dataesr/scientific-paragraphs-categorization huggingface dataset:
51% compared to the RMSEP of 0. 55% for CovSel. For the mango data set, CovSel suggested 8 variables while for di-CovSel some values of λ suggested up to 11 variables. Please note that the mango data set was a complex case involving both instrument and seasonal differences, where the two batches correspond to mango samples of different year harvests measured with two different instruments. The model made with CovSel selected variables resulted in higher RMSEP value (Fig. 3K ) compared to di-CovSel selected variables (Fig. 3L ) when the model was tested on a new season/instrument data. In summary, the better predictive performances of the OLS models made with di-CovSel selected variables compared to CovSel selected variables shows the potential of selecting domain invariant variables to handle common model adaptation tasks such as model adaptation for physical forms of samples, instruments, and seasonal effects.
In the range from 1150 to 1250 nm, the di-CovSel selected several new variables which could be related to overtones of CH bonds in several macromolecules present in rice kernels  [36]. For the wheat data set (case of model adaptation from point spectrometer to spectral camera), the recentered spectra with the selected variables for CovSel and di-CovSel are shown in Fig. 4B. While the mean shift (Fig. 4B ) around 1000 nm was corrected with recentering, a clear difference in the domains can be seen around 500 nm. This is in line with the variable selection by di-CovSel where the spectral range around 500 nm was not included in the final selection by di-CovSel but was included by CovSel leading to inferior performance of the OLS model made on CovSel selected variables. For the pear and mango data sets, the variables selected by di-CovSel were slightly shifted compared to the CovSel selected variables.
To illustrate the difference in the functionality of the CovSel and di-CovSel, each step in the variable selection related to the pear data set is shown in Fig. 6. As a total of seven variables were selected for the pear fruit case, there are seven rows in Fig. 6. Furthermore, the two columns are related to CovSel (left column) and di-CovSel (right column). It can be noted that the first variable selected by the CovSel method was related to the overtone bands (Fig. 6A ). In the case of di-CovSel, it was the shortest wavelength in the dataset (Fig. 6B ) which may be attributed to an offset correction  [34]. The CovSel selected such an offset as a second variable (Fig. 6C ), while the second variable selected by di-CovSel was related to the chemical overtones (Fig. 6D ). It should be noted that the spectral variable around 760 nm selected by CovSel was never selected by the di-CovSel method.
Conclusions A new method called domain invariant covariate selection (di-Cov-Sel) for generalized feature selection across different data domains was presented, which combines the principles of domain invariant PLS (di-PLS) and covariate selection (CovSel). The method was tested on diverse cases of domain adaptation such as adapting models for different physical forms of samples, adapting models for seasonal differences and performing tasks such as calibration transfer for similar modality (point to point spectrometer), as well as different modalities of NIR spectroscopy (point spectrometer to spectral camera). In all the presented cases, the multivariate predictive models based on variables selected by adding the domain invariance to the CovSel approach led to lower standard error of prediction and prediction bias compared to the models based on variables selected with the original CovSel method.
In addition, with the availability of a few reference measurements, the performance of the adapted models was improved by correcting the degradation in the bias term of the calibration models. In this work, it was shown that as low as 60 samples were sufficient to allow di-CovSel to select generalized variables. Although in this study all the presented cases were related to NIR spectroscopy, the method is applicable to any area of multivariate data modelling where often the models fail when they need to be used in a new domain. The new domain can be anything related to the different physical forms of samples, different instruments and modalities and seasonal effects. Note that, in general, the domain invariant techniques such as di-PLS and di-CovSel, are bound to succeed if the required adaptation concerns the spectral variation and no changes in the relationship between spectral values and reference analysis occur.
, 2004; Denoyelle et al. , 2010). Private companies are responsible for many worldwide activities related with offshore HYD routine exploration and extraction. Thus, access to these highly interesting macrofaunal and environmental data is often only possible through environmental assessment or monitoring projects. These projects are frequently a legal imperative to companies exploiting natural resources in the sea and habitually include benthic monitoring. Thus, they regularly generate large macrobenthic datasets, sometimes including reference invertebrate collections and interesting information from often previously poorly known or non-studied areas. This can be seen in a recent study carried out in the Falkland Islands, where only 16% of the 191 species found were known species described from the region, while 13% were uncertain (i.
e. , damaged, presumed cosmopolitan or having poor original descriptions) and the remaining ones (almost 70%) were unknown, likely new  (Neal et al. , 2020). A similar situation occurs with the surveys carried out in association to the oil and gas activities of TotalEnergies in the Atlantic coasts of the Gulf of Guinea facing the Republic of Congo. The datasets of benthic macrofauna generated have been partly managed through the long-term collaboration between the scientists of the Centre d'Estudis Avançats de Blanes (CEAB-CSIC) and the French company CREOCEAN. The in-depth analysis of these datasets proved to have a high scientific and management interest, including studies on sediment toxicity and recolonization  (Dalmazzone et al. , 2004)  or on the contrasted role as indicators of foraminifers vs.
