dataesr/scientific-paragraphs-categorization huggingface dataset:
01 (0. 0001), window size 10 (5), vector dimension 50 (100), and number of epochs 1,000 (5). For all other parameters we use the default values; see https://fasttext. cc/docs/ en/options. html (January 21, 2020). We use fastText to detect words which are most similar to the emotion words we are studying: the similarity of two word vectors indicates that the words in question have a similar semantic function in the language and can appear in similar contexts. If the corpus is large enough, the most similar word vectors are synonymous, antithetical or their meaning is very close to each other in some other way. When studying words of fear, one expects to find other fear words among the most similar words detected by fastText.
4. 4 Combining the Methods Up to now we have described our methods individually. To summarize the sequence of analysis, (1) we built a file with each document of the dataset as one line. This was done separately for both levels of preprocessing. (2) Analyzing the two levels separately, we extracted collocations with PMI for all the emotion words we were interested in and (3) built word vectors with fastText and extracted the closest words for the emotion words under scrutiny. (4) We visualized the emotion words and their collocates and semantically similar words as networks with Gephi. We built graphs for the PMI and fastText results with both the 10 and the 50 closest words. The larger graphs of 50 also contain the 10 closest words, of course, but with additional words we could get an idea of the wider contextual domain of the words of interest.
In the smaller graphs of the 10 closest words for each emotion word, it was easier to spot the links between words and, in some cases, the common contexts as well. 46 (5) The analysis of these networks (presented in Section 5) moved in a hermeneutic circle. After examining the graphs, certain ideas of the context emerged. These different possibilities were examined by going back to the context of individual words. For examining individual words and their contexts, we used the corpus search tool Korp, an online service provided by the Language Bank of Finland, which contains several versions of Oracc data. 47 At the time of writing, the Korp version of Oracc that can be searched in the user interface is "May 2019. "48 The Akkadian text data in that version is very close to the dataset that we used in this article (see Section 3 above).
49 Therefore, in what follows, "Korp" refers to the dataset "Oracc in Korp May 2019," not directly to our research data. The results of a query in Korp are presented as concordances (i. e. , every instance of the word matching the query is listed line by line with all its neighbors). Furthermore, the matching words are highlighted in the middle of the display. Korp also allows for gathering statistics regarding the genre (or other 46 All of the graphs and the data used for creating them are available in the online repository Zenodo: http://doi. org/10. 5281/zenodo. 3634325 (February 4, 2020). In our graphs, the size of a node indicates its degree (i. e. , the number of nodes to which it is directly connected). metadata categories) of the word or words of interest. One can search for individual words or co-occurrences of two words within a certain distance from each other.
Data-Driven Results PMI analyzes words that often appear close to our five verbs of interest. This is to say, it quantifies co-occurrence patterns. Therefore, these collocates reflect the vocabulary context in which our words of interest occur. FastText is designed for handling large datasets efficiently, and instead of analyzing each word attestation in the corpus separately, it uses predictive models to build word vectors. It excels in building accurate word vectors in corpora of billions of words, but our tiny corpus of slightly more than a million words poses challenges for fastText. Relatively few words of synonymous or similar meaning appear on the lists of word vectors similar to the fear words under scrutiny. Instead we get many words which are similar to the PMI results, such as words that appear together (e.
g. , in formulaic expressions or lists). graphs we have produced in Gephi are too large to be easily reproduced in print. Nonetheless, even when comparing these simplified figures, one notices that because PMI requires the words of interest to appear close to each other, there are fewer connections between fear verbs than with fastText, which looks for words that appear in similar contexts as our words of interest. As outlined above, our analysis is based on many different sources: fastText and PMI analysis is produced for two separate datasets (Level 1 and Level 2), and for each of these we have produced graphs for the 10 closest words and the 50 closest words. In the interests of clarity, we present below only our main observations. The intent here is to provide the reader with enough details to support our conclusions, although the main support for our arguments is provided by the PMI and fastText calculations and the graphs themselves.
palāḫu To start with the Level 1 results, both PMI and fastText results offer a unanimous picture of the noun pulḫu. It denotes the terror caused by the Assyrian king and the god Aššur who accompanies him on the battlefield. Pulḫu is nearly always attested together with the word melemmu "radiance" (54 out of 58 occurrences) and almost as often with saḫāpu "to envelop, overwhelm" (48/58). A Korp search reveals that the words are attested in a common phrase in Neo-Assyrian royal inscriptions, of which a royal inscription of Sennacherib gives a good example: "Fear (pulḫu) of my lordly brilliance (melemmu) overwhelmed (saḫāpu) Lulî, the king of the city Sidon, and he fled afar into the midst of the sea and disappeared" (RINAP 3 23). PMI and fastText capture this relationship very well, as melemmu and saḫāpu have unusually high PMI scores in this dataset (0.
