dataesr/scientific-paragraphs-categorization huggingface dataset:
It is possible that zebrafish proteins with functionally similar activities to LIF, OSM and IL-6 exist, but that their sequence divergence is so great that they cannot be recognized as orthologues. Similarly, the zebrafish genome has no BRCA1 orthologue, but does have an orthologue of the BRCA1-associated BARD1 gene, which encodes an associated and functionally similar protein and a brca2 gene, which plays an important role in oocyte development, probably reflecting its role in DNA damage repair  15. Zebrafish have been used successfully to understand the biological activity of genes orthologous to human disease-related genes in greater detail  [3] [4] [5]. To investigate the number of potential disease-related genes, we compared the list of human genes possessing at least one zebrafish orthologue with the 3,176 genes bearing morbidity descriptions that are listed in the Online Mendelian Inheritance in Man (OMIM) database.
METHODS SUMMARY We generated cloned libraries of large fragments of genomic DNA, assembled a physical map of large-insert clones and completely sequenced a set of minimally overlapping clones. In addition, we generated WGS sequences by end-sequencing a mixture of large-and shortinsert libraries. Overlapping clone sequences were combined with WGS sequences and tied to the meiotic map, SATmap, which enabled independent placement and orientation of clones in the genome sequence. The sequence data can be found in the BioProject database, under accession number PRJNA11776. To obtain evidence for a more complete description of protein-coding genes, we used highthroughput short-read complementary DNA sequencing and obtained a deep-coverage data set for messenger RNAs expressed in zebrafish at various stages of development and in adult tissues  6.
Acknowledgments We wish to thank  R. Durbin ,  E. Birney ,  A. Scally ,  C. P. Ponting ,  E. Busch-Nentwich  and  R. Kettleborough  for helpful discussions, as well as  F. L. Marlow  and  P. Aanstad  for critical reading and helpful comments on manuscripts. We thank the  zebrafish information network (ZFIN)  for funding part of the manual annotation of the zebrafish genome and the  ZFIN  staff for support with gene nomenclature and other genome issues. We also thank the  Genome Reference  Consortium for the maintenance and improvement of the zebrafish genome assembly. We are indebted to the  Ensembl team  for providing a browser and database that greatly facilitated the use and the analyses of the zebrafish genome. We thank  A. Pirani  at  Affymetrix  for genotyping advice support, and the  Zebrafish International Resource Center (ZIRC)  for distributing the SAT strain.
J. H. P. was supported by the  National Institutes of Health (NIH)   grantR01 GM085318  (to J. H. P. ),  NIH  grant  P01 HD22486  (to J. H. P. ) and  R01 OD011116  (later changed to  R01 RR020833 ) (to J. H. P. ). We would like to acknowledge the support of the  European Commission 's  Sixth Framework Programme  (contract no. LSHG-CT-2003-503496 ,  ZF-MODELS ) and  Seventh Framework Programme  (grant no. HEALTH-F4-2010-242048 ,  ZF-HEALTH ). R. G. was supported by the  German Human Genome Project  ( DHGP  Grant  01 KW 9627  and  01 KW 9919 ). C. N. -V. , G. -J. R. and R. G. were supported by the  NIH  ( NIH  grant  1 R01 DK55377-01A1 ). The  Zebrafish Genome Project at the Wellcome Trust Sanger Institute  was funded by  Wellcome Trust  grant number  098051.
The four-vectors of particle candidates reconstructed by the CMS global event reconstruction algorithm (also called particle-flow event reconstruction  [16] ) are used as input to the jet-clustering algorithm. The clustering is performed by the FASTJET package  [13]  using four-momentum summation. The global event reconstruction algorithm reconstructs and identifies each particle with an optimized combination of subdetector information. The energy of photons is obtained directly from the ECAL measurements after being corrected for zero-suppression effects. The energy of electrons is determined from a combination of the track momentum at the main interaction vertex, the corresponding ECAL cluster energy, and the energy sum of all bremsstrahlung photons attached to the track. The energy of muons is derived from the corresponding track momentum.
