dataesr/scientific-paragraphs-categorization huggingface dataset:
These data also provide a real-world test bed for the development of algorithms for pattern recognition, event reconstruction and analysis, and they will be used to measure the cross sections of interactions of charged particles in LAr. The ProtoDUNE-SP apparatus is designed to satisfy the stringent new requirements and achieve the improved levels of performance required by DUNE  [6]. The membrane cryostat and its associated cryogenic system are the largest LAr systems ever constructed. The argon purification system is the largest constructed to date. As compared to previous devices, such as ICARUS  [2] , ArgoNeuT  [7] , LongBo  [8] , MicroBooNE  [9] , and the 35-ton prototype  [10]  which had shorter maximum drift distances, the 3. 6 m drift distance in ProtoDUNE-SP makes higher demands on argon purity.
Furthermore, the data acquisition system accommodates a higher data rate and larger event sizes than previous LArTPC systems. ProtoDUNE-SP includes a novel photon-detector design which embeds the photon detectors within the APAs in order to collect scintillation light from ionized LAr. Due to the small available area, the photon detectors are required to be highly efficient for detecting single photons. The performance of the photon detectors in ProtoDUNE-SP is a primary topic of this paper. Cosmic-ray interactions with the detector cause a buildup of positive ions that drift very slowly towards the cathode. The accumulated space charge is proportional to the rate of incident cosmic rays and it depends strongly on the drift distance. The space charge alters the electric field in the detector, changing both its strength and its direction, causing distortions in both the measured positions of particles traversing the detector and their apparent ionization densities.
Acknowledgments The ProtoDUNE-SP detector was constructed and operated on the  CERN Neutrino Platform. We thank the  CERN  management for providing the infrastructure for this experiment and gratefully acknowledge the support of the  CERN EP, BE, TE, EN, IT and IPT Departments for NP04/Proto-DUNE-SP. This document was prepared by the DUNE collaboration using the resources of the  Fermi National Accelerator Laboratory (Fermilab) , a  U. S. Department of Energy, Office of Science, HEP User Facility. Fermilab  is managed by  Fermi Research Alliance, LLC (FRA) , acting under Contract No. DE-AC02-07CH11359. This work was supported by  CNPq ,  FAPERJ ,  FAPEG  and  FAPESP, Brazil ;  CFI ,  IPP  and  NSERC ,  Canada ; CERN; MŠMT, Czech Republic; ERDF,  H2020-EU  and  MSCA ,  European Union ;  CNRS/IN2P3  and  CEA, France ;  INFN, Italy ; FCT, Portugal; NRF, South Korea;  CAM, Fundación "La Caixa " and  MICINN, Spain ;  SERI  and  SNSF, Switzerland ;  TÜBİTAK, Turkey ;  The Royal Society  and  UKRI/STFC ,  United Kingdom ;  DOE  and  NSF ,  United States of America.
Chemogenetic experiments (adult mice): respiration analysis following neuronal circuit inhibition in ROSA +/hM4Di adult mice hM4Di expressing adult mice underwent acclimatization to the plethysmographer for 15 minutes/day for 3 days. Plethysmograph recordings lasting 10 minutes prior to CNO administration via intraperitoneal (i. p. ) injection (0. 25 mg/kg) represented our baseline values. After 60 minutes, respiration was recorded and analyzed during a 10-minute recording interval. Data processing and analysis are detailed in the Supporting Information.
We conclude that Phox2b ∆8 expression in Olig3-derived cells does not alter the hypercapnic nor hypoxic respiratory chemoreflex. In each series, an example animal is plotted for each genotype. Each plot shows a line that represents the 25th percentile of pooled control data, with each data point lying out of the 25th percentile colored in red (note the significant number of red data points in the mutants). Box-whisker plots show the median as the bar, the box is the interquartile range, and the whiskers are 1. A B C D G E F 1 F3 F4 F2 8 dimensions of our data by implementing principal component analysis on the respiratory and respiratory variability data. Specifically, our initial dataset of 48 data dimension per animal were reduced to 30 principal components (PC, see details in Supporting Information).