The relationships between species densities and sediment descriptors were assessed by Pearson correlation. The significance of the differences between the sample groups obtained in the PCA was assessed by analysis of similarity (ANOSIM) and confirmed by PMDA (same procedure as above), both for sediment descriptors and target species. The significance of the differences for each sediment descriptor and target species was then assessed by independent one-way Analysis of Variance (one-way ANOVA), and the group(s) responsible for the observed differences were assessed by a post hoc Tukey test. All analyses were based on log-transformed data to meet with the assumptions of normality and homoscedasticity required for parametric analyses  (Zar, 1984)  and were performed with the  XLSTAT software, version 2016.
ACKNOWLEDGEMENTS We would like to thank  TotalEnergies E&P Congo  for partly sponsoring the field surveys and for their help with the sampling campaigns, the staff of the  N'Kossa platform  for their kind welcome and help during our visits; the staff of the  Laboratoire Municipal et Régional de la Ville de Rouen (France ) for the analyses of the sediment characteristics;  José Manuel Fortuño  from the  SEM Service of the CMIMA-CSIC (Spain) , for his help with SEM micrographs;  Alexandra Rizzo  and an anonymous reviewer for their detailed revisions of the manuscript; and the curators of the different biological collections where the specimens examined were deposited, for their assistance.
1  , with minor modifications. Briefly, ESCs were dissociated by TrypLE (Gibco, 12604013) and counted on a haemocytometer. Cells were washed with DPBS (Sigma-Aldrich, D8537) and re-suspended in ice-cold Growth Factor-reduced Matrigel (Corning, 354230) as a single-cell suspension at 1500 cells per µl. The Matrigel cell suspension was seeded as 25 µl drops and set in ibidi-µ plate 8-well chambers (ibidi, 80821) at 37 °C for 10 min before replenishing FBS (15%) supplemented medium without LIF (-LIF). For experiments with pharmacological inhibition, sources and concentration of the inhibitors can be found in Supplementary Information (see Supplementary Data 1). For molecular experiments, cells were seeded as a "3D-on-top" culture. Briefly, pre-chilled culture plates were coated with Matrigel and set at 37 °C for 30 min.
Single-cell suspension in culture medium (15% FBS; -LIF) was then seeded at 5 × 10 4 cells per cm 2. Once the cells are attached, the medium was replaced by 5% Matrigel supplemented medium (5% Matrigel; 15% FBS; -LIF). For collecting the spheroids, Matrigel cultures were washed with DPBS, followed by gentle scraping and incubation in chilled Cell Recovery Solution (Corning, 354253) for 30 min. The suspension was then collected in equivalent volume of cold PBS, followed by centrifugation (150 × g) to pellet the cells. Pelleted spheroids were washed in cold PBS again, snap-frozen before being processed for subsequent analyses. Molecular cloning. CIDEA WT and point-mutation (R47E and R171E) constructs were previously generated as detailed in Barneda et al. 27. Insertion fragments were amplified by PCR (list of primers can be found in Supplementary Data 1), purified by PCR purification kit (Roche, 11732676001), followed by restriction enzyme digestion.
8. 2. The full z-stack sections of WT and CIDEA KO embryo chimaeras shown in Fig. 3h  can be found in Supplementary Movie 2 and Movie 3, respectively. List of all antibodies used with company names, catalogue numbers and dilution factors can be found in Supplementary Data 1. LD staining. Fixed samples were stained with BODIPY 493/503 (5 µg/ml in PBS protected from light exposure) (Invitrogen, D3922) for 30 min. Samples were then washed 3 times with PBS, followed by DAPI staining for 10 min. BODIPY staining was carried out after secondary antibody incubation, in the case of staining LDs in immunostaining samples. Lipophagy live-cell imaging. Embryos or ESC spheroids were co-stained for lysosomes and LDs with LysoTracker Red (500 nM) (Invitrogen, L7528) and BODIPY 493/503 (5 µg/ml), respectively.
