dataesr/scientific-paragraphs-categorization huggingface dataset:
In the noise MCs, no errors were propagated from Tables  9  and 10 ; only the median values of the three noise parameters were used. Therefore the error bars in the noise MCs represent only the statistical variance in the 101 realizations. If the uncertainty of the estimation of the three noise parameters were propagated to the noise MC, the error bars would be much larger. ning the LDAP authentication server and the DNS and DHCP services. The software used for system management and synchronization includes kickstart and puppet, while parallelization of the computations is guaranteed by the torque resource manager and the maui scheduler. Data products are stored and organized into three different servers that host the Level 1, Level 2, and test databases (Fig. 1 ). For each database, there is associated RAID 6 storage, with up to 40 TB formatted with the JFS filesystem.
Discussion and conclusions We have described the pipeline used to process the first 15. 5 months of Planck/LFI data from Level 1 through temperature frequency maps, and the assessment of the quality of the products, which is largely based on null tests. Companion papers provide full descriptions of three critical aspects of the data analysis and products delivered: Planck Collaboration III (2014) analyzes systematic effects and assesses their impact; Planck Collaboration V (2014) describes photometric calibration; and Planck Collaboration IV (2014) describes beam patterns and window functions. The Planck Explanatory Supplement (Planck Collaboration 2013) provides a detailed description of all the products delivered in this release. The Level 1 pipeline has not changed since the start of the mission in 2009, and has been running flawlessly and continuously, demonstrating the robustness of the design and development approach.
This work was granted access to the HPC resources of CSC made available within the Distributed European Computing Initiative by the  PRACE-2IP , receiving funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement  RI-283493. We thank  CSC -IT Center for Science Ltd (Finland)  for computational resources. We acknowledge financial support provided by the  Spanish Ministerio de Ciencia e Innovaciõn  through the  Plan Nacional del Espacio y Plan Nacional de Astronomia y Astrofisica. We acknowledge the  Max Planck Institute for Astrophysics Planck Analysis Centre (MPAC)  funded by the  Space Agency of the German Aerospace Center (DLR)  under grant  50OP0901  with resources of the  German Federal Ministry of Economics and Technology , and by the  Max Planck Society.
This work has made use of the Planck satellite simulation package (Level-S), which is assembled by the  Max Planck Institute for Astrophysics Planck Analysis Centre (MPAC) Reinecke  et al. (2006). We acknowledge financial support provided by the  National Energy Research Scientific Computing Center , which is supported by the  Office of Science of the U. S. Department of Energy  under Contract No. DE-AC02-05CH11231. Some of the results in this paper have been derived using the HEALPix package Górski et al. (2005). The development of Planck has been supported by:  ESA ;  CNES  and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MICINN, JA  and  RES (Spain) ;  Tekes, AoF  and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ; and  PRACE (EU).
Model data set The data sources that are used to produce the LUC maps include aerial photographs, DEM (digital elevation model, derived from topographic maps at 1:25,000 by interpolation) and field surveys after 1994. LUC maps for 1989 (t 0 ) and 2000 (t 1 ) are used to train the models while data for 2009 (t 2 ) are employed for model validation. The LUC maps are derived from segmentation and maximum likelihood classification by using panchromatic aerial photographs (2000 and earlier) or color orthophotographs  (2004, 2009)   (Paegelow and Camacho Olmedo, 2010). Classification errors are corrected by hand and the results are validated with an in situ survey. Several potential drivers explain the LUCCs, including the type of land cover, elevation, slope, aspect, distance from thalwegs and pasture fire management (maps with the location and approximate date of the fire).
Modelling approaches and tools Three model tools incorporating different modelling approaches were run using the dataset. All of these approaches use Markov chains to predict the quantity of LUCC buts present differing techniques how to get knowledge about spatial distribution and how to allocate land categories or land changes in space. These expected quantities are spatially allocated using suitability or change potential maps. The three following modelling approaches are commonly used and debated by authors: • A supervised method to assign expected conditional probabilities performed by Markov chains in space using a multi-criteria evaluation. CA_Markov implemented this method with the Idrisi software  (Houet and Hubert-Moy 2006, Eastman, 2009). • A dynamic model that spatially assigns expected conditional probabilities performed by Markov chains and uses a neural network which is a self-learning algorithm, such as the Land Change Modeler (LCM).
