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Objective To provide national guidelines for the management of women with severe pre-eclampsia. Design A consensus committee of 26 experts was formed. A formal conflict-of-interest (COI) policy was developed at the onset of the process and enforced throughout. The entire guidelines process was conducted independently of any industrial funding. The authors were advised to follow the principles of the Grading of Recommendations Assessment, Development and Evaluation (GRADE(r)) system to guide assessment of quality of evidence. The potential drawbacks of making strong recommendations in the presence of low-quality evidence were emphasised. Methods The last SFAR and CNGOF guidelines on the management of women with severe pre-eclampsia were published in 2009. The literature is now sufficient for an update. The aim of this expert panel guidelines is to evaluate the impact of different aspects of the management of women with severe preeclampsia on maternal and neonatal morbidities separately. The experts studied questions within 7 domains. Each question was formulated according to the PICO (Patients Intervention Comparison Outcome) model and the evidence profiles were produced. An extensive literature review and recommendations were carried out and analysed according to the GRADE(r) methodology. Results The SFAR/CNGOF experts panel provided 25 recommendations: 8 have a high level of evidence (GRADE 1+/−), 9 have a moderate level of evidence (GRADE 2+/−), and for 7 recommendations, the GRADE method could not be applied, resulting in expert opinions. No recommendation was provided for 3 questions. After one scoring round, strong agreement was reached between the experts for all the recommendations. Conclusions There was strong agreement among experts who made 25 recommendations to improve practices for the management of women with severe pre-eclampsia.

International audience

International audience; Objective To describe and compare the characteristics of women with placenta accreta spectrum (PAS) and their pregnancy outcomes according to the presence of placenta praevia and a prior caesarean section.Design Prospective population-based study.Setting All 176 maternity hospitals of eight French regions.Population Two hundred and forty-nine women with PAS, from a source population of 520 114 deliveries.Methods Women with PAS were classified into two risk-profile groups, with or without the high-risk combination of placenta praevia (or an anterior low-lying placenta) and at least one prior caesarean. These two groups were described and compared.Main outcome measures Population-based incidence of PAS, characteristics of women, pregnancies, deliveries and pregnancy outcomes.Results The PAS population-based incidence was 4.8/10 000 (95% CI 4.2-5.4/10 000). After exclusion of women lost to follow up from the analysis, the group with placenta praevia and a prior caesarean included 115 (48%) women and the group without this combination included 127 (52%). In the group with both factors, PAS was more often suspected antenatally (77% versus 17%; P < 0.001) and more often percreta (38% versus 5%; P < 0.001). This group also had more hysterectomies (53% versus 21%, P < 0.001) and higher rates of blood product transfusions, maternal complications, preterm births and neonatal intensive care unit admissions. Sensitivity analysis showed similar results after exclusion of women who delivered vaginally.Conclusion More than half the cases of PAS occurred in women without the combination of placenta praevia and a prior caesarean delivery, and these women had better maternal and neonatal outcomes. We cannot completely rule out that some of the women who delivered vaginally had placental retention rather than PAS; however, we found similar results among women who delivered by caesarean.Tweetable abstract Half the women with PAS do not have both placenta praevia and a prior caesarean delivery, and they have better maternal outcomes.

International audience; AI-based models for IUCN conservation status prediction (see Fig 1) Digitized Herbarium analysis (phenology, traits, identification) Plant disease recognition Agro-ecological robots (weeds detection and identification, mixed seeds) Biodiversity data quality and uncertainty New AI-based services for citizen science (cos4cloud project)

International audience; The aim of this paper is to present the promising outlooks that lies ahead for the innovative plasma source we introduced recently. The idea of this source is to dynamically control the plasma location by changing the waveform of the transmitted signal to an overmoded cavity. It is then the behavior of the waves inside the cavity that dynamically control the plasma location, hence the name of Space-Time Plasma Steering Source. Since its introduction in 2019, we have produced three main papers, aimed at different scientific communities: applied physics, physics of plasma, signal processing and mathematical and numerical methods. An overview of these works is first presented in this paper. Then, the promising outlooks for this unusual plasma source are identified and classified into four domains, among which the most important and essential one being the unhooking of the plasmas from the initiators in order to control their location anywhere in the cavity. Several solutions for this “unhooking issue” are then proposed, the most straightforward one being the use of non-intrusive probes.

Background Pudendal and cluneal nerve entrapment can cause a neuropathic pain syndrome in the sensitive areas innervated by these nerves. Recently, a new endoscopic minimal invasive approach for pudendal and inferior cluneal nerve neurolysis has been published in a cadaver study. The aim of our study was to describe the feasibility of this new approach and to evaluate the clinical outcome. Methods Fifteen patients underwent the ENTRAMI technique. The Numeric Pain Rating Scale (NPRS) and Patient Global Impression of Change (PGIC) were recorded at baseline and at 3 and 6 months after surgery. Result The average duration of intervention (skin to skin) was 139 min (range 50-270 min) for bilateral pudendal neurolysis and/or cluneal neurolysis and 113 min (range 100-130 min) for unilateral pudendal and/or cluneal neurolysis. No perioperative blood loss occurred. At 3 months, 50% of patients declared a more than 30% improvement of their PGIC, increasing to 57% at 6 months; 31% reported more than 90% improvement of PGIC at 6 months. Overall reduction of the average maximal NPRS score was from 9 (range 7-10) to 6 at 3 months (range 0-10; p value < 0.05) and to 5 at 6 months (range 0-10; p value < 0.05). There were no postoperative complications. Conclusions The ENTRAMI technique is feasibly in patients suffering from pudendal and/or cluneal neuralgia and preliminary results are promising.