Volume of the spheroids was measured by the same plug-in on F-ACTIN channel, which marks the cell boundary of the spheroids. Lineage-specific analysis of LD size and number in freshly harvested peri-implantation embryos was carried out using the Imaris v6. 3 software (Bitplane). Briefly, embryonic tissues were manually outlined on each image plane of the z-stack images, according to E-CADHERIN staining. The 3D structures of the tissue were then re-constituted by the software and LDs in each tissue structure were selected and analysed under automatic segmentation. Details of the lineage-specific LD analysis pipeline can be found in Supplementary Fig. 4a, b. For LD analysis in ESC cultures, Cell Profiler software was used and the "identify primary objects" plug-in was used to determine the number and area of each LD.
Reverse transcriptase was then heat-inactivated at 70 °C for 15 min. Resultant cDNA was diluted to 5 ng/µl for qPCR. For qPCR, 10 ng of cDNA was used in each reaction with 5 µl of SYBR Green qPCR reagent (Merck, kcqs00) and 0. 01 nmol/µl primers. qPCR was run as followed, 95 °C for 15 min; 40 cycles of 94 °C for 15 s, 60 °C for 30 s and 72 °C for 30 s. For analysis, Ct value of analysed genes was normalized to the average of Ct values of the two housekeeping genes L19 and S17. List of primers used can be found in Supplementary Data 1. Western blot. ESCs or spheroid cultures were collected as described above (see the 'Cell culture' and 'ESC spheroid formation' sections, respectively) and lysed in RIPA buffer for protein extraction. Protein concentration was determined by BCA assay (ThermoFisher, 23225) according to the manufacturer's instruction.
Quantification was done by Fiji ImageJ. List of all antibodies and dilution factors used can be found in Supplementary Data 1. Scans of uncropped blots are provided in the Source data file. RNA-sequencing and data analysis. The library preparation and sequencing were conducted by Genewiz. Libraries were sequenced on Illumina HiSeq (2 × 150 bp) to a depth of 30 million reads. Quality filtering of paired-end reads was carried out using FastQC (Simon Andrews, https://www. bioinformatics. babraham. ac. uk/ projects/fastqc/), followed by data trimming with Trimmomatic-0. 33  65  to remove sequencing adaptors and low-quality bases (<15). Quantification of transcripts was performed using Kallisto v0. 43. 1  66. All downstream analysis was carried out in R using Bioconductor packages. Statistics and reproducibility.
All experiments including experiments were performed in triplicate unless specified. All statistical tests were conducted on data pooled from all replicas of experiments. In cases of data shown as proportions, for example, LD size distribution and spheroid phenotype, results were presented as the mean ± s. e. m. of triplicate experiments. Inter-experiment variation is checked by the same statistical test to ensure consistency of the results. Data representation. Box plot: centre line shows the medians; box limits indicate the 25th and 75th percentiles as determined by GraphPad Prism software; the whiskers end at minimum and maximum values.
Acknowledgements We thank  Noboru Mizushima ,  Austin Smith ,  Niall Dillon  and  Ian Chambers  for providing ESC lines constitutively knock-out for Atg5 and matching controls,  ESC-E14TG2a  and  ESC-R1  lines and the pPyCAGIP plasmid, respectively. Thanks to  Megha Prakash-Bangalore ,  Monica Roman-Trufero ,  Luc Jouneau  and  Olivier Jouneau  for their technical, statistical and/or graphic design assistance, and to  James Elliot  at the  Imperial/BRC Flow cytometry Facility. Gratitude also goes to  Alex Gould ,  Aylin Hanyaloglu ,  Tristan Rodriguez , and  Victor Zammit  for discussions and/or critical reading of the manuscript, and to all members of the  Epigenetics and Development group. This work was supported by  Genesis Research Trust -U. K. to V. A. ( P65250 ;  P54998 );  Biotechnology and Biological Sciences Research Council -U.
K. to V. A. ( BB/P005179/1 ;  BB/D52657X/1 ) and to M. C. ( BB/P005209/2 ;  BB/H020233/1 ); Imperial College President PhD scholarships -U. K. (D. K. and R. A. d. S. );  Medical Research Council and UKRI Future Leaders Fellowship -U. K. to Mi. P. (MRC ,  MC_UP_1605/4  and  MC_EX_MR/S015930/1 );  INRAE  and  ANR   Programme Investissements d'Avenir REVIVE -France  (V. B. and  A. J. ,  ANR-10-LABX73 );  Wellcome Senior Investigator Award -U. K. to S. S. ( 103788/Z/14/Z ),  Agro-ParisTech  and  PHASE INRAE -France  (Me. P. ),  University of Oxford -U. K. (S. S. ), University of Warwick  and  Nottingham Trent University -U. K. (M. C. ) and  Imperial College London -U. K. (V. A. and M. C. ).
