dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The CUORE Collaboration thanks the directors and staff of the  Laboratori Nazionali del Gran Sasso  and the technical staff of our laboratories. This work was supported by the  Istituto Nazionale di Fisica Nucleare (INFN) ; the Director,  Office of Science, of the U. S. Department of Energy  under Contract Nos. DE-AC02-05CH11231  and  DE-AC52-07NA27344 ; the  DOE Office of Nuclear Physics  under Contract Nos. DE-FG02-08ER41551  and  DEFG03-00ER41138 ; the  National Science Foundation  under Grant Nos. NSF-PHY-0605119 ,  NSF-PHY-0500337 ,  NSF-PHY-0855314 ,  NSF-PHY-0902171 , and  NSF-PHY-0969852 ; the  Alfred P. Sloan Foundation ; the  University of Wisconsin Foundation ; and  Yale University. This research used resources of the  National Energy Research Scientific Computing Center (NERSC).
In the first step the nuclide inventory and the associated residual radiation field has to be calculated by using a Monte Carlo code like FLUKA  [11, 12]  which includes dedicated high energy nuclear models and is capable of treating the full build-up and decay chain of the radionuclide-production based on a user-defined irradiation pattern and customizable material compositions. In contrast to the simulations conducted with a software package for use in nuclear power plants, these calculations are based solely on the beam loss of the primary beam, followed by an explicit treatment of all subsequent particle showers leading to the production and spatial distribution of the radioisotopes and the resulting residual radiation fields which include not only gamma radiation but also electrons and positrons.
Therefore, an effective shielding surrounding the dump was established in order to limit activation of the structures placed in dump proximity and to protect personnel accessing the machine, for instance during maintenance operations of, or near this beam dump. As part of the design effort of the beam dump, a detailed Monte Carlo calculation, using the FLUKA particle physics simulation package  [11, 21] , has been performed in order to optimize the choice of shielding material and its design in accordance with the ALARA principle (Fig. 3 )  [22, 23] , and to prepare for possible future maintenance operations. Estimations of individual and collective doses for the Linac4 dump replacement and decommissioning are thus used to optimize the design of the dump. Fig. 4  shows dose rate maps resulting of this study, in their conventional visualization.
Preprocessing of the FLUKA data One way of preprocessing the FLUKA  [11, 12]  output data for use in intervention planning is integrating the values of a scored quantity over a vertical interval of 60 cm, representing the torso of a human being. This way, the representative values of effective dose rate or ambient dose equivalent rate due to induced radioactivity is obtained for a horizontal plane. Fig. 2. Areas where workers will be situated during a beam dump core replacement intervention  [17]. This preprocessing operation leads to a reduction in dimensionality: in the vertical direction, only one bin/voxel of 60 cm high is retained. This makes the data easier to handle with the traditional work dose planning approach. It however also makes that our software tool 0 s abilities are not leveraged fully: instead of a full three-dimensional tool, the tool is factually used as to a twodimensional planning tool, although the visualization is still threedimensional and it stays possible to navigate through the facility in 3D.
Trajectory planning around the Linac4 beam dump Fig. 5 (a) shows a volume rendered visualization of a FLUKA simulation of the Linac4 beam dump area. Using our software tool, radiation data can be visualized together with the facility geometry, and the simulation can be probed. In this way, the position of the worker for a certain activity can be optimized interactively and visually, allowing all stakeholders involvement at once. Fig. 5 (b) shows a volume rendered visualization of a FLUKA simulation of the Linac4 beam dump area, together with an interactively positioned trajectory that a maintenance worker could be walking. The trajectory can also be easily updated by just grabbing and moving the control point location interactively. Trajectory points can also interactively be added or deleted.
Monte Carlo algorithms rely on repeated random sampling to obtain numerical results. They are a great tool when it is not feasible to apply a deterministic algorithm, as is the case in the simulations that are of interest here, but as they are a statistical approximation to the physical results, there is an inherent limit to their accuracy. This limit is also influenced by the accuracy in mimicking the real world of the Monte Carlo algorithm itself. FLUKA has been extensively benchmarked for radiation protection purposes  [25] [26] [27] [28] [29] , and has been proven to be sufficient for this purpose. In Ref. [29] , it is confirmed that "FLUKA is the most suitable particle interaction and transport code for calculating induced radioactivity at high-energy hadron accelerators", and mentions that "FLUKA reproduces measured specific activities to within 20-30% for most isotopes".