International audience; Highly fluorinated fullerenes with a chemical composition of C60F48 have been prepared and characterized. Their solubility of the new nanomaterials in the ionic liquid (IL) has been compared with raw fullerenes C60. Fluorination may appear as a convenient method to enhance the solubility of carbonaceous nanomaterials in a fluorinated based anion IL. Indeed, the ability of 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([BmIm][NTf2]), to solubilize C60 and C60F48 was investigated using optical microscopy and UV–vis spectroscopy. While C60 has very poor solubility in this IL (below 0.07 mmol/L), the solubility increases at least eightfold, i.e. more than 1.0 mg/mL (or 0.61 mmol/L) for C60F48 samples. The presence of fluorine atoms on fullerene seems to enhance the stability of the solvated molecules in IL thanks to the limitation of the aggregation of C60F48 in [BmIm][NTf2]. This improvement of the solubility could be explained by the beneficial effect of the fluorination that induces a decrease of the π-π interaction between carbonaceous solutes as the carbon hybridization changes from sp2 to sp3 through the formation of C-F covalent bonds and thus enhances the interaction between the solvent and the solute. Fluorination appears as an efficient approach to limit the use of volatile organic compounds usually used as co-solvent to enhance the dispersion mechanism.

It was fair to assume that the 2019 United Kingdom (UK) general election would be all about Brexit. The reality is actually more complex and other issues came into play in the run-up to 12 December ballot, in particular the question of environmental emergency. Several key elements can be used to explain why the environment topped the voters’ main concerns, slightly behind Brexit and health, but on a par with crime and the economy: recent natural catastrophes such as the summer heatwaves or the floods in the North of the country and the growing climate awareness among younger citizens. Chris Stark, chief executive of the Committee on Climate Change, even claimed that “this election really is the climate election” (Shukman).When one takes the general political mood into consideration, it seems quite legitimate to question this assertion and to wonder how much of an impact the environmental emergency really had during the campaign. This is what this paper will examine. Through an analysis of the media coverage and the official literature from the different political parties, we will try to define the role and the importance of the climate emergency during the 2019 campaign. Pour beaucoup, la question du Brexit allait être au centre des élections législatives de 2019 au Royaume-Uni. La réalité est toutefois plus complexe et d’autres préoccupations ont émergé durant la campagne, notamment la question de l’urgence climatique. Plusieurs éléments peuvent expliquer le fait que l’environnement fut l’une des principales préoccupations des électeurs, légèrement derrière le Brexit et la santé, mais à égalité avec l’économie et la sécurité : le pays a été frappé par une série de catastrophes naturelles qui a renforcé la conscience climatique, notamment chez les jeunes. Le directeur général du Committee on Climate Change, Chris Stark, a même affirmé que « cette élection est réellement celle du climat » (Shukman).Lorsque le contexte politique général est pris en considération, il semble légitime de remettre en question cette affirmation et d’évaluer le véritable impact de l’urgence climatique sur la campagne. Cet article va tenter de répondre à cette interrogation. L’analyse à la fois de la couverture médiatique ainsi que des documents officiels des différents partis politiques nous permettra de comprendre l’impact de la question de l’urgence climatique durant les élections législatives de 2019.

Background Severe acute maternal morbidity, accounting for any life-threatening complication during pregnancy or after delivery, is a major issue in maternal health. Measuring and monitoring it seems critical for assessing the quality of maternal health care. We explored the relevance of maternal ICU admission as an indicator of severe acute maternal morbidity by characterizing, among maternal ICU admissions, the profile of women with severe acute maternal morbidity and their ICU stay, according to the association with other criterion of severe acute maternal morbidity. Methods Secondary analysis of a multiregional prospective population-based study of 2,540 women with severe acute maternal morbidity according to a multicriteria definition based on national experts’ consensus and including ICU admission. Results 511 women were admitted to an ICU during or up to 42 days after pregnancy (2.8 per 1,000 deliveries; 20.1% of women with severe acute maternal morbidity); 15.5% had no other severe acute maternal morbidity criterion. Among women with severe acute maternal morbidity, on multivariable multinomial analysis and adjusting for cause, the odd of intensive care unit admission with another morbidity criterion was increased for migrant from outside of Europe or Africa (adjusted odds ratio = 2.1 [95% CI 1.3-3.4]), with multiple gestation (adjusted odds ratio =1.5 [1.0-2.2]), and intrapartum cesarean (adjusted odds ratio =1.5 [1.1-2.2]). The odd of intensive care unit admission with no other morbidity criterion was increased with pre-existing medical conditions (adjusted odds ratio =2.2 [1.2-4.0]) and cesarean before labor (adjusted odds ratio =3.0 [1.4-6.1]). Women admitted to an ICU with no other morbidity criterion had no interventions for organ support. Conclusions Among women with severe acute maternal morbidity, one in five is admitted to an ICU; 15.5% of these have no other severe acute maternal morbidity criterion and their admission appears mostly indicated for continuous monitoring. The use of ICU admission alone as a single criterion morbidity is misleading to define severe acute maternal morbidity; this criterion needs to be refined to be included in the definition of severe acute maternal morbidity. These results also challenge the current organization of acute care for women with severe maternal morbidity.