Measurement of R 32 and comparison with theoretical predictions The measured ratio R 32 as a function of p T1,2 is the ratio of the number of selected inclusive 3-jet events to the number of selected inclusive 2-jet events in each p T1,2 bin. The ratio R 32 is corrected for detector smearing effects and unfolded to stable-particle level. The unfolding method is the iterative Bayesian method  [20] , as implemented in the ROOUNFOLD software package  [21]. Unfolding uses a response matrix that maps the true distribution onto the measured one. The response matrix is derived from a simulation, which uses as input the true R 32 distribution from PYTHIA6 tune Z2 and introduces the smearing effects by taking into account the p T1,2 resolution  [17]. After unfolding R 32 to stable-particle level, the final statistical uncertainties include the correlation among the various p T1,2 bins.
The upper panel of each plot shows the ratio R 32 (solid circles) together with the NLO prediction (solid line) corrected for nonperturbative effects, the scale uncertainty, and the PDF uncertainty. At the bottom of each plot, the ratio of data over theory is shown together with bands representing the scale (dotted lines) and PDF uncertainties (solid lines). The error bars in the figure correspond to the total uncertainty, for which the statistical and systematic uncertainties are added in quadrature. For each PDF set the respective default value of α S (M Z ) is used in this comparison as indicated. (µ r / p T1,2 , µ f / p T1,2 ) = (1/2, 1/2), (1/2, 1), (1, 1/2), (1, 2), The measured ratio rises with increasing p T1,2 as the phase space opens up for the production of a third jet, reaching a plateau value for 600 < p T1,2 < 1000 GeV.
The comparison of data with the predictions of pQCD in Fig. 1  demonstrates that the NLO calculations using the NNPDF2. 1, MSTW2008, and CT10 PDF sets are in agreement with the measured ratio R 32 throughout the range of this measurement. The NLO result employing the ABM11 PDF set underestimates R 32 , especially for p T1,2 < 600 GeV.
1214 ± 0. 0020 (exp. ) with χ 2 /N dof = 20. 6/20. For the ABM11 PDF set at NLO with a default value of α S (M Z ) = 0. 118, the series in α S (M Z ) values ends at 0. 130 such that a fit can be performed which yields α S (M Z ) = 0. 1214 ± 0. 0018 (exp. ), consistent with the extrapolation above. The fit exhibits, however, a somewhat larger value of χ 2 /N dof = 28. 5/20 compared to the other results. It is observed that with ABM11 PDFs a higher value of α S (M Z ) is preferred. This is in accord with the fact that the ABM11 gluon density in the phase space relevant for this analysis is significantly smaller than that of all other PDF sets. Thus, the fit favors a larger α S (M Z ) value to compensate for this effect. In summary, the ABM11 PDF set does not describe the data as well as the alternative PDF sets, as shown in Fig.
Acknowledgements This work was supported by grants from  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ,  Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ)  and  Programa CAPES-COFECUB. Luiza Stankevicins  was recipient of fellowship from  CAPES-COFECUB. Evelin dos Santos Ferreira were recipient of fellowship from  FAPERJ/Brasil.
Statistical Methods To compare C q values of dissected, IT inoculated C. sonorensis a linear mixed model was used with C q value as the dependent variable, body component (abdomen/thorax, head or saliva) as a fixed effect and individual as a random effect. The methods were implemented using the nlme package  [35]  in R  [36]. The C q values obtained when Culicoides were infected by membrane feeding and processed as whole insects were analysed using a two-component mixture model  [37]. In this approach we assume that the C q values for Culicoides with transmissible infections are drawn from one distribution, while those with subtransmissible infections are drawn from another distribution. Based on these distributions we can assign each Culicoides to either the ''transmissible'' or ''sub-transmissible'' group with a certain probability based on its C q value and, hence, estimate the proportion of Culicoides with a transmissible infection.
A normal prior was used for m 1 with mean equal to the estimated mean (24. 24) and standard deviation (0. 70) chosen so that 50% of the prior covered the 95% confidence interval. An exponential prior was used for s 1 with mean equal to the estimated standard deviation (0. 66). The priors were assumed to be independent of one another. Parameter estimation was implemented in OpenBUGS (version 3. 2. 2; www. openbugs. info); the OpenBUGS code is supplied in the supporting information (Text S1). Two chains, each of 200,000 iterations, were run, with the first 50,000 iterations discarded to allow for burn-in of the chain. The chains were then thinned (taking every twentieth iteration) to reduce autocorrelation amongst the samples. Convergence of the MCMC scheme was assessed visually and using the Gelman-Rubin statistic in Open-BUGS.