50 The graphs have been created with the Gephi software (downloaded from https://gephi. org). The full networks are available in our Zenodo repository (http://doi. org/10. 5281/ zenodo. 3634325) in the "network-data" folder, whereas the visualizations of the networks as PDF files are in the "visualizations" folder. The noun puluḫtu resembles pulḫu. They share melemmu and saḫāpu on their lists of the ten closest PMI collocates and most similar word vectors in fast-Text. Moreover, the word namurratu "radiance" also has a word vector which is similar to both puluḫtu (cosine similarity 0. 77) and pulḫu (0. 77). Co-occurrence numbers and PMI scores show, however, that unlike pulḫu, puluḫtu is not attested in a single phrase or set expression. There are several relatively frequent PMI collocates (e.
The words "terror" and "fear" seem to be legitimate English translations for adirtu, gilittu and pirittu. The verb parādu is mainly attested in the disregard formula in our dataset, which is very clearly shown in the PMI and fastText results. A handful of other occurrences were studied using Korp. The word appears in medical omens as a symptom (either translated as "tremble" or "to be terrified"; e. g. , SpTU 4 152), and it can denote fear experienced by mortals and gods (e. g. , RINAP 4 106). Regarding the derivative pardu, it is very frequently used in the expression "frightening dreams," which explains the strong connection it has to šuttu ("dream"). The word has clear connotations related to magic and divination (e. g. , ittu "sign"), and therefore it follows the general flavor of the verb parādu.
As noted at the beginning of this article, the concept of "emotion" itself is not universal and the existence of universal emotional states is debated. Furthermore, there are many other ways of expressing the emotion of fear than these five verbs. In this article, our modest and concrete goal has been instead to examine how emotions were expressed linguistically via these particular words and examine how the native Akkadian users themselves might have perceived the relationships between these five words and their relationships to other words. This aim rests on the idea that the meaning of any word lies in its relationship with other words. By using language technological methods (PMI and fastText), we traced semantically relevant properties of the five fear verbs with other words of Akkadian.
This enabled us to create for the first time a semantic map of how these verbs relate to each other and other lexemes in our corpus. This provided us with tools to reflect on the semantic domains of the words beyond what is covered by existing research, even comprehensive volumes like the CAD. In the article, we have discussed previous lexical research on "fear" in Akkadian (Section 2), described our dataset (Section 3), and outlined our research process and methods (Section 4) in detail. The results of the quantitative analysis are discussed in Section 5. In this final section, we summarize our results from the perspective of our main aims. The aims of our research have been twofold: 1) to develop methods that allow us to construct semantic maps of lexemes, thus getting closer to understanding the semantic domains in the mind of Akkadian language users, and 2) to use these methods to contribute something new to Assyriological research regarding lexical semantics of fear.
6. 1 Methods As in all historical research, we are constrained by our source material. In our case, this is the text material that is available in Oracc. The existing genres influence the results strongly, as do the methodological choices that we have made. Working on this article has shown us that in the future it will not be necessary to process the text data quite so much. Most of the interesting differences could be perceived immediately after the first round of processing, before combining the nouns with their verbal roots (see Section 3. 2). The overall results highlight the difference between Level 1, with its basic simple modifications, and Level 2, where roots were combined with their derivatives and homonyms separated. Although both were useful, Level 1 should be our primary concern in future work, since a verb and its derivatives do not necessarily share the same semantic field.
