dataesr/scientific-paragraphs-categorization huggingface dataset:
The first method to describe this novel class of techniques, Wanderlust  [1904] , was applied to mass cytometry, but since the advent of single-cell sequencing techniques the field of trajectory inference methods has exploded and currently more than 70 techniques are available  [1905]. Many of these techniques have been developed for single-cell transcriptomics data, but often they can be applied equally well to cytometry data. An example of the result of a trajectory inference method applied to a dataset of reprogramming fibroblasts is presented in Fig. 212. Here the black, bifurcating curve represents the model, where MEF cells either develop further to neurons or to myocytes  [1906]. As cell developmental processes can be quite complex, e. g. , resulting in very complex tree structures, accurately reconstructing the underlying topology of the trajectory is a very complex and challenging computational problem.
An example of immunofluorescent staining in cytometry-Figure First, is there any difference between these two datasets? This is addressed with a K-S analysis, which reveals that there is a maximum normalized vertical displacement of 0. 0655 at channel 37 with 8976, N 1 , and 8570, N 2 , cells in the control and test sample, respectively (Fig. 215 ). K-S statistic gave P < 0. 05, suggesting there is a statistical difference between the two datasets at the 1:20 probability level. The remaining data shown in this figure will become apparent later. Second, can we establish the "meaning" of the discernible shoulder in the lower histogram of Fig. 214 ? This is addressed analytically using a concept derived from mechanics; namely, taking moments about a point. Imagine a weightless beam with two different weights hanging from the beam that will balance according to equation (  22 ) W 1 B -X 1 = W 2 X 2 -B (22) where W 1 and W 2 are the "weights" hung from the beam, B is the balance point, and X 1 and X 2 are the distances of the respective weights from the balance point, B.
O'Neill and Brinkman have suggested that certain data besides compensation, gating details and mathematical algorithms, should be shared for reproducible FCM bioinformatics  [1945]. One major aim is to make FCM data easily accessible to the users by open-access databases for flow data (e. g. , FlowRepository), as well as the code sources. A series of data sets have already been provided by the FlowCAP (Critical Assessment of Population Identification Methods) project, comparing different mathematical models and automated methods for analysis. The cytometry community has already made great steps toward reproducible research by standardizing instrumentation, measurement, and data analysis, but still looks forward to optimize the reproducibility in different cytometry fields. 3. 4 Advantages-The major advantage of the MIFlowCyt concept is that each reader of MIFlowCyt-compliant manuscripts can access all necessary data so that experiments can be reproduced step by step as easily as cooking recipes, because the focus is on the transparency of scientific data.
If your lab is using Cytobank already, then choosing its Community version presents a straightforward option of sharing your data publicly. In addition, all versions of Cytobank give you the option of sharing data privately with your collaborators. A potential drawback of Cytobank is that public datasets can be set back to private-and thus "disappear. " FlowRepository is a public repository allowing researchers to deposit, annotate, analyze, share, and publish FCM data, mainly those associated with peer-reviewed manuscripts. The repository is provided free of charge by the International Society for Advancement of Cytometry. While FlowRepository was developed by extending Cytobank's code base, the two platforms drifted apart significantly over the past 8 years. However, there are still some common aspects allowing users of one system to adapt to the other easily.
Depositing data to FlowRepository is recommended by Nature, Cytometry Part A, and PLoS journals. The Immunology Database and Analysis Portal (ImmPort) system provides an archive of immunology research data generated by investigators mainly funded through the National Institutes of Health (NIH), National Institute of Allergy and Infectious Diseases (NIAID), and Division of Allergy, Immunology, and Transplantation (DAIT). It is an extensive data warehouse containing an integration of experimental and clinical trial data generated by dozens of assay types, including 131 FCM and 24 CyTOF data sets. In addition, the ImmPort system also provides data analysis tools and it contains implicit knowledge and "best practices" for clinical and genomic studies in the form of ~50 templates for data deposition, management, and dissemination.
