dataesr/scientific-paragraphs-categorization huggingface dataset:
Participants and fieldwork procedure Hundred and seventy-six inhabitants of the area of West Sardinia from three different municipalities responded to our questionnaire in a public setting (Figure  2 ). The chosen municipalities belong to the area of wetlands, Oristano being the main city of the province, and Cabras and Arborea being villages with fishing and agriculture activities that depend heavily on these wetlands. Questionnaires were tested prior to the study with twenty participants chosen randomly among partners of the project Maristanis, with the questionnaire being reduced and some additional changes introduced. This questionnairebased survey was conducted during May and June 2019. Data were collected face-to-face, by targeting general population. Questionnaires were also prepared online using LimeSurvey software, for those respondents that preferred fulfilling it in that way (only 29 among the total 176 questionnaires).
However, it is to underline that not all questions from the questionnaire were used in this analysis. The data were firstly described in Excel order to get an initial view of the population from the region of interest. The contingent valuation exercise was calculated using the software Stata. Finally, the regression model used was binary logistic model, using software SPSS, since it permits both binary and continuous data jointly, dividing variables into more blocks, which allows tracing the model's improvement by adding new variables. The first set of variables used in the regression model consists of demographic variables: age, gender, education, annual net income, housing surface, number of household members, number of kids under 12 years old in the household and municipality of living (Arborea, Cabras, Oristano).
Acknowledgements Most of the research activities were undertaken under the framework of the  MARISTANIS  project, coordinated by the  Mediterranean Sea and Coast Foundation (MEDSEA)  and cofinanced by the  MAVA Foundation. The authors thank the two institutions for their support. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement  No713750. Also, it has been carried out with the financial support of the  Regional Council of Provence-Alpes-Côte d'Azur  and with the financial support of the A* MIDEX  (n°  ANR-11-IDEX-0001-02 ), funded by the  Investissements d' Avenir  project funded by the  French Government , managed by the  French National Research Agency (ANR). The authors are grateful to the local stakeholders that provided useful information and the people that supported the survey administration activity, as well as to  Vanessa Melas , for her contribution to questionnaire adaptation and helping with the field work.
Mutation detection by next generation sequencing (NGS) is routinely used for cancer diagnosis. Selecting an optimal set of genes for a given cancer is not trivial as it has to optimize informativity (i. e. the number of patients with at least one mutation in the panel), while minimizing panel length in order to reduce sequencing costs and increase sensitivity. We propose herein Panel Informativity Optimizer (PIO), an open-source software developed as an R package with a user-friendly graphical interface to help optimize cancer NGS panel informativity. Using patient-level mutational data from either private datasets or preloaded dataset of 91 independent cohort from 31 different cancer type, PIO selects an optimal set of genomic intervals to maximize informativity and panel size in a given cancer type.
Different options are offered such as the definition of genomic intervals at the gene or exon level, and the use of optimization strategy at the patient or patient per kilobase level. PIO can also propose an optimal set of genomic intervals to increase informativity of custom panels. A panel tester function is also available for panel benchmarking. Using public databases, as well as data from real-life settings, we demonstrate that PIO allows panel size reduction of up to 1000kb, and accurately predicts the performance of custom or commercial panels. PIO is available online at.
e. increase sensitivity of detection of low variant allele frequency events). This constraint is even more important when working on circulating tumor DNA (ctDNA) because the tumor DNA is diluted in circulating DNA from normal cells and because very high sensitivity is needed to monitor residual disease. On the other hand, a large panel is required to ensure detection of mutations with clinical impact, and optimal informativity (at least one detectable mutation in all patients). Whereas the list of genes with clinical impact in a given cancer is well described and consensual  [2] , there is no generic tool to optimize the informativity of the panel. We present herein the Panel Informativity Optimizer (PIO), an open-source, publicly available software to optimize panel informativity. Based on individual mutational profiles available in public cancer genomics databases or in private databases, PIO enables the design of an optimal panel (defined at the level of whole genes or exons) to achieve absolute informativity (100% of the patients with at least one mutation).
