dataesr/scientific-paragraphs-categorization huggingface dataset:
S3  at https://doi. org/10. 5281/zenodo. 6597409). However, the similarities between LCHF and other deep-sea hydrothermal systems highlighted by our hierarchical clustering have already been reported through enrichments of genes involved in stress responses, homologous recombination, and chemotaxis  (63). Microbial communities in these systems are subjected to numerous physical constraints (e. g. , hydrostatic pressure and temperature) and chemical stresses (e. g. , heavy metals and radionuclides) that are probably specific to deep-sea hydrothermal vents and reflected in their genomic adaptations. Additionally, high abundances of mobile genetic elements such as transposases were also reported as a distinctive characteristic of LCHF and deep-sea hydrothermal vents  (64, 65). It has been suggested that lateral gene transfer is frequent and could be an important source of phylogenetic diversity  (65)  as well as a way to promote bacterial adaptation to ecological niches.
These genes, encoding the functional core of the C-P lyase, should enable microorganisms to utilize phosphonates as source of P. In phosphate-limited marine environments, phosphonates are often considered an important source of P and could be metabolized by diverse microbial taxa, notably among Proteobacteria  (45, 48). Among the enzymes involved in phosphonate catabolism, the C-P lyase pathway has been extensively studied because its enzymatic activity acting upon methylphosphonate esters as the substrate has been proposed as an explanation for the biological production of methane in oxygenated environments  (48, 67, 68). Considering the abundance of genes involved in this pathway in serpentinizing systems (Table  S3  at https://doi. org/10. 5281/zenodo. 6597409), as well as the enrichment of its regulon-encoding gene (phoB [Table  S3 ]), we hypothesize that phosphonate catabolism could play an important role in the growth of microbial communities.
Furthermore, several mechanisms for organophosphate synthesis under prebiotic conditions have been reported  (78)  and may contribute to the tapestry of geochemical processes occurring in modern-day serpentinizing environments. At present, geochemical data are missing to identify the origins of phosphonates in serpentinizing sites. Alternatively, the detection of a phosphonate synthesis gene (pepM) could be associated with the presence of phosphonate in dissolved organic matter  (68)  and be a path to understand its distribution. However, genetic markers (pepM, ppd, phpC, and mpnS) (79) of this biosynthesis pathway were rarely retrieved in the metagenomes in this study (see Tables  S3  and S5  at https://doi. org/10. 5281/zenodo. 6597409). Furthermore, abundances of these genes were not in concordance with those of C-P lyase-encoding genes.
MATERIALS AND METHODS Metagenomic data sets. Twenty-one publicly available metagenomic data sets from 6 serpentinizing systems (2 submarine, 4 terrestrial, and 4 other hydrothermal systems [2 deep-sea basaltic hydrothermal vents and 2 hot springs from continental volcanic area]) were retrieved from the NCBI Sequence Read Archive (SRA) and MG-RAST databases and are described in Table  S1  at https://doi. org/10. 5281/ zenodo. 6597409. To limit the bias arising from the use of different sequencing technologies and protocols, we selected only metagenomic data sets containing Illumina raw paired-end reads with a partial overlap. Comparative metagenomic pipeline. With the objective of standardizing data processing, all 21 metagenomes (see Table  S1  at https://doi. org/10. 5281/zenodo. 6597409) were reprocessed using an in-house bioinformatic pipeline for quality control, assembly, and annotation against taxonomic and functional databases.
This pipeline was implemented in Snakemake  (85)  and deposited on the Github repository at https://github. com/elfrouin/MetaGPipeline. git. The raw paired-end reads of metagenomes were first merged using PandaSeq v2. 8  (86)  with default parameters. The merged reads were next processed by Trimommatic v0. 32  (87) , which trimmed reads once the average quality in a 4-bp window fell below 15, cut bases off the extremities if below a quality score of 20, and removed short reads (,35 bp). The high-quality trimmed reads were finally assembled into contigs, longer than 200 bp, with IDBA-UD v1. 1. 1  (88)  reads mapped to the contigs with BWA v0. 7. 12 (89). Gene prediction was done with Prodigal v47 (90), using the "-p meta" flag for metagenomic sequences. Gene coverage was computed with Bedtools v2.
