dataesr/scientific-paragraphs-categorization huggingface dataset:
(B) Fluorescence in situ hybridisation (FISH) localisations of the gene groups (GrA to GrI) obtained in Fig. 1A  drawn on the chicken standard idiograms  [32]  from the measures summarised in Table  1. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. ) For each sequenced BAC A to I, the total gene number was identified by FGE-NESH in Softberry. For the details of each BAC annotation (positions of predicted genes) see Supplemental Table  4. The number and numbering* of PHF7-like genes is based on BLASTP results (as described in Supplemental Table  5 ). bp = base pair. * The numbering is as described on the BAC maps in Fig. 2  (BAC I) and Supplemental Fig. 3  (BAC A to H). 1 GenBank accession numbers: ON022098 (BAC A), ON022099 (BAC B), ON022100 (BAC C), ON022101 (BAC D), ON022102 (BAC E), ON022103 (BAC F), ON022104 (BAC G), ON022105 (BAC H), ON022106 (BAC I) D, testis-expressed protein 264, metabotropic glutamate receptor 2 in BAC G and centrosomal protein of 126 kDa in BAC I; or 2) families with many members: chemokine in BAC A (6 genes), reverse transcriptase dispatched in BAC B, BAC C and BAC I (16 genes); and translation initiation factor IF-2-like (9 genes) in BAC I.
A limit of our approach is that it does not allow sequencing BAC that were not detected by the screen with the initial primers (which are dependent on sequences available in the databases). Nevertheless, the chicken genome is relatively 'young' compared with the well-sequenced mouse genome (in which we have characterised massive duplications of reproductive genes  [3] [4] [5] [6] 10] ) or the human genome. The quality of the chicken genome assembly may be optimised further in the future. Moreover, the chicken genome exhibits microchromosomes that are very difficult to sequence (GC-rich sequences). In the present study, we used the PacBio method to sequence the BAC  [20]. The advantage of using PacBio's Circular Consensus Sequence (CCS) method is that it provides very high-fidelity, quality reads and allows obtaining a unique contig for each BAC clone.
Coupled with FISH to target specific genomic regions of the chicken bank of BAC available in our lab, it is very efficient to  Another source of difficulty and thus variability in results is the annotation process. High duplication is relatively rare, and thus the more recent EnsEMBL versions have probably simplified annotation with automatic algorithmic processes that eliminate massive duplications in the sequence, aiming to avoid putative false-positive gene redundancies. We have already observed this phenomenon with the expanding FFAR2 family: a version of EnsEMBL described a family of 23 paralogues, then in the following versions (including the current) only one FFAR2 gene was present, whereas we experimentally found that the chicken genome contains 22 (± 2) paralogues  [10]. Concerning our 'own' ab initio annotation of BAC sequences, we had the choice between three classical annotation programs available: Augustus, GENESCAN and Softberry, the last one based on FGENESH program that is the subject of a number of publications (as reviewed previously  [24] ).
One inconvenience of GENESCAN is that no species can be targetedonly the vertebrate class. A convenient advantage of Softberry compared with the other two programs is the availability of a large amount of datain particular, we retrieved each exon sequence so that we could align them. Annotation of predicted genes was almost identical for the three programs, with few differences. One concerns gene 1 in BAC F: Augustus and GENSCAN instead predicted three and two genes, respectively (the long gene 1 is split into three and two smaller genes, that are also PHF7-like genes). Subtleties in the annotation process/program may explain such variations  [25] , which may be a further source of underestimation of PHF7-like genes in the present results. Of note, all three programs predicted the strange long gene 5 in BAC H exhibiting 70 exons, corresponding to a repetition of seven PHF7 'core-structures'.
In our more recent study, we identified, in addition to PHF7, two other chicken testicular family genes with many paralogues (whereas a unique gene in the mouse): SUN3 (11 paralogues) and SPAG4/SUN5 (20 paralogues)  [14]. As for PHF7 and for reasons discussed above, the presence of these numerous paralogous genes fluctuates according to databases and their successive versions (for example, there is a unique FFAR2 gene in the chicken genome according to current EnsEMBL version 105). Studies similar to the one we have conducted here for PHF7 would be of interest to characterise such families; however, such work is time-consuming and expensive. Better annotation of the G. gallus genome (Galgal7) may improve identification of massively duplicated regions. Another interesting point would be to improve annotation in other bird species, as well as in other sauropsids, aiming to conduct evolutionary studies to date massive duplications.
