dataesr/scientific-paragraphs-categorization huggingface dataset:
Higher sensitivity of the EXPRSS method EXPRSS revealed differential expression in more genes than the other two methods and we wanted to verify if differential expression was observed for these additional genes in previous flg22 experiments. Therefore microarray data from previously published flg22 treatments of plants at various developmental stages were compared with EXPRSS Tag-seq data. ATH1 microarray data from seedlings treated with 1 μM flg22  [67] , mature leaves infiltrated with 1 μM flg22  [68]  and leaf discs generated from mature leaves incubated in 100 nM flg22 solution  [41] , were used for the analysis. Flg22 samples were compared against 1 hr water treated samples for leaf and seedling arrays and with untreated samples for leaf disc arrays. Details of differential expression are provided in Additional files 9 and 10.
The Rank products method  [66]  was used to generate differential expression lists (< 0. 05 FDR) from the 1 hr flg22 treatment microarrays and resulting lists were compared against the EXPRSS data. The outcome of these comparisons is summarised as Venn diagrams in Figure  5A  and B. The majority of flg22induced genes from various tissues are in good agreement among microarray experiments. In pair-wise comparisons of microarray data, the overlap for upregulated genes ranged from 48 to 74% (see Additional file 11). For upregulated genes EXPRSS has 92% overlap with leaf disc data (487 out of 529), 77% overlap with seedling data (626 out of 808) and 74% overlap with leaf data (445 out of 604). There is a substantial overlap of EXPRSS Tag-seq with three microarray datasets (hypergeometric probability 0.
00 for three comparisons), for common as well as specific identifications. 64% of the upregulated genes identified by EXPRSS that are spotted on ATH1 chip, were found to be differentially expressed in at least one of the three microarray datasets. Although some these genes were only identified at a different developmental stage or with differing concentration of flg22, EXPRSS Tag-seq is sensitive enough to identify responding genes. Compared to upregulated genes, the overlap of downregulated genes between three microarray experiments is less. In pair-wise comparisons of microarray data, the downregulated gene overlap ranged from 11 to 37% (see Additional file 11). However, EXPRSS profiling data has better overlap with two of the three microarray data sets. EXPRSS has 64% overlap with leaf disc data (236/370), 49% overlap with seedling data (181/372) and 23% overlap with leaf data (45/199) for downregulated genes.
EXPRSS tag-seq reliability and paired-end sequencing Expression profiling of flg22 responses in Arabidopsis ecotype Col-0 was carried out over a time course to study transcriptional regulation during flg22 triggered PTI and to validate the reliability of the EXPRSS Tag-seq. Additionally we performed a flg22 time course on mutants from three major hormonal pathways (npr1-1salicylic acid  [69] , jar1-1jasmonic acid  [70]  & ein2-5ethylene  [71] ). We collected three replicates each of 7 time points of flg22 treatment  (8, 15, 30, 45, 60 , 120 and 180 minutes) and a water treatment control. Comparison of correlation between 3 replicates of 60 minutes flg22 samples from time course data with 4 replicates of data presented earlier, from validation of EXPRSS Tag-seq, show significantly high correlation (>0.
925, see Additional file 1, Figure  S8 ). Differential expression analysis over 60 minutes of flg22 treatment revealed ~80% (1987/2504 at < 0. 01 FDR) and ~90% (2259/2504 at < 0. 05 FDR) of genes found differentially expressed using 4 replicate data from EXPSS validation. The high correlation (>0. 94) between log2 fold changes of 2504 genes from two experiments supports the consistency of EXPRSS Tag-seq (see Additional file 12). Additional genes found in the time course experiment could be due to comparison using a different control sample than with the validation experiment. High correlations (>0. 9) have been observed between three biological replicates of 32 data points collected (4 genotypes and 8 time points; see Additional file 1, Figure  S9 -S12 ). Samples were sequenced on different lanes of the same flow cell (wildtype and npr1-1), or on different flow cells from separate runs (jar1-1 and ein2-5).