Objectives: The aim of this study was to assess the predictive value of the Vandenberg and Kuse Mental Rotation Test (MRT) on performance of novice medical students for manipulation of a nasal endoscope on a cadaveric model. Material and Method: We randomly selected 39 medical students who had never handled a nasal endoscope and subjected them to the MRT. General information including experience in manual, technical, or surgical activities and testing of anatomical knowledge were collected to exclude possible confounding factors. They were then asked to perform series of cadaveric model exercises using a nasal endoscope. Their cadaver performance was evaluated by 2 blinded observers, using a standardized scale. Results: We found that medical students with higher mental rotation skills had significantly increased endoscopic sinus performance ( P = .0002 using multivariate regression adjusted for specialty choice, previous surgical exposure, and anatomy knowledge). Higher anatomy knowledge was also associated with better endoscopic sinus performance ( P = .0141). Other parameters had no impact on endoscopic sinus performance measured by the endoscopic scale ( P > .05). Conclusion: The score obtained on the MRT was correlated with the practical performance of manipulating the nasal endoscope in cadaver. It could therefore be a useful spatial ability tool for directing targeted training in rhinology.

Toxoplasma gondii is a prevalent parasite of medical and veterinary importance. Tachyzoïtes and bradyzoïtes are responsible for acute and chronic toxoplasmosis (AT and CT), respectively. In immunocompetent hosts, AT evolves into a persistent CT, which can reactivate in immunocompromised patients with dire consequences. Imiquimod is an efficient immunomodulatory drug against certain viral and parasitic infections. In vivo, treatment with Imiquimod, throughout AT, reduces the number of brain cysts while rendering the remaining cysts un-infectious. Post-establishment of CT, Imiquimod significantly reduces the number of brain cysts, leading to a delay or abortion of reactivation. At the molecular level, Imiquimod upregulates the expression of Toll-like receptors 7, 11, and 12, following interconversion from bradyzoïtes to tachyzoïtes. Consequently, MyD88 pathway is activated, resulting in the induction of the immune response to control reactivated Toxoplasma foci. This study positions Imiquimod as a potent drug against toxoplasmosis and elucidates its mechanism of action particularly against chronic toxoplasmosis, which is the most prevalent form of the disease.

International audience; Several recent studies have proposed an automatic plant disease identification system based on deep learning. However, these approaches are generally based on learned classification models with target classes of joint host species-disease pairs that may not allow optimal use of the available information. This is because these approaches require distinguishing between similar host species or diseases, and more importantly, they have limited scalability due to the size of a network gradually increases as new classes are added, despite the fact that information on host species or diseases is already available. This constraint is all the more important as it can be difficult to collect/establish a specific list of all diseases for each host plant species in an actual application. In this paper, we address the problems by proposing a new conditional multi-task learning (CMTL) approach which allows the distribution of host species and disease characteristics learned simultaneously with a conditional link between them. This conditioning is formed in such a way that the knowledge to infer the prediction of one concept (the diseases) depends on the other concept (the host species), which corresponds to the way plant pathologists used to infer the diseases of the host species. We show that our approach can improve the performance of plant disease identification compared to the usual species-disease pair modeling in the previous studies. Meanwhile, we also compose a new dataset on plant disease identification that could serve as an important benchmark in this field