Overall, we have characterised a new expanding germ cell-specific gene family in the chicken genome. The PHF7-like proteins and genes exhibit a strong level of similarity (as stated by rakes in their phylogenic tree) that may be explain by gene conversion process; this hypothesis would need future specific studies. Also, its functional and evolutionary significance (as for other germinal specific gene families as Oogenesin or Nlrp5 in the mouse) remain to be investigated further, given the lack/ absence of information in the genome database due to the technical problems mentioned above. We need to clarify and illuminate 'the dark side' of the chicken genome, such regions with recent duplications still being the blind spot of genomic sequencing programmes.
The DNA was extracted based on alkaline lysis 2 https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE133401 (available in 2023). using the Qiagen Plasmid Midi Kit. The presence of each group of genes in the corresponding BAC clone was checked by PCR as described previously. PCR products were sequenced by using the Sanger technique on the Get-Plage Genotoul Platform (GeT-PlaGe INRAE Auzeville F-31326 Castanet-Tolosan Cedex France) to confirm the gene identities. The sequenced were visualised with Chromas software and aligned by Blast. FISH was carried out on metaphase spreads obtained from fibroblast cultures of 7-day-old chicken and duck embryos, arrested with 0. 05 μg/ ml colcemid (Sigma). After a 10 min hypotonic treatment (1:5 foetal calf serum hypotonic solution mixed equal parts with 0.
Chromosomes were counterstained with DAPI in antifade solution (Vectashield with DAPI, Vector Laboratories-H-1200). The hybridised metaphases were screened with a Zeiss fluorescence microscope; a minimum of 20 spreads were analysed for each experiment. Spotbearing metaphases were captured and analysed with a cooled CCD camera using Cytovision software (Leica Biosystem). We defined the precise localisations for macrochromosomes by the fractional length from the p arm telomere (Flpter) after measurement of 10 chromosomes (Cytovision software), except for ChrZ (GGAZ), for which we used the fractional length from the centromere (Flcen) because this chromosome is difficult to orientate. We used Flpter and Flcen to determine the FISH localisation on the G-banded chicken standard karyotype as shown on Fig.
Gene annotation For gene structure prediction of the BAC nucleotide sequences, we performed ab initio annotation by using the commonly used FGENESH program on the Softberry site  [39] , which relies on hidden Markov model (HMM) statistical models to identify promoters, coding or noncoding regions, and intron-exon junctions (available at http://www. softberry. com/) [24]. When needed, we consulted two other classical programs: Augustus (http://bioinf. uni-greifswald. de/augustus/submi ssion. php) and GENESCAN (http://hollywood. mit. edu/GENSCAN. htm l).
Similarity with sequences in databases using NCBI BLAST We searched for similarity and/or functional homology by using the BLAST tool of NCBI (https://blast. ncbi. nlm. nih. gov/Blast. cgi). We performed BLASTN with the nucleotide sequence of each BAC on the gal-Gal7 genome (GCRg7w, white Leghorn layer, NCBI 106). We performed BLASTP with the 116 peptides (sequences in Supplemental Table  6 ) obtained with FGENESH. The criteria of the BLASTP interrogation were 'by default' or, when no results were obtained, the threshold was upgraded (we arbitrarily chose 1000; in this case, E-value may be high, i. e. > 0).
Phylogeny and sequence alignment and similarity We constructed the phylogenetic tree from the PHF7-like protein sequences with http://www. phylogeny. fr/alacarte. cgi (MUSCLE for Multiple Alignment; Gblocks for Alignment curation; construction of phylogenetic tree with PhyML; visualisation of phylogenetic tree with TreeDyn). The bootstrap values were estimated with 1000 replications and the tree was rooted using midpoint rooting method. We created a representation of the PHF7 sequence alignment with the program ESPript (Easy Sequencing in PostScript, available at https://espript. ibcp. fr/ESPript/ESPript/) [40] , which displays sequence similarities from aligned sequences.