25. 0  (91). The microbial taxonomic diversity of each metagenome was determined from the taxonomic annotation of predicted genes. Similarity searches were first performed against the NCBI nonredundant (nr) database with DIAMOND BLASTP v0. 8. 34 (92) using a maximal E value of 1 Â 10 25. MEGAN6 (93) was used to assign the annotated genes to their most confidently predicted taxonomic rank, by applying the lowest-common-ancestor (LCA) algorithm. A taxonomic contingency table was created by weighting annotated genes by their sequencing coverage. A first functional annotation of genes was performed by RPS-BLAST search (E value cutoff of 1 Â 10 25 ) against the Cluster of Orthologous Groups (COG) database  (94). The second functional annotation was performed using the online tool GhostKOALA (the only nonautomated step) against the KEGG Orthology (KO) database with a minimum alignment score threshold of 50  (95).
These specific microbial genera were added on the PCoA biplot. Regarding the functional capabilities of the microbial community, the Pearson correlation and Ward's method were employed for agglomerative hierarchical clustering of metagenomes and annotated genes. The gene coverage per metagenome was visualized using heat maps, generated with the R package gplots v3. 0. 1. To discriminate the functional capabilities of serpentinizing versus nonserpentinizing systems, a random forest analysis was carried out using the R package Random Forest v4. 6. 12 (101) with 3,000 independent decision trees. Finally, a heat map was built with the top 50 genes contributing to segregate metagenomes related to serpentinizing ecosystems from the others (i. e. , the genes with the highest value of mean decrease Gini, a measure of variable importance).
phn operon analysis. Analyses of phn genes were carried out to identify their associated taxonomies and genomic organizations. Genes were queried against the NCBI nr database using BLASTN (with an E value of 1 Â 10 26 ). The determination of gene taxonomy was next assessed in two ways: (i) via the best BLAST hit and (ii) via the LCA algorithm implemented in MEGAN6. To estimate the percentage of microorganisms possessing a phn operon, the occurrences of phnGHIJKLM genes were normalized by the median abundance of 10 single-copy genes (listed in Table  S6  at https://doi. org/10. 5281/zenodo. 6597409) in each sample. Finally, the structural organization of the operon phn was investigated from the metagenomic contigs. All contigs with at least the seven genes of the catalytic unit were reannotated with PROKKA v1.
11  (102)  to characterize the genomic context of the operon. Phosphonate transport and biosynthesis. Despite the abundance of C-P lyase-encoding genes in our data set, well-known genes involved in phosphonate biosynthesis (i. e. , pepM, ppdA, ppdH, and mpnS) were poorly detected with the approach described above. Therefore, we further examined the occurrence of these genes to confirm our results. For this purpose, we searched for homology against collections of hidden Markov models (HMMs) using the hmmsearch tool implemented in HMMER v3. 3. 2 (hmmer. org). First, homologs of genes were searched against the HMM database proposed by Acker and colleagues  (80)  and publicly available in their GitHub repository (https://github. com/slhogle/ phosphonates/tree/master/data/phosphonate_biosynthesis/HMM_models).
Furthermore, we looked for the presence of the highly conserved active-site motif EDK(X)5NS in sequences identified as PepM, since HMMs of PepM present significant similarities with the isocitrate lyase superfamily  (78). Additionally, we carried out a similar approach on the recently described aepX gene (74), encoding a Pi-independent phosphonate transport, using a curated database. Briefly, homologs of the aepX gene were identified using the Integrated Microbial Genomes (https://img. jgi. doe. gov/) homolog display tool and aligned with ClustalOmega implemented in the IMG/JGI website. Then, aligned amino acid sequences were profiled with the hmmbuild function. Sequence homology was searched in each metagenome applying the hmmsearch function with an E value of 1 Â 10 280.