ImmPort has been developed under the Bioinformatics Integration Support Contract (BISC) by the Northrop Grumman Information Technology Health Solutions team for the NIH NIAID/DAIT. If your research funding comes from this source and you are generating immunology data, you should deposit it in ImmPort. Immporťs support for different data types can be another reason to choose it if you are generating FCM data as well as data from different types of assays. A (free) registration and approval by DAIT is required in order to deposit and access data from ImmPort. A potential drawback of using this resource is that there can be a long delay in publication of deposited datasets. ImmuneSpace is a database and analysis engine built by customizing the LabKey server for the Human Immunology Project Consortium (HIPC).
2. 5 De-multiplexing of barcoded data-Original sample data can be extracted from barcoded, pooled samples by deconvolution through consecutive manual gating in standard FCM software, by Boolean gating for combinatorically barcoded samples  [1988] , or using scripts developed for that purpose  [1992, 1994, 1995]. Debarcoding software can be developed in-house or retrieved from https://github. com/nolanlab/single-cell-debarcoder (accessed  August 15, 2016 August 15, ) [1992]] , Premessa (https://github. com/ParkerICI/premessa), and has been implemented in the regular CyTOF software (Fluidigm). The better the cytometric separation of the barcoded samples from each other, the better the recovery of original sample cells in the deconvolution. When different cell types in a given sample show heterogeneous barcoding marker staining intensity, resulting in suboptimal cytometric separation in the barcode channels, one should consider separating those first (e.
As such, the EpiTOF (Epigenetic landscape-profiling using cytometry by time-of-flight) Ab panel allows the assessment of different classes of histone modifications and variants. Functional probes available for mass cytometry include 5-iodo-2-deoxyuridine for assessing cell proliferation [2050], enzymatic activity  [2054] , and a tellurium-based hypoxia probe  [2055]. Wheat germ agglutinin (WGA) and osmium tetroxide staining were proposed as a proxy for cell size in mass cytometry  [2056] , besides ASCQ-Ru for cell volume respectively, which, in conjunction with the Cell tracer software has been used to correct for confounding cell size effects in signaling studies  [2057]. Further, osmium tetroxide has been used to stain functionalized polystyrene beads, making beads manufactured for conventional FCM readily detectable by mass cytometry  (Budzinski et al.
Combinatorial cytometry Combinatorial cytometry is the subfield of cytometry, or single-cell analysis, whereby researchers describe, study, and model complex relationships between multiple combined cytometry samples exposed to varying stimuli, environment, treatment, and so on. Examples include various techniques of multiplexing, such as fluorescence barcoding  [1984] , high-throughput cytometry, and cytometry-based compound screening  [2070] , as well as multiple computational techniques that combine multiple data files either during the data collection  [2071]  or post hoc in order to create multifactorial and multidimensional datasets to allow for analytical comparisons across properties not readily available or accessible via a single experiment  [1795]. Combinatorial cytometry approaches have been implemented successfully with innovative mass cytometry (CyTOF) systems (For more information on the equipment and concept, see Section VIII.
This approach creates a new opportunity for combining fluorescent probes that may not be feasible in conventional FCM  [2079]. For example, dyes such as GFP and FITC can be used together because chemometric techniques to process spectral cytometry data can be utilized to classify and/or unmix the resultant signals. There are several excellent recent examples of this approach in FCM  [32, 33]  in which combinations of fluorescent proteins, together with a variety of fluorochromes, allowed a total of 11 markers to be used simultaneously and then separated by spectral unmixing. A final example of combinatorial cytometry and one that demonstrates the extraordinary power of multiparameter datasets can be seen in data collected by the CyTOF technology and demonstrated in Fig. 230  (for an overview of the equipment, see Section VIII.
Conclusion- The key requirements for combinatorial cytometry are (i) well-defined reproducible assay layout, (ii) highly controlled, preferably automated, assay setup and preparation, (iii) data-collection method recognizing the relationships between the collected FCS files and organizing the measurements in higher-order data structures, and (iv) automated data analysis and reporting software. When this combination of tools is available, complex multiparameter and multifactorial experiment designs can be executed and the resultant data can be rapidly processed to produce useful insight leading to mechanistic models of the studied biological systems. 5 High dimensional FCM 5. 1 Overview-The characterization of the complex nature of immunological processes in health and disease requires multi-dimensionality as well as high resolution to detect all targets of interest.