Funding This work was supported by grants from the  European Union 's  Horizon 2020 Research and Innovation Programme  under grant agreement N • 677353 (IMAGE), the  Région Centre Val de Loire  (FERTILMALE: n • 2017 119977), the  French National Infrastructure of Research CRB Anim  funded by ' Investissements d'avenir' ,  INRAE (Institut National de la Recherche pour l'Agriculture , l'alimentation et l'Environnement). F. Picolo received a fellowship from  CRB Anim  and  S. Ronsin  received a fellowship from  FERTILMALE.
ATR-MIR analysis Infrared measurements were performed with a portable 4100 ExoScan FTIR spectrometer (Agilent, California, USA), equipped with an interchangeable spherical ATR sampling interface with a diamond crystal window. The spectroscopic range was from 4000 to 650 cm -1 , and spectra were recorded with a resolution of 8 cm -1 and 32 scans. An air-background was collected before each sample to avoid interferences due to the variation in room conditions. All samples were measured in triplicate. From each one of the 2 mL eppendorfs, a drop of the sample was placed on top of the crystal and the spectrum was recorded immediately afterwards. Spectra were collected using the Microlab PC software (Agilent, California, USA) and data were saved as. spc files. The mean of the triplicates was used in subsequent data analysis.
Multivariate curve resolution-alternating least squares (MCR-ALS) Every ith fermentation experiment monitored by ATR-MIR provides a data matrix, D i (K x J), where rows (K) are the spectra collected at different k process times and the columns (J) are the spectral wavelengths. The D matrix is defined by the following bilinear model: D = C⋅S T + E (1) where C (K x F) is the matrix of the kinetic profiles of the resolved compounds (F), S T (F x J) is the matrix containing the resolved pure spectral profiles, and E (K x J) is the residual matrix containing the data unexplained by the bilinear model. In order to obtain a more complete information about the different fermentations, all acquired ATR-MIR data (for all fermentation samples, I) were treated together. For this purpose, all D i data matrices were arranged into a new augmented data matrix, setting one on top of each other and keeping the common wavelengths in the same column.
The quality and reliability of the MCR-ALS models were evaluated by calculating two parameters that allow assessing the dissimilarity between the experimental data matrix (D aug ) and the data modeled by MCR-ALS. These parameters are the lack of fit (% LOF) and the explained variance (% r2): % LOF = 100 × ̅̅̅̅̅̅̅̅ Σe 2 ij Σd 2 ij √ (3) % r2 = 100 × ( 1 - Σe 2 ij Σd 2 ij ) (4) where d ij is an element of the experimental data matrix D aug and e ij is the corresponding element of E aug (Cf Equation (  2 )). The number of components included in the MCR-ALS model is a compromise between model simplicity, maximum variance explained by the model, and model interpretability. MCR-ALS models were built using the MCR GUI (MCR UB, Barcelona) working under Matlab R2015 (The MathWorks, Natick, USA). More details about the MCR-ALS method are given in  (Jaumot et al.
'Inverse' MSPC charts In process control involving multivariate data, MSPC charts are a valuable tool to monitor the effect of different variables at the same time. Due to the high of correlation in spectroscopic data, MSPC methods are usually based on principal component analysis (PCA), where changes in the covariance structure of the process variables are detected. Conventional MSPC-PCA models identify process disturbances as soon as they occur using T 2 and Q statistical limits  (Sahni et al. , 2005). Typically, a calibration model is developed using data collected from samples running in normal operating conditions (NOC) to define the design-space limits covering the NOC space, and deviations are detected when changes in the covariance matrix occur or abnormal signals arise. The T 2 statistic calculates the distance from an observation to the center of the "in-control" set and determines whether a future observation has a systematic deviation in relation to the samples considered in statistical control.