EXPRSS Tag-seq was initially designed with Illumina single end adapters that are compatible with single read flow cells of GAII and Hiseq. We have modified EXPRSS to be compatible with the Illumina paired-end flow cell, so we can now sequence EXPRSS tag-seq samples from both ends on a GAII, a HiSeq, a HiSeq2500 and a Miseq. The second sequence read from polyadenylation junction enables verification of polyadenylation site assignments of transcripts. We have performed paired end sequencing on cDNA libraries made from temperature-shifted (28°C to 21°Ccollected at 0, 6, 9 and 24 hr) samples of Arabidopsis ecotype No-0 and slh1 leaves. We pooled eight libraries and sequencing was carried out on a Miseq and resulted in 4. 6 million paired reads. As our primary focus was on the technical feasibility of the paired-end sequencing the biological relevance of this data is not further considered here.
However, RNA-seq as a tool for differential expression analysis has its drawbacks. Tag-seq methods generate one tag per transcript, mostly from a defined position, thereby avoiding transcript length biases and length-based data transformation for differential expression analysis. According to TAIR10 transcript data, the mean cDNA length of Arabidopsis is 1. 5 kb. Thus, for every one fragment sequenced per transcript by EXPRSS there would be 10 ~150 bp fragments from RNA-seq, one could reasonably assume that 2 million reads from EXPRSS are equivalent to ~20 million reads of RNA-seq data, without transcript length and reverse transcription biases. Illumina single end sequencing on GAII provides about 35-40 M reads per lane, while a Hiseq lane provides about ~100-150 M reads. This means sequencing of only 2 RNA-seq samples per lane on GAII and 4-6 RNA-seq samples on Hiseq is required to provide sufficient depth for differential expression analysis.
Differential expression analysis performed on absolute tag counts facilitates interpretation of expression levels. Analysis using EXPRSS has identified more than 80% of the genes found by NlaIII-DGE and ATH1 microarray, though one could argue this is due to the higher numbers of genes found differentially expressed by EXPRSS compared to NlaIII-DGE and ATH1 microarray. A comparative study of flg22-treated microarray data from three different developmental stages in Arabidopsis has shown that EXPRSS data had a better overlap with the three microarray data sets than the overlap observed between the three microarray data sets. Two thirds of the upregulated genes from EXPRSS (spotted on ATH1 chip) were also found differentially expressed in at least one of the three array experiments. This supports the view that EXPRSS is sensitive enough to detect response-specific differential expression.
With respect to downregulated genes, EXPRSS had good overlap with two of the three-microarray data sets; although the three-microarray data sets had poor overlap among themselves. The number of genes found downregulated, using the EXPRSS Tag-seq, was greater than those detected from other microarray experiments. Therefore, one could argue this higher overlap might be due to the higher number of genes identified as downregulated with the EXPRSS method. There appears to be a detection bias against identification of down-regulation with microarrays, explaining the poor overlap between the three microarray data sets tested. The down-regulated genes found in this study could have been previously undetected in microarray analysis. Additionally, qPCR verification of selected genes from EXPRSS data confirmed that differential expression identification is reliable.
Q-RT-PCR For quantitative Reverse Transcription PCR, RNA samples were reverse-transcribed into complementary DNA (cDNA) using SuperscriptII reverse transcriptase (Invitrogen). The cDNA was used to quantify gene expression using a SYBR Green quantitative PCR kit (Sigma) and gene-specific primers (see Additional file 2). Themocycling and intensity detection was carried out with Chromo4 system on MJ Research Thermal cycler and data extracted using Opticon Monitor software.
12. 8 (-a -m 10 -best -strata)  [49]  and selected reads with up to 10 reportable alignments. Unaligned reads from previous step are used to align to transcript sequences of Arabidopsis [61] using Bowtie version 0. 12. 8 (-a -m 100 -best -strata). Tag to gene association is carried out using following considerations. Reads aligning in sense orientation with in each gene limits are assigned to that gene. A read aligning to all splice variant of a gene is counted once, and the reads uniquely aligning to various splice variants of a gene are pooled. Reads aligning to genes with overlapping gene limits are split equally between them. Reads aligning to more than 10 genes are discarded. Reads aligning to up to 10 genes are split equally between them. Reads aligning on the anti-sense strand of any gene are tested for if the read falls within 500 bp from 3′ end of another gene in sense direction.