These complementary views provide the best possible results. Although we only selected verbs denoting fear that had a frequency of more than twenty instances in our dataset, during analysis we noticed that for words that appear less than fifty times in our material (e. g. , galātu, pirittu and šaḫātu), simply using the myriad search functions in Korp is a powerful research tool. Although the text corpus in Korp was not entirely identical with the text corpus used for this article (see Section 3), the results of Korp queries were often essential for our analysis. For example, the results of the Korp search demonstrated that the verb šaḫātu "to fear" is almost exclusively used to express fear in the genre of royal inscriptions. Certainly in Akkadian some genres are so specialized that they might constitute a context (a prototypical scenario where a word appears) in themselves.
6. 2 Meaning in Networks of Lexemes The statistical results (PMI and fastText) on how specific words relate to each other give new perspectives on semantics and the relations between lexemes of fear in a way that has not been previously possible. Figures 18. 1 and 18. 2 above illustrate this to a small degree, although we again refer to the full dataset (available in the Zenodo repository). For a concrete example, the close connection between certain anger and fear verbs (especially in martial contexts) was very apparent in our results. 57 The relationship of šaḫātu to love (through piousness toward gods) is worth mentioning here as well. Furthermore, parādu and galātu could be identified as semantically similar words. There are several interesting future research topics related to this approach.
Acknowledgements We gratefully acknowledge the funding received from the  Academy of Finland  that has made this article possible. Furthermore, we thank the  Open Richly Annotated Cuneiform Corpus (Oracc)  steering committee, in particular  Niek Veldhuis , who provided access to Oracc data in JSON format. We are indebted to everyone who has been involved in making this research data available. This naturally includes the authors of the original publications, but also the people who have made the data Oracc-compatible and enriched it through lemmatizations and by adding other metadata. As the total number of people involved would amount to hundreds (the current number of individual subprojects in Oracc is approximately seventy), it would be impractical to list them all individually. However, much of our data is Neo-Assyrian, and much of that  Neo-Assyrian linguistic data was created in the State Archives of Assyria  project.
Patients, clinical information and sequencing data The study protocol was approved by the Ethics Committee of Rennes Hospital. The full retrospective cohort was constituted of all the cases referred to our laboratory for molecular diagnosis during a 10-year period  from eight French Centers Labeled for Developmental Anomalies (CLADs), centres of prenatal diagnosis and several European centres. Study participation involved informed written consent as well as the availability of clinical information and associated molecular data (whole exome, gene panel and Sanger sequencing with comparative genomic hybridization array/multiplex ligation-dependent probe amplification) for known/suspected genes associated with holoprosencephaly and related midline defects (Supplementary Table  6 ). For the 931 patients involved in the present study, we retrieved all the quality checked sSNVs located in the coding sequence of SHH (NM_000193.
Bioinformatic annotation of SHH sSNVs Patient-associated SHH sSNVs were analysed through a semiautomated bioinformatics pipeline (genome version hg19) using Variant Effect Predictor (release 97),  ANNOVAR, SpiCE (v2. 1. 3) and SilVA (v1. 1. 1) software, combined with manual annotations by Human Splicing Finder, TrAP and RegRNA 2. 0 web servers. Alamut Visual v2. 8. 1 was used to retrieve splicing predictions of MaxEntScan and SSF-like models. gnomAD database (v2. 1, controls dataset) was used to assess variant population frequency. Eighty-nine sSNVs of SHH (NM_000193. 3) found in the gnomAD controls dataset are provided in Supplementary Table  3.
Relative synonymous bicodon usage index Some bicodons are scarcely used in both samples. In these cases, the number of occurrences of a bicodon observed in one sample of sequences is not significantly different than the number of occurrences observed in the other sample of sequences, thus leading to p $ 0. However, they are associated to a very low significance to estimate the pause propensity, as in the case of the variant c. 897G4C. For that reason, we used a complementary measure for such cases, which is the RSBU index, computed over both samples of sequences. Mathematically, it is similar to the RSCU index and is defined as: RSBUij ¼ qðfijL þ fijHÞ=Nap (5) Similar to the RSCU index, RSBU is a measure of a relative rareness of a given bicodon. Codon/bicodon variations were calculated for patient-associated sSNVs of SHH and compared to those of the sSNVs present in the gnomAD control database.