5 (Supporting Table  S9  -Online Resource 1). These data indicate that the statistically significant loss of ISLET1 positive visceral motor neurons identified in our E18. 5 experiments may begin as early as E10. 5 in Nkx2. 2 Cre , Phox2b ∆8 mice. Proliferation assays occurred by pulsing animals with CldU (2. 5 hours) and EdU (0. 5 hours), a technique permitting calculation of S-phase time in neural progenitor cells  (47)  as well as S-phase localization of progenitor cells  (23). Note that the brief EdU pulse-labeled progenitor cells distant from the ventricular surface, whereas CldU pusling delinates signals spanning both EdU and nuclei at the ventricular surface (Figure  7B1 ,B2,C1,C2, note that CldU is revealed using anti-BrdU antibody). We demarcated the ventral neuroepithelium by marking the dorsal and ventral extremes of the ventral Phox2b immunoreactive region (a region that included both Phox2b+ and Phox2b-cells), and in that region counted the number of BrdU positive, EdU positive and BrdU positive/EdU negative nuclei for S-phase cycle estimation.
ACKNOWLEDGMENTS This work was supported by  National Institute of Health/ National Heart, Lung, and Blood Institute (NIH/NHLBI   R01HL132355  for CMC, JJO;  R01HL138738  to KIS) and by  American Heart Association (AHA)   17CSA33610078  to KIS. This research was also supported by public funding from  São Paulo Research Foundation (FAPESP)  (Grants:  2016/23281-3  to ACT;  2015/23376-1  and  2015/23376-1  to TSM). TMS acknowledges 2017/12678-2 funding from  FAPESP. This research was supported by ( NIH/NHLBI PO1HL 144454 ),  National Institute of Health/ The National Institute of Neurological Disorders and Stroke  ( NIH/NINDS   R01 NS10742101  to AJG), and  National Institute of Health/ National Institute on Drug Abuse  ( NIH/NIDA T32DA043469  supported CCS).
We present an interactive tool for selection and evaluation of global climate models. The tool is implemented as a web application using the "Shiny" R-package and is available at https://gcmeval. met. no. Through this tool, climate models of the CMIP5 and CMIP6 ensembles can be ranked and compared based on their representation of the present climate, with user-determined weights indicating the importance of different regions, seasons, climate variables, and skill scores. The ranking can be used to eliminate the climate models with poorest representation of the present climate. As further guidance, the projected regional mean temperature and precipitation changes for all climate models are displayed in a scatterplot for a chosen season, emission scenario, and time horizon. Ranks and projected changes for a subset of climate models are compared to the whole ensemble.
She obtains her data preferably from global research initiatives such as the CMIP5 archive. She wants free and easy access to raw or only slightly post-processed data, since she starts trusting the information by conducting her own investigations, e. g. , with a tool such as GCMeval. The GCMeval tool analyzes the performance of GCMs from the CMIP5 and CMIP6 ensembles under current climate conditions and illustrates the spread of the projected temperature and precipitation changes of a subset of models relative to the whole ensemble. GCMeval is inspired by the principles and concepts described in  McSweeney and Jones (2016)  and  Gleckler et al. (2008). McSweeney and Jones (2016)  concluded that at least 20 models would be required to capture 90% of the range of the projections, but that a strategic selection of fewer ensemble members could lead to a suitable representation of climate change regionally.
Reference data The GCMs are evaluated based on various reference data sets. Currently, there are evaluation statistics calculated using precipitation and temperature data from two global reanalyses of the European Centre for Medium-Range Weather Forecasts, ERA5  (Hersbach and Dee, 2016)  and ERA-Interim  (Dee et al. , 2011) , and precipitation from the Global Precipitation Climatology Project (GPCP, version 2. 3) data set  (Adler et al. , 2003). The data from ERA5 is available at a resolution of about 31 km, while ERA-Interim provides data with approximately 80 km resolution. The GPCP data provides estimations of monthly precipitation on a °2. 5 grid, combining stations, satellites, and sounding observations. All three data sets are covering both land and sea and are available for the selected reference period.