Data availability The RNA-sequencing data generated in this study have been deposited in the Gene Expression Omnibus database under accession code GSE165563. The RNA-sequencing data of mouse embryo development used in this study are available in the ArrayExpress database under accession code E-MTAB-2958. Source data include RT-qPCR data (Fig. 2c ; Fig. 3b ; Supplementary Fig. 1c, d, f, g ; Supplementary Fig. 2a, f, g, h ), quantification of immunofluorescence data (Fig. 1b, c ; Fig. 2e, f, j ; Fig. 3c, d ; Fig. 4c ; Fig. 5a, e ; Supplementary Fig. 4d ; Supplementary Fig. 8c, e ), records of spheroid phenotypes (Fig. 3e , f; Fig. 4e ; Fig. 5b, h ; Supplementary Fig. 6b ; Supplementary Fig. 7d ; Supplementary Fig. 8h ), triglyceride assay (Supplementary Fig. 5a ; Supplementary Fig. 7f ), growth and self-renewal assays (Supplementary Fig.
Results The main goal of this study was to assess the effect of porcine genome copy number variants (CNVs) on the diversity and composition of pig gut microbiota. For this purpose, we used whole-genome sequencing data to undertake a comprehensive identification of CNVs followed by a genome-wide association analysis between the estimated CNV status and the gut bacterial diversity in a commercial Duroc pig population. A CNV predicted as gain (DUP) partially harboring ABCC2-DNMBP loci was associated with richness (p-value=5. 41×10 -  ) and Shannon α-diversity (p-value=1. 42×10 -4 ). The insilico predicted gain of copies was validated by real-time quantitative PCR (qPCR), and its segregation, and positive association with the richness and Shannon α-diversity of the porcine gut bacterial ecosystem was confirmed in an unrelated F1 (Duroc×Iberian) cross.
Animal samples Samples employed in this study are a subset of pigs reported in  [14]  and  [9]. In brief, a total of 100 weaned piglets (50 males and 50 females) from a commercial Duroc pig line were used as a discovery dataset (Table  1 ). The pigs were distributed in three batches, all animals were raised on the same farm and had ad libitum access to the same commercial cereal-based diet. Furthermore, a subset of 24 unrelated F1 (Duroc×Iberian) crossbred pigs with phenotypically extreme gut microbial diversity index (12 high and 12 low) from  [7]  were employed as independent validation dataset (Table  1 ).
ASVs were classified to the lowest possible taxonomic level based on a primer-specific trained version of GreenGenes Database 13. 8  [16]. Before the estimation of the diversity indices, to correct for the sequencing depth, samples were rarefied at 10,000 reads. Diversity metrics were estimated with the vegan R package v2. 6-2  [17]. The α-diversity was evaluated with the Shannon index  [18] , and the β-diversity was assessed using the Whittaker index  [19].
Host-genome data analysis and CNV-calling Simultaneously with fecal sampling, blood was collected at 60 ± 8 days of age via the external jugular vein. Host genomic DNA was extracted from blood using the NucleoSpin Blood (Macherey-Nagel). Whole genome was paired-end sequenced (2 × 150 bp) in an Illumina NovaSeq6000 platform (Illumina) at Centro Nacional de Análisis Genómico (CNAG-CRG; Barcelona, Spain). Reads were mapped to the porcine reference assembly Sscrofa. 11. 1 with BWA-MEM 0. 7. 17  [20]. Alignment files containing only properly paired, uniquely mapping reads without duplicates were processed using Picard [ http://broadinstitute. github. io/picard/ ] to add read groups and to remove duplicates. Variant calling was performed with the HaplotypeCaller tool from Genome Analysis Tool Kit (GATK 4.
1. 8. 0)  [21]. Applying GATK Best Practices, variants with minimum read depth of 5 on at least one sample were retained. Joint genotyping was conducted with combined gVCFs. Functional annotations were added using SnpEff v. 5  [22]  against the Sscrofa. 11. 1 reference database. CNV prediction was performed with ControlFREEC 11. 5  [23] , using a pool of samples as CNV baseline and using intervals of 20kb windows. CNV calls from all samples that were less than 10kb apart were merged with Survivor  [24]. Individual CNVcalls were combined into copy number variant regions (CNVR) following the reciprocal overlap approach  [11]  with CNVRange  [25]. Therefore, contiguous CNVs intervals with at least 50% of mutual overlap were merged into the same CNVR. Nucleotide diversity pattern estimates were calculated considering the SNPs present in each individual separately.
