dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental Protocol We performed three main experiments, one to evaluate our approach compared to other domain adaptation methods on popular benchmark datasets, a second one to evaluate WMSSDA on a real-world medical multi-source domain adaptation dataset, and finally, an ablation study to evaluate the impact and usefulness of each component of our method. We evaluate all approaches on a supervised domain adaptation classification task with limited data and class imbalance, all unsupervised domain adaptation methods have been slightly modified to handle a labeled target domain as described in the previous section. All our experimental results are compared using the three following classification metrics: the balanced Accuracy (bACC), the Area Under the Receiver Operating Characteristic Curve (AUC), and the F 1 -score.
All datasets are split between a training set and a test set, all approaches are trained on the same training data and evaluated on the same test data. To obtain significant results we conduct each experiment 5 times and report the mean and standard deviation of each evaluation metric. To be as fair as possible in our comparisons, in each experiment, we define the model architecture for each approach as similarly as possible across them, while taking account of their architectural differences. For image datasets, our feature extractor is a convolutional network, and all other modules are fully connected networks, for the Covid tabular dataset all modules are fully-connected. In the same line of thought, identical hyper-parameters, tuned on the NN approach, are used across all compared approaches for each experiment.
Comparative Study on Benchmark Datasets This first experiment is a comparative study of several baseline and state-ofthe-art domain adaptation approaches to evaluate WMSSDA performance on benchmark domain adaptation datasets. We aim to show that our approach is able to compete against, and even outperform, other state-of-the-art domain adaptation approaches on well-known image benchmark multi-domain datasets in a context of limited data and class imbalance. Table  3  reports our entire experimental results on the 5-Digits dataset, with visual indications of the results of the t-tests between our best-performing models and each compared approach in all settings. As can be seen in the table, our two models, WMSSDA and WMSSDA-β, perform largely better than other approaches in the vast majority of cases, obtaining the best or second-best result for almost all metrics and target domains.
05 Table  3 : Comparative Study on the 5-Digits domain adaptation Benchmark Dataset, with Limited and Imbalanced Data. The best and second-best results for each metric and each target domain appear in bold and underlined respectively. Results are evaluated on a multi-class classification task using metrics: balanced Accuracy, AUC, and the F 1 -Score. The fact that we drastically reduced the amount of available training data worsens classification results, but this does not affect the comparison potential of the results. We can see that our proposed WMSSDA-β obtains the best or second-best results in the majority of cases, while WMSSDA obtains overall good results but not better than other approaches. It is important to note that the overall best prediction results for the Quickdraw domain are reached by a simple neural network trained on the target domain only.
This probably shows that adding a short phase of pre-training to other state-of-the-art approaches would drastically improve the overall results of all methods. Another well-performing approach in this setting is MFSAN, which leads to significantly better balanced Accuracy than ours on average. Overall, our two versions of WMSSDA lead to competitive experimental results on this dataset, with WMSSDA-β leading to significantly better results than other approaches in the vast majority of cases. When considering the average over all target domains, WMSSDA obtains significantly better results than all other approaches on the three evaluation metrics except for Fine-Tuning and MFSAN on the balanced accuracy metric. We can conclude from this experiment on benchmark datasets that our proposed approach is able to compete, and even surpass, other baseline and state-of-the-art domain adaptation approaches in our supervised multi-domain with limited and imbalanced data context.
Comparative Study on Real-World Tabular Medical Dataset The second experiment is a comparative study between all tested domain adaptation approaches and our proposed WMSSDA, on the real-world mixed-type tabular medical Covid dataset. We aim to show that our approach can reach good results on mixed-type tabular data in addition to image data. We also aim to show that WMSSDA competes and outperforms other domain adaptation approaches in a real-world medical context with limited data and class imbalance. Table  5  reports our entire experimental results on the Covid dataset. Results show that both WMSSDA variations lead to the best or second-best results in most cases, and in all cases on average. Those experimental results show that our approach can perform very well on tabular data. In this particular setting, we note that it is our standard version of WMSSDA that performs the best, which is probably an indication that there is almost no concept shift in this dataset, unlike with the two benchmark image datasets, rendering the β version of the approach no better than the standard one.
