dataesr/scientific-paragraphs-categorization huggingface dataset:
PCA results of this new dataset is given in Fig. 2d. Under these conditions where saturation is omnipresent, six significant contributions are now detected. The first principal components are more and more perturbed. They are now undeniably different from pure spectra extracted on unsaturated spectra. As examples, the first principal component contains distinct contributions from all three elements and the following ones, which contain more and more artifacts, have equally increasing explained variances. Two new principal components 5 and 6 are also extracted in these conditions with quite singular scores maps. The presence of these additional principal components is explained by the fact that the variance of all the saturated peaks must of course be explained, but also that they are not necessarily saturated at the same time in all the spectra of the dataset.
e. retrieve lost spectral information and consequently a full data matrix. Imputation was therefore applied to the previous datasets by considering the three levels of saturation. In order to appreciate the quality of the data reconstruction, we simply reapplied PCA on these three new datasets. Fig. 3  shows the results concerning the intermediate saturation level equal to 20. The results for the other levels are presented in the supplementary material (SI Figure  5S ). By comparing these new extractions with those obtained on unsaturated data, we observe rather spectacular results. First of all, we recover the three significant components on the eigenvalues scree plot, which is consistent with the initial results. Moreover, principal components and corresponding scores maps are also very comparable to the initial ones.
These good results can be explained by the fact that the multivariate regressions used in the MICE approach predict missing values rather well. By way of illustration, Fig. 4  shows the emission predicted by the imputation model as a function of known values at the wavelength 323. 96 nm from non-saturated data, this silver emission line being the most often saturated for a saturation level equal to 20. Looking specifically at the results concerning the most saturated dataset (SI Figure  S5 , saturation level equal 10), some readers might say that despite the three significant contributions detected on the scree plot, it is possible to observe information related to a fourth component at both spectral and spatial levels. This would be quite commendable but we must not lose sight of the fact that these contributions are very close to the noise level.
Moreover, these results were obtained from a dataset for which almost all the spectra were saturated, which could not be more challenging. In this second part, we propose to explore a lung biopsy sample. This sample is particularly interesting because the analyzed area of lung presents a certain diversity of materials since we have naturally biological tissues but also mineral phases and metal particles localized in specific sub-areas. In these conditions, we quickly understand that it is almost impossible to find acquisition conditions allowing us to avoid saturation over the entire surface analyzed. In a way, one always make a bet before such an analysis because the considered spectroscopy is destructive and it is not possible to return to this sample area with new acquisition parameters.
Conclusion As we have seen in the work, it is crucial to consider the phenomenon of saturation present in the spectra. Through different datasets we have indeed shown that its presence quickly induces artifacts on spectral profiles but also on generated images when multivariate tools are used for their exploration. Make no mistake, even the presence of a limited percentage of saturated spectra in a given dataset can have an impact on the veracity of the chemometric results. It is obvious that it is absolutely necessary to avoid the presence of saturation in the acquired spectroscopic data whenever possible by modifying, for example, the sample preparation or the acquisition parameters. Unfortunately, there are many situations where this phenomenon is observed as in LIBS imaging and we have to find solutions to exploit these acquired data anyway.
The usual column-or row-wise deletion is not a satisfactory solution because it can be accompanied by a large loss of spectral information in the dataset. As a consequence, we would have a partial or even biased view both at the spectral and spatial level of the sample. All the originality of our work was to consider the saturated signals as values that had not really been measured and by extension as missing values. The goal being to preserve all the spectral and spatial dimensions of the dataset, statistical imputation allowed us to retrieve complete data cubes consistent with the analytical reality of the samples considered as shown in the results. With this new approach, we will potentially have a chance to explore all those datasets that we think are being lost due to saturated signals.
To synchronize USV recording with the tracking, we use the ''Trigger control'' of Avisoft-RECORDER. This function allows launching of an external program at each start and end of records. We use the free software PacketSender 2  to perform communication between Avisoft-RECORDER and Live Mouse Tracker (LMT). Through PacketSender, we send a UDP string packet containing the file number currently recorded by Avisoft-RECORDER. This information is recorded by LMT within the database as an ''USV event. '' The goal of the synchronization is to match the USV record with the current data frame number recorded by LMT.