The dataset in VCF format was converted to FASTA and from FASTA to transposed FASTA (tFASTA). This tFASTA file was read with the software mstatspop (https://github. com/CRAGENOMICA/mstatspop) to obtain the frequencies of SNPs from each of the pigs at the desired region. Finally, we calculated RTajima per fragment using self-made R scripts. All these estimates were finally divided by the effective length size of the studied region to obtain comparative estimates per nucleotide.
Quantitative real time PCR Real-time quantitative PCR (qPCR) was used to validate the CNV on the ABCC2 gene in a total of 72 samples including a subset of 48 Duroc samples (24 diploid and 24 in silico predicted as DUP), and 24 unrelated F1 Duroc×Iberian cross (Table  1 ). The CNV breakpoint was re-estimated with Manta: v1. 6. 0  [26]. All primers were designed using the Primer Express 2. 0 software (Applied Biosystems). The pair of primers ABCC2_CNV_F 5'-.
Identification of microbial signatures The identification of ASVs that discriminate samples according to the number of copies of the ABCC2-DNMBP loci was performed based on the compositional kernel as implemented the function 'classify' of kernInt R package  [28]. The 'classify' function run a supervised classification model based on Support Vector Machine. For that purpose, the available dataset was split at random into training set (80% of data) and validation set (20%). The C hyperparameter's optimal value was obtained by 10×10 cross-validation on the training set. To estimate the mean classification accuracy the 'classify' function was run ten times using different training/test splits of the dataset. Microbial signatures were obtained from the hyperplane vector w, and the importance of the ASV k was computed by kernInt as (wk) 2  [29].
Detection of copy number variants and association analysis In this study we used whole-genome sequencing data from 100 healthy 60-day-old Duroc pigs to undertake a comprehensive identification of CNVs. A total of 1,292 CNVs distributed across 531 CNVR on autosomal pig chromosomes were identified (Figure  1 , Supplementary table  1 ). After quality control, 1,005 CNVs grouped into 291 CNVR, presented in at least the 5% of the samples, were used for the association analysis. Among them, a CNV predicted as gain (DUP) located on CNVR454 (SSC14:111000000-111075999) that partially contain the ATP Binding Cassette Subfamily C Member 2 (ABCC2) and the Dynamin Binding Protein (DNMBP) genes showed a significant association with richness (p-value= 5. 41×10 -5 ) and the Shannon α-diversity (p-value=1.
A bidirectional crosstalk between gut microbiota and the metabolism of bilirubin and conjugated BAs has been documented. Bilirubin can regulate the composition of gut microbiota by being potentially toxic towards Gram-positive bacteria, while promoting the proliferation of Gram-negative species  [42]. In a similar way, a higher BA tolerance is evidenced by Gram-negative bacteria  [43]. In agreement with these studies, the gut microbiota of DUP samples in both the discovery and validation datasets showed a higher relative abundance of Gram-negative bacteria, such as members of the Desulfovibrio, Phascolarctobacterium, Faecalibacterium, Succinivibrio, and Anaerovibrio genera. On another note, gut microbiota composition can regulate BA and bilirubin production and signaling. Evidence from germ-free (GF) rats reveals that gut microbiota is a key player in the reduction of bilirubin to urobilinoids with significant lower fecal urobilin levels in GF rats compared with conventional ones  [44].
The closest relative of Parhyale with an assembled genome is Hyalella azteca, whose genome assembly is significantly smaller (estimated genome size ~1 Gb, genome assembly 550 Mb) and more fragmented (N50 of ~200 kb for v2. 0,  Poynton et al. 2018). It is estimated that Parhyale and Hyalella have diverged for 40-90 million years  (Copilas-Ciocianu et al. 2020) , which means that non-functional sequences will be too dissimilar in these species to be properly aligned. Gene identification and annotation: Before genome se-quencing, several transcriptomic datasets were produced in Parhyale, including EST data generated by Sanger sequencing and de novo transcriptome assemblies generated by using early NGS sequencing technologies (454 pyrosequencing and SOLiD) on mRNA and small RNAs from maternal and embryonic tissues  (Zeng et al.