In addition, PIO can be used to assess the informativity of a given panel in a cancer of interest. PIO also includes everal other features, such as the exploration of mutation Genome Atlas (TCGA) dataset from the 2018 TCGA pancancer study were selected  [8]. The complete list of datasets used and their corresponding studies is provided in the Supplementary Table  S1. Additional genomic information were retrieved from the GRCH37 human genome reference downloaded from the NCBI Reference Sequence Database (Refseq) (GCF_000001405. 13). Exon coordinates were extracted using the RefSeq "Select" annotation. Genes with no RefSeq "Select" annotations (such as pseudogenes) were attributed their unique RefSeq ID. Gene and exon length were calculated using the difference between end and start site from each position.
Exon ID was attributed using the nearest exon from the mutation start site. Intronic mutations were numbered according to the nearest exon ID and introns were systematically attributed a 500 bp length. Mutations with no feature found at less than 10 kb and with a nearest feature distance exceeding their respective gene's total size were omitted. All gene names were updated to their last HUGO symbol using the alias2SymbolTable function from the limma R package  [9].
Informativity module algorithm The PIO algorithm takes as input pre-formatted mutational data from whole-exome or genome sequencing, and it can be run grouping mutations by gene or exon. For the optimal panel selection (optimal mode), the most informative gene on the overall cohort is first selected. Patients presenting this mutation are then removed from the cohort, and the most informative gene in the remaining cohort is then selected. The algorithm reiterates until all patients are removed or no patients are added. The number of unique patients per kilobase (UPKB) or the number of unique patients (UP) can be used as informativity metric. A minimal number of patients per mutation, calculated on the overall cohort, can be set in order to avoid overfitting to private mutations. For the custom panel interrogation (custom mode), only genes/exons from the provided list of genomic intervals are considered.
Pancancer optimal panel length comparisons Panel length to reach full informativity were compared across all cancer types using the PIO length analysis module. For each cancer, an optimal panel was defined using the 4 different approaches proposed by PIO. These approaches were first compared using the overall proportion (%) of patients diagnosed according to panel size in kb smoothed using a general additive model. A focus was then proposed for each metric, comparing the panel size differences allowing to reach 100% informativity between PIO and classical approach. This optimal panel size was then extracted for each cancer type and used for exploratory correlations with the total number of patients and different markers of DNA alterations extracted from Thorsson et al. [12].
Examples of application For panel size optimization, a 293-gene (802-exon) panel targeting genes recurrently mutated in hematological malignancies  [4]  was shrunk using the PIO custom mode with UP or UPKB metric and mutations grouped by exon. The performance of the resulting 140-exon panel was then evaluated in 928 DLBCL patients who benefited from the full panel  [3]. For the evaluation of our local routine 110-gene panel, PIO was first used in custom mode (mutations grouped by gene, UP metric, at least 1 patient per mutation) to evaluate the predicted performance in the pre-loaded dataset (predicted). The proposed optimized 33-gene panel was then evaluated in test mode on our local cohort (observed). For panel benchmarking in AML, 5 commercial myeloid panels  [4]   the patients harboring a mutation in this first genomic interval, the second genomic interval selected is the most informative on the remaining patients.
This procedure is reiterated until all patients have at least one mutation selected or if no more patients can be added (see methods). Importantly, the algorithm can be run by gene or by exon/intron, and two informativity metrics can be used: the number of UP or the number of UPKB. The datasets can be interrogated with three different modes: in the so-called "optimal" mode, PIO automatically selects an optimal set of genomic intervals to maximize the informativity in the selected cohort; in the "custom" mode, PIO selects genomic intervals among a custom panel proposed by the user. In the latter case, PIO suggests genomic intervals to add according to their informativity computed on the remaining patients, unless optimal informativity has already been reached. In the panel test mode, the software takes the exact list of genomic intervals from a provided custom panel, ranks them according to the selected metric and presents the panel's performance in the cohort.
