dataesr/scientific-paragraphs-categorization huggingface dataset:
Besides modeling assumptions, our results may be influenced by the inherent biases of metabarcoding data. Bacterial relative abundances characterized using metabarcoding techniques are a distortion of the actual relative abundances  (Knight et al. 2018; Lavrinienko et al. 2021) , since metabarcoding is sensitive to the number of rRNA copies in the bacterial genomes, primer biases, and the quality and completeness of the reference database for taxonomic assignation (at the bacterial order/phylum level in our case). These issues are unlikely to artefactually generate phylosymbiosis or covariations across bacterial taxa because we expect such biases to be homogeneous across host species; nevertheless, they are likely to affect our ancestral reconstructions of microbiota compositions. Our approach to quantifying phylosymbiosis characterizes microbiota composition in terms of the relative abundances of higher bacterial taxa (orders or phyla).
Hence, microbial interactions combined with phylogeneticallyconserved host traits shape microbiota composition over millions of years, supporting the view of vertebrate gut microbiota as 'ecosystems on a leash'  (Foster et al. 2017). positive values. Furthermore, model selection using Bayes factors led to many false negatives on simulated data (Supplementary Methods 2 & Results 1). Therefore, we assessed the significance of l using permutations. We shuffled at random the extant host species to break the phylogenetic structure and ran again model inference of the randomized dataset. We performed 100 replications and compared the distribution of l values thus obtained to the original l estimate: if the original l was greater than at least 95% of the l values obtained through permutations, we considered that there was a significant impact of host evolution on microbiota evolution.
Simulations: We evaluated our approach using simulations. We simulated the evolution of a microbiota along a host phylogeny using Multivariate Brownian motions for logabundances. We simulated phylogenies with n = 20, 50, 100, or 250 extant host species using a pure birth model (pbtree function in the phytools R-package  (Revell 2012) ). We considered microbiota with p = 3, 5, 10, or 15 microbial taxa and uniformly sampled the logarithms of their ancestral abundances at the root of the host phylogeny between -4 and 0 before normalizing them so that ∑  "9 9 = 1. We generated random positive definite variance-covariance matrices R following  (Uyeda et al. 2015)  and  (Clavel et al. 2019 ) with eigenvalues of 1/4. Finally, we applied Pagel's l transformations with l = 1, 0. 75, 0. 5, 0. 25, or 0.
Empirical application: We downloaded the dataset of  (Song et al. 2020 ) that gathered the gut microbiota of 2,677 mammal individuals from >200 species and 1,630 bird individuals from >300 species, characterized by metabarcoding using the V4 region of the 16S rRNA gene. Only studies using the standard protocol of the Earth Microbiome Project  (Thompson et al. 2017)  were included (see  (Song et al. 2020)  for details), making samples comparable across different studies  (Knight et al. 2018 ). Song et al. converted bacterial reads into amplicon sequence variants (ASV), assigned each ASV taxonomically using the Greengenes database  (DeSantis et al. 2006; Song et al. 2020) , and rarefied ASV tables at 10,000 reads per sample. We complemented their dataset with the consensus phylogenetic trees of  (Upham et al.
Effect of host traits on phylosymbiosis: We gathered data on host species traits from  (Song et al. 2020)  for diet, geographic location, and flying ability. We assigned a dominant diet to each host species as either "plants", "fruits", "invertebrates" or "meat" following the EltonTraits database  (Wilman et al. 2014). We assigned a geographic location to each species by picking the biogeographic realm (Afrotropical, Antarctic, Australasian, Nearctic, Neotropic, Oriental, or Palearctic) where the highest number of wild individuals were sampled, or if not available, where the highest number of captive individuals were sampled (this was the case for 48% of the mammalian species and 18% of the avian ones). We treated flying ability as binary (yes/no). First, we assessed the influence of flight on the gut microbiota by performing inferences on non-flying mammal species only (i.
e. excluding bats) and on flying bird species only. Similarly, we investigated the effect of captivity on our inferences by replicating them using only the gut microbiota of wild or captive individuals. Second, we tested whether the evolutionary conservatism of diet, geographic location, or flying ability may explain phylosymbiosis in mammals and birds by performing permutations. We shuffled host species having the same diet, geographic location, and/or flying ability and re-ran the inferences on these randomized datasets. For each tested trait, we performed 100 independent randomizations. Finally, we verified that phylosymbiosis did not artefactually arise from the concatenation of the separate studies composing this dataset by randomizing the species that came from the same study.