This study was funded by the  Department for Environment, Food and Rural Affairs (Defra)  (grant code  SE0542 ), the  European Union  (grant code EDENext:  FP7-HEALTH-353 2010/nr. 261504 ) and the  Biotechnology and Biological Sciences Research Council (BBSRC)  (grant code:  BBS/E/I/00001445 ). This manuscript is cataloged as paper 066 of EDENext. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Materials and Methods Ethics statement. Parents of all infant participants provided written informed consent prior to the experiment. The experimental protocol and consent procedure were both approved by the CERES (Comited'evaluation ethique des projets de recherche) of the UniversiteParis Descartes. All data were obtained according to the principles expressed in the Declaration of Helsinki. Participants. Twenty-eight full-term 16-month-old infants from French-speaking families were tested and included in the analyses (mean age = 16 months 9 days; range: 16 months 1 day -16 months 23 days; 14 girls, 14 boys). Fifteen additional infants were tested and excluded from the analyses due to fussiness/crying (N = 3), technical problems (N = 1) or because they did not meet the inclusion criteria (N = 11; see paragraph on data analysis for details).
15; p =. 33, VC 2 t (15) = 0. 48; p =. 66, and C 1 VC 2 t (15) = 0. 81 p =. 44) according to the Lexique 3 database  [60]. Therefore, while in Saffran and Graf Estes  [52]  ''high probability'' sequences were defined in terms of adjacent phonemes, in our experiment all the adjacent frequencies were fully controlled, so that the only difference between the two lists of items used here was the overall relative frequency for the LC and CL non-adjacent sequences in the French lexicon. All items were recorded in a sound-attenuated booth by a female French native speaker. The duration for the LC and the CL pseudo-words was similar (386 vs. 375 ms, t (127) = 1. 34; p =. 22). Object Stimuli. Images of eight pairs of objects differing in shape, color and texture (Fig. 1 ) were created for the current study.
Apparatus and Procedure The films were presented on a 170 TFT monitor (128061024 pixel resolution) with an integrated Tobii T60 eyetracking system which was run by a DELL PC computer. A camcorder was mounted above this display to monitor the participants' behavior. The presentation of the stimuli and the storing of the data were performed with the Tobii Studio software. Participants were tested individually in a dimly lit, sound-proof laboratory room. Each infant sat approximately 65 cm from the screen on a caregiver's lap in the center of the test booth. The caregiver was wearing opaque glasses to prevent them from seeing the stimuli and thus minimize the potential for biases. The experimenter controlled the presentation of the stimuli from an adjacent room and monitored the participant's behavior through a video camera.
Data Analysis and Exclusion Criteria The eye-tracking data used for the analysis consisted of the binocular gaze position (X and Y coordinates) at each timestamp, that is, every 16. 6 msec. First, the proportion of on-screen looks during the course of the 8 trials was calculated for each infant. We excluded six infants with less than 50% on-screen data (between 32% and 49%) to ensure that infants were sufficiently engaged in the task (see also  [65] , for a similar inclusion criteria). For each trial, we then calculated the proportion of on-screen looks as well as the proportion of time infants spent looking at the target and the distractor (D) in both the prenaming and the postnaming phases. Therefore, two areas of interest were defined (5756895 Pixel), each including one object. Trials in which infants had a strong object bias in the pre-naming phase (.
To evaluate if learning of phonotactically high and low probability sound sequences was modulated by the receptive vocabulary size, correlational analyses were conducted between target looking and the CDI score. Therefore, the mean difference score of target looking between the pre-and postnaming phases ([% target looking in postnaming phase 2% target looking prenaming phase]) was calculated for each participant and both structures. For both the LC words and the CL words, there was no significant relationship between object label recognition and receptive vocabulary size (LC condition: r = 2. 01; p =. 94; CL condition: r =. 11; p =. 57). Time course and latency analysis. Since the analyses on the proportion of target looking failed to reveal differences in performance for the LC and CL words, we further looked for such differences between conditions in exploring the data of the postnaming phase in terms of its time course and infants' latency to shift from the distractor to the target after naming the target (as done in e.