Data analysis Single-cell RNA sequencing (scRNA seq) development opened in the last couple of years new approaches to answer important questions in developmental biology  [2102] , cancer  [2103] , and neural network analysis  [2104]. Detection and quantification of gene expression at single-cell level encompasses many challenges regarding data analysis. As an example, the very low starting material from a single-cell can lead to dropout genes in some cells and not in others. The stochastic nature of gene expression might imply an important cell-to-cell biological variability in single cell measurements although the specific cell is currently in a different expression cycle. These confounding factors, including variable detection sensitivity, batch effects, and transcriptional noise, complicate the analysis and interpretation of scRNA sequencing datasets.
Adaptation of formerly developed methods for bulk RNA sequencing could also be used The transcriptional landscape of a single cell can be compared based on co-expressed genes. Here, cells are grouped into clusters and marker genes, which are driving the expression signature of sub-clusters, are identified and annotated. Before the identification of cell clusters, visual exploration is usually achieved by dimensional reduction, where the dataset is projected to only a couple of dimensional spaces. Among these approaches, principal   [2116]. We would also like to mention two interesting resources, listing software packages dedicated to the different scRNAseq applications (https://www. scrna-tools. org/ and https:// github. com/seandavi/awesome-single-cell).
241 ). Equipment-Most currently commercially available cell sorters are capable of index sorting. Sorting for the example in Fig. 241   Experimental workflow and acquisition-Staining, preparation of single cell/particle suspensions, and the sorting process are identical to (single cell) sorting without index (see Chapter IV: Cell sorting). Index sorting has to be activated in the sorter software before starting the sorting process. When sorting single events into multi-well plates or onto slides, special attention should be paid to plate targeting. Accurate plate alignment should be confirmed in regular intervals (e. g. , after every fourth plate). Singlet gates should be defined stringently to reduce the chance of sorting doublets. Make sure to activate the "single cell mode" (or equivalent) in the sorter software when sorting single cells.
The HTFC assay described in Fig. 243  was designed to identify compounds inducing the expression of Foxp3-the master transcription factor of regulatory T cells. Therefore, spleen and lymph node cells from reporter mice were incubated with 40 000 small synthetic molecules (provided by the screening unit of the FMP Berlin) and the expression of enhanced green fluorescent protein (EGFP) under the control of the Foxp3 promotor was analyzed by FCM. Equipment-To perform HTFC with reasonable throughput the assays should at least be set up in 96-well formats. Necessary hardware components are the autosampler, a FCM, and a computer with data analysis software. Several FCM offer already build in autosampler for 96-or even 384-well formats: iQue screener (intellicyt), MACS ® Quant X  (Miltenyi) , ZE5 (BioRad), Cyte (ACEA Biosciences), Cytoflex (Beckman Coulter), and the spectral analyzer SA3800 (Sony).
Other distributors offer compatible addon autosampler for their devices like Attune (Thermo Fisher) or Beckton Dickinson instruments that can be easily connected. For the phenotypic assay described here, the HyperCyt ® autosampler (intellicyt)  [2236]  connected to a FCM (Accuri) and a computer equipped with a specialized software capable of handling and analyzing the data (ForeCyt ® ) (Fig. 243A ) was used.
Acknowledgements Work in the laboratory of  Dieter Adam  is supported by the  Deutsche Forschungsgemeinschaft (DFG ,  German Research Foundation )- Projektnummer 125440785 -SFB 877 , Project  B2. Petra Hoffmann ,  Andrea Hauser , and  Matthias Edinger  thank  BD Biosciences ®  ,  San José , CA, USA, and  SKAN AG, Bale, Switzerland  for fruitful cooperation during the development, construction, and installation of the GMPcompliant cell sorting equipment and the  Bavarian Immune   Therapy Network (BayImmuNet)  for financial support. Edwin van der Pol  and  Paola Lanuti  acknowledge  Aleksandra G. asecka  M. D. for excellent experimental support and  Dr. Rienk Nieuwland  for textual suggestions. This work was supported by the  Netherlands Organisation for Scientific Research -Domain Applied and Engineering Sciences  ( NWO-TTW ) , research program  VENI 15924.