Ablation Study Our method WMSSDA is composed of several important elements, in this section we perform an ablation study in order to evaluate the pertinence and usefulness of each component of WMSSDA. An ablation study is a type of experiment that is conducted to investigate the impact of removing, or disabling, specific components or features of an approach. To do so, we eliminate parts of our model and evaluate the results to understand their individual contributions to the overall performance and validate the pertinence of each component. In this ablation study we compare five ablated versions of our WMSSDA approach with our complete method. Table  6  shows all WMSSDA versions compared in this study. In the column "Branches" it is indicated if the method contains both the common modules branch and the source domain specific modules branch, or only one of the two.
Column "Regus" indicates if both  Table  7 : Ablation study results. Table  7  shows our experimental results on the Covid dataset for this ablation study. We compare our method with only the common modules branch with method WMSSDA-A and only the source domain specific modules branch with method WMSSDA-B. In both cases, average results are similar, with slightly better results for WMSSDA-A, showing the importance of a shared latent space. Method WMSSDA-E contains both branches and obtains largely better results than WMSSDA-A and WMSSDA-B, showing the pertinence of an architecture combining both the common and specific branches to obtain the best possible results. We believe that the two branches architecture naturally decreases negative transfer as classifiers with higher confidence are given more importance in the ensemble pooling of results, which highly contributes to improving prediction quality.
Discussion and Conclusion In this paper, we proposed an innovative multi-source supervised domain adaptation approach, Weighted Multi-Source Supervised Domain Adaptation (WMSSDA). We evaluated and compared WMSSDA and compared our results to those obtained by other state-of-the-art approaches, on limited and imbalanced data, on both benchmark and real-world medical datasets. Our proposed approach is composed of a two branch architecture, learning both a shared domain invariant latent space and source domain specific latent spaces. The shared latent representation is learned and regularized both statistically and adversarially, the statistical regularization relies on a MD measure between source and target domains. The output of the MD regularization is used to compute transfer contribution weights that are applied to weight the impact of each source domain during training, limiting negative transfer.
We show that our proposed WMSSDA outperforms most state-of-the-art approaches on both image benchmarks datasets and a real-world tabular medical dataset. We further analyze the relevance and importance of each component of our method by performing an ablation study, validating the overall architecture of our approach. Overall, our experimental results seem to show that most multi-source domain adaptation approaches do not obtain significantly better results than single-source approaches in our experimental scenario. This seems to show that despite researchers efforts, negative transfer in multi-source domain adaptation is still an open critical problem that seems to limit the overall potential performance of state-of-the-art multi-source domain adaptation approaches. Best performing domain adaptation approaches are still not able to fully avoid negative transfer.
https://drive. google. com/file/d/ 1XvgWqkHA4LuK6bPE9ktIlO9SFY1MemnC/view?usp=sharing 9 https://github. com/CuthbertCai/pytorch DAN 10 https://github. com/fungtion/DANN 11 https://github. com/mil-tokyo/MCD DA 12 https://github. com/easezyc/deep-transfer-learning/tree/master/UDA/pytorch1. 0/DSAN 13 https://github. com/hanzhaoml/MDAN.
Availability of data and material • Covid dataset: https://www. kaggle. com/datasets/meirnizri/ covid19-dataset. • Digits dataset: -MNIST: http://yann. lecun. com/exdb/mnist. -MNIST-M: https://www. kaggle. com/datasets/aquibiqbal/mnistm. -Street View House Numbers (SVHN): http://ufldl. stanford. edu/ housenumbers. -Synthetic Digits (SYN): https://www. kaggle. com/datasets/prasunroy/ synthetic-digits. -USPS: https://www. kaggle. com/datasets/bistaumanga/usps-dataset. • DomainNet dataset: http://ai. bu. edu/M3SDA/#dataset.