The second and third source of inaccuracy lies in the fact that Eq. (  4 ) is in fact a simplification of  [30] E ¼ ∑ N i ¼ 0 Z te k ts k ∑ p ∬ V p ðtÞ ρ p ðx; y; z; tÞ _ Hðx; y; z; tÞ dx dy dz dt The difference between Eqs. (  6 ) and (  1 ) lies in two separate approximations that have been made. The first one is the timedependency of the radiation field that has been neglected. Indeed, as we currently work with one set of FLUKA simulation data per trajectory planning instance, the radioactive decay over the time of the intervention is not accounted for. As the simulation for an intervention that is used is always one at a time point at the start of the intervention, this can only leads to an overestimate of the resulting dose, thus not negatively impacting the radiological safety. Because of the particularity of the radiation field around high-energy accelerators, the time dependance of the radiation field should be calculated with a Monte Carlo simulation such as FLUKA, which would result in a big number of 3D simulation results which should then be appropriately processed by the proposed software.
Technically, this is certainly possible, as the toolbox that is used for the visualization is capable of handling time-dependent volumetric data  [31] [32] [33]. Computing the time dependence would however make the FLUKA simulations even more computing and time intensive. þ ∑ N À 1 i ¼ 0 Z m i þ 1 s ¼ m i v À 1 i Z te i t ¼ ts i ∑ p ∬ The second approximation that is visible when comparing Eqs. (  6 ) and (1) lies in the fact that the human phantom used in Eq. (  1 ) is reduced to one single 3D location. Given the fact that the dose are in real-life also measured at one point in 3D space using a personal dosimeter, this is a natural approximation. If it would be possible to make very detailed Monte Carlo simulations, leading to very low uncertainties with regard to all of the aforementioned aspects, it might become instrumental to have detailed human phantom be implemented in software.
Challenges for the software In its current form, the developed software can only deal with mono-simulation scenarios, meaning that only situations where the activated equipment (or other sources of radiation) are static. In the case of the Linac4 beam dump replacement and decommissioning scenarios that were described before, this means that the trajectory planning has to be done in multiple steps, according to the number of simulations that have to be used. It is currently under consideration to implement support for multiple simulation scenarios, where care will have to be taken to not diminish the user-friendliness of the application by adding the necessary extra features for this update. The interactiveness of the software is a big asset, but at the same time it can also be a burden to process a big number of datasets.
Conclusion and outlook The particle accelerators and HEP detectors, as used at CERN, for instance, are subjected to ionizing radiation and their components can become activated. To protect the maintenance personnel from ionizing radiation during interventions, the radiation dose received by the workers during an intervention has to be minimized. Our goal is to provide software to plan an intervention which enables minimization of the contracted radiation dose, taking practical conditions concerning maintenance tasks into account. This optimization cannot easily be automated and therefore requires human assessment. The visualization of the several layers of data involved in the planning process, i. e. the facility geometry, the radiation levels and the trajectory, therefore needs to be clear, intuitive and interactive.
Not all TAP families have sufficient experimental evidence to allow robust statistical classification. However, all families were classified into the three categories, resulting in three different types of annotations: quality class 1 supported at 95% using FDR-corrected P values, class 2 at 95% based on non-adjusted P values, and class 3 which are solemnly based on the selection of the process class with the maximal number of annotated family members. The resulting classifications were used to analyze the fold changes of the TAP-coding transcripts along the time course using generalized linear models (GLMs). All steps were performed using a combination of custom bash, Perl and R scripts.
Full MS1 spectra and targeted MS2 spectra were recorded using a normalized collision energy of 20%. Internal standardbased quantification was performed using the MS data and Tracefinder 3. 1 software (Thermo Fisher Scientific). The exact mass of ABA (m/z À 1 = 263. 12888) and the MS2 daughter ions (m/z = 219. 13866 and 204. 11307) were used for identification. The unlabeled authentic reference (AE)-ABA was from Sigma-Aldrich.