4% of all the trials. From these trials, we excluded trials in which infants shifted before the postnaming phase began as these saccades were probably programmed before the name of the target was processed (23%), or did not shift at all (7%) as well as outliers, that is values greater or smaller than two standard deviations from the mean (1%). Finally, only infants who provided both LC and CL data were included (N = 15, so only about half of the 28 infants tested). Mean latencies were of 775 ms (SD = 315 ms) for LC words, and of 782 ms (SD = 377 ms) for CL words. This was not significantly different, t (14) ,1, therefore again failing to provide evidence of performance differences between the two conditions. Taken together, the results of Experiment 1 show that 16month-old infants are able to link both the LC and CL labels to the unfamiliar object referents presented in the present word learning task.
Materials and Methods Participants. Twenty-eight full-term 14-month-old infants from French-speaking families were tested and included in the analysis (mean age = 14 months 10 days; range: 14 months 2 days -14 months 22 days; 10 girls, 18 boys). The data of eleven additional infants were not included in the analyses due to technical problems (n = 1), fussiness/crying (n = 2) or given that they did not fulfill the inclusion criteria (n = 8, see paragraph on data analysis for details). The age of these infants did not differ from the age of the included infants (mean age = 14 months 13 days, range: 14 months 3 days -14 months 22 days), and both female (N = 6) and male (N = 5) participants were excluded. Stimuli, apparatus and procedure. The material, apparatus and procedure were the same as in Experiment 1.
40, Monte Carlo p =. 003 (as done by  [76] ). Second, as in Experiment 1, switch latencies were calculated for those trials in which the participants fixated the distracter at the onset of the target word (49. 1% of all the trials). From these trials, we excluded those trials in which infants shifted before the postnaming phase began (23%) or did not shift at all (9. 6%) as well as outliers (4. 8%). Only infants who provided both LC and CL data were included (N = 10, out of 28 infants). Latencies were of 970 ms (SD = 243 ms) for LC words and of 884 ms (SD = 329 ms) for CL words. This was not significantly different from each other, t (9) ,1, providing no evidence of differences in LC and CL. However, it is important to note that the small sample size (N = 10) limits the power available for this analysis.
x i (t) = d i (t)p i (t) is defined as the product of the spreading code or pseudo random noise (PRN) code, p i (t) and the navigation data bits, d i (t). w(t) is input additive white Gaussian noise. Here the noise w(t) is assumed independent of the signal and has a flat power spectrum over the pre-correlation bandwidth. In the indoor case, the channel gain series can be further broken down as follows  [16] : ' , , 0 0 0 cos cos ( cos cos ) 1 ,, 11 ( ) ( ) 1 1 ( ) ( ) i D Max m m m Di max i i i i i i M j t j jt i im m i ii LOS i NLOS i K h t e A t e K KM h t h t                  (2) From Equation (2), The NLOS channel gain has M i multipath components, ,, i i i m m m A  are the weighting factor, azimuth and elevation angles for m i th multipath component. K i is the Ricean factor for i th satellites, which is the ratio between LOS and NLOS signal powers.
Conventional and Direct Vector GNSS Software Receiver Architectures The architecture of a conventional high sensitivity GNSS software receiver that uses block processing technique is illustrated in Figure  1. The received intermediate frequency (IF) data is first fed to a Doppler removal and correlation (DRC) block. After the integration module, which might have coherent or non-coherent form, the despread and demodulated signals are then processed by a block processing technique. The pseudorange and Doppler measurements are estimated by selecting the maximum power in the correlator outputs (code phase and Doppler domain). With such measurements, the user position and velocity can be estimated by either least squares or Kalman filtering. When it comes to weak signal conditions, these measurements are often themselves biased and lead to very large residuals.
Data Collection Description The experimental data was collected on the campus of the University of Calgary. The primary pieces of equipment were an NI front-end, a Novatel SPAN receiver and a LCI IMU  [22] , all of which are shown in Figure  5 , mounted on a backpack. The raw IF data was collected with the NI front-end at a rate of 5 Msps (complex). The reference trajectory is shown in Figure  6. This trajectory is generated using Novatel Inertial Explorer TM with a Novatel SPAN TM system. The SPAN TM system used here includes a tactical grade IMU (LCI) and a high precision GNSS receiver, i. e. , SPAN TM receiver. The Inertial Explorer TM uses differential GNSS measurements to tightly integrate with IMU. However, the high precision SPAN TM receiver cannot provide GNSS measurements indoors due to the fact that the signal C/N 0 is too weak for standard tracking loops.
