dataesr/scientific-paragraphs-categorization huggingface dataset:
Genotypes and imputation The iPSYCH 2015 case-cohort sample is a genotyped dataset from neonatal dried blood spots (DBS) nested within the entire Danish population born between 1981 and 2008, including 1,657,449 persons. After genotyping and sample quality control (described in detail elsewhere  22, 23  ), it includes 92,765 individuals diagnosed with a major psychiatric disorder i. e. attentiondeficit/hyperactivity disorder (ADHD), affective disorder (AFF), autism (ASD), schizophrenia (SCZ) and bipolar disorder (BD). We also included the anorexia nervosa (AN; ANGI-DK) samples from the Anorexia Nervosa Genetics Initiative (ANGI)  43  , as they were samples within the same framework as iPSYCH 2015. The dataset also includes 42,912 individuals randomly sampled from the same birth cohort, making it representative of the general Danish population.
Pearson's correlation estimates. large cohorts. Nat. Genet. 47, 284-290 (2015). 50. Loh, P. -R. , Kichaev, G. , Gazal, S. , Schoech, A. P. & Price, A. L. Mixed-model association for biobank-scale datasets. Nat. Genet. 50, 906-908 (2018). 51. Lee, S. H. , Goddard, M. E. , Wray, N. R. & Visscher, P. M. A better coefficient of determination for genetic profile analysis. Genet. Epidemiol. 36, 214-224 (2012).
Acknowledgements C. A. , B. J. V. and F. P. were supported by the  Danish National Research Foundation  ( Niels Bohr Professorship  to  Prof. John McGrath ), the  Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH  ( R102-A9118 ,  R155-2014-1724  and  R248-2017-2003 ), and a  Lundbeck Foundation  Fellowship ( R335-2019-2339 ). C. A. was also supported by a  Willam Demant Fonden travel grant. High-performance computer capacity for handling and statistical analysis of iPSYCH data on the GenomeDK HPC facility was provided by the  Center for Genomics and Personalized Medicine and the Centre for Integrative Sequencing , iSEQ,  Aarhus University, Denmark  (grant to A. D. B. ). IB was also supported by the  Swedish Brain Foundation  and  Fredrik och Ingrid Thurings Stiftelse.
Code availability All code used in this project is available in GitHub (https://github. com/ClaraAlbi/paper_multiPGS). We used the following tools: BOLT-LMM (https://data. broadinstitute. org/alkesgroup/bolt-lmm), XGBoost (https://xgboost. readthedocs. io/en/latest/), SMTpred (https://github. com/uqrmaie1/smtpred), bigsnpr/LDpred2 (https://privefl. github. io/bigsnpr/articles/LDpred2. html).
Data availability All relevant iPSYCH and Danish ANGI data are available from the authors after approval by the iPSYCH Data Access Committee and can only be accessed on the secured Danish server as the data are protected by Danish legislation. More information about getting access to the Danish data can be obtained at http://ipsych. au. dk/about-ipsych/. We utilized the list of GWAS Catalog (https://www. ebi. ac. uk/gwas/) summary statistics downloaded on 09/09/2020, GWAS summary statistics from the PGC (https://www. med. unc. edu/pgc/download-results/) and GWAS Atlas UKB2 data freeze v20191115 (https://atlas. ctglab. nl/).
4 ) 4 Pearson's correlation estimates. large cohorts. Nat. Genet. 47, 284-290 (2015). 50. Loh, P. -R. , Kichaev, G. , Gazal, S. , Schoech, A. P. & Price, A. L. Mixed-model association for biobank-scale datasets. Nat. Genet. 50, 906-908 (2018). 51. Lee, S. H. , Goddard, M. E. , Wray, N. R. & Visscher, P. M. A better coefficient of determination for genetic profile analysis. Genet. Epidemiol. 36, 214-224 (2012).
Data process for detection frequency (DF) method The data obtained in GC-O and Olfactoscan were processed using the DF method  (Pollien et al. , 1997)  to perform an overall grouping of all the responses given by all the subjects into Odor Zones (OZs) on the basis of their retention time closeness. Because of the background odor in Olfactoscan, it was more difficult to determine OZs; thus, a semiautomatic method was established to define and standardize the OZs between GC-O and Olfactoscan. The GC-O result of the odor cocktail solution (Supplementary Table  1 ) in the training session and the GC-O result of the icewine extract were used to optimize different parameters of the semiautomatic method to obtain the OZs as precisely as possible. In this semiautomatic method, retention time was first transferred to Kovats retention indices (RIs) by means of n-alkane injections (C8-C32), and then the detection frequency was calculated from the number of odor events that occurred in a range of 5 RI values.