Train2 The mouse is following another mouse (speed higher than 5 pixels per frame) while sniffing its ano-genital region. LMT USV Toolbox, an Open-Source, Free, and Online USV Analysis Pipeline The currently available methods to detect and analyze mouse USVs need specific installations and software. To facilitate the testing of our own algorithm, we provide a website to test the method or to process data online 1. The user simply drags and drops his/her wave file, waits a few seconds (depending on the length of the sample file), and finally evaluates the quality of the USV segmentation and the data extracted from the sound file. The goal of this website is to provide immediate access to the method without installing any software. The first panel of the website is dedicated to evaluating USV detection.
-the duration of each USV (in ms). -the power (i. e. , amplitude) of each USV, depicted in arbitrary unit. -the proportion of USVs with frequency modulations. -the proportion of USVs containing one or more frequency jump(s). -a table gathering all the acoustic variables extracted on each USV of the sound file. The user can download all these results for his/her own sound file. These results are deleted after 1 h. Data downloaded from this web page can be directly used with the scripts that we provide in the present study. To perform the analysis on thousands of files, we also provide the desktop version of the analysis program, working in batch mode (download through the Live Mouse Tracker website: http://livemousetracker. org).
The USVs were then synchronized with behaviors and the acoustic characteristics extracted and stored as metadata in the database (Supplementary Tables II, III-Description of the acoustic variables measured for each USV and burst). We provide a library in Python to access and query these data. We developed an online application that allows the segmentation and analysis of wave files without software installation 1. Annotated spectrograms are generated online to allow visual inspection of the results. We also provide online examples. The desktop version of this resource is also available and open source.
Uncorrected p-values: ns: non significant, *p < 0. 05, **p < 0. 01, * * * p < 0. 001. P-values followed by • survived a correction for multiple testing over age classes. all age classes over the 3 days of recording (Figure  3B ). The total number of USVs recorded for 3 months-old Shank3 -/- females over the 3 days (5,527. 0 ± 3,236. 8) was not statistically different from that of USVs recorded for age-matched B6 females (6,589. 3 ± 1,979. 8; Mann-Whitney U-test: U = 8. 0, p = 0. 228; Figure  3C ). Shank3 -/-mice emitted significantly fewer USVs than B6 mice only during the second night (U = 2. 0, p = 0. 021; data not shown).
Comparisons of Behaviors With and Without USVs We next hypothesized that behaviors accompanied by USVs were more intense (i. e. , longer and with higher mean speed) than behaviors not accompanied by USVs. As a proxy for arousal, we considered the speed of the animals. We thus quantified the mean speed of the animals during behaviors accompanied or not by USVs, as well as the duration of these behaviors. Our hypothesis was not verified for Train2 events in any age class at the individual level. Indeed, within each individual, the duration of the event and the speed of the mice did not vary significantly, regardless of whether USVs were emitted or not (Figure  6A ). This might be related to the fact that this behavior was the most specific to USVs (Figure  3E ). In contrast, follow (Figure  6B ), approach contact (Figure  6C ), contact, getaway, and oral-genital contact (data not shown) showed both a significantly FIGURE 5 | Acoustic variations of USVs between contexts of emission in B6 and Shank3 -/-mice.