Automated plant identification has recently improved significantly due to advances in deep learning and the availability of large amounts of field photos. As an illustration, the classification accuracy of 10K species measured in the LifeCLEF challenge (Goëau et al. 2018) reached 90%, very close to that of human experts. However, the profusion of field images only concerns a few tens of thousands of species, mainly located in North America and Western Europe. Conversely, the richest regions in terms of biodiversity, such as tropical countries, suffer from a shortage of training data (Pitman 2021). Consequently, the identification performance of the most advanced models on the flora of these regions is much lower (Goëau et al. 2019). Nevertheless, for several centuries, botanists have systematically collected, catalogued, and stored plant specimens in herbaria. Considerable recent efforts by the biodiversity informatics community, such as DiSSCo (Addink et al. 2018) and iDigBio (Matsunaga et al. 2013), have made millions of digitized specimens from these collections available online. A key question is therefore whether these digitized specimens could be used to improve the identification performance of species for which we have very few (if any) photos. However, this is a very difficult problem from a machine learning point of view. The visual appearance of a herbarium specimen is actually very different from a field photograph because the specimens are dried and crushed on a herbarium sheet before being digitized (Fig. 1). To advance research on this topic, we built a large dataset that we shared as one of the challenges of the LifeCLEF 2020 (Goëau et al. 2020) and 2021 evaluation campaigns (Goëau et al. 2021). It includes more than 320K herbarium specimens collected mostly from the Guiana Shield and the Northern Amazon Rainforest, focusing on about 1K plant species of the French Guiana flora. A valuable asset of this collection is that some of the specimens are accompanied by a few photos of the same specimen, allowing for more precise machine learning. In addition to this training data, we also built a test set for model evaluation, composed of 3,186 field photos collected by two of the best experts on Guyanese flora. Based on this dataset, about ten research teams have developed deep learning methods to address the challenge (including the authors of this abstract as the organizing team). A detailed description of these methods can be found in the technical notes written by the participating teams (Goëau et al. 2020, Goëau et al. 2021). The methods can be divided into two categories: those based on classical convolutional neural networks (CNN) trained simply by mixing digitized specimens and photos and those based on advanced domain adaptation techniques with the objective of learning a joint representation space between field and herbarium representations. those based on classical convolutional neural networks (CNN) trained simply by mixing digitized specimens and photos and those based on advanced domain adaptation techniques with the objective of learning a joint representation space between field and herbarium representations. The domain adaptation methods themselves were of two types, those based on adversarial regularization (Motiian et al. 2017) to force herbarium specimens and photos to have the same representations, metric learning to maximize inter-species distances and minimize intra-species distances in the representation space adversarial regularization (Motiian et al. 2017) to force herbarium specimens and photos to have the same representations, metric learning to maximize inter-species distances and minimize intra-species distances in the representation space In Table 1, we report the results achieved by the different methods evaluated during the 2020 edition of the challenge. The evaluation metric used is the mean reciprocal rank (MRR), i.e., the average of the inverse of the rank of the correct species in the list of the predicted species. In addition to this main score, a second MRR score is computed on a subset of the test set composed of the most difficult species, i.e., the ones that are the least frequently photographed in the field. The main outcomes we can derive from these results are the following: Classical deep learning models fail to identify plant photos from digitized herbarium specimens. The best classical CNN trained on the provided data resulted in a very low MRR score (0.011). Even with the of use additional training data (e.g. photos and digitized herbarium from GBIF) the MRR score remains very low (0.039). Domain adaptation methods provide significant improvement but the task remains challenging. The best MRR score (0.180) was achieved by using adversarial regularization (FSDA Motiian et al. 2017). This is much better than the classical CNN models but there is still a lot of progress to be made to reach the performance of a truly functional identification system (the MRR score on classical plant identification tasks can be up to 0.9). No method fits all. As shown in Table 1, the metric learning method has a significantly better MRR score on the most difficult species (0.107). However, the performance of this method on the species with more photos is much lower than the adversarial technique. In 2021, the challenge was run again but with additional information provided to train the models, i.e., species traits (plant life form, woodiness and plant growth form). The use of the species traits allowed slight performance improvement of the best adversarial adaptation method (with a MRR equal to 0.198). In conclusion, the results of the experiments conducted are promising and demonstrate the potential interest of digitized herbarium data for automated plant identification. However, progress is still needed before integrating this type of approach into production applications.

