinical information, is undisputed, but it is challenging in practice because of the resources required to do this. Clinicians (and patients) are strongly advised to ensure that the testing services they use are comprehensive and undertaken in accredited labs that meet relevant ISO standards, offering inhouse validation of potentially pathogenic variants that are identified.
People with AS often do well following renal transplantation, with graft and patient survival that is better than the general end-stage renal disease population; however, they are at risk of anti-GBM disease post-transplant due to formation of autoantibodies  [32, 33]. The incidence appears to be lower than previously reported, with only 0. 4% of patients affected in a recent retrospective review  [34]. Evidence to guide recommendations for monitoring and treatment of anti-GBM disease is not available, and this topic deserves further study. The group considered an international clinical registry to be an important milestone in developing clinical research in AS further. A working group that will concentrate on developing the International Alport Registry was formulated. Such a registry could potentially provide data on the epidemiology and natural course of AS.
Estimate of biologically relevant parameter values The biologically relevant range of parameters can be extrapolated from large-scale measurements of gene expression at the single cell level. For E. coli in particular, proteins and mRNAs have been measured with singlemolecule sensitivity for ∼ 10 3 genes  [21]. In this dataset, the average mRNA lifetime is 5 minutes, which corresponds to a degradation rate of γ m = 0. 2. Protein lifetime is often longer then the duration of the cell cycle, which may span from 20 minutes to several hours in fast-growing bacteria like E. coli. Thus, protein dilution in fast-growth conditions defines the maximum value γ p 0. 03 min -1 for effective protein degradation. The higher stability of proteins with respect to mRNAs (i. e. , the approximation η 1) is generally valid in yeast  [19]  as well as in mammalian cells  [25] , although in higher eukaryotes the many layers of regulation of molecule stability can give rise to a more complex scenarios.
Laboratory Analyses In the main study, blood specimens (3 ml) were collected by venipuncture at baseline (i. e. day 0) and at days 28 and 56 after intervention (either IPV, bOPV or no vaccination). Serum samples were tested for neutralizing antibody titers to poliovirus types 1, 2, and 3, using a microneutralization assay  [20]. Seroconversion was defined as a change from a negative (baseline titer <1:8) to a positive (1:8) titer after vaccine administration  [20] [21] [22]  and boosting as a 4-fold rise in antibody titer for children with a baseline titer of 1:8-1:362  [23]. Stool specimens were collected on day 28 (before challenge) and 3, 7 and 14 days (i. e. on day 31, 35 and 42) after challenge with OPV, and titrated for poliovirus types 1, 2 and 3 as per WHO guidelines  [3]. Data are expressed as log10 CCID50/gram of feces.
After washings, zones of solid phase-bound secreted IgA and IgG antibodies were visualized by stepwise incubation with corresponding enzyme chromogen substrates followed by washing with water  [9]. After drying, plates were scanned and blue (IgA) and red (IgG) spots enumerated using an automated ELISPOT reader. Total and mucosal ASCs as well as ISCs were enumerated against each of poliovirus type 1, 2 and 3 antigens and net ASC and ISC counts were determined after subtracting corresponding non-specific counts detected in control (BSA-coated) wells. Data are expressed as ASC or ISC numbers per milliliter of blood.
Results Of the 199 subjects enrolled in this study, 190 (95. 5%) had complete data and were included in the analysis. Subjects were excluded if they were lost to follow up or if their blood sample was rejected due to clotting or hemolysis during transport to field laboratories (Fig  1 ). Of the 190 subjects, one was excreting poliovirus type 1 and seven excreting type 3 at day of bOPV challenge. All subjects excreting virus before challenge were in the bOPV arm. These subjects were included in the analysis; however, exclusion of these subjects produced similar results.
Acknowledgments We thank all volunteers who participated in this study and the entire team from the  National Polio Surveillance Project (NPSP) ,  World Health Organization (WHO) ,  India , for setting up the field laboratory and providing field support in Moradabad and Sambhal,  Uttar Pradesh ,  India. We are particularly grateful to  Prof. Pearay Ogra  for advice and inspiring discussions. Special thanks are extended to  Drs Eugene Butcher  ( Stanford University ) and  Sanofi-Pasteur (France)  for providing α4β7 antibody and purified poliovirus antigens respectively. This work was technically and financially supported by the  Global Polio Eradication Initiative (GPEI) ,  WHO ,  Geneva  (through a grant from  Rotary International). Part of this study was funded by the  Bill & Melinda Gates Foundation  and the  Swedish Agency for International Cooperation (SIDA).
This work was technically and financially supported by the  Global Polio Eradication Initiative (GPEI) ,  WHO ,  Geneva  (through a grant from  Rotary International). Part of this study was funded by the  Bill & Melinda Gates Foundation  and the Swedish 20. Agency for International Cooperation (SIDA). The  International Vaccine Institute  is supported by the governments of the  Republic of Korea and Sweden. Support to AD through  National Research foundation of Korea  (http://www. nrf. re. kr), grant number  2013K1AZA1058633  is gratefully acknowledged. Support to CC through " ADITEC grant  (http://www. aditecproject. eu), European Union Framework Program 7 , is gratefully acknowledged.
The Centers for Disease Control and Prevention (CDC) Emerging Infections Program (EIP) initiated population-based surveillance for CDI in 2009, providing important information on persons who develop CDI, the settings where CDI onset occurs, and prevalent strains in the community and hospitals  [4]. However, since data are obtained only from CDI cases, it is not possible to determine the incidence of CDI for different subpopulations  [10]. The Healthcare Cost and Utilization Project (HCUP) Nationwide Inpatient Sample (NIS) has been used to calculate national estimates of CDI hospitalizations. The NIS does not link patients stays over time, resulting in an over-estimation of CDI incidence and prevalence  [1, 11]. The CareFusion and Premier Perspective clinical research databases have also been used to study CDI  [12, 13].