Jessica G Borger ,  Kylie M Quinn ,  Mairi McGrath , and  Regina Stark  thank  Francesco Siracusa  and  Patrick Maschmeyer  for providing data. Larissa Nogueira Almeida  was supported by  DFG  research grant  MA 2273/14-1. Rudolf A. Manz  was supported by the  Excellence Cluster "Inflammation at Interfaces"  ( EXC 306/2 ). Susanne Hartmann  and  Friederike Ebner  were supported by the  German Research Foundation  ( GRK 2046 ). Hans Minderman  was supported by  NIH   R50CA211108. This work was funded by the  Deutsche Forschungsgemeinschaft  through the grant  TRR130  (project  P11  and  C03 ) to  Thomas H. Winkler. Ramon Bellmàs Sanz ,  Jenny Kühne , and  Christine S. Falk  thank  Jana Keil  and  Kerstin Daemen  for excellent technical support. The work was funded by the  Germany Research Foundation   CRC738/B3  (CSF).
Yeast Strains All yeast strains have a W303 background and are listed in Table  S3. The wt strain (RZ71) containing the HA tagged Rpb3 RNAPol2 subunit was obtained from a cross between ES3086 (courtesy of E. Schwob) and YMTK2567 (courtesy of T. Lee). The Rtt109Δ strain (RZ72) (Figure  5 ) was obtained by crossing RZ71 with ZGY929 (courtesy of Z. Zhang). The Rtt109Δ strain (RZ23) (Figure  S3 ) was obtained from a cross between ES3086 and ZGY954 (courtesy of Z. Zhang). The Rtt109Δ (RZ72 and RZ23) and hst3, 4ΔΔ (PKY4220, courtesy of P. Kaufman, Figure  S5 ). Strains have been additionally validated by anti H3K56ac western blotting of bulk mid-log cell extracts (data not shown). The Mcm2-3A strain (AC38) (Figures 6, S7, S8 and S9) was derived from a cross between strains 8566 (courtesy of K. Labib) and RZ71.
Images were scanned at 5μm with the InnoScan 710 MicroArray scanner (Innopsys) and processed with the Mapix software. Data was normalized by dividing the Cy3/Cy5 (or Cy5/Cy3 ratio for the dye flip) ratio for each probe with the average Cy3/Cy5 ratio for the whole array. The average of the pair of normalized ratios from the dye flip technical replicates was used in the final analysis. The GEO accession number for the microarray data is Western Blot (Figure  S10 ) 10 mL aliquots from time points above were mixed with 2 mL 100% TCA and kept on ice for 10 min. Cells were then pelleted and washed twice with 500 µL 10% cold TCA. Pellets were resuspended in 300 µL 10% cold TCA and bead beated with Zirconium Sillicate beads (0. 5 mm) in a bullet blender (Next Advance) for 3 times x 3 min (intensity 8).
Data Analysis All analysis was done using in house Perl and R scripts available upon request. ChIP, ChIP-NChAP, NChAP: Sequences were aligned to S. Cerevisiae genome using BLAT (Kent Informatics, http://hgdownload. soe. ucsc. edu/admin/). We kept reads that had at least one uniquely aligned 100% match in the paired end pair. Read count distribution was determined in 1bp windows and then normalized to 1 by dividing each base pair count with the genome-wide average base-pair count. Forward and reverse reads were treated separately. The repetitive regions map was constructed by "BLATing" all the possible 70 bp sequences of the yeast genome and parsing all the unique 70bp sequences. All the base coordinates that were not in those unique sequences were considered repetitive. Normalized read densities for all genes were aligned by the transcription start site  (51)  and median read densities for each coding region (from the tss to the transcription termination site) were determined for all datasets.
Replication origins mapping: Origins were mapped from the nascent chromatin fraction in the early S-phase dataset from Figure  1C  and Figure  2  (54% non-replicated). The resolution for origin centers was higher in this dataset because small fragments (<100bp) were not removed from this fraction (see the Chromatin Immunoprecipitation (ChIP) section). We identified local peaks within Replication Origin Associated Domains (ROADS, i. e. replicated regions around known origins of replication) on every chromosome (Table  S1 ). We then looked for ACS consensus sequences (52) within +-200bp of each identified peak and kept the ACS sequence closest to the peak (Table  S2 ). Peaks without ACS sequences were eliminated from further analysis. Replication timing (Figure  S8 A-E ) Replication timing in Figure  S8A  was determined as described in  (22).