Pl@ntnet is a citizen observatory that relies on artificial intelligence (AI) technologies to help people identify plants with their smartphones (Joly 2014). Over the past few years, Pl@ntNet has become one of the largest plant biodiversity observatories in the world with several million contributors (Bonnet 2020b). Based on user demands, a set of tools and services following the FAIR (Findable, Accessible, Interoperable, Reusable) principles (Wilkinson 2016) were implemented to allow the development of e-floras. After a short description of the platform (see Joly (2015) and Joly (2016) for details), we present three complementary services dedicated to the customization of e-floras. The general workflow of Pl@ntNet can be divided into three main components. First, the Pl@ntNet mobile Android and iOS apps, dedicated to plant identification, allow both anonymous and authenticated users to take a picture of a plant, and to send it to a server for recognition at the species level. This recognition, which is performed by a convolutional neural network (Affouard 2017), allows the user to get a list of candidate species, each associated with a confidence score. The second component of Pl@ntNet's workflow is the enrichment of the database to improve the observation quality of the data collected. Because of the huge volume of data (over four million observations shared in 2020, Pl@ntNet statistics), this enrichment cannot be done solely by expert botanists. This is why various crowdsourcing mechanisms have been put in place allowing the collaborative curation of the data. The third part of the Pl@ntNet workflow is the exploitation of the database, which is made available for the needs of stakeholders such as researchers. For example, the recent sharing of Pl@ntNet data on GBIF's (Global Biodiversity Information Facility) platform, through two complementary datasets, has increased the benefit to the scientific community (examples are available here). To support the development of customized e-floras, three complementary concepts have been developed: micro-projects, groups, and monitoring work spaces, whose services are detailed below: (a) Micro-projects allow full adaptation of all the interfaces of the Pl@ntNet apps to a species list of interest. This e-flora can be linked to a specific geographical area, which allows it to be automatically selected according to the user's location. When several specific geographical areas overlap for a given geolocation, the smallest one is automatically selected. This adaptation increases the accuracy of the identification, as the number of potential species for a given identification request is reduced to the checklist of the micro-project (e.g., Bonnet (2020a)). Up to now, 12 micro-projects adapted to European, African or Asian contexts are running on the platform. A full description of this service is provided in Bonnet (2020c). The API (Application Programming Interface) for the identification service of each e-flora is available on My-Pl@ntNet. (b) Groups allow any user to create a private or public space on the platform (https://identify.plantnet.org/groups), to permit everyone to aggregate a part or all of their observations in the group. A group is "observation-centered" as opposed to a micro-project, which is species-centered. If the group is public, any authenticated user can join and contribute to it; if it is private, only users validated by the group's moderators can become members. When a group is restricted to a specific geographical zone (such as a school, city, or natural area), only observations found in that area are displayed in the group, contrary the micro-project. As all the group's observations can be downloaded (as tabbed or comma separated values) by any of the group's members, group features can be used to conduct statistical analyses on the data in order to study plant plots, plant phenology or user profiles. These groups are used by people who want to structure the activity of a group of people interested in monitoring the biodiversity of a given area, a taxonomical group, or a type of plant habitat. Over 260 groups have already been created by e.g., professional land managers, educators, and plant enthusiasts. (c) Monitoring work spaces allows a given stakeholder to access all the observations and identification requests of a given species list in a particular area. Micro-projects and Groups only allow exploration of plant observations explicitly shared by the authenticated users. However, Pl@ntNet's database contains hundreds of millions of plant identification requests submitted by anonymous users. Monitoring work spaces was set up to allow access by land managers to this rich and important material. These work spaces provide the maps, the list of plant observations, and identification requests (with a very high confidence score on the species identification), for all the species of interest to a given partner. For example, this service has been mobilized to follow the recent development of an invasive species (i.e., Hakea sericea Schrad. & J.C. Wendl.) in and around a natural reserve on the Mediterranean coast. All of these on-demand e-floras and monitoring services accelerate the use of daily-produced data, and inform land managers and scientists of the changes in the floristic composition of monitoring areas.

Building accurate knowledge of the identity, the geographic distribution and the evolution of species is essential for the sustainable development of humanity, as well as for biodiversity conservation. However, the difficulty of identifying plants and animals in the field is hindering the aggregation of new data and knowledge. Identifying and naming living plants or animals is almost impossible for the general public and is often difficult even for professionals and naturalists. Bridging this gap is a key step towards enabling effective biodiversity monitoring systems. The LifeCLEF campaign, presented in this paper, has been promoting and evaluating advances in this domain since 2011. The 2021 edition proposes four data-oriented challenges related to the identification and prediction of biodiversity: (i) PlantCLEF: cross-domain plant identification based on herbarium sheets, (ii) BirdCLEF: bird species recognition in audio soundscapes, (iii) GeoLifeCLEF: location-based prediction of species based on environmental and occurrence data and (iv) SnakeCLEF: image-based snake identification.

Objective To provide national guidelines for the management of women with severe pre-eclampsia. Design A consensus committee of 26 experts was formed. A formal conflict-of-interest (COI) policy was developed at the onset of the process and enforced throughout. The entire guidelines process was conducted independently of any industrial funding. The authors were advised to follow the principles of the Grading of Recommendations Assessment, Development and Evaluation (GRADE®) system to guide assessment of quality of evidence. The potential drawbacks of making strong recommendations in the presence of low-quality evidence were emphasised. Methods The last SFAR and CNGOF guidelines on the management of women with severe pre-eclampsia were published in 2009. The literature is now sufficient for an update. The aim of this expert panel guidelines is to evaluate the impact of different aspects of the management of women with severe preeclampsia on maternal and neonatal morbidities separately. The experts studied questions within 7 domains. Each question was formulated according to the PICO (Patients Intervention Comparison Outcome) model and the evidence profiles were produced. An extensive literature review and recommendations were carried out and analysed according to the GRADE® methodology. Results The SFAR/CNGOF experts panel provided 25 recommendations: 8 have a high level of evidence (GRADE 1+/−), 9 have a moderate level of evidence (GRADE 2+/−), and for 7 recommendations, the GRADE method could not be applied, resulting in expert opinions. No recommendation was provided for 3 questions. After one scoring round, strong agreement was reached between the experts for all the recommendations. Conclusions There was strong agreement among experts who made 25 recommendations to improve practices for the management of women with severe pre-eclampsia.

International audience; Building accurate knowledge of the identity, the geographic distribution and the evolution of species is essential for the sustainable development of humanity, as well as for biodiversity conservation. However, the difficulty of identifying plants and animals is hindering the aggregation of new data and knowledge. Identifying and naming living plants or animals is almost impossible for the general public and is often difficult even for professionals and naturalists. Bridging this gap is a key step towards enabling effective biodiversity monitoring systems. The LifeCLEF campaign, presented in this paper, has been promoting and evaluating advances in this domain since 2011. The 2021 edition proposes four data-oriented challenges related to the identification and prediction of biodiversity: (i) PlantCLEF: cross-domain plant identification based on herbarium sheets, (ii) BirdCLEF: bird species recognition in audio soundscapes, (iii) GeoLifeCLEF: remote sensing based prediction of species, and (iv) SnakeCLEF: Automatic Snake Species Identification with Country-Level Focus.