The GCMeval tool GCMeval consists of an interactive front-end and a back-end that contains the code to calculate statistics and extract metadata. The frontend tool was created in the R environment using the Shiny  (Chang et al. , 2017)  package for web application development. The tool can be accessed online at https://gcmeval. met. no. The source code can be found at the Github repository https://github. com/metno/gcmeval. The GCMeval front-end provides a ranking of the GCMs based on two user-defined focus regions and weights representing the relative importance of those regions as well as different variables (temperature and precipitation), seasons (annual mean, winter, spring, summer, autumn), and skill scores. The included skill scores are the bias, the spatial correlation, the spatial standard deviation ratio, and the root mean square error (RMSE) of the mean annual cycle.
Skill scores To evaluate the performance of the GCMs, a set of skill scores highlighting different aspects of performance has been selected. The following skill scores have been calculated for all available reference data sets for the common reference period 1981-2010: • The absolute bias of the GCM compared to a reference data, i. e. , the difference between the mean values of the two data sets. • The spatial correlation of the GCM and reference data. This metric indicates if the model and observations have a similar structure in space. • The ratio of the spatial standard deviations of the GCM and re- ference data (σ GCM /σ ref ), i. e. , a comparison between the spatial variability of the data sets. • The root mean square error (RMSE) of the mean annual cycle of the GCM compared to the reference data.
For the other parameters, the settings are as listed in Table  3. To derive the ranking, GPCP data was used as reference for precipitation and ERA5 data for temperature. For A, the 10 best performing simulations include several CCSM4, CESM1-CAM5, CESM1-BGC, and EC-EARTH runs (Table  4 ). For B, focusing on summer temperature, the selection now includes several MPI-ESM-LR and EC-EARTH, as well as MPI-ESM-MR, CCSM4, CESM1-CAM5 and HadGEM2-ES runs. The scatterplots of future climate change in North Europe (Fig. 3a  and b ) show that a selection based solely on the model ranking does not represent the full range of possible climate change. For A, the spread of the annual mean climate change (present day to far future, assuming RCP8. 5) of the 10 best performing simulations was only 16% of the whole ensemble for temperature and 40% for precipitation.
Discussion The ensemble selection includes many subjective choices, both in deciding regions and weights, and how to use the resulting ranking and the spread of future climate change. There are many strategies, variables, and skill scores not included in the GCMeval tool that could have been considered and may have changed the ranking and selection. Our code is open source (https://github. com/metno/gcmeval) and can be further developed to include other parameters, strategies, metrics, and reference data sets for ensemble selection and evaluation. Working with a small subset of models (e. g. , 5-10) makes it difficult to preserve the statistical properties of a large ensemble of available GCMs. If for practical reasons it is unavoidable, one should be careful not to over-interpret statistical quantities like the standard deviation or quantiles when evaluating a small ensemble.
Due to the variety of user aims and interests, there is no objective way to define a single optimal sub-ensemble, even for a specific region. The grounds for selection should be clearly documented and the selected models should be put in context with the whole ensemble. This can be done either by explicitly taking some extreme models into account, or discussing the possible outcome when taking them into account. The CMIP5 and CMIP6 ensembles are ensembles of opportunity and the members are not all independent of each other  (Knutti et al. , 2013). They include several runs from the same GCMs, and different GCMs can be related because of shared code and development history. Ensuring independence would require a more in depth model genealogy and clustering (e. g. , as in  Knutti et al. , 2013)  that is beyond the scope of our work.
One of the largest recent efforts to provide online climate model analysis is the development of the CDS toolbox, built as part of the Climate Data Store (https://cds. climate. copernicus. eu). The CDS toolbox allows users to write their own applications for data analysis and visualization using Python and in this sense also acts as a development environment. The toolbox has been designed to serve a broad audience and while examples of how to write applications exist, it requires some skill and knowledge of climate data and programming. Another example of a set of interactive tools are provided in the Climate analytics web page (https://climateanalytics. org/tools), where easy-touse web-tools have been provided for non-expert users. However, global model simulations are not covered by these applications, as they are designed for illustrating regional climate change for specific sectors and geographical locations.
Acknowledgements We acknowledge the  World Climate Research Programme , which, through its Working Group on Coupled Modelling, coordinated and promoted  CMIP5  and  CMIP6. We thank the climate modeling groups for producing and making available their model output, the  Earth System Grid Federation (ESGF)  for archiving the data and providing access, and the multiple funding agencies who support  CMIP5 ,  CMIP6  and  ESGF.