DNA sequencing and analyses Total DNA was isolated from cultures by phenol-chloroform extraction as described previously  11. Sequencing of the Thermococcus sp. 33-3 strain (ENA accession GCA_946300405) was carried out at Genoscope (Centre National de Sequençage, France), using Illumina MiSeq and Oxford Nanopore MinION. Reads were assembled with NaS (https://github. com/institut-de-genomique/NaS) and corrected with Canu  46. Mapping of Illumina reads to assembled genomes (as an estimate of copy number) was performed and visualised using Geneious (BioMatters). 16S, RpoB and PheS sequences were each aligned with MAFFT  47. Whole genomes were aligned with Gepard v2. 1  48. Functional annotations of pT33-3 were predicted using each ORF as a query sequence for BLASTp  49  against the nr database (cutoff E=0.
01); HHblits (HHSuite v3)  50  against the UniRef30_2020_06 database (n=2 iterations, cutoff E=0. 01). To analyse CRISPR spacer matches, CRISPR arrays were predicted in all Thermococcales genomes available through NCBI (November 2021) using CRT  51  in Geneious. Spacers were extracted and used as query sequences for BLASTn search against pT33-3 (cutoff parameters of 85% identity over the entire spacer length).
Mobilization assays Candidate oriT sequences were cloned into a pLC70-derived plasmid encoding only a mevinolin resistance cassette by Gibson Assembly (sequences and plasmid maps of all plasmids available in supplementary data on Dryad). These plasmids were used to transform CRISPR-null T. kodakarensis harboring a trp-selectable pT33-3 plasmid. The resulting strains were used as donors in mating assays (as above), with transconjugants selected by resistance to mevinolin (mobilised plasmid marker) and agmatine prototrophy (donors are agmatine auxotrophs). When mobilised DNA was designed to integrate into recipient genomes, a transfer cassette was cloned as above. The transfer cassette consists of a mevinolin-resistance marker surrounded by 1kb homology arms for pop-in/pop-out allelic exchange with the recipient chromosome, all surrounded by tandem oriT sequences.
Phylogeny For RpoB phylogenetic reconstruction, sequences aligned with MAFFT  47  , alignment gaps removed with BMGE  55  , evolutionary model selected with ModelFinder 56 , and phylogenetic trees generated with IQ-TREE v1. 6. 12  57. Trees were visualised with iTol  58. For VirB4 phylogenetic reconstruction, sequences of VirB4 proteins from MPFFATA , and MPFFA used previously  10, 33  were retrieved from Genbank. Additional archaeal sequences were retrieved by BLASTp search. Sequences of the MPFF class were used as an outgroup. Phylogenetic trees were generated as for RpoB as above. The maximum likelihood tree was constructed using IQ-TREE with the MFP option for model selection. Branch supports were either computed through SH-like aLRT  59  (10,000 replicates) and UFBoot 60 (10,000 replicates).
Acknowledgements We thank  Nicolas Soler  for advice while writing this manuscript. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 Research and Innovation Program  (grant agreement no. 772725 , project  HiPhore  to PF), under the  European Union 's  Seventh Framework Program  (grant agreement no. 340440 , project  EVOMOBIL  to PF) and by the  National Institutes of Health  ( R35GM118160  to MT).
Participants recruitment, inclusion criteria, and data collection Women were eligible for recruitment in the SABCHO study if they were at adults (aged 18 or more years of age, enrolled between July 1, 2015, and December 31, 2019, newly diagnosed with stage I-IV invasive BC, had no prior history of cancer, and received their BC treatment at one of the tertiary/quaternary hospital sites. Participants were clinically staged at the time of diagnosis using the 7 th edition of the American Joint Committee on Cancer (AJCC). Each participating hospital had dedicated study interviewers who collected sociodemographic characteristics, risk factors, comorbidities, and breast cancer clinical information knowledge and awareness of breast cancer and delay periods to diagnosis as previously described  [6, 17].