International audience; PURPOSE: To provide recommendations for the appropriate choice of fluid therapy for resuscitation of critically ill patients. DESIGN: A consensus committee of 24 experts from the French Society of Anaesthesia and Intensive Care Medicine (Société française d’anesthésie et de réanimation, SFAR) and the French Society of Emergency Medicine (Société française de médecine d’urgence, SFMU) was convened. A formal conflict-of-interest policy was developed at the onset of the process and enforced throughout. The entire guideline elaboration process was conducted independently of any industry funding. The authors were advised to follow the principles of the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system to guide their assessment of quality of evidence. The potential drawbacks of making strong recommendations in the presence of low-quality evidence were emphasised. Some recommendations were left ungraded. METHODS: Four fields were defined: patients with sepsis or septic shock, patients with haemorrhagic shock, patients with acute brain failure, and patients during the peripartum period. For each field, the panel focused on two questions: (1) Does the use of colloids, as compared to crystalloids, reduce morbidity and mortality, and (2) Does the use of some specific crystalloids effectively reduce morbidity and mortality. Population, intervention, comparison, and outcomes (PICO) questions were reviewed and updated as needed, and evidence profiles were generated. The analysis of the literature and the recommendations were then conducted according to the GRADE methodology. RESULTS: The SFAR/SFMU guideline panel provided nine statements on the appropriate choice of fluid therapy for resuscitation of critically ill patients. After two rounds of rating and various amendments, strong agreement was reached for 100% of the recommendations. Out of these recommendations, two have a high level of evidence (Grade 1 +/-), six have a moderate level of evidence (Grade 2 +/-), and one is based on expert opinion. Finally, no recommendation was formulated for two questions. CONCLUSIONS: Substantial agreement among experts has been obtained to provide a sizable number of recommendations aimed at optimising the choice of fluid therapy for resuscitation of critically ill patients.

International audience; BACKGROUND: Placenta accreta spectrum is a life-threatening condition that has increased dramatically in recent decades along with cesarean rates worldwide. Cesarean hysterectomy is widely practiced in women with placenta accreta spectrum; however, the maternal outcomes after cesarean hysterectomy have not been thoroughly compared with the maternal outcomes after alternative approaches, such as conservative management.OBJECTIVE: This study aimed to compare the severe maternal outcomes between women with placenta accreta spectrum treated with cesarean hysterectomy and those treated with conservative management (leaving the placenta in situ).STUDY DESIGN: From a source population of 520,114 deliveries in 176 hospitals (PACCRETA study), we designed an observational cohort of women with placenta accreta spectrum who had either a cesarean hysterectomy or a conservative management (the placenta left in situ) during cesarean delivery. Clinicians prospectively identified women meeting the inclusion criteria and included them at delivery. Data collection started only after the women had received information and agreed to participate in the study in the immediate postpartum period. The primary outcome was the transfusion of >4 units of packed red blood cells within 6 months after delivery. Secondary outcomes were other maternal complications within 6 months. We used propensity score weighting to account for potential indication bias.RESULTS: Here, 86 women had conservative management and 62 women had cesarean hysterectomy for placenta accreta spectrum during cesarean delivery. The primary outcome occurred in 14 of 86 women in the conservative management group (16.3%) and 36 of 61 (59.0%) in the cesarean hysterectomy group (risk ratio in propensity score weighted model, 0.29; 95% confidence interval, 0.19-0.45). The rates of hysterectomy, total estimated blood loss exceeding 3000 mL, any blood product transfusion, adjacent organ injury, and nonpostpartum hemorrhage-related severe maternal morbidity were lower with conservative management than with cesarean hysterectomy (all adjusted, P 4 units of packed red blood cells within 6 months than cesarean hysterectomy.

Background: The initiation of chronic subdural hematoma (cSDH) is traditionally explained by rupture of bridging veins. Recent descriptions of the embryology and anatomy of the meninges and their vascularization, however, point to the dural vascular plexus (DVP) as a plausible origin of cSDH. This dural plexus is supplied by meningeal arteries. Their endovascular occlusion is efficient in cSDH treatment. Dural arteriovenous fistulae (dAVF) may also present with subdural hematoma. Case Description: A 65-year-old female patient presented with parietal parasagittal dAVF and bilateral cSDH requiring surgical disconnection followed by complete clinical and imaging resolution of dAVF and cSDH. Conclusion: In common cSDH, pressure in the DVP may be normal and subdural bleeding may occur due to mechanical traction on the DVP. In the setting of dAVF, it may be the increase in pressure due to the fistula, within the DVP, that causes subdural hematoma. The DVP, supplied by meningeal arteries, thus not only allows for convergent pathophysiological explanation of subdural bleeding in both cSDH and dAVF but may also be the actual target of the emergent endovascular treatment of cSDH trough meningeal artery embolization.