PIO offers several other exploratory analyses: the "mutations" module presents different mutation metrics calculated on the total cohort, such as gene/exon length and UPKB, together with a summary of the cumulated number of mutations. A graphical output illustrates the frequency and nature of the most frequently mutated genomic intervals in the overall cohort, and the data are summarized in a downloadable table. A second graphical output presents a co-occurrence plot of mutations in the selected genomic intervals at the individual level. In addition, the "length" module compares four different approaches to optimize panel design to maximize the informativity/size ratio (see methods). Figure  1 : PIO Graphical user interface for dataset interrogation. In panel A, preloaded or custom datasets and panels to be analyzed can be downloaded and the analytical parameters selected.
NGS: Next Generation Sequencing, PIO: Panel Informativity Optimizer Pancancer mutations and optimal panel length exploration We first used PIO to obtain insight into pancancer mutations at the gene-level. Using PIO default parameters (optimal panel, mutations grouped by gene, UP as informativity metric), we explored the pre-loaded datasets of 23,203 patients from 91 independent cohorts spanning 31 different cancer types (Supplementary Table  S1 ). Analysis of the merged dataset found differences in term of the number of genes required to reach absolute informativity (Figure  2A ). Prostate adenocarcinoma (PRAD) and breast invasive carcinoma (BRCA) were the two cancers requiring the largest number of genes to reach exhaustive informativity (284 and 193 genes, respectively). Conversely, uterine carcinosarcoma (UCS) and lung squamous cell carcinoma (LUSC) required a panel of only 5 and 12 genes, respectively, to cover all patients.
Panel length optimization One of the main goals of PIO is to offer a tool to help optimize the informativity/size ratio. The PIO length analysis module allows the comparison of four panel optimization strategies, with the aim to catch the highest number of patients with the most parsimonious (i. e. short) panel. In the "classic" approach, mutated genes or exons are ranked according to their mutation frequency in the total cohort. This strategy is compared with the PIO algorithm (see methods) with an optimization strategy based either on absolute (UP) or relative quantification (UPKB). We compared these four optimization strategies across all cancer datasets, grouping mutations by exon. As expected, the UPKB informativity metric performed better than UP metric to optimize panel size (Figure  3A ).
Examples of application We assessed the performance of PIO in different settings. We first set out to optimize the size of a 293-gene (802-exon) panel used in DLBCL  [3]  while retaining maximum informativity. Using the PIO custom mode with UP metric and mutations grouped by exon, we predicted on the preloaded DLBCL datasets that 93. 8% of DLBCL patients could be diagnosed with a subset of only 140 exons from this panel (Figure  4A ). We compared this prediction to sequencing results of a public dataset of 928 DLBCL who were analyzed by the full panel  [3]. In this cohort, 95. 8% of patients presented at least one mutation in one of these 140 exons (Figure  4A ). Using PIO with either UP or UPKB metric allowed to reduce the panel size from ~300kb to less than 100kb whilst keeping the same level of informativity (Figure  4B ).
We then used the PIO in custom mode with UP metric and mutations grouped by gene to evaluate the performance of a custom 110-gene NGS panel used for routine diagnosis of hematological malignancies. Based on the preloaded AML cohorts, PIO predicted that 91. 6% patients would have at least one mutation with only 33 of these genes (Figure  4C ). Indeed, 700/714 (94. 5%) AML patients referred to our laboratory between 2014 and 2020 had at least one mutation in one of these 33 genes (Figure  4C ). The total number of mutations per patient was also roughly similar between PIO predictions and the local cohort data for up to 3 cumulated mutations per patient (Figure  4D ). Finally, we used the PIO panel test mode to compare 5 commercial myeloid panels  [4]  together with our custom panel to the preloaded AML datasets.