Otherwise reads are assigned as antisense tags for the specific gene (see Additional file 1, Figure  S19 ). The tag assignment process was implemented in perl and scripts are available on request. Our analysis pipeline modules for implementation in Galaxy web-based research platform are provided in Additional file 3. Differential expression analysis was performed using the R statistical language version 2. 15. 3  [82]  with the Bioconductor  [83]  package baySeq version 1. 12. 0  [65]  using 10000 iterations to estimate empirical distribution on the parameters of the Negative Binomial distribution. The Multi-Experiment Viewer software from the TIGR website  [84]  was used to cluster similarly expressed genes using Hierarchical clustering  [85]. Eight samples were pooled and sequenced at 151 bp paired end reads in a Miseq from each end of the EXPRSS library.
The insert size of the library was > = 150 bp and also to be comparable with other runs of this manuscript sequence reads were truncated to 36 bp and quality filtered and used for downstream analysis. Paired end reads were aligned to Arabidopsis genome sequence (TAIR10) using Burrows-Wheeler Aligner (BWA)  [86]  sampe with default parameters. Generated Sequence Alignment/Map (SAM) format files are converted to sorted bam files and indexed using SAMtools  [87]. Sorted bam files with index are loaded on to Integrative Genomics Viewer (IGV) v2. 3. 8  [88]  for visualization of paired reads.
Microarray data analysis Microarray data cel files from flg22 treatment on the seedlings (NASCARRAYS-409)  [67] , mature leaves (NASCAR-RAYS-122)  [68]  and leaf discs generated from mature leaves (GEO accession number GSE17479)  [41]  were used. Data analysis was performed using the R statistical language with the Bioconductor packages limma  [89]  and affy  [90]. Robust multiarray average background-corrected, quantile normalized, and log-transformed perfect match only expression values were obtained using medianpolish summary method  [91]. Differentially expressed genes were identified using the rank products method with a false discovery rate of <0. 05  [66].
Acknowledgements This research was supported by the  Gatsby Charitable foundation  grant to JDGJ and a  Dorothy Hodgkin Postgraduate award  to GR. We sincerely thank  Cintia Kawashima ,  Alexi L. Balmuth , and  Jodie Pike  for technical assistance;  Michael Burrell  for computational assistance;  Jane Rogers ,  Darren Heavens , and  The Genome Analysis Centre  for allowing us to use Covaris  S220  for cDNA shearing and optimization and for Miseq sequencing.
Our processing pipeline also includes toponym recognition and geo-coding of mountains, glaciers, cabins, valleys, lakes and towns. Furthermore we recognize and co-reference person names  (Ebling et al. , 2011) , and we annotate temporal expressions (date, time, duration and set) with a variant of HeidelTime  (Rettich, 2013). Finally we analyze the parallel parts of our corpus and provide sentence alignment information that is computed via BLEUalign (Sennrich and  Volk, 2011). In order to process our texts with languagespecific tools (e. g. PoS tagging and person name recognition) we employed automatic language identification on the sentence level. We used Lingua-Ident 2  (developed by Michael Piotrowski) to determine for each sentence in our corpus whether it is in English, French, German, Italian or Romansh.
Tim Baldwin and his group  (Hughes et al. , 2006)  have surveyed the approaches to language identification at the time. They found a number of missing issues, such as language identification for minority languages, open class language identification (in contrast to identification within a fixed set of languages), sparse training data, varying encodings, and multilingual documents. Subsequently they  (Lui and Baldwin, 2011)  introduced a system for language identification of 97 languages trained on a mixture of corpora from different domains. They claim that their system Langid is particularly well suited for classifying short input strings (as in Twitter messages). We therefore tested Langid in our experiments for code-switching detection.