Then, the user walks through the inside of the building with periodic returns outside in order to maintain an accurate reference solution (details below). Finally, circular motion is repeated again at the end of the test in open sky scenario in order to facilitate backward processing of the data. The raw IF data was processed using the GNSRx-ss™ software receiver  [23]  in order to get the raw correlator values over a pre-defined Doppler search range from which Doppler MLE can be easily obtained. In the GSNRx-ss™ receiver, assistance information such as raw data bits, nominal trajectory and broadcast ephemerides are also provided as input. At each measurement epoch, the reference trajectory is used to compute nominal pseudorange and Doppler values for each satellite in view, which are then passed to the signal processing channels.
The selective advantage is likely the result of changes in the growth factor receptor-RAS and related signaling pathways caused by the mutant protein  (19, 21, 25). It has previously been suggested that the ACH mutation in FGFR3 is driven by selection mechanisms similar to those seen for Apert mutations in FGFR2 (reviewed in 2,3), but so far the available data supporting this view are inconclusive. ACH mutation frequencies were measured in two small biopsies taken from one testis in each of two old men (81 and 85 years). For each man, the two biopsies showed a marked difference in the ACH mutation frequency (one was high and the other low). When the same experiment was carried out on two middle-aged men (65 and 66 years), no significant intra-individual difference was observed, and for each testis both biopsies had low mutation frequencies  (14).
We also developed a new mutation assay based on bead-emulsion amplification (BEA)  (26) (27) (28) (29)  that achieves an extremely high sensitivity with a false-positive rate of ,2. 7 × 10 26. We then measured the c. 1138G. A mutation frequency in 192 testis pieces from an unaffected 80-year-old donor and analyzed the data using computational modeling of spermatogonial stem-cell division schemes.
Bead-calling For each experiment two sets with three images each were analyzed. Brightfield images between scans (normal scan and dye switch scan) were aligned by computing the normalized crosscorrelation matrix of pairs of images in Matlab (Mathworks) as described previously  (26). A mask constructed from the combination of both bright fields was employed to extract the intensity values for defined regions of interests (ROI) representing each bead across the fluorescent channels (two per scan). The average pixel intensities for each ROI was compiled for each fluorescent channel to classify the beads into different clusters (10, 01, 11, 00) based on the signal intensity for each fluorophore (the 11 cluster represents beads fluorescing in both channels and the 00 cluster represents empty beads with no fluorescence).
88) for 500 ms, with a random ISI (1200-1600 ms). Subjects were comfortably seated on a chair and were told that they would be shown a set of photographs without any further information. To ensure that subjects paid attention to the stimuli, we asked the participants to stare at a fixation cross that preceded each stimulus. They were naı ¨ve regarding the specific issues being investigated. Total recording time lasted 15 min including breaks. Electrophysiological acquisition and analysis EEG data were recorded from 18 Ag/AgCl active electrodes fixed on the scalp according to the international 10-20 system  (Oostenveld and Praamstra, 2001) : Fz, Cz, F3, C3, O1, T3, T5 and their homologous locations on the right hemiscalp. Additional electrodes were placed at M1 and M2 (left and right mastoid sites), and vertical electro-oculography (EOG) were recorded with electrodes at the superior and inferior orbital ridge.
A nose electrode was chosen as reference during the recording, an average reference being calculated offline  (Picton et al. , 2000; Joyce and Rossion, 2005). Impedance was maintained below 5 k. The EEG and EOG were amplified with a band-pass filter (0. 1-70 Hz) and digitised at a sampling rate of 500 Hz. The whole experiment was controlled by a Compumedics NeuroScan EEG system (Synamps amplifier, Scan 4. 3 and Stim2 software). EEG contaminated with ocular and motor activity was rejected before averaging. Trials were then averaged according to the emotions expressed by the faces (a threshold of 50 trials per subject per emotion was required) over a 1100 ms analysis period, including a 100 ms prestimulus baseline, and were digitally filtered (0-30 Hz). The ELAN software package for analysis and visualisation of EEG-ERPs was used  (Aguera et al.