International audience; We propose a simple semi analytical model that allows to compute the transmittance and reflectance of a one dimensional subwavelength graphene strip grating under an external static magnetic field. In this model graphene is treated as an anisotropic layer with atomic thickness and a frequency dependent complex permittivity tensor. The model is based on an effective medium approach (EMA) and a rigorous phase correction. The scattering matrix approach is also used to take into account the different resonant phenomena occurring in the structure. The approach is validated against the Polynomial Modal Method (PMM) through numerical examples.

International audience; Background: The mechanisms of disparities in maternal and perinatal health between migrant and native women are multiple and remain poorly understood. Access to and quality of care are likely to participate in these mechanisms, and one hypothesis is the existence of implicit biases among caregivers through which ethno-racial belonging can influence medical decisions and consequently engender healthcare disparities. Their existence and their role in the generation of non-medically justified differential care have been documented in the United States apart from perinatal care, but remain largely unexplored in Europe. In this article, we present the study protocol and theoretical framework of a study that aims to test and quantify the existence of implicit bias toward African Sub-Saharan migrant women among caregivers working in the perinatal field, and to explore the association between implicit bias and differential care. Material and methods: This study is based on an online survey to which French obstetricians, midwives, and anesthetists were invited to take part. The potential existence of implicit biases toward African Sub-Saharan migrant will be quantified through a validated tool, the Implicit Association Test. Then we will assess how implicit biases are likely to influence clinical decisions and lead to differential care using clinical vignettes designed by an experts group. Discussion: Implicit bias and differential care are concept that are tricky to capture and interpret. This research program opens up in France a field of research on certain forms of health discriminations and sheds new light on the issue of social inequalities in perinatal health. Study registration: Registration in the Open Science Framework portal: https://osf.io/djva7/?view_only=c6012ace3fe94165a65b05c2dc6aff9e

International audience; This paper presents the integration of sensor communication using the diagnosis signal of wired networks. In order to cover the entire network, the diagnosis of branched networks requires the use of distributed diagnosis systems. The objective of this paper is to propose a novel technique of ensuring efficient communication between distributed reflectometers (sensors) by using the transmitted part of the Multi-Carrier Time Domain Reflectometry (MCTDR) signal. Our approach provides unambiguous fault location thanks to sensor data sharing and fusion, which improves the accuracy of fault location and the quality of the diagnosis. MCTDR has already been proven as an efficient method for online diagnosis by enabling precise bandwidth control while avoiding interferences. The main novelty of our technique is to inject an MCTDR signal carrying information which is capable of ensuring network diagnosis and reliable communication between several distributed sensors at the same time. This is done by exploiting, simultaneously, both the transmitted part and the reflected part of the MCTDR signal. The effectiveness of the proposed approach is evaluated by a series of simulations and experiments (based on field-programmable gate array (FPGA) implementation) on different types of cables and with different configurations.

Background This study aimed to compare incidence, management and outcomes of women transfused their blood volume or more within 24 hours during pregnancy or following childbirth. Methods Combined analysis of individual patient data, prospectively collected in six international population-based studies (France, United Kingdom, Italy, Australia, the Netherlands and Denmark). Massive transfusion in major obstetric haemorrhage was defined as transfusion of eight or more units of red blood cells within 24 hours in a pregnant or postpartum woman. Causes, management and outcomes of women with massive transfusion were compared across countries using descriptive statistics. Findings The incidence of massive transfusion was approximately 21 women per 100,000 maternities for the United Kingdom, Australia and Italy; by contrast Denmark, the Netherlands and France had incidences of 82, 66 and 69 per 100,000 maternities, respectively. There was large variation in obstetric and haematological management across countries. Fibrinogen products were used in 86% of women in Australia, while the Netherlands and Italy reported lower use at 35–37% of women. Tranexamic acid was used in 75% of women in the Netherlands, but in less than half of women in the UK, Australia and Italy. In all countries, women received large quantities of colloid/crystalloid fluids during resuscitation (>3·5 litres). There was large variation in the use of compression sutures, embolisation and hysterectomy across countries. There was no difference in maternal mortality; however, variable proportions of women had cardiac arrests, renal failure and thrombotic events from 0–16%. Interpretation There was considerable variation in the incidence of massive transfusion associated with major obstetric haemorrhage across six high-income countries. There were also large disparities in both transfusion and obstetric management between these countries. There is a requirement for detailed evaluation of evidence underlying current guidance. Furthermore, cross-country comparison may empower countries to reference their clinical care against that of other countries.

Field photographs of plant species are crucial for research and conservation, but the lack of a centralized database makes them difficult to locate. We surveyed 25 online databases of field photographs and found that they harboured only about 53% of the approximately 125,000 vascular plant species of the Americas. These results reflect the urgent need for a centralized database that can both integrate and complete the photographic record of the world’s flora. Field photographs are essential for research and conservation, but are difficult to access since there is no central database or connection between archives. The 25 leading databases hold only 53% of the plant species of the Americas, illustrating the need for increased work on collecting and standardizing this valuable data.