ACKNOWLEDGMENTS This project was financially supported by  ANR projects deepOASES  ( 14-CE01-0008-06 ) and  MICROPRONY  ( 19-CE02-0020-02 ), the  Deep Carbon Observatory (Census for Deep Life : Comparative metagenomics of archaeal biofilms in carbonate chimneys associated with geographically isolated sites of serpentinization; principal investigator, M. O. Schrenk; funded by the  Alfred P. Sloan Foundation ). E. F. was awarded a Ph. D. fellowship from the  French Ministry of Education and Scientific Research.
Data and methods We drew our sample from the 2019 S&P Global Sustainability Yearbook  (S&P Global, n. d. ). The Yearbook, released annually, presents the list of best-performing firms according to the RobecoSAM (SAM) Corporate Sustainability Assessment and in line with the criteria of the Dow Jones Sustainability Index (DJSI). The SAM assessment is an annual evaluation of firms' sustainability practices. Each year over 7,300 firms around the world are assessed across 61 different industries. Firms ranking in the top 15% of each industry are included in The Sustainability Yearbook and those with scores that are within 10% of the industry-leading scores are classified into three categories: SAM Gold Class, SAM Silver Class, and SAM Bronze Class. We initially included in our sample the 220 firms that received one of these Olympic Medal-inspired distinctions in 2019.
Data sources To answer our research question, we collected two sustainability reports for each of the firms in our sample: one published prior to and another after the launch of the SDGs. Sustainability reports provide substantive information to corporate stakeholders  (Rost & Ehrmann, 2017) , improving the transparency on corporate social and environmental practices  (Bebbington, 2014). We acknowledge previous research on signaling and image theory in corporate sustainability  (Bansal & Kistruck, 2006; Cho et al. , 2018; Hossain et al. , 2019). However, in recent times, firms increasingly recognize the link between sustainability and financial performance  (Knox, 2020) , stakeholders exercise increasing pressures and punish irresponsible behaviors  (Nason et al. , 2018) , and both auditing  (Boiral et al.
, 2019)  and the use of formalized and comparable reporting practices (e. g. , GRI) have increased. These efforts have collectively improved the connections between reporting and real practices, especially for publicly traded, global firms. We collected sustainability reports from publicly available databases and corporate websites when necessary. We used the following criteria for the data collection: for the "pre-SDG" reports, we included the most recent pre-2015 report, which included reports covering 2011 to 2014, and for the "post-SDG" report, we included the most recent report prior to 2020 (the year of our data gathering), which included reports covering 2016 to 2019. We downloaded both reports for 190 firms, for a total of 380 reports. We excluded 30 firms from the original sample of 220 because at least one of the two reports was not available online.
We removed 28 reports (both reports for 14 firms) because at least one of their reports was not published in English or contained nonreadable characters when transformed into a text file format, and we removed 24 additional reports (both reports for 12 firms) because the firm shifted from separate sustainability reporting to integrated reporting, confounding the comparison of the two reports. After these adjustments, our final dataset included 164 firms based in 23 countries from the North American, European, Asia Pacific, and Latin American regions.
2. Analysis of each corporate sustainability report to quantify the usage/weighting of each of the SDG terms and computation of the alignment of each report to each of the 17 SDGs, based on the corresponding term weighting scores. 3. Pre-SDG versus post-SDG analysis of the alignment scores. SDG language quantification. As our source for the "language of the SDGs," we accessed the UN Sustainable Development website (UN, 2015a) and downloaded the descriptions for each of the 17 goals as raw text documents. To quantify this SDG language, we processed these 17 documents using the open-source Tf-idfVectorizer routine from SciKit-Learn  (Pedregosa et al. , 2011). The Tf-idfVectorizer computes the term frequency (Tf) for each term within each document from a group of documents, modified by an inverse document frequency (idf) that reduces the score as terms occur more commonly across documents in the group.