Microscopy Stereomicroscopy was performed with the Stemi 2000C (Zeiss, Jena, Germany). Images were taken with a charge-coupled device camera (AxioCam MRc5 and ICc1; Zeiss) and scaled with Axio-Vision software 4. 8 (Zeiss). For scanning electron microscopy (SEM), gametophores were fixed on a holder and frozen in slush nitrogen. In the prechamber of the scanning electron microscope (Philips XL30 ESEM), the samples were warmed to À80°C and sputtered with 20 nm gold. The SEM images were taken at À150°C.
GO annotation and classification For all DEGs, GO term annotations  (Zimmer et al. , 2013)  were analyzed; 2558 of the 3220 DEGs were annotated in at least one of the three GO categories 'cellular component', 'biological process' and 'molecular function', whereas 662 (20. 5%) had no available GO annotation (Table  S3 ). Pairwise gene set enrichment analyses using Fisher's exact test was performed to search for over-and under-represented GO terms (0 vs 1 h/3 h/8 h/ 24 h). The datasets of the early time points in comparison with the control yielded no significantly over-or under-represented GO terms, whereas the datasets of the intermediate and late time points in comparison with the control yielded numerous significantly over-and under-represented GO terms (Table  S9 ). Based on this, the transcriptomic data were divided into three groups, representing early (1, 3 h), intermediate (8 h) and late (24 h) responses.
patens were classified according to their responsiveness to external stimuli/stress and their involvement in the regulation of developmental processes. This revealed 34 TAP families as preferentially stress responsive and 20 families as preferentially involved Fig. 2  Differential gene expression in response to cold stress. Significantly differentially expressed genes of Physcomitrella patens in response to cold were determined with an ANOVA, followed by a post hoc test for comparison in pairs. Statistical analyses were performed with Analyst 7. 0 software (www. genedata. com). Significantly induced genes for the comparisons in pairs (blue); significantly repressed genes (green). Differentially expressed genes with a Bonferroni-corrected P value of < 0. 05 (3220 genes, light blue and light green).
Acknowledgements This work was supported by the  Deutsche Forschungsgemeinschaft  ( GRK 1305 ), the  Excellence Initiative of the German Federal and State Governments  ( EXC294 ),  German Federal Ministry of Education and Research  ( BMBF FRISYS 0313921 ) and EU-cofunded by  INTERREG IV  Project  A17  ' ITP-TIP ' (ERDF). We are grateful to  Evi Bieler  from the  Microscopy Center (ZMB) at the University of Basel  for taking scanning electron microscopy images, and  Anne Katrin Prowse  for proofreading of the manuscript.
combined by SSPACE  (14)  and Opera software v1. 2  (15)  complemented by GapFiller v1. 10 (16). The genome was improved using CLC Genomics v5 software (CLC bio, Aarhus, Denmark). The M. austroafricanum draft genome sequence consists of 20 scaffolds of 69 contigs containing 6,682,536 bp, with an estimated genome size including gaps of 6,772,357 bp. The GϩC content of this genome is 66. 79%. Noncoding genes and miscellaneous features were predicted using RNAmmer  (17) , ARAGORN  (18) , Rfam  (19) , and PFAM  (20). Open reading frames were predicted using Prodigal  (21) , and functional annotation was achieved using BLASTp against the GenBank database (  22 ) and the Clusters of Orthologous Groups (COG) database  (23, 24). The genome was shown to encode at least 112 predicted RNAs, including 4 rRNAs, 85 tRNAs, 2 transfer-messenger RNAs, and 21 miscellaneous RNAs.
Also, 6,419 genes were identified, which yields a coding capacity of 6,124,875 bp (coding percentage, 90. 4%). Among these genes, 850 (12. 77%) were found to encode putative proteins and 1,324 (16. 88%) were assigned as encoding hypothetical proteins. Moreover, 6,549 genes matched at least one sequence in the COG database with BLASTp default parameters. Nucleotide sequence accession numbers. The Mycobacterium austroafricanum strain DSM 44191 (ϭ E9789-SA12441 T ) genome sequence has been deposited at DDBJ/EMBL/GenBank under the accession no. HG964450 to HG964469. The whole-genome shotgun master numbers are CCAW010000001 to CCAW010000069.