Exploratory Experiments with the SAC Yearbook 1925 In order to assess the performance of Langid for the detection of code-switching we performed an exploratory experiment with the SAC yearbook 1925. We extracted all word sequences between pairs of quotation marks where at least one token had been assigned the "unknown" lemma by our PoS tagger. The "unknown" lemma indicates that this word sequence may come from a different language. The word sequence had to be at least 4 characters long, thus skipping single letters and abbreviations. In this way we obtained 333 word sequences that are potential candidates for intrasentential code-switching. We then ran these word sequences through the Langid language identification system with the restriction that we expect the word sequences only to be either English, French, German, Italian or Latin (Romansh and Swiss German are not included in Langid).
For a given string Langid delivers the most likely language together with a confidence score. We then compared the language predicted by the Langid system with the (automatically) computed language of the complete sentence. In 189 out of the 333 sentences the Langid output predicted a code-switch. We then manually graded all Langid judgements and found that 225 language judgements (67. 5%) were correct. But only 89 of the 189 predicted code-switches came with the correct language. 40 of the 100 incorrect judgements were actually code-switches but with a different language. The remaining ones should have been classified with the same language as the surrounding sentence and are thus no examples of code-switching. A closer inspection of the results revealed that the book contained not only code-switches in the expected 5 languages, but also into Romansh (6), Spanish (4) and Swiss-German (13).
All others need to be discarded. In general, if precision is the most important aspect, then Langid should only be used for strings with 20 or more characters. In our test set only 4 strings that were longer than 20 characters were incorrectly classified within the selected language set. Among the errors was the famous Latin phrase conditio sine qua non (length : 21 characters including blanks) which Langid incorrectly classified as Italian. Another reason for the considerable number of misclassifications can be repeated occurrences of a word sequence. Our error count is a token-based count and thus prone to misclassified recurring phrases. In our experiment, Langid misclassified the French book name Echo des Alpes as Italian. Unfortunately this name occurs 18 times in our test set and thus accounts for 18 errors.
We suspect that an -o at the end of a word is a strong indicator for Italian. In a short string like Echo des Alpes (14 characters), this can make the difference. Another interesting observation is that hyphens speak for German. Our test set contains the hyphenated French string vesse-de-neige which Langid misclassifies as German with a clear margin over French. When the same string is analyzed without hyphens, then Langid correctly computes a preference for French over German. A similar observation comes from the Swiss German phrase uf's Wiederluege being classified as English when spelled with the apostrophe (which is less frequent in German than in English). Without the apostrophe Langid would count the string as German. With short strings like this, special symbols have a visible impact on the language identification.
We also observed that Langid is sensitive to all-caps capitalization. For example, AUS DEM LEBEN DER GEBIRGSMUNDARTEN (EN : The Lives of Mountain Dialects) is misclassified as English (with the default score) while Aus dem Leben der Gebirgsmundarten is correctly classified as German. Overall, we found that code-switching within the same article rarely targets different languages. For example, if the article is in German and contains code-switches into English, then it hardly ever contains code-switches into other languages. In analogy to the one-sense-per-discourse hypothesis we might call this the one-code-switchlanguage-per-discourse hypothesis.
Detecting Intra-sentential Code-Switching Based on exploratory studies and observations we decided on the following algorithm for detecting and annotating intra-sentential foreign language segments in the Text+Berg corpus. We search for sub-sentential token sequences (possibly of length 1) that are framed by a pair of quotation marks and that contain at least one "unknown" lemma. There must be at least two tokens outside of the quotation marks in the same sentence. As a compromise we restrict our detection to strings longer than 15 characters so that we get relatively reliable language judgements by Langid. The strings may consist of one token that is longer than 15 characters (e. g. Matterhornhochtourist) or a sequence of tokens whose sum of characters including blanks is more than 15. We feed these candidate strings to Langid for language identification and compare the output language with the language attribute of the surrounding sentence.
If the languages are different, then we regard the token sequence as code-switch and mark it accordingly in XML as shown in figure  2. In order to determine the precision of this algorithm, we checked 10 yearbooks from 1868 to 1878 (there was no yearbook in 1870) and from 1926 to 1935. The results are in table 1. From the 1180 code-switch candidates that we computed based on the above restrictions, Langid predicted 456 code-switches (39%). This means that in 39% of the cases Langid predicted a language that was different from the language of the surrounding sentence. We manually evaluated all 456 predicted codeswitches and found that 354 of them (78%) were correctly classified and labeled. These segments were indeed in a different language than the surrounding sentence and their language was correctly determined.