This integration process was applied because of the variability of subject response times. Then, the detection frequency as a function of the RI was analyzed by R software (version 4. 0. 1) using the findPeak function of the quantmod package  (Ryan et al. , 2020)  to determine detection frequency peaks. In this procedure, a noise level of 3 for frequency was chosen as a threshold to consider a significant peak corresponding to an OZ. The obtained OZs were further manually checked in the raw data to evaluate whether any important OZs were missing or duplicated considering the odor descriptors given by the subjects. Finally, OZs from GC-O and Olfactoscan analysis were defined, and each OZ was characterized by: 1) its nasal impact frequency (NIF, %), which corresponded to the proportion of detection by the panelists of each OZ (number of subjects who detected / total number of subjects) c; 2) its odor descriptors given by subjects; and 3) the first, last and average retention indices of the response given by the subjects.
Identification of the impact compounds The compounds responsible for OZs were identified by: 1) GC-MS  (Ma et al. , 2017)  and comparing the RI and odor descriptor of a candidate compound with the RI and odor descriptor of its pure standards under the same GC conditions as GC-O; 2) comparing the odor descriptor of a candidate compound with its odor descriptor reported in the database; 3) comparing the experimental RI of a candidate compound with its RI reported in the National Institute of Standards and Technology (NIST) mass spectral library and 4) comprehensive twodimensional gas chromatography and time-of-flight mass spectrometry (GC × GC-TOFMS) analysis. GC × GC-TOFMS analysis was performed on a LECO Pegasus 4D® GC × GC-TOFMS instrument (LECO Corporation, St. Joseph, MI, USA), basically consisting of an Agilent GC model 7890B, LECO dual nozzle thermal modulator system, and secondary column thermostat connected to a time-of-flight mass spectrometer.
Within individual chromatograms, subpeaks in the 2nd dimension were required to meet a S/N ≥ 6 and a minimum spectral similarity match of 650 (65%) to be combined. The reference peak was determined by the unique mass ion and the overall purity and shape of the peak. All chromatograms were compared spectrally with the reference peak chromatogram from the NIST Mass Spectral Library and Wiley Regis-try™ of Mass Spectral Data Library. The mass spectra of a reference peak with similarity scores >700 were selected as candidate peaks, and its name was assigned to the automated peak detection result. Kovats retention indices (RIs) of peaks were calculated by injection of a reference solution of n-alkanes under the same GC × GC conditions (C8 -C29). The RI of each peak was compared with its RI reported in the NIST library, and peaks with RI differences exceeding 20 units were excluded from the peak identification.
Data analysis Statistical analyses were performed with R software (version 4. 0. 1). Principal component analysis (PCA) was carried out on the nasal impact frequency (NIF, %) of every odor descriptor for the high impact odor peaks over the icewine background odor level by using the prcomp function of the tempR package  (Castura, 2016). The PCAs were used to provide a global representation of the trajectory of impact odor peaks in relation to the evolution of odor descriptors based on the first and second principal components. The categorized odor descriptors trajectories in each impact odor peak are illustrated by connecting three different icewine background odor levels. The icewine background odor levels were none for GC-O analysis, low level for Olfactoscan analysis (OLFL) and high level for Olfactoscan analysis (OLFH).
Odor zone defined in GC-O and Olfactoscan analysis by the detection frequency (DF) method A total of 2430 odor events were recorded from 19 subjects during all the analysis methods. These events were distributed as follows: GC-O analysis (GCO, 820), Olfactoscan analysis at a low background odor level (OLFL, 870), and Olfactoscan analysis at a high background odor level  (OLFH, 740). The raw detection frequency data are reported in Fig. 2a  for each analysis method. A first observation is that the number of odor events in the OLFH method is lower than in other methods, suggesting a mixture-induced masking effect of the icewine background odor on the detection of odorants. A semiautomatic method was applied to define the odor zones (OZs) in each analysis condition. First, an automatic peak detection function led to the identification of 75 OZs in GCO, 65 OZs in OLFL, and 56 OZs in OLFH.