FUNDING This work was partially funded by the Institut Pasteur, the Bettencourt-Schueller Foundation, the Cognacq-Jay Foundation, the Conny-Maeva Foundation, the ERANET-NEURON ALTRUISM program, the Agence Nationale de la Recherche through grant number ANR-10-LABX-62-IBEID, France-BioImaging infrastructure through grant number ANR-10-INBS-04 and the INCEPTION program through grant number ANR-16-CONV-0005, the Centre National de la Recherche Scientifique, the University Paris Diderot, the BioPsy Labex, and the Foundation for Medical Research (Equipe DEQ20130326488). This study also benefited from the financial support of GIS ''Autisme et Troubles du Neuro-Développement''. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Methods combining antigen restimulation with the detection of upregulated cellular response markers can also be used for this purpose  (11, 12). Although these approaches allow a powerful interrogation of the T cell response, antigen-specific cells against non-specific background binding are rare, meaning some memory T cell responses are below the limit of detection, and the peptide:MHC multimer approach depends on the a prior identification of appropriate peptide:MHC combinations. Thirdly, 'sequence-based prediction' describes a new family of methods in which growing catalogs of defined TCR:antigen combinations are used to train machine learning algorithms to predict specificity directly from TCR sequences  (13) (14) (15). These have great potential to enable generalizable decoding of the repertoire, especially as the training datasets grow, however they do not yet enable the confident assignment of specificities within deep repertoires using TCR sequences alone.
Following the incubation time, cells were washed three times in 1X BD Permwash and resuspended in BD stabilizing fixative (BD Biosciences) diluted 1:3. All analysis plots were pre-gated on live (using Live/Dead stain) and singlet events. Cells were analyzed with a FACSCanto II or an LSR Fortessa (BD Biosciences). Data were analyzed with FlowJo ® Software. The following antibodies were used: hCD3-BV711, clone UCHT1, ref 563725  (BD).
Incubation was for 20 mins at RT and 2 mL PBS was used for washing. Beads were then resuspended into 200 mL PBS. Sorting was on a FACS Aria using BD FACS Diva software for gating analysis: 9. 0. 1. Gating was FCS/SSC; Singlets FCS; Singlets SSC; Live/Dead Aqua; Dump channel (APC) CD19, CD14, CD16; CD3+; CD8+/CD4-; CD8 +/Tet+. Following the sort, the cell pellet was spun at 2,500 RPM, washed once in 2% FCS/PBS, and 650 mL RLT (RLT Lysis Buffer, Qiagen) with 1% 2ME (2-mercaptoethanol) added with vortexing for 45 sec followed by a quick spin and freezing at -80°C. If cell numbers were less than 10e6, only 350 mL of RLT with 1% 2ME was used.
Significance was assigned to each cluster by determining the frequency with which clusters containing the same number of members, and a greater or equal mean frequency, were observed within 1000 random trials. The random trials used randomly-generated TCR clonotypes of the corresponding chain, constructed using the 212,651 a and b sequences published in  Howie et al. , 2015 (23)  as a base. Each TCR sequence from this public dataset was decomposed into 14 elements using mixcr (bestVHit, V3dels_or_Ps, nSeqVPSegment, nSeqVDJunction, nSeqDLeftPSegment, D5dels_or_Ps, bestDHit, D3dels_or_Ps, nSeqDRightPSegment, nSeqDJJunction, nSeqJPSegment, J5dels_or_Ps, bestJHit, nSeqVJJunction). These were permuted to generate each new random TCR. For each trial, 1000 such TCRs were generated and each assigned a frequency from the 1000-member clonotype shortlist being tested, before being clustered as described above.
Clusters were considered significant at p<0. 01 (i. e. , <10 occurrences in the 1000 random trials). Clonotypes from significant clusters detected across all TCRdist thresholds were combined into a single master clonotype list and reclustered at the maximum threshold of 50 for final output. R Code is available at https://github. com/TGenNorth/TCR_framework. git. Raw sequence data is available at SRA (https://trace. ncbi. nlm. nih. gov/ Traces/sra/) under BioProject: PRJNA752634.
FUNDING We acknowledge funding support from the  Roche   Collaborative Research Grant  to the  Baylor Scott and White Research Institute  (supporting the initial development of anti-CD40 antibodies with enhanced agonist potency), the  Vaccine Research Institute  via the  ANR-10-LABX-77  grant, and the  NIH  grant  U19 AI111211. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.