Acknowledgments: The authors acknowledge  Julien Clavel ,  Jonathan Drury ,  Félix Foutel--Rodier , and members of the  BioDiv team at IBENS  for helpful discussions, as well as the Editor  Aurélien Tellier  and two anonymous reviewers for their constructive comments. They also acknowledge the  Hubert Curien Alliance program  for funding workshops that initiated the project. GSK acknowledges support from the  Academy of Finland  (decision  340314 ) and the  Sakari Alhopuro foundation  (grant  20210172 ). This work was performed using HPC resources from  GENCI-IDRIS  (Grants  2021-A0100312405  and  2022-AD010313735 ).
Data availability: Raw data and processed data from  (Song et al. 2020 ) used to perform the empirical applications are available in Qiita (https://qiita. ucsd. edu/study/description/11166). Our phylogenetic comparative method, referred to as ABDOMEN (A Brownian moDel Of Microbiota EvolutioN), is available on GitHub with a tutorial: https://github. com/BPerezLamarque/ABDOMEN.
Model inference: To infer the model parameters, we sampled from their joint posterior distribution (log  " , , , log  > C , … , log  > E | CC , … ,  89 , … ,  EG , ) using a No U-turn Hamiltonian Monte Carlo sampler, a computationally efficient Markov Chain Monte Carlo algorithm for continuous variables (Supplementary Methods 1). We implemented it in the probabilistic programming language Stan and we ran and compiled it through the RStan interface (R Core Team 2022; Stan Development Team 2022). Inferences were performed with 4 independent chains and a minimum of 4,000 iterations per chain including a warmup of 2,000 iterations. We checked the convergence of the chains using the Gelman statistics and effective sample sizes (ESS). We extracted the mean posterior value of each parameter and its associated 95% credible interval across posterior samples.
This article aims to analyze the transformations in medical prescription work and infrastructures brought by digitalization. Our fieldwork takes place in the context of precision medicine development based on genomics High Throughput Sequencing (HTS) in France, through the Plan France Médecine Génomique (PFMG ). The Plan aims at industrializing the production of genomic testing in clinical context at a national scale, particularly in oncology. To ensure the intensified flow of information between hospitals and HTS platforms required, a centralized process has been organized around two sequencing platforms and the introduction of a new e-prescription software (E-PRES). We start by analyzing how the e-prescription software changes the practices of health professionals by imposing new technological and professional standards.
Yet, the French Plan, with its focus on High Throughput Sequencing (HTS) technologies , takes genetic analysis for the clinical care of cancer into a new dimension. In the field of rare diseases, Timmermans has proposed a rich analysis of the transformations in the genetic diagnosis work associated with HTS. He emphasizes the importance of new standards, in this work, namely international databases and the specific expertise required to use them properly  (Timmermans, 2015). He also outlines the new ways in which these standards are articulated, through a collective endeavor, with both individualized molecular and clinical data to produce new forms of "causality for clinical purposes"  (Timmermans, 2017). Digital tools are a central part of this emerging HTS diagnostic process. In the case of the PFMG2025, if international databases are essential, a couple of additional tools have been developed among which a software, referred to by the actors as an eprescription software, is of particular interest, given its role in the national access to HTS in different care context, including cancer.
Some of this literature has renewed the analysis of "the ties between records and the social system that services and is serviced by these records"  (Bittner and Garfinkel, 1967; Garfinkel, 1967; p. 192)  and has highlighted the invisible work done by technicians to make the infrastructures operational  (Shapin, 1989). This includes and shapes diagnostic work associated with intensive data entry, data care and logistical organization to ensure physical links between the stages of production organized from afar, that remains largely imperceptible to many members of the infrastructures when the device is working. Yet, this work of "little hands" requires specific skills  (Denis and Pontille, 2012). While the updating of a database is a matter of expertise specific to so-called scriptural activities, its cognitive dimension has often been underestimated  (Pontille, 2010).
The invisible work behind databases consists of a set of tasks such as updating reports, cleaning fields, classifying, building a query, but also making the data compatible with the chosen digital This is high-throughput sequencing that allows the analysis of the entire human genome, and no longer only a panel of genes targeted and previously identified by biologists. format. These tasks must constantly be legitimized by the actors to show the extent of their work  (Dagiral and Peerbaye, 2012). The present article analyzes the implementation and the ways in which the new e-prescription software developed in the PFMG2025 is used. With this case study, we aim at contributing to the existing literature on HTS diagnostic work in care setting by introducing an analysis of the specific effects of digital tools on the organization of work and the forms of expertise involved.
We also highlighted the decisive role the eprescription software, showing that its usages went well beyond this sole act. The software, we claimed, actively contributed to the coordination of the complex sequencing pathways that goes from the initial prescriptions by oncologists to their final impacts on the patients. The sequencing pathways generate a very large amount of data for a significant number of patients, that conveys an image of abundance medicine. The handling of the case flow from a diversity of hospitals all over the country required the setting of an ad hoc digital infrastructure. Following the guidelines of the PFMG2025, each of the two national HTS platforms (referred to as platforms A and B) developed their own system, and referred to them as e-prescription software. Although the two have specificities, they share important common characteristics.