Peak identification and odor-active compound contribution in GC-O and Olfactoscan analysis To identify the odor-active compounds responsible for each OZ obtained in GC-O and Olfactoscan analysis, GC-MS and GC × GC-TOFMS analyses were conducted. The identification of several compounds was further checked through injection in GC-MS of pure standards under the same GC conditions as in GC-O (marked by 'S' in Table  2 ). Finally, 57 OZs were associated with 63 compounds identified by GC-MS and GC × GC-TOFMS analysis; there was coelution for 4 OZs. These results were confirmed by injection of pure standards under the same GC conditions  (Ma et al. , 2017). The OZs that failed to be related to the compounds identified by GC × GC-TOFMS analysis were defined by at least two of the following methods: 1) comparing the RI and odor descriptor of a candidate compound with the RI and odor descriptor of its pure standards under the same GC conditions; 2) comparing the odor descriptor of a candidate compound with its odor descriptor reported in The Good Scents Company database; and 3) comparing the experimental RI of a candidate compound with the RI reported in the NIST Mass Spectral Library.
Y. Ma et al. a RI calculated from GC-O and Olfactoscan analysis; b RI calculated from GC × GC-TOFMS analysis; c RI reported in NIST library on similar column; d Compounds tagged with an '*' were found to have different RI calculated from the GCO analysis and from the GC × GC-TOFMS analysis; the identification of these odor zones have Y. Ma et al. been verified by injecting pure standards; e descriptors obtained from the database of The Good Scents Company (http://www. thegoodscentscompany. com/); f Peak identified by: 1) GC × GC-TOFMS analysis (TOFMS), the first six odorants were not identified by TOFMS due to the setting of solvent delay; 2) GC-MS (MS) and comparing the RI and odor descriptor of a candidate compound with the RI and odor descriptor of its pure standard under the same GC conditions as in GC-O (S); 3) comparing the odor descriptor of a candidate compound with its odor descriptor reported in the database (O); and 4) comparing the experimental RI of a candidate compound with the RI reported in the NIST Mass Spectral Library (RI).
Results The pipeline is implemented as a makefile orchestrating multiple bioinformatics tools, as well as R functions to process the intermediary data. Everything is packaged as an R package containing the analysis functions and the visualization tool as a shiny web application. The makefile, reference sequences for HPV and the HeLa example dataset are installed along the R package. The makefile allows for batch processing of multiple samples, and multithreading is enabled for bowtie2, samtools, blat, and most of the R logic. Viral capture data are usually small. For example, the HeLa sample dataset is 13Mb per paired FASTQ file, and executes in 12 minutes using 4 threads on a standard laptop computer (Intel i7 CPU, 16GB of RAM). The pipeline code can be obtained from https://github. com/maximewack/viroCapt.
A good proportion of those resources that contain syntactic analyses have been annotated according to the Universal Dependencies (UD) scheme  (Nivre et al. 2016 ), a dependency-based annotation scheme which has become a popular standard reference for treebank annotation because of its adaptability to different domains and genres. At the time of writing (in UD version 2. 6), as many as 92 languages are represented within this vast project, with 163 treebanks dealing with extremely varied genres, ranging from news to fiction, medical, legal, religious texts, etc. This linguistic and textual variety demonstrates the generality and adaptability of the annotation scheme. On the one hand, this flexibility opens up the possibility of also adopting the UD scheme for a broad range of user-generated text types, since a framework which is proven to be readily adaptable is more likely to fit the needs of diverse UGC data sources, and the wealth of existing materials makes it potentially easier to find precedents for analysis whenever difficult or uncommon constructions are encountered.
As such, it can be used as a reference when considering alternative solutions, and it is hoped that the survey of treatments of similar phenomena across resources will help future projects in making choices that are as comparable as possible to common practices in the existing datasets. 2 Linguistics of UGC Describing all challenges brought about by UGC for all languages is beyond the scope of this work. Nevertheless, following  Foster (2010) ;  Seddah et al. (2012) ;  Eisenstein (2013)  we can characterize UGC's idiosyncrasies along a few major dimensions defined by the intentionality or communicative needs that motivate linguistic variation. It should be stressed that one and the same utterance, and indeed often a single word, can instantiate multiple categories from the selection below, and that their occurrence can be either intentional or unintentional.