There was an overall better informativity for Trusight, Ion AmpliSeq, Archer Varianplex and our custom panel, though no panel reached full informativity (Figure  4E ). Differences in terms of cumulated number of mutations were also observed (Figure  4F ). By runing PIO in custom mode with each panel on the preloaded AML datasets, we observed that optimal informativity could be reached with only half of each panel size (Supplementary Figure  S1 ). Newman et al. proposed a method to optimize panel informativity in the design phase of CAPP-Seq with an approach that aims to maximize the number of mutations per patient in order to increase the sensitivity of the analysis of ctDNA for minimal residual disease monitoring  [5] ; this differs to PIO that aims to maximize the informativity/panel size ratio.
Accordingly, PIO will select smaller panel sizes than the one proposed by CAPPseq, but at the cost of identifying fewer targets for minimal residual disease monitoring. Moreover, PIO stands out by proposing an open-source tool with a user-friendly interface. As shown in the present study, PIO can also be used to explore mutation frequencies and other metrics in a given cancer or across a pancancer dataset. Furthermore, as an open-source software, any user may integrate the code on a routine pipeline, suggest improvement or new features implementation online or directly modify/adapt the code to match its specific needs. Compared to an empirical approach based on raw UP or UPKB metrics, PIO systematically improved panel size while keeping the same informativity. We observed a correlation between the magnitude of panel size reduction and the size of the cohort which can be explained by higher heterogeneity in large cohorts, necessitating an important number of genes to reach an acceptable informativity.
The use of PIO is of particular interest for optimizing panel size in these cancer types. Considering potential cohort bias is also important when using PIO. Although a low variance in the number of genes needed for full informativity was observed for most cancers, some were characterized by high heterogeneity between cohorts. This heterogeneity could be explained by disease heterogeneity (several molecular subtypes) or differences in patient inclusion criteria. To circumvent this, PIO allows to interrogate any custom dataset, hence enabling the user to select representative studies according to its specific clinical settings. While allowing to maximize informativity, PIO does not account for specific settings such as the prognosis or theragnostic value of a given mutation. Hence, although clinically relevant events will be included according to their relative frequency and informativity,.
AVAILABILITY AND REQUIREMENTS Project name: Panel Informativity Optimizer Project home page: https://github. com/VincentAlcazer/PIO Operating system(s): Platform independent Programming language: R Other requirements: NA License: MIT Any restrictions to use by non-academics: No Availability of data and materials: The dataset(s) supporting the conclusions of this article together with software code are available in the Github repository https://github. com/VincentAlcazer/PIO.
frequency and co-occurrence. By combining metrics on informativity and panel length, PIO can help make accurate choices in the design of panels or to benchmark commercial solutions. We provide 3 different examples of applications of PIO in real-life settings: size optimization of a 293-gene (802-exon) panel used in Diffuse Large B Cell Lymphomas (DLBCL) [3], benchmarking of five different commercial panels proposed for myeloid malignancies [4], and validation of PIO predictions for a custom 110-gene panel in a local cohort of 714 Acute Myeloid Leukemia (AML) patients. METHODS & IMPLEMENTATION Preloaded mutation dataset selection and genomic annotations Preloaded datasets available in PIO have been downloaded from cBioPortal (http://www. cbioportal. org/) [6, 7]. Only diseases with at least one available The Cancer.
Mitochondrial diversity within Carnivora The first dataset, composed of complete carnivoran mitogenomes available in GenBank combined with the newly generated sequences of the two subspecies of P. cristatus, the two subspecies of O. megalotis, Parahyaena brunnea, Speothos venaticus and Vulpes vulpes, plus the sequences extracted from Ultra Conserved Elements (UCE) libraries for Bdeogale nigripes, Fossa fossana, and Viverra tangalunga (see Methods for more details), comprised and 10 P. cristatus individuals. Maximum likelihood (ML) analyses reconstructed a robust mitogenomic phylogeny, with 91. 4% of the nodes (128 out of 140) recovered with bootstrap support higher than 95% (Fig. 2a ). The patristic distances, extracted from the phylogenetic tree inferred with complete mitogenomes between the allopatric subspecies of aardwolf and bat-eared fox, were 0.