In what follows, they will be designated in a non-specific way, as E-PRES. In the present article, we analyze the specific issues raised by the production and use of digital data associated with E-PRES, with a focus on the initial prescription stage. While this informational infrastructure links the different professions and spaces involved in the genomic analysis production chain, as a technological mediator, it also translates and distorts information  (Latour, 2007; p. 58). Its impact on the ways in which professionals organize themselves is therefore significant. Our survey has been carried out at the peculiar and transitional moment of the implementation of these new genomic analysis pathways and associated computer system, as part of the PFMG 2025. We show that this centralizing intention clashes with the information systems and genomic medicine analyses that have been in place for several years in some expert centers.
E-PRES is in competition with existing software in hospitals, and around which logistical processes and work organization of health professionals are well-established. In this context, clinicians have difficulties in appropriating this additional software and its interface, and they finally organize themselves to delegate the e-prescription related work. To this aim, they negotiate with reformers (as we propose to call the health professionals to whom the State has entrusted the implementation of the PFMG2025 Plan) the creation of new positions, mostly held by women qualified as "prescription assistants" or ensure that other professionals already present, such as genetic counselors, free up time to carry out the work of monitoring, updating and validating the various stages of treatment in E-PRES.
At the time of our survey, the new positions were referred to as "prescription assistants. " Late 2022, their title was changed for "genomic pathway managers. " Using this type of profession to perform the dirty work  (Hughes, 1997) , corresponding to the administrative and time-consuming tasks performed by the bottom of the hospital hierarchy, is a common practice in the medical profession. Moreover, this invisible organizational work is mostly performed by overqualified women in often precarious jobs  (Avril and Vacca, 2020). Similarly, the work of prescription assistants seems to be invisible as it is carried out in the name of doctors and in a software environment that leaves little trace of their input, except when it is not performed correctly and the production chain is blocked. The moment chosen for our survey, during the phase of introduction of BOX E-PRES: the di erent steps of the e-prescription software.
Many actors are involved in the steps followed by E-PRES, whether they produce the data it aggregates, use them and/or are concerned by the decisions it allows to be made. the E-PRES software, enables us to reveal the importance of this work done by "little hands. " Difficulties of appropriation by the clinicians, negotiations between professionals, training issues and the need to recruit professionals assigned to these technical tasks of prescription assistance are all indicative of their importance. In this context, our article examines the ways in which the introduction of the E-PRES software reaffirms the existence of little hands within the hospital, and more broadly within the medical order, and renews the forms of invisibilization of their work.
Methods The data used in this study were obtained from a field survey interested in the deployment of genomic medicine in France , conducted from January 2021 to June 2022, in the two sequencing platforms of the PFMG (platforms A and B) and four hospitals or cancer centers in the Paris and Lyon areas. Given the COVID 19 pandemic context the fieldwork was conducted by different members of the team in the Lyon and in the Paris area. Previous fieldworks (on INCa platforms and on early phase clinical trials in several French hospitals) and contacts helped getting access to the platforms, hospitals and interviewees (see Table  1 ). In the present article, we rely on qualitative data, i. e. , semistructured interviews based on grids adapted to the different interviewees and observations. Twenty six interviews were conducted with PFMG managers; staff recruited on the two platforms: managers, biologists, quality manager, medical manager; referring physicians and other professionals involved in the genomic test prescription chain, such as prescription assistants or genetic counselors.
These observations were crucial for to identify difficulties linked to the handling of the E-PRES software and the decisive but often silent role of prescription assistants. Physicians, prescription assistants and genetic counselors were interviewed and a 1-day observation of the work of a prescription assistant was carried out. Transcriptions of the interviews, subcontracted to an external provider, were analyzed with a double thematic coding and completed with the field notes from the meeting and workday observations. Interviewees were given an information leaflet presenting the objectives of the survey and the conditions for data storage and processing. Finally, compliance with the CNIL's MR-004 reference methodology of the project was validated by the CNRS's Data Protection Department (DPD) (declaration n • 220740).
It also received the favorable opinion of the Groupe de Réflexion Éthique du Center Léon Bérard (GRET-CLB 2021-003) as well as that of the Inserm Ethics Evaluation Committee (CEEI-IRB00003888). In what follows, we start by presenting the complex path of genomic analysis in a care context and analyze the ways in which E-PRES challenges existing practices. Secondly, we show that the implementation of E-PRES requires important adjustments on the part of clinicians and implies the work of "little hands" of new professions, to select and translate relevant data, sort documents and digitize the information required in the process. The introduction of genomic prescription software in the French healthcare system The introduction of e-prescription in care pathways linked to genomics appears to be a tool for controlling and transforming the nature of work.