Fluorescence microscopy and analysis Medium was removed, and cells were fixed in 4% formaldehyde in phosphate-buffered saline for 20 min. Cells were permeabilized with 0. 2% Triton TM X-100 for 10 min, DNA was counterstained with DAPI (Invitrogen, ThermoFisher). Coverslips were mounted to microscope slides with ProLong V R Gold (Life Technologies). Images were finally acquired using an Olympus IX71 inverted fluorescence microscope (DeltaVision, MRic platform). Quantification of the fluorescence intensities mEGFP (green, I meGFP ) and mCherry (red; I mCherry ) for each cell was done with ImageJ software. After background intensity substraction (I meGFP BGD and I mCherry BGD) , signal intensities were expressed as an I meGFP /I mCherry ratio: R = (I meGFP -I meGFP BGD )/ (I mCherry -I mCherry BGD ).
SHH sSNVs are more prevalent in holoprosencephaly patients compared to controls We performed a retrospective analysis of clinical sequencing data obtained from 931 patients affected by holoprosencephaly and associated disorders who were referred to our laboratory for molecular screening of SHH and related genes. Variant analysis identified eight different sSNVs of SHH (Fig. 1A ) in 21 patients (Supplementary Table  1 ). Among the identified sSNVs, two variants (c. 1206C4T and c. 1354C4T) were never reported among 60 146 healthy individuals present in Genome Aggregation Database (gnomAD)  (Karczewski et al. , 2019). Two variants (c. 522C4T and c. 552G4C) were detected only once in gnomAD. Three variants (c. 630C4T, c. 885C4T and c. 897G4C) were rare in gnomAD [minor allele frequency (MAF) 5 0.
To assess the significance of each D-value, we performed the same calculations for 89 SHH sSNVs reported in the general population (gnomAD), which were used as control dataset. Five of eight patient-associated sSNVs (c. 552G4C, c. 630C4T, c. 897G4C, c. 1206C4T and c. 1354C4T) presented at least one significant D-value (above the 3rd quartile of the control dataset) and were predicted to impact the translation (Table  2 ). The c. 1354C4T variant introduced the largest decrease in codon usage (i. e. replacement of a very frequent codon by a very rare one), indicating a slowdown in an otherwise fast translating region (Fig. 3A  and B ). The largest increase was observed for c. 552G4C, indicating an accelerated translation rate in a slow translating region. c. 522C4T and c. 885C4T were associated with the smallest changes among the studied variants and showed conflicting results between different D measures, and were therefore considered of low to null impact.
897G4C CTG CTC # ~# c. 1206C4T GGC GGT ~~~# c. 1354C4T CTG TTG # # # # For each variant, we calculated differences in usage between the wild-type and the mutated codon/bicodon using RSCU, MinMax, RSBU and PPI algorithms. For this analysis, %MinMax values were averaged over a sliding window of three codons to assess with better resolution the effect of each sSNV. To assess the significance of changes, the resulting D-values were compared to those of known SHH sSNVs reported in gnomAD control database. Tilde ($) and downward and upward arrows (#") indicate low (below 3rd quartile) and high (above 3rd quartile) D-values, respectively. For RSBU and PPI, each sSNV is associated with two bicodon variations, since one codon can be part of two different bicodons. For each sSNV, bicodons showing the largest change were taken into account.
1206C4T-is associated with a reduction of both the mRNA and the protein levels. Decreased mRNA levels could originate from a reduced transcriptional activity  (Helmlinger et al. , 2006) , although the precise mechanism by which this sSNV could impact on transcription remains unknown. Alternatively, this reduction in mRNA level could be a consequence of a negative feedback loop from suboptimal translation leading to mRNA degradation, as described recently  (Presnyak et al. , 2015). In both cases, further investigations will be required to elucidate the impact of this particular sSNV. We also show that sSNVs influencing the SHH protein amount are present in the general population. The c. 630C4T and c. 897G4C variants (associated with 5% and 13% protein reduction, respectively) were found in 389 individuals of the gnomAD database, including seven homozygous carriers for the c.
Acknowledgements The authors acknowledge the  Centre de Ressources Biologiques (CRB)-Sante (http://www. crbsante-rennes. com) of Rennes for managing patient samples. We thank  Marie-Dominique Galibert  for carefully reading this manuscript and for scientific discussion. We thank  Alinoe Lavillaureix  for retrieving the clinical data. We thank  Charlotte Andrieu  for helping with figure design. We thank  Alexandra Martins  for her assistance with the minigene. We thank  Luc Paillard.