AGB is the largest carbon pool in most tropical forests, and also tends to be the best characterized because it is relatively easy to measure, with other carbon pools often estimated as a simple ratio of AGB  (GOFC-GOLD, 2009). Biomass maps of the Amazon region have been created in a number of ways. Some have used direct extrapolations from field-plot measurements, either multiplying the total area of forest by mean biomass density values  (Olson et al. , 1983; Fearnside, 1997; FAO, 2010)  or by two-dimensional kriging  (Malhi et al. , 2006) ; others have used environmental gradients to co-krig field-plot measurements (U. S. Agency for International Development, 2012); and others have used remotesensing (RS) data  (Saatchi et al. , 2007). In the absence of continuous field measurements throughout an area of interest, RS datasets should provide the most accurate maps, because every location can be directly observed.
, 2011 ; subsequently called RS1) and 500 m  (Baccini et al. , 2012; RS2)  resolution, aimed specifically at providing baseline data for REDD+, with the data being widely disseminated and used. Both maps use similar methods and datasets: they take millions of discrete 0. 25-ha canopy height estimates from the Ice, Cloud and Land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) LiDAR sensor, convert these to estimates of AGB using empirically derived models that relate LiDAR variables to AGB using field plots located under some GLAS footprints, and use ancillary full-coverage RS layers to extrapolate these point AGB estimates across the landscape. The ancillary RS layers are visual and infra-red spectrum optical data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, elevation data from the Shuttle Radar Topography Mission (SRTM), and, in the case of RS1 only, QuikSCAT radar scatterometer data.
These are approximated into four zones  (Feldpausch et al. , 2011 (Feldpausch et al. , , 2012)) , with the use of a different D:H model in each zone, greatly reducing the error in the prediction of H from D compared to a pan-Amazonian model  (Feldpausch et al. , 2012)  equations used coming from the same study:  Chave et al. , 2005) , regional differences in D:H ratios and ρ are not detected by GLAS, and thus the continental-scale GLAS-AGB calibrations used could smooth out these differences. This is likely to result in significantly higher regional uncertainties than estimated by  Saatchi et al. (2011)  or  Baccini et al. (2012). In order to test this, we use a unique dataset of 413 field plots located throughout tropical South America, compiled as part of RAINFOR (Red Amazónica de Inventarios Forestales; Amazon Forest Inventory Network;  Malhi et al.
, 2002) , the Amazon Tree Diversity Network  (ter Steege et al. , 2003) , TEAM (Tropical Ecology Assessment and Monitoring) and PPBio (Brazilian Program for Biodiversity Research) (Fig. 1 ). Data in these plots were collected using a consistent methodology, and AGB was calculated using a T-SQL query to a single database. We compare these field plots directly to the two remote-sensing-derived maps, and additionally create a plotbased AGB map using simple two-dimensional kriging (K DHρ) to allow a spatial comparison.
MATERIALS AND METHODS Details of field methods and error checking procedures involved in the RAINFOR permanent plot network are discussed in detail elsewhere  (Phillips et al. , 2008 (Phillips et al. , , 2009a, b), b). The individual stem data for every plot used in this study are held in a database (http://www. forestplots. net/), which allowed us to calculate plot-level AGB consistently with a single T-SQL query  (Lopez-Gonzalez et al. , 2009 , 2011). The TEAM plots were downloaded and added to the database in April 2013, with data set identifier codes of 20130415013221_3991 and 20130405063033_1587. We only used plots where data were available for every stem and trees had been measured consistently above buttresses. Plots above 1000 m elevation were excluded, as were plots in nonforest ecosystems.
On average across plots, 77% of stems were identified to the species level, and 92% to the genus level. The dates at which the plots were most recently measured, and the number of times they had been re-censused, varied: in order to dampen the influence of short-term disturbances and to produce values that most closely represented the landscape AGB distribution, the value for each plot was calculated as the mean of all census values, weighted by census interval lengths before and after each measurement. Censuses collected from 2010 onwards were excluded as these post-date the remote-sensing data, apart from 41 plots that were only measured for the first time during or after 2010, in which case the earliest available census was used. The principal AGB dataset was calculated using the threeparameter moist tropical forest model from  Chave et al.
(2005) , with height estimated from d. b. h. individually for each stem using the region-specific Weibull models from  Feldpausch et al. (2012) , and wood density values estimated for each stem using the mean value for the species in the Global Wood Density Database  (Chave et al. , 2009; Zanne et al. , 2009) , or the mean for the genus using congeneric taxa from Mexico, Central America and tropical South America if no data were available for that species (K DHρ). For comparison, AGB was also calculated using the same allometric equation but with the pan-Amazon Weibull model from  Feldpausch et al. (2012)  (KDρ), regional height models but with a dataset mean wood density value of 0. 63 applied to every stem (KDH), and with the pan-Amazonian height model and mean wood density applied to every stem (KD).