Thus, this weighting captures both the frequency of each term in a document and the specificity or uniqueness of that term to that document versus the rest of the documents in the group. If a term does not occur in a document, that term receives a score of zero for that document. The results are output in separate columns (or vectors) for each document. For a detailed description of the open-source Tf-idfVectorizer program routine and its features, an excellent online reference guide is available from the developer (Scikit-Learn, n. d. ). Using an approach similar to that of  Lavin (2019) , we instructed the Tf-idfVectorizer to extract "terms" from the SDG description documents that consisted of each individual distinct word used in the 17 SDG descriptions, along with all sequential combinations of up to three words as they occurred in the SDG descriptions.
The presence of the multi-word terms (such as "climate change"), when present in both documents as a phrase, amplifies the alignment score significantly compared to single-word terms. 4 An alignment score was computed for each firm's pre-2015 sustainability report vector paired with each of the 17 SDG vectors. As a measure of the alignment with the overall SDG language, the average of the 17 individual SDG scores (SDG Avg) was also computed. This was repeated for the firms' post-2015 sustainability reports, resulting in two frames of data for our sample of 164 firms, each with 18 variables.
It is the statistical significance of these changes, before and after 2015, which is the subject of our subsequent analysis. Our calculated ∆SDG scores for individual firms resulted in considerable variation across the dataset, with values ranging from -48. 690 to +51. 124. SDG14 (Life Below Water) showed the smallest variation in ∆SDG scores (SD = 4. 884), with SDG7 (Affordable and Clean Energy) showing the largest spread in ∆SDG scores (SD = 14. 692). To explore the variation in the ∆SDG scores, we began by investigating whether or not the change in the incorporation of overall SDG vocabulary (indicated by ∆SDG Avg scores) was significantly greater than zero in each of the firm subgroups in our study. Table  4  presents the results of this analysis for all three dimensions of our firm groupings.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research work is partially funded by the  Spanish Ministry of Economy and Competitiveness  (Grant No. PID2019-106725GB-I00 ), the  European Regional Development Fund  (Grant No. B-SEJ-398-UGR20 ), and the  Andalusian Regional Government  (Grant No. P20_00019 ). Dante I. Leyva-de la Hiz  is member of the  LabEx Entrepreneurship center  ( University of Montpellier, France ), funded by the  French government (Labex Entreprendre ,  ANR-10-Labex-11-01 ).
Data processing, mapping, and SNP calling Clean data were obtained by trimming primer dimers and adaptors, discarding low-quality bases (Q20 as cutoff). Burrows-Wheeler Alignment Tool (BWA) software (http://bio-bwa. sourceforge. net/bwa. s-html) (44)  was used to map the clean data to the Pacific oyster genome v9  (45). Duplicated reads generated by PCR amplification were removed. SAMtools (http://samtools. sourceforge. net/samtools. -shtml) (46) , Picard (http://broadinstitute. github. io/picard/), and BCFtools  (46)  were used for SNP calling. Ambiguously mapped reads and reads with low depth were removed. The major filter parameters are: SNPs with sequencing depth no less than 10 and no more than 150, RMS >= 20, Qual >= 20, no indel within 15 bp of SNPs.
To detect large genomic regions in oyster that had experienced selection by the mortality, we mapped all the disease associated SNPs, regions, and genes onto the chromosomes of the genome. Ragtag (version=1. 1. 1) (48)  was first used to assemble the v9 reference genome into chromosomes based on VN1 version information (GCA_011032805. 1  (49) ). The positions of the associated SNPs, regions, and genes were converted to the new chromosomal assembly through custom Python script. Circos software (version=0. 69-9) (50) was used to draw the circle diagram.
5 (multiple of fold change, FC: difference) and FDR ≤ 0. 05. In addition, the immune geneset used for comparison were obtained from  Zhang L. et. al. , 2015 (33). The gene ontology (GO) and KEGG enrichment analyses were conducted with TBtools software  (54)  with the threshold value p-value ≤ 0. 05. Fisher's LSD and c 2 test was used to test significant enrichment when the number of genes in a GO term was< 5 and ≥ 5, respectively. Transcription factor binding sites of upstream and downstream 300 bp of SNPs were predicted using an online transcription factor-binding site database, TRANSFAC via the PATCH ™ 1. 0 platform (http://gene-regulation. com/cgi-bin/ pub/programs/patch/-bin/patch. cgi) with default parameters.