Model implementation The three modelling tools use Markov chains to compute the expected conditional probabilities for land cover categories. Nevertheless, the software algorithms are slightly different, as shown by  Mas et al. (2011 Mas et al. ( , 2014)) , and the default options are different. The LCM model computes a Markov chain based on a 100 % confidence level and the Markov module, which is used in CA_Markov, allows the user to specify the probability that LUC is incorrect. Called proportional error, the documentation of Markov module suggests a value of 0. 15 for remote sensing data. To assure comparability, we computed a 0. 15 proportional error Markov chain and introduced this matrix into all models. In our case study land cover maps for 1989 and 2000 are used as inputs for the three models.
Ahlqvist (2008)  provides fuzzy change estimation about the closeness of LUC categories. Here, the authors propose to evaluate the magnitude of error between the predicted and observed LUC. The LUC is expressed by qualitative data (categories). However, the used LUC legend is a ranking that reflects spontaneous vegetation succession from bare soil to wood. There is no physical limit for spreading vegetation, such as altitude or soil, that block this evolution. In addition, authors are unable to indicate a specific distance between categories. Therefore, they rank the categories from 5 (bare soil) to 1 (wood). Inaccurate prediction is measured by the absolute categorical distance between the observed and simulated LUC. This error distance scale reaches from 1, e. g. observed grassland and predicted broom land or bare soil, to 4, e.
To do so, authors apply Markov chain on all available dates: two previous training dates are used to predict the amount of LUC at the next date. The disagreement between the observed and the Markov chain predicted LUC quantities are measured as half of the sum of the categorical absolute differences. This test is i) independent from LUCC modelling performed by CA_Markov, LCM and Dinamica and ii) driven afterwards. When performing LUCC modelling, 2009 validation data still was not available.
The spatial prediction potential of the two LUCC models is always lower than the soft prediction potential of the CA_Markov for both persistence and changes. When comparing the LCM and Dinamica Ego performances, the Dinamica Ego generally obtains higher ROC scores, which may be caused by the Dinamica Ego specific tools that are used to model the spatial pattern. This software distinguishes between linear growth on existing pattern limits and disconnected appearance of new pattern. The two modules controlling this process, called "Exapnder" and "Patcher" provide also the ability to set the mean patch size and form. However, the LCM obtains some transition specific ROC values that are higher than those in the Dinamica Ego. Considering the LUC changes to wood, the LCM's soft prediction potential to model changes from broom land to wood fit the observed changes.
Discussion For this dataset, applying various validation and error analysis techniques to map comparisons produces contrasting results. Although all models use the same Markov transition matrix, only quantitative validation (cf. table 2, left) shows differences in the fit with the observed LUC in 2009. As previously mentioned, the Markov transition matrix use differs in each modelling tool. The CA_Markov model uses predicted areas directly and only precedes the plausible allocation of predicted quantities. In contrast, LCM and Dinamica Ego model transitions and allow the inhibition or selection of specific changes. The authors avoided improbable changes. Otherwise,  Mas et al. (2011)  showed that the Markov algorithms in the considered models slightly differ. The most common map comparison technique, a pixel by pixel comparison between the simulated and observed LUC, provides a higher prediction score in the CA_Markov model than in the LCM and Dinamica models.
This result occurs because persistence is the dominant process. The CA_Markov models LUC. The two other tools are especially designed to handle changes. Furthermore, these results correspond with the predicted amount of change and its swap component, in which both are substantially lower in the CA_Markov model (figure  3 ). Applied to this dataset, the CA_Markov model is a more conservative modelling tool in that the model is closer to the null hypothesis, which is also noted in other studies  (Paegelow and Camacho, 2005, Mas et al. 2011). Figure  5  confirms this fact and shows that the CA_Markov model performs a larger fit with reality for persistence (category E) than the LCM and Dinamica models and performs the lowest fit for the correct predicted change (category B). Regarding the realism of the predicted spatial pattern (figure  6 ), we cannot note any evident advantage for any model.