Statistical analyses Statistical analyses were performed using GraphPad Prism software (version 5. 0; GraphPad Software, Inc. , San Diego, CA). Differences between two groups were evaluated using 2-way analysis of variance (parametric), followed by the Bonferroni correction for parametric samples, or the Dunn's test for nonparametric samples as a post hoc test. Statistical significance was set at p < 0. 05. All authors had access to the study data and had reviewed and approved the final manuscript.
Data collection Data were collected from computerized patient files and anonymized. We collected the data of patients with HTLV-1positive serologies by ELISA (enzyme-linked immunosorbent assay) with confirmation of HTLV-1 infection by Western blot or PCR (polymerase chain reaction). Serum samples from patients were tested for the detection of HTLV-1/2 antibodies using a CMIA (chemiluminescent microparticle immunoassay) method (ARCHITECT rHTLV-I/II, Abbott Laboratories, Wiesbaden, Germany) following the manufacturer's instructions. Patients for whom the first screening result was positive benefitted from a confirmatory assay with a Line immune assay test (INNO-LIA HTLVI/II Score, FURIJEBIO Europe, Belgium). This confirmation was performed at the Cayenne Hospital laboratory between 2012 and 2021; the samples were taken from adult patients who were hospitalized in Cayenne Hospital or in the remote health centers (providing care in the most isolated areas of the Amazonian forest), or from outpatients with at least one thyroid-stimulating hormone (TSH) assay performed by the hospital laboratory.
In the case of patients with confirmed HTLV-1 infections, we collected data on TSH (laboratory value, 0. 4-4. 2 mIU/ml), along with the date of sample collection. As we could not confirm the TSH values, we excluded patients for whom TSH measurement was performed in a private laboratory and documented on the computerized patient files. In cases of HTLV-1 positivity, patients are routinely tested for Epstein-Barr virus and hepatitis C. Concerning the rubeola virus, we only test pregnant women (excluded from the study) and parvovirus B19 is only tested if there are clinical symptoms. In our study, none of the patients had associated hepatitis C or EBV serology and none of the patients were tested for parvovirus B19 or rubella. Furthermore, we collected data on age, sex, and place of residence classified into savannah plateau, coastal center, eastern French Guiana, and western French Guiana, and missing information.
Control population Controls were defined as patients who were hospitalized and had both negative HTLV serology and a thyroid checkup during hospitalization. Patients were randomly selected from the laboratory databases of clinical wards unrelated to thyroid disease management from 2012 to 2021. The maximum interval between requests for HTLV serology and thyroid checkups was less than 1 month. In addition, only patients with a single thyroid checkup request during this period were selected to ensure the absence of thyroid pathology and, therefore, medical follow-up. Thus, the controls were individually matched to the cases by sex and age (1:2 for women and 1:3 for men) using the Mahalanobis optimal algorithm  (XLSTAT 2022 (XLSTAT. 3. 1. 1344)).
Statistical analysis The statistical analysis was performed using the STATA 16 software (STATA-CORP ® ) (StataCorpLLC, Lakeway Drive College Station, Texas, USA). A descriptive analysis of the study population and clinical data was performed. Quantitative data are expressed as means and standard deviations for continuous variables, and prevalence data are expressed as frequencies and percentages. Age was categorized as ≤30 years, 30-65 years, and >65 years. The proportions of HTLV-1-positive patients with and without hyperthyroidism and hypothyroidism were compared using crude odds ratios and the c 2 test. Furthermore, we used two unconditional  multiple logistic regression models to control for age, sex, and residence location (there may be ethnic or iodine input differences among the different parts of French Guiana): one with hyperthyroidism as the dependent variable and the other with hypothyroidism as the dependent variable.
The Firalink bioinformatics pipeline has been developed to analyse long non-coding RNA (lncRNA) data generated by targeted sequencing. This pipeline has been first implemented for use with the FIMICS panel containing 2906 lncRNAs useful for investigations in cardiovascular disease. It has been subsequently tested and validated using a panel of lncRNAs targeting brain disease. The pipeline can be adapted to other targeted sequencing panels or other transcriptomics data (e. g. whole transcriptome) through a change of the reference genome/panel. Therefore, Firalink can be applied to different lncRNA panels and transcriptomics data targeting multiple diseases.