Supplementary File 1). To further assess the genetic distances between the two pairs of subspecies and compare them to both polymorphism and divergence values observed across Carnivora, two supplemental datasets, including at least two individuals per species, were assembled by retrieving all COX1 and CYTB sequences, which are the two widely sequenced mitochondrial markers for carnivorans, available on GenBank. These datasets include 3,657 COX1 sequences for 150 species and 6,159 CYTB sequences for 203 species of Carnivora. After adding the corresponding sequences from the newly assembled mitogenomes, ML phylogenetic inference was conducted on each dataset. The patristic distances between all tips of the resulting phylogenetic trees were measured and classified into two categories: (i) intraspecific variation (polymorphism) for distances inferred among individuals of the same species and (ii) interspecific divergence for distances inferred among individuals of different species.
Phylogenomics of the Carnivora Phylogenetic relationships within the Carnivora were inferred from a phylogenomic dataset comprising 52 carnivoran species (including the likely new Proteles septentrionalis species), representing all but two families of the Carnivora (Nandiniidae and Prionodontidae). The non-annotated genome assemblies of these different species were annotated with a median of 18,131 functional protein-coding genes recovered for each species. Then, single-copy orthologous gene identification resulted in a median of 12,062 out of the 14,509 single-copy orthologues extracted from the OrthoMaM database for each species, ranging from a minimum of 6,305 genes for the California sea lion (Zalophus californianus) and a maximum of 13,808 for the dog (Canis familiaris) (Supplementary File 5).
Indeed, estimation of the genetic differentiation either within well-defined species (polymorphism) or between two closely related species (divergence) allowed us to define a threshold between genetic polymorphism and genetic divergence across the Carnivora (Fig. 5 ). Given these estimates, and in accordance with mitochondrial data, the two subspecies of P. cristatus  (1)  present more genetic differentiation between each other than the two well-defined species of golden jackal (Canis aureus) and wolf (C. lupus), and (2) present more genetic differentiation than the more polymorphic species of the dataset, the lion (P. leo). Despite known cases of natural hybridisation reported between C. aureus and C. lupus  (Galov et al. , 2015; Gopalakrishnan et al. , 2018) , the taxonomic rank of these two species is well accepted.
In that sense, given the species used as a reference, both subspecies of P. cristatus seem to deserve to be elevated to species level. The situation is less clear regarding the subspecies of O. megalotis. Indeed, while the genetic differentiation observed between the two subspecies is significantly higher than the polymorphic distances observed for all the well-defined species of the dataset, there is no species in our dataset that exhibits equivalent or lower genetic divergence than a closely related species. This illustrates the limits of delineating closely related species due to the continuous nature of the divergence process  (De Queiroz, 2007). The subspecies of O. megalotis fall into the "grey zone" of the speciation continuum  (De Queiroz, 2007; Roux et al. , 2016)  and are likely undergoing speciation due to their vicariant distributions.
To be congruent with the genetic divergence observed across closely related species of the Carnivora (according to our dataset), we thus propose that (1) the taxonomic level of the P. cristatus subspecies be reconsidered by elevating the two subspecies P. c. cristatus and P. c. septentrionalis to species level, and (  2 ) the taxonomic level for the two subspecies of O. megalotis be maintained. Although there is a distinct genetic difference between Eastern and Southern aardwolves, the evidence for a clear morphological difference is less obvious (Fig. 6 , Appendix 2 -Figure  1 -3, Supplementary File 6-7). The earliest available name for the East African aardwolf subspecies is P. c. septentrionalis  (Rothschild, 1902). This subspecies was first distinguished based on pelage characteristics of a specimen from Somaliland, which has a creamy white pelage without any grey tinge, but washed slightly with buff in the neck and side of the rump  (Rothschild, 1902).