Conclusions and outlook The specificity of any LUCC model tool does not allow for strict comparisons. However, the three tested tools represent commonly used LUCC modelling approaches that use past dynamics and Markov matrices to simulate LUCC and show a high congruence of correct predicted LUC. The only choice of LUC dates for calculating conditional transition probabilities may change the simulation map. Thus, we showed that the most recent data are not always the most appropriate data. In our case, the LUCC depends on the unknown fire management schedule that is designed to stop spontaneous recolonization and maintain pastureland value. While most popular GIS embedded modelling tools only use two LUC maps for their simulation, a longer time series analysis appears necessary to estimate the degree of significance of the chosen training dates.
Outcome Parameters The following parameters were evaluated at follow-up after a minimum of one year: HSS Score (0-100), SF 12 Score, range of motion, complications, surgical revisions, radiological union and mal-union, or non-union. Union was defined as visible callus in two planes and full weight bearing without pain, non-union as no callus formation or fracture of the plate after six months and mal-union as a shortening of > 2cm or valgus/varus >5°. Also return to work was evaluated. Results are presented descriptively using mean and range for continuous variables and percentages for categorical variables. Statistics were performed with SPSS Software 19. 0 (SPSS Inc. , Chicago IL, USA).
Statistical analysis Data were analysed using SAS version 8 (SAS, www. sas. com). The time spent in the two arms of the Y-tube was expressed as mean values obtained for the 30 tested females. These data were submitted to ANOVA. Separation of means was done using a t-test for paired series to compare differences between means. The values of parasitism rate are presented as mean ± SE of the 20 recorded values. They were then submitted to one-way ANOVA followed by a Fisher LSD test at a significance level of 5%.
We describe a search for two members of the extended Higgs sector, H and A, via their decays H → hh and A → Zh, where h denotes the recently discovered SM-like Higgs boson  [1] [2] [3]. The final states used in this search consist of three or more charged leptons or a resonant photon pair accompanied by at least one charged lepton. (In the remainder of this paper, "lepton" refers to a charged lepton, e, µ, or hadronic decay of the τ-lepton, τ h ). The H → hh and A → Zh decays can yield multileptonic final states when h decays to WW * , ZZ * , or ττ. Similarly, the resonant decay h → γγ can provide a final state that contains a photon pair and one or more leptons from the decay of the other daughter particle. Using the same dataset and technique, we also investigate the process t → ch, namely the flavor changing rare decay of the top quark to a Higgs boson accompanied by a charm quark in the tt → (bW)(ch) decay.
The t → ch process can occur at an observable rate for some parameters of the 2HDM  [7]. Depending on how the h boson and t quark decay, both the multilepton and the lepton+diphoton final states can be produced. Both ATLAS  [8]  and CMS  [9]  have searched for this process using complementary techniques. The CMS upper limit for the branching fraction of 1. 3% at 95% Confidence Level (CL) comes from an inclusive multilepton search that uses the dataset analyzed here. We describe here a t → ch search using lepton+diphoton events and combine the results of the previously reported multilepton search with the present lepton+diphoton search. This combination results in a considerable improvement in the t → ch search sensitivity. In this paper, we first briefly describe the CMS detector, data collection, and the detector simulation scheme in Section 2.
Detector, data collection, and simulation The central feature of the CMS detector is a superconducting solenoidal magnet of field strength 3. 8 T. Within the field volume are a silicon pixel and strip tracker, a lead tungstate crystal calorimeter, and a brass-and-scintillator hadron calorimeter. The tracking detector covers the pseudorapidity region |η| < 2. 5 and the calorimeters |η| < 3. 0. Muon detectors based on gas-ionization detectors lie outside the solenoid, covering |η| < 2. 4. A steel-and-quartz-fiber forward calorimeter provides additional coverage between 3 < |η| < 5. 0. A detailed description of the detector as well as a description of the coordinate system and relevant kinematical variables can be found in Ref. [10]. The data sample used in this search corresponds to an integrated luminosity of 19.