Eligibility has two dimensions. First, general clinical indications have been set by the Plan-in oncology, two broad categories: rare cancers and refractory metastatic cancers-and only patients corresponding to these indications can get an HTS analysis. Second, the MTB also selects patients according to health indicators (number of metastases, "RMH" score , etc. ) and sometimes, more informally, according to other individual characteristics: age, social situation, location, but also according to the lifestyle and behavior of certain patients, such as whether or not they smoke. The collective decision made during the MTB meeting are based on the information collected in the patient's clinical file and made available from a software that is different from E-PRES. Indeed, each hospital uses a specific software to handle the consultations or medical acts, the clinical information, evolution of the disease and its management, and the results of the biological or genomic tests already performed.
Once the members of the MTB have collectively validated the HTS prescription, a new entry must be created in E-PRES that includes the prescription, clinical data and the patient's consent. This registration triggers the logistical and technical process of the genomic test. New software, specific Molecular Tumor Board are multidisciplinary meetings in which clinicians, biologists, clinical research associates and bioinformaticians participate. Their objective is either to refer a patient for a sequencing or to analyze the molecular results of the sequencing, in order to prescribe a treatment to the patient. https://pfmg. aviesan. fr/professionnels/preindications-et-mise-enplace/cancers-avances-en-echec-therapeutique/ The RMH (Royal Marsden Hospital) score is a biological test used to assess the patient's health status.
Frontiers in Sociology frontiersin. org to the PFMG pathways, E-PRES thus requires supplementary data work. "There, the patient is entered into [E-PRES] which is a software that allows the follow-up of different patients, and the interface between the different centers. From there, as soon as the patient exists in [E-PRES], we can ask the pathologist to transfer the tissues to the platform where the nucleic acids will be extracted. And so, there is again a bit of. I would say formalities, trying to mediate and coordinate between different people, with the pathologists, saying that this patient has been included. You have to send the tissue. The pathologists order a transporter. The tumor comes out of the tumor library. " Interview with a neuro-oncologist, prescription physician, June 17, 2021.
The samples are then sent to the sequencing platform, where various preparation steps are executed by specialized technicians. These final steps in the preparation of biological samples and sequencing require meticulous logistical development. The DNA must be extracted and prepared so that the molecules can be analyzed by the machines, the very high-speed sequencers, which are the cornerstones of the platforms. The sequencing of the samples is a particularly important step in the technical process: the biological material is transformed into digitalized information. While other softwares are used during the sequencing process, E-PRES plays an important role at the end of these technical steps because it aggregates the genomic data analyzed by the biologists. Indeed, the subsequent steps of bioinformatics data processing require technical expertise developed by dedicated teams.
The quality of the digital data generated by the sequencers is controlled before they are analyzed by biologists, using ad-hoc software, different from E-PRES, and developed by the bioinformaticians. Thus, E-PRES organizes the work of linking and articulating  (Star, 1986; Strauss, 1988)  data of different natures: genomic data, clinical data, legal documents such as the consent signed by the patient, but also external international databases containing, in particular, information on the role of variants. E-PRES is thus at the center of a complex, distributed information infrastructure of storage and computing, in which material and logistical issues play a key role. This makes it a priority for the manager of bioinformatics on platform B to have a system engineer in his team, who is able to operate and control the hardware infrastructure: "In fact, yes, my profile [.
] that I like to recruit first, [. ] is the system engineer. The one who will manage all the hardware infrastructure, the calculation server. Because without it, we don't really do anything. Without a good infrastructure that holds up and with good systems engineers who monitor and manage all that, you don't get very far. It's really the essential engine. " Interview with Platform B bioinformatics manager, April 29, 2021. Beyond this function of organizing digital prescription, E-PRES is, for the platforms and prescribers, a tool used for logistical followup and work organization. Each step on the pathway is recorded in the software. The web interface displaying the list of patient files submitted for genomic testing indicates by a band at the top of the screen the number of files according to their status along the genomic testing chain: "waiting to be received, " "waiting for sequencing, " "waiting for results, " "completed.
The centralizing e ect of E-PRES added to the localized systems already in place In addition to its function as a tool for logistical follow-up and organization of the prescription work, E-PRES also has a role of centralizing information. Yet, as E-PRES is superimposed on existing local solutions, specific work is required to adapt to its interface and computer input. The changes in practice required are a source of resistance from certain clinicians, negotiations between professionals and, consequently, frictions  (Beaudevin et al. , 2019). The PFMG 2025 aims to generalize and harmonize access to genomic medicine. In this context, the E-PRES software allows the platforms to be electronically linked to referring physicians located throughout France. As the management of platform B explains, the particularity of their sequencing laboratory is that it is entirely dematerialized and goes beyond the boundaries of the hospital: Platform B medical manager: "You can't go to something like that without having a tool for prescription.