This could be explained because during the first 65 h of alcoholic fermentation, the concentration curve for lactic acid does not indicate lactic acid production for both LAC and NOC samples (in this time interval, the production of lactic acid is below the limit of detection of the ATR-MIR instrument). Atypical T 2 values for NOC samples start to appear after 60 h, and all NOC samples are completely out of the limit after 96 h. Hence, ATR-MIR spectra from LAB samples were distinguishable from NOC samples before 100h, which is before the end of alcoholic fermentation. Oliveira et al. used local PCA models and were able to build Fixed Size Moving Window MSPC charts and evolving MSPC charts to detect    faulty batches during the distillation process using NIR data  (de Oliveira et al. , 2017).
Acknowledgements Authors would like to thank the scientific advice received from  Anna de Juan (Universitat de Barcelona, Spain). Julieta Cavaglia  would like to thank to the  ChemHouse Research Group (Montpellier, France)  for accepting her as a visiting research fellow. Financial support is acknowledged for the  Secretaria d'Universitats i Recerca del Departament d'Empresa i Coneixement de la Generalitat de the European Union (UE)  and the  European Social Fund (ESF)  (predoctoral fellowship number  2018 FI_B00844 ), and the  Spanish Ministry of Science and Innovation  (Project  PID2019-104269RR-C33 ).
Renal transplantation datasets usually belong to this case, making the use of such methods the way to move forward. Our study is among the first that explore such methods for renal transplantation, solving challenging clinical questions. In this work, we propose a method to forecast the renal transplant function through the serum creatinine prediction from follow-up exams of Dynamic Contrast-Enhanced (DCE) MRI data post-transplantation. The main contributions of this work are twofold. First, we propose the use of contrastive schemes, generating informative manifolds of DCE MRI exams of patients undergoing renal transplantation. Different self-supervised and weakly-supervised clinical pertinent tasks are explored to generate relevant features using the cosine similarity. Secondly, we introduce a transformer-based architecture for forecasting serum creatinine score, while proposing a tailored method to deal with missing data.
Implementation Details Starting with the contrastive learning, we used data augmentation with horizontal flipping and random affine transformation with a 0. 5 probability, as well as random Gaussian blur (σ ∈ [0, 0. 5]) and random Gaussian noise (σ ∈ [0, 0. 05]), using TorchIO python library  [20]. Having approximately a set of pairs of V 2 = V (V -1) 2 , where V is the number of available volumes, we proposed to fix the training set size to V t = 5000. We decided to fix the number of positive samples, as well as its balance to 25%, and to randomly sample every epoch the remaining from the negative samples. Concerning the optimization of our models, a 10% dropout has been used for the linear layers of both the contrastive and sequential models. For the contrastive model, the Stochastic Gradient Descent optimizer with a momentum equal to 0.
Data Our study was approved by the Institutional Review Board, which waived the need for patients' consent. The data cohort corresponds to study reference ID-RCB: 2012-A01070-43 and ClinicalTrials. gov identifier: NCT02201537. All the data used in this study were anonymized. Overall, our imaging data are based on DCE MRI series collected from 89 subjects at 4 follow-up exams which took place approximately 15 days (D15), 30 days (D30), 3 months (M3), and 12 months (M12) after the transplant surgery, resulting in respectively 68, 75, 87, and 83 available scans at each follow-up. The MRI volumes sized 512 × 512 × [64 -88] voxels included spacing ranging in [0. 78 -0. 94] × [0. 78 -0. 94] × [1. 9 -2. 5] mm. All volumes were cropped around the transplant using an automatic selection of the region of interest in order to reduce dimensionality while no information about the transplant is discarded.
Additional sets of feature representations were used to compare the significance of our approach. First simple statistics from the serum creatinine captured from the available blood test results between each follow-up (number of points, mean, median, standard deviation, minimum, maximum) are calculated and used as input to the models. Second, a set of predefined radiomics features  [10]  are obtained from the segmentation of the kidney transplant following the unsupervised method presented in  [17]. Finally, we investigate generating MRI features from SimCLR  [4]  contrastive scheme, while we report the performance of different transfer-learning approaches, pre-trained on ImageNet  [6]  by duplicating the weights to 3D, Kinetics  [23] , and medical image datasets MedicalNet  [3]. Quantitative results for all the methods are reported in Table  1.