5 fb -1 recorded in 2012 with the CMS detector at the LHC. Dilepton triggers (dielectron, dimuon, muon-electron) and diphoton triggers are used for data collection. The transverse momentum (p T ) threshold for dilepton triggers is 17 GeV for the leading lepton and 8 GeV for the subleading lepton. Similarly, the p T thresholds for the diphoton trigger are 36 and 22 GeV. The dominant SM backgrounds for this analysis such as tt quark pairs and diboson production are simulated using the MADGRAPH (version 5. 1. 3. 30) [11]  generator. We use the CTEQ6L1 leading-order parton distribution function (PDF) set  [12]. For the diboson + jets simulation, up to two jets are selected at the matrix element level in MADGRAPH. The detector simulation is performed with GEANT4  [13]. The generation of signal events is performed using both the MADGRAPH and PYTHIA generators, with the description of detector response based on the CMS fast simulation program  [14].
Since the simulation of the rare fluctuations that lead to such a misidentified prompt lepton can be unreliable, we use the data with two reconstructed leptons to estimate this SM background using the number of isolated prompt tracks in the dilepton dataset. The tt decay can result in two prompt leptons and is a source of background when the decay of one of the daughter b quarks reconstructs as the third prompt lepton candidate. This background is estimated from a tt Monte Carlo sample and using the probability of occurrence of a misidentified third lepton derived from data. For search channels that contain τ h , we estimate the probability of a (sparse) jet misidentified as a τ h candidate by extrapolating the isolation distribution of the τ h candidates. Since the shape of this distribution is sensitive to the extent of jet activity, the extrapolation is carried out as a function of the hadronic activity in the sample as determined by the summed p T of all tracks as well as the leading jet p T in the event.
Summary We have performed a search for the H → hh and A → Zh decays of heavy scalar (H) and pseudoscalar (A) Higgs bosons, respectively, which occur in the extended Higgs sector described by the 2HDM. This is the first search for these decays carried out at the LHC. We used multilepton and diphoton final states from a dataset corresponding to an integrated luminosity of 19. 5 fb -1 of data recorded in 2012 from pp collisions at a center-of-mass energy of 8 TeV. We find no significant deviation from the SM expectations and place 95% CL cross section upper limits of approximately 7 pb on σB for H → hh and 2 pb for A → Zh. We further interpret these limits in the context of Type I and Type II 2HDMs, presenting exclusion contours in the tan β versus cos(βα) plane. Using diphoton and multilepton search channels that are sensitive to the decay t → ch, we place an upper limit of 0.
Alterations in the percentage of mitochondria containing PA-GFP were calculated for each time point to determine mitochondrial fusion. Thresholding and image analysis was performed using Volocity (PerkinElmer). Data were represented with boxplots. Boxes represent data between the 25th and 75th percentiles, lines extend between the 10th and 90th percentiles, the horizontal line indicates the median value, and the circle the mean value. Changes in fusion were evaluated by a Student's t test.
Materials and methods Generation and culture of cell lines Yme1l loxP/loxP and Oma1 loxP/loxP conditional mice were generated by conventional ESC targeting using pRapidFlirt constructs designed to insert intronic LoxP sites flanking exon 3 of either Yme1l or Oma1. Germline chimeras were generated on a C57Bl6/N background and subsequently intercrossed to generate Yme1l loxP/loxP Oma1 loxP/loxP animals. Primary fibroblasts were isolated from Yme1l loxP/loxP , Oma1 loxP/loxP , and Yme1l loxP/loxP Oma1 loxP/loxP embryonic day 13. 5 embryos and immortalized using a plasmid encoding SV40 large T antigen. Immortalized fibroblasts were transduced with recombinant His-TAT-NLS-Cre (HTNC) fusion protein (4 µM), incubated for 20 h, washed with PBS, and supplemented with growth medium. Individual clones were sorted by flow cytometry into 96-well plates (FACS Aria III; BD) using the FASC DIVA software (BD), expanded, and subsequently genotyped for deletion of Yme1l and/or Oma1 by PCR and immunoblotting.