For example, the French seg- Out of the 102 segments whose language was wrongly classified, only 36 were no codeswitches. For example, the Latin segment cum grano salis africani is indeed a code-switch in a German sentence although Langid incorrectly classifies it as English. In fact, our evaluation showed that Langid is "reluctant" to classify strings as Latin. Latin strings are often misclassified as English or Italian. Overall this means that only 8% of the predicted code-switches are no code-switches. Therefore we can safely add the module for code-switch detection into our processing and annotation pipeline. In order to estimate the recall of our quotation filtering approach we manually evaluated a sample of the quotations that our algorithm excluded. Table  2  presents the numbers for the two time periods for two cases : first for sequences that are longer than 15 characters and contain only known lemmas, second for sequences that are shorter than 16 characters and contain at least one "unknown" lemma.
We have not included language identification for Swiss German nor for Romansh in the intra-sentential code-switch experiments reported in this paper. We will train language models for these two languages and add them to Langid to check the impact on the recognition accuracy. Since code-switches into Romansh are rare, and since Romansh can easily be confused with Italian, it is questionable whether the addition of this language model will have a positive influence. We have used the "general-purpose" language identifier Langid in these experiments. It will be interesting to investigate language identifiers that are optimized for short text fragments as discussed by  Vatanen et al. (2010). Given the relatively high number of short quotations (31%) that contain code-switches, recall could improve considerably.
In this paper we have focused solely on codeswitching candidates that are triggered by pairs of quotation marks. In order to increase the recall we will certainly enlarge the set of triggers to other indicators such as parentheses or commas. We have briefly looked at parentheses as trigger symbols and found them clearly less productive than quotation marks. To also find code-switches that have no overt marker remains the ultimate goal. Finally, we will exploit the parallel parts of our corpus. If a sentence in German contains a French segment, then it is likely that this French segment occurs verbatim in the parallel French sentence. Based on sentence and word alignment we will search for identical phrases in both language versions. We hope that this will lead to high accuracy code-switch data that we can use as training material for machine learning experiments.
Acknowledgments We would like to thank  Michi Amsler  and  Don Tuggener  for useful comments on literature and tools for language identification and codeswitching, as well as  Patricia Scheurer  for comments and suggestions on the language use in the SAC corpus. This research was supported by the  Swiss National Science Foundation  under grant  CRSII2 147653/1  through the project " MO-DERN : Modelling discourse entities and relations for coherent machine translation ".
We propose non-negative matrix factorisation with iterative restarts (iNMF) to model a noisy dataset of highly overlapping fluorophores with intermittent intensities. We can recover high-resolution images of individual sources from the optimised model, despite their high mutual overlap in the original data. Each source can have an arbitrary, unknown shape of the PSF and blinking behaviour. This allows us to use quantum dots as bright and stable fluorophores for localisation microscopy. We compare the iNMF results to CSSTORM, 3B and bSOFI. iNMF shows superior performance in the challenging task of super-resolution imaging using quantum dots. We can also retrieve axial localisation of the sources from the shape of the recovered PSF.
Under continuous excitation QDs exhibit stochastic blinking  [4]  which simplifies the acquisition procedure as no active ON-OFF switching (fPALM, STORM), nor presence of specialised buffers (dSTORM) is required. However, the blinking cannot be controlled and the QD-labelled data typically contain highly overlapping PSFs. Super-resolution imaging with QDs remains a challenging task. Several methods dealing with overlapping sources have been proposed recently. Among methods analysing each frame of the LM dataset individually  [5, 6]  CSSTORM  [7]  has shown a superior performance. Another group of algorithms models the whole LM dataset as a collection of blinking emitters. These techniques take the reappearance of the fluorophores in different frames into account, and allow the localisation of higher densities of ON sources.