Aire-sensitive genes show a preference for short-3'UTR transcript isoforms in mTEChi To assess the proportion of long and short-3'UTR transcript isoforms in mTEChi, we selected the genes that harbor potential proximal alternative pAs in their annotated 3'UTRs according to the Pol-yA_DB 2 database which reports pAs identified from comparisons of cDNAs and ESTs from a very large panel of peripheral tissues  (Lee et al. , 2007 ; Figure  1 -source data 1). For each gene, the relative expression of the long 3'UTR isoform versus all isoforms could be defined as the distal 3'UTR (d3'UTR) ratio, that is the expression of the region downstream of the proximal pA (d3'UTR) normalized to the upstream region in the last exon (Figure  1A ). To determine whether the Aire-sensitive genes exhibit a biased proportion of long and short-3'UTR isoforms in mTEChi, we first performed RNA deep-sequencing (RNA-seq) of mTEChi sorted from WT and Aire-KO mice in order to identify the Aire-sensitive genes, that is those upregulated by Aire (Figure  1B  and Figure  1 -source data 2).
To determine whether the genes sensitive to Aire exhibit shorter 3'UTR isoforms in mTEChi than in their normal tissue of expression, we set out to identify the Aire-sensitive genes showing tissuespecific or selective expression and to calculate the d3'UTR ratios of the identified Aire-sensitive TRA genes in their tissue(s) of expression. To this end, we collected RNA-seq datasets corresponding to a variety of tissues  (Shen et al. , 2012; van den Berghe et al. , 2013; Warren et al. , 2013)  and selected in each tissue the set of Aire-sensitive genes for which we identified a restricted expression in comparison to the other tissues by applying the SPM (Specificity Measurement) method  (Pan et al. , 2013 ; Figure  1 -figure supplement 1D,E). We then calculated the d3'UTR ratios of these genes in each peripheral tissue and found variable d3'UTR ratios across tissues with the Taking the Aire-sensitive TRA genes individually, we compared their d3'UTR ratios in mTEChi versus their respective tissue of expression and found a dramatic bias towards higher ratios in peripheral tissues, confirming the preference of Aire-sensitive TRA genes for short-3'UTR transcript isoforms in mTEChi, as compared to the periphery (Figure  1H ).
The effect of CLP1 knockdown on Aire-sensitive genes lacking annotated proximal pAs was much less than for the genes with proximal pAs, but a smaller effect on the annotated single-pA genes was observed for one of two CLP1 shRNAs (Figure  3C ). This might simply be because some genes tagged as "proximal pA-" in the incomplete PolyA_DB 2 database possessed undiscovered proximal pAs in mTEChi and were therefore responsive to the action of CLP1. Our findings regarding the differential response of pA+ and pA-genes to CLP1 loss-of-function suggested that the effect of CLP1 on Airesensitive gene expression in HEK293 cells was dependent on the potential of Aire-sensitive genes to switch from using a distal to a proximal pA site. To assess the effect of CLP1 on 3'UTR shortening at Aire-sensitive genes, we performed RNA-seq experiments in Ctr and CLP1 knockdown HEK293 cells.
A role for Clp1 in mTEChi was further supported by its higher expression in mTEChi in comparison to a wide variety of tissues for which we collected RNA-seq datasets  (Shen et al. , 2012; van den Berghe et al. , 2013; Warren et al. , 2013 ; Figure  4D ). As a comparison, none of the other candidate factors, Hnrnpl, Ddx5, Ddx17 and Paprp1 that contributed to 3'UTR shortening in HEK293 cells, were over-represented in mTEChi (Figure  4 -figure supplement 4). Importantly, we also validated higher expression of Clp1 at the protein level in mTEChi versus their precursor cells, mTEClo  (Ga ¨bler et al. , 2007; Hamazaki et al. , 2007) , cells from the whole thymus, and also the predominant thymus CD45+ leukocyte fraction (Figure  4 -figure supplement 5). In addition, a role for Clp1 independent of Aire's action on genes expression was supported by the observed similar levels of Clp1 expression in WT and Aire-KO mTEChi (Figure  4 -figure supplement 6A) and the lack of evidence for Aire and CLP1 interaction (Figure  4 -figure supplement 6B).