In some cases, the resulting non-standard tokens might even be homographs of existing words, creating more ambiguities if not properly analyzed. Such phenomena are frequent in the corpora of UGC surveyed below, and they require specific annotation guidelines. -Marks of expressiveness: orthographic variation is often used as a mark of expressiveness, e. g. , graphical stretching (yes → yesssss), replication of punctuation marks (? → ?????), as well as emoticons, which can also take the place of standard language words, e. g. a noun or verb (FR Je t'aime → Je t'<3, 'I love you', with the heart emoticon representing the verb 'love'). These phenomena often emulate sentiment expressed through prosody, facial expression and gesture in direct interaction; however the written nature of UGC data means that they need to be assigned analyses in terms of tokens, parts of speech and dependency functions.
-Medium-dependent phenomena: Some deviations from standard language are direct results of the electronic medium, including client-side automatic error correction, masking or replacement of taboo words by the server, artifacts of the keyboard or other user input devices, and more. In some cases, and especially for languages other than English, some apparent English words in UGC represent automatic 'corrections' of non-English inputs, such as Irish coicíse 'fortnight' → concise. These cases raise questions   1. relating to the degree of interpretation, such as reconstructing likely UGC inputs before error correction, which may need to be annotated either as typos (in UD, the annotation Typo=Yes), or at an even greater level of detail in lemmatization. -Context dependency: Given the conversational nature of most social media, UGC data often exhibits high context-dependence (much like dialogue-based interaction).
As the main focus of this work is on the syntactic annotation of web content and user-generated texts, we discarded all papers that presented system descriptions, parsing experiments or POS-tagged resources (without syntactic annotation). The results of our search are summarized in Table  2. Based on the selection criteria mentioned above, we found 21 papers describing a resource featuring web/social media texts; most of them are freely available, either from a GitHub/BitBucket repository, a dedicated web page or upon request. Dataset sizes vary widely, ranging from 500 (DWT) to approximately 6,700 tweets (Pst) for the Twitter treebanks, and from 974 (xUGC) to more than 16,000 sentences (EWT) for the other datasets.
Although not all of these corpora have been released in the official UD repository, and some of them do not strictly comply with the latest format specifications, the large number of UD resources, as well as their occasional divergences, highlight the need to converge on a single syntactic annotation framework for UGC within UD, to allow for a better degree of comparability across the resources and arrive at tested best practices. In the next section, we provide an analysis of the guidelines of the surveyed treebanks, highlighting their similarities and differences, and a preliminary classification of the phenomena to be dealt with in UGC data from social media and the web with respect to the standard grammar framework for each language.
9 n the other hand, there are counterbalancing considerations that motivate performing medium-independent segmentation on UGC data, among these a possible overuse of syntactic relations that define side-by-side (or run-on) sentences (e. g. parataxis in UD); second, as mentioned previously, at least for some UGC data collections (e. g. blog posts), punctuation is found frequently enough and can be used. Third, given that Twitter doubled its character limit for posts from 140 to 280 at the end of 2017, treating tweets as single utterances might pose a usability problem for manual annotation. Finally, for NLP tools trained on multiple genres and for transfer learning, inconsistent sentence spans are likely to reduce segmentation and parsing accuracy. Due to these considerations, tweeDe manually segmented tweets into sentences while introducing an ID system that enables reconstruction of complete posts, if needed.
Sentential unit of analysis In the interest of maintaining compatibility with treebanks of standard written language, we propose splitting UGC data into sentential units to the extent to which it is possible and keeping token sequences undivided only when no clear segmentation is possible. To facilitate tweet-wise annotation if desired, a subtyped parataxis label, such as parataxis:sentence in Figure  3 , could be used temporarily during annotation. Since some relation label will be needed to connect multiple sentential units within a tweet no matter what, this recommendation is mainly meant to help with later processing or comparison with other data sets, serving as a pointer to identify where the tweet could be split into sentences and distinguishing such junctures from other types of parataxis.