Results: To evaluate whether pCADD captures sites with biological meaning, we used transcripts from miRNAs and introns, sequences from genes that are specific for a particular tissue, and the different sites of codons, to test how well pCADD scores differentiate between functional and non-functional elements. Furthermore, we conducted an assessment of examples of non-coding and coding SNVs, which are causal for changes in phenotypes. Our results show that pCADD scores discriminate between functional and non-functional sequences and prioritize functional SNVs, and that pCADD is able to score the different positions in a codon relative to their redundancy. Taken together, these results indicate that based on pCADD scores, regions with biological relevance can be identified and distinguished according to their rate of adaptation.
Training and test set construction To create the set of derived variants, which consists of putatively benign/neutral variants, we identified (nearly) fixed alleles in a pig population that differ from those of the reconstructed ancestral genome of pig, cow and sheep (Fig. 1a , Sus scrofa  [20] , Bos taurus  [21] , Ovis aries  [22] ). These alleles have become fixed in the pig population due to genetic drift or positive selection, thus they are depleted in deleterious variants and can be assumed to have a benign or neutral effect. The ancestral sequence was obtained from the 25-eutherian-mammals EPO (Enredo, Pecan, Ortheus)  [23, 24]  multiple alignment files (MAF), downloaded from the Ensembl v. 91 database. To avoid errors due to misaligned InDels, only SNVs that are not adjacent to another variant site, between the pig population and the inferred ancestor, were retained.
In total, 61,587,075 proxy benign/neutral SNVs were derived and a similar number of SNVs was simulated. To form the training and test sets, the dataset was randomly split into two sets with an equal number of samples from both classes. The training dataset contained 111,976,500 SNVs whereas the test set consisted of 11,197,650 SNVs. To assess the dependency on the genomic location of the variants, the test set was split into six overlapping subsets: (i) intergenic (non-cDNA) variants; (ii) all transcribed sites (cDNA); (iii) transcribed but not translated sites (5′UTR5, 3′UTR3 and introns); (iv) coding regions; (v) synonymous SNVs in coding regions and (vi) nonsynonymous SNVs in coding regions.
Variant annotation Genomic annotations were obtained from the Ensembl Variant Effect Predictor (VEP v91. 3) database  [26]  and supplemented by PhyloP  [27] , PhastCons  [28]  and GERP  [29]  conservation scores as well as Grantham  [30]  aminoacid substitution scores and predictions of secondary DNA structure (DNAshape)  [31]. VEP-predicted consequences of SNVs were summarised in 14 categories. They were either used directly or combined with other data to create composite annotations (see Additional file 1 and Additional file 2: Table  S3 ). Annotations that rely on a gene build, such as the SIFT protein score, reference and alternative aminoacid, variant position within a transcript and coding region were also used. PhyloP and PhastCons scores are based on three differently sized multiple species alignments: a 6-taxa laurasiatheria, a 25-taxa eutherian-mammals and a 100-taxa vertebrate alignment.
Genomic evolutionary rate profiling (GERP) neutral evolution, GERP conservation, GERP constrained element and GERP constrained element p-values were retrieved from Ensembl91 using a custom Perl script. Predicted differences in the secondary DNA structure between reference and alternative alleles were added as annotations to the dataset, as computed by DNAshape  [31] : minor gap width (MGW), Roll, propeller twist (ProT) and helix twist (HelT). After computing all annotation combinations, imputing missing values and recoding all categorical values to binary variables (see Additional file 1), the final number of features was equal to 867. Each feature was scaled by its standard deviation obtained from the variants in the training set.