Domain adaptation is a popular paradigm in modern machine learning which aims at tackling the problem of divergence (or shift) between the labeled training and validation datasets (source domain) and a potentially large unlabeled dataset (target domain). The task is to embed both datasets into a common space in which the source dataset is informative for training while the divergence between source and target is minimized. The most popular domain adaptation solutions are based on training neural networks that combine classification and adversarial learning modules, frequently making them both data-hungry and difficult to train. We present a method called Domain Adaptation Principal Component Analysis (DAPCA) that identifies a linear reduced data representation useful for solving the domain adaptation task.
DAPCA algorithm introduces positive and negative weights between pairs of data points, and generalizes the supervised extension of principal component analysis. DAPCA is an iterative algorithm that solves a simple quadratic optimization problem at each iteration. The convergence of the algorithm is guaranteed, and the number of iterations is small in practice. We validate the suggested algorithm on previously proposed benchmarks for solving the domain adaptation task. We also show the benefit of using DAPCA in analyzing single-cell omics datasets in biomedical applications. Overall, DAPCA can serve as a practical preprocessing step in many machine learning applications leading to reduced dataset representations, taking into account possible divergence between source and target domains.
Introduction The main and fundamental presumption of the traditional machine learning approach is that there is a probability distribution and that it is the same or very similar for the training and test sets. However, when the training set and the data that the model should use when operating are different, this assumption can be readily broken. The worst is that the new data lack known labels. Such situations are typical and lead to the problem of domain adaptation which became a popular challenge in modern machine learning  [1] [2] [3] [4]. The domain adaptation problem can be stated as follows. Let S be a labeled source dataset and T be an unlabeled target dataset, and let us further assume S and T are not sampled from the same probability distribution. The idea is to find a new representation of the data so that the non-labeled data would be as close to the labeled one as possible, with respect to the given classification problem (Figure  1 ).
In domain adaptations, one usually talks about two complementary subsystems that ideally must be trained simultaneously. The first one is a classifier that distinguishes the feature vector as either source or target and whose error is maximized. The second one is a feature generator that learns features that are as informative as possible for the classification task. Theoretical foundations of domain adaptation based on H-divergence between source and target domains and its estimates from finite datasets have been suggested in  [5]. Here we understand domain adaptation as an approach to a more general outof-distribution (OOD) generalization problem  [6] , and understand OOD as the situation where the unlabeled dataset has a distribution different from the labeled one. One of the most popular applications of domain adaptation in computer vision was implemented using the framework of neural networks known as Domain Adaptation Neural Networks (DANN)  [1, 7] , based on outlined above principle of combination of classification and adversarial learning modules.
Methods for aligning multidimensional data point clouds are well known in machine learning, and they can be used for solving domain adaptation tasks even without considering labels in the source domain. In particular, various generalizations of PCA or other matrix factorization approaches computing a joint linear representation of two and more datasets are widely exploited in machine learning  [8] [9] [10] [11]. Other linear methods such as Transfer Component Analysis minimizing the maximum mean discrepancy (MMD) distance  [12]  between linear projections of the source and the target datasets  [3]  and subspace alignment method  [13]  have been suggested. Correlation Alignment for Unsupervised Domain Adaptation (CORAL) aligns the original feature distributions of the source and target domains, rather than the bases of lowerdimensional subspaces and is claimed to be "frustratingly easy" but still effective in many applications approach to domain adaptation  [14].
As with many quasi-quadratic optimization iterative algorithms, convergence is guaranteed and, in practice, the number of iterations can be made relatively small. There exist several linear domain adaptation methods, each of which is characterized by specific features: for example, some of them produce low-dimensional embedding of the source and target datasets, and some of them do not. A summary with a short description of their working principles is provided in Table  1. Stretches the source distribution onto the target distribution. The source is whitened and then "recolored" to the covariance of the target, without reducing dimensionality. The transformation of the source optimizes the functional min A ||A T C s A -C t || F , where C s , C t are the source and target covariance matrices.