0] [11]  We call this an 'Optimal Bayes One Stage' design; (2) the value of the prior mean favours one of the two technologies so strongly that the expected cost of conducting any trial outweighs the expected benefit. In this scenario, the Optimal Bayes Sequential policy is to run no trial and base the adoption decision on the sign of the prior mean alone. Figure  1  presents a representation of the stopping policy for the problem in (pairwise allocations 3 prior/posterior mean) space. If it is optimal to run a sequential trial, recruitment of patients takes place during Stage I but no outcomes are observed. At the start of Stage II, health outcomes and treatment costs for the first pairwise allocation are observed and used to update the prior mean. Outcomes then arrive sequentially, the posterior mean is updated sequentially and interim analyses of the data are permitted.
Acknowledgements We thank two anonymous reviewers, the Deputy Editor and Associate Editor, together with  Helen Handoll , for comments on earlier drafts of the paper. We thank  Alan Brennan  for suggestions concerning the tracking of health outcome and treatment cost data over the course of the trial. We thank participants in the International Clinical Trials Methodology Conference 2019, Brighton; the  Health Economics and Decision Sciences seminar and EcoNomics of Adaptive Clinical Trials  (ENACT) Workshops 1 and 2 held at the  School of Health and Related Research, University of Sheffield ; and the seminar held by the  Institute of Advanced Studies at the University of Bologna. We thank  Matthew Dore  and  Andrea Palfreeman  for preparation of the research cost data used in the ProFHER trial.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: A. R. was supported by the  National Institute for Health Research (NIHR) Oxford Biomedical Research Centre. S. C. and A. A. acknowledge the support of the  European Union  through the  MSCA-ESA-ITN  project [676129]. M. F. acknowledges funding from the  Research Infrastructure Support Fund of the Department of Economics and Related Studies, University of York. No funding source played a role in the design of the research; collection, analysis and interpretation of data; the writing of the report; and the decision to submit the article for publication.
Profiles may sometimes be represented by b-spline coefficients associated with segments of the profile curve (e. g. Adler et al. , 2002; Hlaváčková-Schindler et al. , 2001; Kampel and Sablatnig, 2007 , 2003 , 2002 , 1999; Laflin, 1986; Schurmans et al. , 2001) , by polylines  (Lucena et al. , 2016) , or by dominant feature points that are extracted using the medialness measurement  (Piccolli et al. , 2015). Similarities between two pottery fragments can be calculated from Euclidean distances between their profiles  (Maiza and Gaildrat, 2005) , or from the similarity of the coefficients expressing their shape  (Gilboa et al. , 2004). The best match for a given fragment (and hence its morphological class) is obtained by minimising differences with potential candidates in a referential database.
The goal is not to reconstruct complete vessels, but to identify the best matches to the fragment within the referential dataset, by shape similarity, thus indicating which shape label or labels would best suit the fragment. Attribution accuracy is evaluated on an already labelled real-world dataset. All the approaches tested are made available as a set of functions encoded in R programming language (R Core Team, 2021). An easy-touse graphical interface was also developed, using Shiny GUI  (Chang et al. , 2019) , and is made freely available to the archaeological community, to simplify data retrieval.
Profile acquisition and data preparation The drawings of the 319 vessels thus selected were scanned at 600 dpi from the paper catalogue. Profile outlines were extracted using a modified set of functions described in Claude (  2008 ), and positioned according to their rotational axis. The outlines were automatically divided into outer and inner segments, using the rim as a reference point (Fig. 2A ). To evaluate the potential of the four methods under comparison, two test subsets of synthetic data (rim fragments and non-rim fragments) were then produced from these outlines, by virtually fragmenting the profiles of the original complete vessels. Rim fragment analysis was based only on shape, and not on size. For rim fragments, all outlines were size-normalised using baseline registration  (Bookstein, 1991) , sending the rim point and the point of intersection between rim plane and the rotational axis to the (-1, 0) and (0, 0) coordinates (Fig.
Programming The code for archaeological pottery identification was written in R language, version 4. 0. 3 (R Core Team, 2021), with the aid of libraries 'sp'  (Pebesma et al. , 2020) , 'MASS'  (Ripley et al. , 2016; Venables and Ripley, 2002) , and 'kmlShape'  (Genolini and Guichard, 2016). The graphical user interface was programmed with the 'shiny' package  (Chang et al. , 2019) , combined with RStudio (RStudio Team, 2019). All software and packages used here are freely available. The application, with manual and sample data, is provided as Supplementary Materials SM3, and is also accessible via the public Git repository (https://github. com/jwilczek-dotcom/RACORD).