Codon analysis From the Ensembl v. 93 pig gene build, we retrieved 10,942 genes with only one annotated transcript to avoid complications due to overlapping transcripts. We created three sets, consisting of the minimum pCADD score found at a site, per transcript, one for each of the three positions of a codon. We computed one-tailed Mann-Whitney U-tests between each of the three sets. The resulting p-values were Bonferroni corrected. All calculations were performed in Python version 3 using SciPy v. 1. 1. 0 [38]  and Statsmodels v. 0. 9. 0  [39].
miRNA analysis We obtained all annotated (pre-)miRNA sequences from the Ensembl v93 database, i. e. 484 sequences, and, after removal of sequences that overlapped with any of the training SNVs, 294 sequences remained. As a second set, equally long sequences up-and downstream of the miRNA sequence were selected. For each position in both sets, the miRNA sequences and surrounding sequences were annotated with the maximum pCADD score. To test whether miRNA sequences had a significantly higher pCADD score than their neighbouring sequences, we applied a one-tailed Mann-Whitney U-test using SciPy v. 1. 1. 0 in Python 3.
pCADD data characteristics The class distribution in the training and test sets were balanced, but subsets of SNVs found in different genomic regions displayed varying proportions of simulated and derived SNVs (Table  1 ). These imbalances were similar to those found for the human (hCADD) and mouse (mCADD) datasets in our previous study  [19]. The largest difference among the three models is the total number of SNVs used for model training: ~ 31 million for hCADD, ~ 67 million for mCADD and ~ 112 million for pCADD. This results from the use of a more distant ancestor of the pig than the ancestors used for mouse in mCADD (mouse and rat) and for humans in hCADD (human and chimpanzee). A more distant ancestor yields more differences between the inferred ancestor and the species of interest, resulting in a larger derived class and, thus, in a larger total number of SNVs to create a balanced dataset.
Fig. 4  pCADD scores per intron compared to all other introns, for the first 20 introns. The blue bar indicates the number of introns tested against the intron of interest, the red bar shows how many of these tests resulted in an adjusted p-value < 0. 05 (scale on the left axis). As the intron position increases, the number of tests that can be conducted decreases (with the number of transcripts that have at least that many introns). The black line represents the normalised number of significantly enriched introns, normalized by the number of conducted tests per intron position (scale on the right axis).
We found that 16 of the 40 SNVs are located within a (NCBI) coding region (one example shown in Fig. 5 ) and six SNVs overlap with a (NCBI) long non-coding RNA (Table  5 ). In addition, we mapped the genomic locations of the candidate SNVs to the human assembly GRCh38. p12 and Ensembl gene builds, which revealed nine additional genic regions that consisted of six lncRNAs, one region considered as a miscRNA and two genes. For all 40 SNVs, synteny of the surrounding genes was conserved except for 18:4227731C>A. The relatively large number of prioritized SNVs that overlap with lncRNAs can be explained in two ways. First, there might be a considerable number of missing annotations in the gene builds that we used because the RNA-seq databases are incomplete and are the basis for lncRNA annotations.
It has to be emphasized that only performance trends can be meaningfully compared between the different mCADD/ pCADD models due to the different datasets used for computation. In spite of the large number of neutral variants, which is expected in both sets of variants, the performance seems to indicate that patterns to differentiate between the derived and simulated datasets have been picked up and can be used to evaluate variants and regions based on their potential interest. The performance of pCADD scores to discriminate between simulated and derived variants in the test set increased as the number of features increased, depending on the genomic regions in which they are embedded. The consequence is that missense mutations are the best classified, although the most interesting application of pCADD is to annotate non-coding and intergenic variants, for which a plethora of functional candidates exist but there are only a few methods for further prioritization.
This is a clear advantage compared to alternative methods to evaluate non-coding sequences, such as conservation scores, which may not be available for the entirety of the genome. This was the case in the analysis of intron sequences, for which more than 80,000 fewer tests could be conducted due to missing conservation scores. Using pCADD, candidate regions in which annotations are potentially missing can be identified. For example, no annotation was found for the 6:149549021T>C SNV, even though pCADD scores were within a range typical for exons and displayed patterns of islands of high importance (see Additional file 3: Figure  S6 ), which is more compatible with coding regions than with intergenic regions. Ensembl gene annotations rely strongly on transcript data from public databases, which implies that incomplete databases may lead to missing gene annotations.
Funding This research was funded by the  TTW-Breed4Food Partnership , project number 14283: From sequence to phenotype: detecting deleterious variation by prediction of functionality. This study was financially supported by  NWO-TTW  and the  Breed4Food partners Cobb Europe, CRV, Hendrix Genetics and Topigs-Norsvin. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.