As core components of the trial, lung and heart function were purposefully assessed. Now in its 14th year, TomoBreast has gathered the largest known open database of longitudinal lung and heart function in breast cancer  [15] , making it uniquely relevant to investigate the impact of the pandemic. Follow-up of the trial was continuous and unabated except for a disruption in 2020 due to the COVID-19 pandemic. Recent studies have observed disruptions in patient care during the lockdown period of the pandemic, including cancer-related care  [16]. Such lockdown-related disruptions have been correlated with increased morbidity in various patient populations  [17, 18] , suggesting that adverse outcomes may be indirectly related to COVID-19, especially in populations of patients with pre-existing vulnerabilities  [19].
Description of the Cohort The study population includes participants in the TomoBreast clinical trial which randomized post-surgery stage I-II breast cancer patients into two groups: adjuvant image-guided radiotherapy vs. adjuvant conventional radiotherapy. The trial's design, summary, flowchart and cardiopulmonary quality of life related results have been detailed elsewhere  [14]. The trial started in June 2007 and ended in July 2011. A total of 123 women consented to participate (Table  1 ). The follow-up cutoff date was 16 July 2021. The collected data included patients' self-reported outcome, clinical assessment, echocardiographic measurements, and pulmonary function tests, as detailed in the next sections. All patients were analyzed in the present study as a single cohort, irrespective of the randomization assignment.
Clinical Data Collection Patients' information was abstracted from individual patient's electronic medical files and from the Belgian federal eHealth platform  [20] [21] [22] , at each date of medical contact related to breast cancer or any other condition. If available, clinical assessments done less than 365 days before randomization were also abstracted. The total number of medical records was 3013, averaging 24. 5 per patient. The abstracted data included Karnofsky performance status (KPS), weight, and echocardiographic and pulmonary function test results.
Interpolation and Handling of Missing Data All clinical toxicity and echocardiography and pulmonary function test data were merged into a single data frame ordered by patient and by contact date, resulting in a full data frame with n = 4647 rows, with each row representing a unique patient time-record containing a toxicity score and a test measurement. Empty data cells were interpolated between non-missing time points. To illustrate with a fictional example, consider a patient who has, at day D20, an LVEF measurement but no adverse event (AE) score, at D30, an AE score but no LVEF measurement, and at D40, an LVEF but no AE score. The LVEF at D30 is interpolated from the D20 and D40 LVEFs; the AEs at D20 and D40 cannot be interpolated and remain missing.
Introduction Language complexity is a notion that has hovered in linguistics for more than a century, both as an implicit scale in a language description (with one feature being considered as more complex than another, such as passive vs. active syntactic constructions for instance) and as a general backdrop against which differences among languages could be dismissed or highlighted, depending on one's position on the equi-complexity hypothesis (see  Joseph & Newmeyer 2012, for a discussion). For most of the twentieth century, those discussions have arguably remained impressionistic but in the last two decades, considerable progress has been achieved in giving language complexity a more tangible substance. On the one hand, large databases based on thorough linguistic descriptions, such as LAPSyD  (Maddieson et al.
1  The paper is organized as follows: Section 2 offers a short introduction to the measurement of linguistic complexity. Section 3 describes the corpus and our subsampling strategy. We introduce in the first part of Section 4 the grammar-based complexity indices that provide the expert background against which our corpusbased study is elaborated and interpreted. In the second part of Section 4, we describe the four indices of morphological complexity assessed in the paper, along with the methodology implemented to test its robustness. Results on morphological complexity are then provided. In Section 5, we study the interaction between the within-word and across-word information distribution, taking advantage of the Word Information Density index introduced in Section 4. In Section 6, a proof of concept is proposed and evaluated to alleviate the need for a parallel dataset in quantitative typological studies.
Phonological complexity is often operationalized as the number of units (syllables, phonemes, features or gestures) or contrasts between units implemented in 1. The paper is provided with a Rmarkdown html document incorporating the analysis code in R, the main results detailed in the paper, and results from additional analyses. This document is referred to as Supplementary Information throughout the paper. It is freely available in the GitHub repository (https://github. com/yoonmioh/RobustMorphComp, last access 2 December 2022).