Rim fragment retrieval Rim retrieval results can be seen in Table  3  (see also Supplementary materials SM4 for confusion matrices obtained with k = 1). The RTC representations, although used with success to create a classification of rim fragments dated to the Iron Age from the region of Levante  (Karasik and Smilansky, 2011) , did not correctly classify ca. 20% of the test dataset, while the other three methods all produced better results, reaching 95. 9% for ICP with k = 5. Interestingly, these successful retrieval rates, based only on rim fragments, were very close to those obtained from analyses of whole ceramic profiles  (Lucena et al. , 2016). Examples of visual outputs of rim fragment retrieval can be seen in Fig. 7. This figure shows that, when a direct correspondence for the fragment is present in the corpus, the correct morphological class is identified, and the superimposition of the source fragment and target complete vessel is almost perfect (Fig.
g. deformed fragments, cubic shapes, fragments with plastic features, etc. ). All the algorithms presented here were implemented in a simple computer application, with the open-source R software (Fig. 9 ). This program provides an extension to (semi-)automatic systems dedicated to pottery fragment orientation and profile extraction, based on 3D models (e. g. Karasik and Smilansky, 2008; Mara and Sablatnig, 2006; Wilczek et al. , 2018). The user has full control over the criteria used for bestmatch retrieval (e. g. , the range of searching values in the optimisation step). Criteria should be set in relation to the presupposed quality of the rotational axis position and orientation, the size of the objects stored in the dataset, and the number of points sampled along profile outlines. By arbitrarily fixing several criteria (e.
g. the percentage of unique classes within k-best candidates), problematic fragments (e. g. typical of many classes) can be identified, and the entire process can be fully automated. The proposed class labels can then be verified, both visually by inspection of superimposed profiles, and quantitatively from the RMSD values. The quality of the reference database and existing typological schemes will obviously affect the quality of the output, whatever the algorithm used. The archaeologist always retains full control over the final and most important options with regard to fragment attribution (i. e. whether attribution is possible, and if so, into which class). This freely available tool can be maintained, developed, and tested by the archaeological community. It can be adapted to suit the requirements of various classification strategies.
Archaeological material We collected a total of 102 well preserved right petrosal bones dated to Antiquity, Byzantine, Medieval and Modern time periods, that were excavated from 10 archaeological sites in France and one archaeological site in Turkey. Information on the respective archaeological contexts is provided below. The total sample set was subjected to ancient DNA analysis and successful sex and species determination based on the Zonkey software  (Schubert et al. , 2017). It included a total of 41 horses (27 males, 14 females), 24 donkeys (20 males, 4 females), 36 mules (16 males, 20 females) and one single female hinny (the offspring of a donkey mare and a horse stallion, i. e. the mule reciprocal cross). Overall, our archaeological material includes 11 archaeological sites, all from France except a single one from Turkey.
Read demultiplexing, adapter removal and quality filtering DNA reads were performed using AdapterRemoval v2. 3. 0  (Schubert et al. , 2016)  and mapped against the horse reference nuclear genome (Equ-Cab2  (Wade et al. , 2009) ) and mitochondrial genome (Genbank accession number NC_001640  (Xu and Arnason, 1994 )) using PALEOMIX  (Schubert et al. , 2014) , with default parameters, except that the minimal mapping quality was set to 25 and that the local sensitive mode of Bowtie 2  (Langmead and Salzberg, 2012)  was used instead of BWA  (Li and Durbin, 2010) , following  Poullet and Orlando (2020). To establish the authenticity of ancient DNA data recovered, rates of terminal fragmentation patterns and nucleotide misincorporation were evaluated using mapDamage2  (Jónsson et al. , 2013). Species and sex determination were performed using the Zonkey package, which is part of PALE-OMIX (https://github.