Four to five neurons per animal were reconstructed by ImageJ software. For spine quantification, a 1006 oil-immersion objective was used to identify spines in dendrites longer than 10 mm. Quantification of the number of primary dendrites (defined as dendrites longer than 21 mm emanating directly from the soma) and total dendrites (defined as the amount of all dendritic branches) was done on images acquired with 256 objective, in which a circle was drawn around the cell body and the number of dendrites crossing each circle (primary dendrites) and the total branches were manually counted. Quantitative analysis for spines was performed using NeuronStudio and ImageJ in images acquired with 1006 objective. The dendritic branching patterns were analyzed using Sholl's method of regularly spaced concentric circles centred on the neuronal soma  [40].
Statistical Analysis Mean, standard deviation and p values were calculated using Excel software. For behavioural analyses SYSTAT 11. 0 statistical software was used. The results were analyzed using one or twoway ANOVAs, or a repeated measure ANOVA when appropriate. Post-hoc multiple comparisons were carried out when allowed, using Tukey's Honestly Significant Distance (HSD) test. Student ttest was performed to determine significant differences between samples. A value of p,0. 05 was considered statistically significant. For dendritic complexity and spine density, the results are presented as mean 6 S. E. M. Statistical differences were determined by Student's t test for two-group comparisons.
Data analysis Context dependence was analyzed taking into account only TALEN activity on their cognate targets. For each nucleotide pair (N i , N j ), we studied how the presence of N j just on the right (or on the left) of N i was influencing activity. For all targets containing the subsequence N i N j (or N j N i ), we computed the ratio R(N i ,N j ) = A(N i ,N j )/ <A(N k ,N j )> where A(N i ,N j ) is the activity on the target and <A(N k ,N j )> is the average activity on all targets where N i is replaced by any of the four nucleotides. If there is complete context independence, R(N i ,N j ) should not depend on N j , and for a given N i , all values for the four N j nucleotides should be the same. The average value and standard deviation of R(N i ,N j ), N i is indicated on the axis, and N j represented by various colors is shown in (Supplementary Figure  S7A  and B ).
These 110 TALEN were thus tested in our CHO-K1 model cell line, and the percentage of GFP-negative cells was recorded. We first analyzed the global effect of mismatch numbers on the activity, taking into account all datasets and found a decent correlation, although this correlation could mask particular positioning effects (r = À0. 68, P = 4eÀ16, Figure  1C ). We thus decided to further decipher the RVD/nucleotide pairing specificity by monitoring the effect of increasing number of mismatches (1, 2 or 3) as a function of their position in the array (sliding of the experimental window from the N-to the C-terminus). The analysis of the data pointed out that while the presence of a single mismatch had a limited and similar impact regardless its position in the array (P = 0. 08), multiple mismatches present a more pronounced effect when positioned at the N-terminal end rather than at the C-terminal end (P = 4eÀ4 for two mismatches and P = 2eÀ4 for three mismatches, Figure  1D, E  and F ).
To address this technical limitation, we used a plasmid-based assay in yeast cells to perform high-throughput nuclease activity screenings  (25). To validate a single-strand annealing (SSA) assay as readout, we performed a comparative nuclease activity study of a subset of 13 TALEN from the original GFP dataset (Collections 1-5). We found a particularly good correlation between results from an extrachromosomal SSA assay in CHO-K1 and our previous chromosomal disruption experiments (r = 0. 88 with P = 3. 8eÀ05, Supplementary Figure  S1 ). In addition, taking into account that we have previously reported a very good correlation between the yeast and CHO-K1 extrachromosomal SSA assays  (26) , we anticipated that the yeast model system could serve as an appropriate and representative high-throughput assay to study TALEN activity and specificity.