Treatment of morphology: alignment with UniMorph Like the UD group, the collaborative UniMorph project  (Kirov et al. 2016 (Kirov et al. , 2018;; McCarthy et al. 2020 ) is developing a cross-lingual schema for annotating the morphosyntactic details of language. While UD's main focus is on the annotation of dependency syntactic relations between tokens in corpora of running text, UniMorph focuses on the analysis of morphological features at the word type level. Nevertheless, UD corpora also include more detailed annotations of lexical and grammatical properties of tokens beyond POS. The attribute-value pairs used for morphological features in UD's schema are constructed in a bottom-up fashion: features are adapted and newly included as evidence comes in from languages for which resources are created.
By contrast, UniMorph is top-down: its design is guided by surveys of the typological literature and aims to be complete, accounting for all attested morphological phenomena. Accordingly, UniMorph provides some attributes that UD lacks, such as those which describe information structure and deixis. In other cases, UniMorph provides more values for certain attributes: for instance, it covers 23 different noun classes used by Bantu languages. As reported by  Kirov et al. (2018) , a preliminary survey of UD annotations shows that approximately 68% of UD features have direct UniMorph schema equivalents, with these feature sets covering 97. 04% of the complete UD tags. 19 As the authors note, some UD features are outside the scope of UniMorph, which marks primarily morphosyntactic and morphosemantic distinctions.
Acknowledgements We warmly thank  Nathan Schneider  for his comments on a previous version of this paper. All remaining errors are ours. The work of  C. Bosco  is partially funded by  Progetto di Ateneo/CSP   2016  ( Immigrants, Hate and Prejudice in Social Media ,  S1618_L2_BOSC_01 ). The work of  M. Sanguinetti  is funded by  PRIN 2017  ( 2019-2022 ) project  HOPE -High quality Open data Publishing and Enrichment. Ö. Çetinoglu is funded by  DFG  via project  CE 326/11 Computational Structural Analysis of German Turkish Code-Switching (SAGT ). D. Seddah is partially funded by the  ANR  projects  ParSiTi  ( ANR-16-CE33-0021 ) and SoSweet ( ANR15-CE38-0011-01 ). T. Lynn  and  L. Cassidy  are funded by the  Irish Government Department of Culture, Heritage  and the Gaeltacht under the  GaelTech  Project, and also supported by  Science Foundation Ireland in the ADAPT Centre  (Grant  13/RC/2106 ) at  Dublin City University.
Simulation Setup The evaluation process was carried out over python implementation of our TS-based approach. Python is developed under an OSI-approved open source license, making it freely usable and distributable  [28]. In our implementation, we consider one tracking zone covered by a MEC node, in addition to n UAVs flying in this tracking zone and m MEC applications requiring federated learning. Specifically, we evaluate our UAV selection algorithm in terms of objective function outcomes, while varying the number of MEC applications as well as the number of UAVs. In our simulation, we aim to validate the efficiency of our scheme in maximizing the objective function value (learning accuracy) with means of the resource capacity of selected UAVs. Table  3  provides the main simulation parameters we used.
Quantities to measure and associated questions In order to analyze and understand how collagen fibers behave when they are under a mechanical load, it is necessary to quantify them using some relevant information. For example, for arteries, the quantitative information extracted from corresponding SHG images can be introduced in previously developed mechanical models to better characterize the behavior of arteries  (Holzapfel et al. 2000; Morin et al. 2021 ). In the literature, researchers focused on three types of information that can be extracted from SHG images of collagen fibers: its geometry, its composition, and its morphology. However, they dealt with different types of input data (thus, the output data were different) at different scales of measure. In the present section, we will exhibit how that information was extracted through the literature.
The most known one is the NeuronJ plugin of ImageJ software  (Meijering et al. 2004 ). This plugin was designed to characterize neurons which have a linear shape. NeuronJ was used for tracing the fibers and analyzing their waviness  (Zyablitskaya et al. 2017; Chow et al. 2014; Zeinali-Davarani et al. 2013). Besides, a 3D implementation of this technique was proposed and tested on SHG images. For example, to determine the fiber arc length,  Hill et al. (2012)  proceeded to a reconstruction of the SHG stack using a fast-marching algorithm to trace the fibers. Once an accurate extraction of the collagen fibers is reached, it is possible to compute the waviness as a ratio between the Euclidean distance between the starting and ending points of a fiber and its actual length  (Ayyalasomayajula et al.