In order to compare the AGB dataset directly with the field plots, we averaged the field plots within 20 km × 20 km boxes and compared the mean value for these boxes to the mean AGB of RS1 and RS2. This was intended to reduce the noise involved in comparing single field plots to their surrounding remotesensing pixel. This resulted in comparisons being made with 107 unique points, with a mean of 3. 9 field plots in each (range 1-14). We attempted to produce the kriged maps using universal kriging  (ter Steege et al. , 2003) , but this proved impossible because of high local variation in AGB values of neighbouring plots, resulting in little spatial autocorrelation. Plots located within a 250-m search radius were averaged, which reduced the total number of independent points entering the kriging procedure from 413 to 378; this assisted matters, but a semivariogram showed that there was still little spatial autocorrelation in the dataset (Fig.
S1 ). We therefore used an inverse distance kernel with a smoothing distance of 100 km, which removed local variation and produced output layers showing the broad spatial trends in the dataset. The output kriged maps were produced at a 500-m resolution using the MODIS sinusoidal projection, an equal-area projection used in the creation of RS2. RS1 was provided by S. Saatchi (NASA Jet Propulsion Laboratory, CA, USA) in a geographic projection with a pixel size of 0. 00833°; RS2 was provided by A. Baccini (Woods Hole Research Center, MA, USA) in a MODIS sinusoidal projection at 500 m resolution. RS1 was warped to the projection of RS2 using an exact mathematical transformation. Pixel values were assigned during warping using the 'nearest neighbour' algorithm, so no pixel values were changed by the warping procedure.
RESULTS AND DISCUSSION The field plots with our best estimate of AGB (P DHρ) show a robust trend of increasing AGB with increasing latitude, longitude and distance along a SW-NE line (Fig. 2a-c ; the parameters of the best-fit lines are given in Table  S1 ; input plot biomass data are available in  Lopez-Gonzalez et al. , 2014). The kriged map of the same field plots (Fig. 3c ) shows that the latitudinal and longitudinal trends seen in the graphs are clearly driven by the dominant SW-NE gradient. By contrast, the remote-sensing layers RS1 and RS2 show significant decreasing trends with dis-tance along a SW-NE line (Fig. 2c , Table  S1 ). Subtracting the two RS layers from the plot AGB values emphasizes the trends described above, with positive differences (i. e. RS1 and RS2 greater than P DHρ) in the south and west, and negative differences in the north and east (Fig.
2d-f , Table  S1 ). Two-dimensional kriging of the plot dataset (KDHρ) allows area-based comparisons to be made between the field-plot and RS datasets. The total Amazon basin AGB stocks in RS1, RS2 and KDHρ did not differ greatly for intact forest landscape (IFL) areas  (Potapov et al. , 2008)  (Table  1 ), with KDHρ and RS2 having very similar total stocks, and the RS1 estimate being 11% lower. The patterns of AGB differ greatly among the maps, however, as shown in Fig. 3  and demonstrated by the high root mean squared error (RMSE) values comparing RS1 and RS2 to KDHρ (Table  1 ). The two RS maps do not show the strong SW-NE AGB gradient seen in KDHρ (Figs 2 & 3). RS1 shows similar AGB in western and eastern Amazon forests, with distinctly lower AGB in central Amazonia than either of the other maps, explaining its lower total stock estimate.
RS2 has less variation in AGB overall, but with the highest values in the central-western Amazon, opposite to the pattern seen in the KDHρ map. We believe that the AGB gradient in the field plots cannot be an artefact of our analysis: the ground-based estimates use consistent field measurements  (Phillips et al. , 2008 (Phillips et al. , , 2009a) )  in wellsurveyed plots, with AGB calculated at a stem level using a trusted allometric equation  (Chave et al. , 2005)  involving tree diameter, wood density and height. We are confident that diameters are measured to high precision, as a primary purpose of these plots is to track small changes in diameter through time; we are confident that our wood density values are accurate due to careful species identification and the use of a reliable wooddensity dataset  (Chave et al.