Introduction Long non-coding RNAs (lncRNAs) represent a large group of RNA molecules longer than 200 nucleotides and lacking protein-coding potential. Instead, they regulate gene expression mostly at the epigenetics level and are involved in the development and progression of multiple types of diseases such as cardiovascular disease  [1]. During the past few years, multiple tools and databases have been developed to identify lncRNAs and study their expression  (Sun et al. , 2017 , Zhao et al. , 2018 , Iyer et al. , 2015 , Zhao et al. , 2015 , Bryzghalov et al. , 2021). Hence, lncRNAs are attracting a lot of attention by the biomedical research community. To facilitate the research on the role and therapeutic potential of lncRNAs in cardiovascular disease, we conducted a deep RNA sequencing experiment in human failing and non-failing hearts through which we identified 2906 lncRNAs that were either enriched in the cardiac tissue or differentially expressed between failing and non-failing hearts.
With these 2906 lncRNAs, we developed the FIMICS panel which can be used to identify novel disease markers or therapeutic targets  [2]. The FIMICS panel is based on targeted sequencing to increase the sensitivity of the detection of lncRNAs, which are often weakly expressed, especially in blood samples. Therefore, the FIMICS panel allows detecting novel lncRNA biomarkers in blood samples that can be translated to the clinic and help in patient management  [2]. Translation of research findings to clinical application in the RNA field is a challenge which can be overcome by attention to methodological issues  [3]  and collaborative work between researchers with complementary expertise  [4]. To facilitate this translation and improve the quality of the analysis of sequencing data generated by the FIMICS panel, we developed the Firalink bioinformatics pipeline, which was applied to RNA sequencing (RNA-seq) data obtained from blood samples of COVID-19 patients.
RNA-seq data generation We conducted targeted sequencing of 2906 lncRNAs using the FIMICS panel  [2]  in whole blood samples obtained from COVID-19 patients involved in the COVIRNA project aiming to discover lncRNAs predicting cardiovascular dysfunction in COVID-19 patients (www. covirna. eu). FIMICS libraries were prepared from total RNA extracted from whole blood samples and sequenced at 50bp reads paired ends (2x50bp) using the Illumina NextSeq2000 sequencer in an ISO17025 accredited lab. Flow cells of 400 M reads allowed the simultaneous quantification of 48 patients and resulted in an average of 8 million reads per sample. As described in details in Ref. [2] , targeted sequencing uses both amplification and capture to enrich lncRNAs which are mostly weakly expressed. A first amplification occurs as the last step of library preparation (15 PCR cycles).
Pipeline description Similar to other RNA-seq data analysis workflows, the Firalink pipeline is composed of five steps: 1) quality control of FASTQ files; 2) removal of low-quality reads; 3) evaluation of potential contamination with sequences from other species; 4) alignment of reads against the FIMICS panel (sequences of the 2906 lncRNAs) and generation of lncRNA counts using Kallisto method  [5] ; 5) compilation of count files into a matrix and quality control. The following tools are used for each step. 1. Quality control of FASTQ files. FastQC software generates a report on descriptive metrics of the sequences contained in FASTQ  [6]. Information about the number of reads and their duplication level, the average Phred quality score, GC composition, and the presence of adapters in sequences is provided.
2. Removal of low-quality sequences. Trimmomatic software removes low-quality sequences (reads with the Phred score below 30), adaptors or low-quality read extremities  [7]. Sequences are compared to the classical Illumina adaptors to detect contaminations. 3. Evaluation of potential contamination. Kraken is a taxonomic classification tool used for the detection of inter-species contamination (bacterial, fungal or viral) in samples  [8]. It proceeds by alignment of each sequence against the provided database and labels each sequence with a taxonomic level. 4. Alignment of reads and generation of count values. Kallisto  [5]  is a pseudo-alignment software used to obtain the count tables for each sample against a reference in FASTA format that contains the sequences of the FIMICS panel. The tool generates K-mer from the reference and compares sequences to determine the most likely position of the sequence on the given reference.