RNA-seq normalized to S. Pombe spike-in (Figure  S5 ): S. Cerevisiae and S. pombe reads were aligned to their respective genomes using BLAT and the read density distribution was determined for each species in each dataset separately. The average S. pombe genomic read density per bp (F and R reads were processed together) was determined for each dataset. For spike-in normalization, S. Cerevisiae read densities per bp were then divided with the corresponding S. pombe average genomic read density. For internal normalization S. Cerevisiae read densities were divided with its average genomic read density as described above. Normalized read densities for each gene were aligned by the transcription start site and divided into sense and antisense transcripts. The median read density for each gene (from the tss to the end of the coding sequence) was then determined for each transcript.
For example, early genes (705 genes shown in A) were sorted by increasing lagging/leading nascent RNAPol2 ratio from the early S-phase dataset and then divided into 7 bins of ~100 Figure Legends genes each, and we determined the box plot distribution of lagging/leading ratios for each bin. The average lagging/leading ratios for each bin are in the y axis on the left. For example the bottom group of genes in row 1 column 1 has on average 5. 6 times more RNAPol2 on the leading copy than on the lagging. RNAPol2 distribution that was asymmetrical in early S-phase appears to even out later on in S-phase (rows 1 and 3, column 2). The bar graphs on the left show the enrichment of "same" genes for each bin indicated in the Y axis calculated as the ratio of "same" orientation genes versus "opposite" genes for each group normalized to the same/opposite ratio of all 705 early (rows 1 and 2) or 1868 mid-early genes (rows 3 and 4).
Acknowledgments We thank  Susan Forsburg ,  Paul Kaufman ,  Traci Lee ,  Etienne Schwob ,  Karim Labib  and  Zhiguo Zhang  for yeast strains. Thank you to  Marta Gut  and  Julie Blanc  from  CNAG (Barcelona, Spain ) and  Veronique Pantesco  ( Plateforme Transcriptome-IRMB Montpellier ) for deep-sequencing services. Thank you to  Robert Feil  for critical reading of the manuscript. This work was supported with the  ERC-Consolidator  grant-NChIP 647618 (MRL).
5aaaARS911. 5aaa ARS912aa ARS912aa ARS913aaa ARS913aaa ARS914aa ARS914aa ARS916aa ARS916aa ARS919aa ARS919aa ARS920aa ARS920aa ARS922aa ARS922aa Yabuki et al. , 2002 origins replication Figure 1:RNAPol2 ChIP-NChAP in early S-phase. chr coordinate (bp) 1e+05 2e+05 3e+05 -0. 68 -0. 36 -0. 16 0 0. 16 0. 36 0. 74 -1 0 1 nascent RNAPol2 log2( lagging/leading) early-mid S-phase (repl. 1) 4e+05 Nieduszynski et al. , 2007 this dataset Vasseur et al. , 2016 35<=replication timing <45min (n=1868 genes, n (genes)/bin=267) log2(nascent RNAPol2 (lagging/leading)) late early certified by peer early genes (r. t. <=35min) -2. 17.
In this paper, we consider the named entity linking (NEL) problem. We assume a set of queries, named entities, that have to be identified within a knowledge base. This knowledge base is represented by a text database paired with a semantic graph, endowed with a classification of entities (ontology). We present state-of-the-art methods in NEL, and propose a new method for individual identification requiring few annotated data samples. We demonstrate its scalability and performance over standard datasets, for several ontology configurations. Our approach is wellmotivated for integration in real systems. Indeed, recent deep learning methods, despite their capacity to improve experimental precision, require lots of parameter tuning along with large volume of annotated data.
Introduction 1. Basic concepts and definitions The purpose of Named entity discovery (NED) in information retrieval is two-fold. First, it aims at extracting pre-defined sets of words from text documents: this corresponds to Named entity recognition (NER). These words are representations of named entities (such as names, places, locations,. ). Then, these entity mentions paired with their context are seen as queries to be identified within database: this corresponds to named entity linking  (NEL). NEL is also refered as named entity disambiguation. The interest in NEL has grown recently in several fields: in bioinformatics, to obtain locations of viral sequences from databases  (Weissenbacher et al. , 2015) , or to process biomedical litterature  (Zheng et al. , 2015). It also revealed to be useful in recruitment in order to identify employer names in a database  (Liu et al.