To determine the rate and profile of repeated maternal ICU admissions during or after pregnancy and to compare the characteristics of these women's first and second ICU admissions.A descriptive analysis from the French national hospital discharge database that included all women admitted to an ICU during pregnancy or within 42 days after delivery, between 2010 and 2014.During the 5-year study period, there were 371 women with more than one maternal ICU admission, representing 2.5% of all women admitted during or after pregnancy (371/15,096) and a 0.9 per 10,000 deliveries (371/4,030,409) rate of repeated maternal ICU admission. Compared with women with only one maternal ICU admission, those with repeated maternal ICU admissions were more often admitted during the pregnancy rather than during or after the delivery stay (P 0.001), for organ failure or sepsis (P 0.001), and with a SAPS-II score25 (P 0.001). Women with repeated admissions were usually readmitted for the same indications and had similar SAPS-II scores. Half of ICU readmissions occurred within 72 h of first ICU discharge, with similar causes and levels of severity for both stays.Although the rate of women with repeated maternal ICU admissions was low, their initial stay had a specific profile of causes of admission and greater severity compared with the stay of women admitted only once. The pattern and similar characteristics of both first and second ICU admission and the short interval for readmission suggests that some ICU discharges may have been potentially premature.

Dès son arrivée au pouvoir, Theresa May a clairement exprimé son souhait de respecter le choix du peuple britannique de quitter l’Union européenne. Elle a adopté une position ferme vis-à-vis de l’Europe afin de satisfaire l’aile droite de son parti, les Conservateurs, dans le but de renforcer sa crédibilité et garantir l’unité de son parti. Sans chercher de compromis avec les autres partis politiques britanniques, elle a déclenché l’article 50 du traité sur l’Union européenne et commencé les négociations de sortie. Entre temps, le gouvernement a accepté de faire entériner son accord de sortie par le Parlement (« meaningful vote »). Au sortir d’âpres négociations, May doit alors convaincre le Parlement que l’accord trouvé respecte le choix du peuple tout en garantissant la pérennité économique et diplomatique du Royaume-Uni. Après trois refus du Parlement, May doit démissionner. Le présent article propose d’étudier cette entreprise périlleuse en analysant un corpus de déclarations (discours, déclarations, lettre à la nation) afin de comprendre les raisons de cet échec. Le livre de Ruth Amossy, L’argumentation dans le discours, servira de base à notre recherche. Il s’agira de voir pourquoi l’argumentation déployée par May n’a pas réussi à convaincre les députés britanniques. As soon as she reached power, Theresa May said that she wanted to respect the will of the British people to leave the European Union (EU). She took a firm stance concerning Europe so as to please the right wing of her party, the Tories, reinforce her credibility and preserve the unity of her party. She triggered Article 50 without trying to find any form of compromise with any other party beforehand. In the meantime, the government agreed to put any EU/ UK deal to the vote in Westminster (the so-called “meaningful vote”). At the end of tough negotiations, May had therefore to convince Parliament that the deal she had struck with the EU respected the will of the British people and guaranteed Britain’s economic and diplomatic continuity. After three crushing defeats, May was forced to resign. The present article will study this perilous endeavour through a corpus-based analysis of Theresa May’s declarations (speeches, addresses, letter to the nation) to understand the reasons behind this failure. Ruth Amossy’s book, L’argumentation dans le discours, will be used as the basis of our research. We will try to explain why the arguments used by May failed to convince MPs.

Summary The increasing availability of digital images, coupled with sophisticated artificial intelligence (AI) techniques for image classification, presents an exciting opportunity for biodiversity researchers to create new datasets of species observations. We investigated whether an AI plant species classifier could extract previously unexploited biodiversity data from social media photos (Flickr). We found over 60,000 geolocated images tagged with the keyword “flower” across an urban and rural location in the UK and classified these using AI, reviewing these identifications and assessing the representativeness of images. Images were predominantly biodiversity focused, showing single species. Non-native garden plants dominated, particularly in the urban setting. The AI classifier performed best when photos were focused on single native species in wild situations but also performed well at higher taxonomic levels (genus and family), even when images substantially deviated from this. We present a checklist of questions that should be considered when undertaking a similar analysis., Highlights • AI image classifiers can create biodiversity datasets from social media imagery • Flickr hosts many images of plants; some can be accurately classified to species by AI • Images are spatially aggregated around tourist sites and under-represent native species • Images focused on a single, non-horticultural, plant are most reliably identified, The Bigger Picture Recent reports of global biodiversity decline make it more important than ever to monitor biodiversity so that we can detect changes and infer their drivers. Online digital media, such as social media images, may be a new source of biodiversity observations, but they are far too numerous for a human to practically review. In this paper we apply an AI image classifier, designed to identify plants from images, to social media imagery to assess this method as a way to generate new biodiversity observations. We find that this approach is able to generate new data on species occurrence but that there are biases in both the social media data and the AI image classifier that need to be considered in analyses. This approach could be applied outside the biodiversity domain, to any phenomena of interest that may be captured in social media imagery. The checklist we provide at the end of this paper should therefore be of interest to anyone considering this approach to generating new data., We apply newly developed AI image classifiers to large social media image datasets in order to assess whether new datasets of biodiversity observations can be generated in this way. We explore biases in both the dataset of images as well as in the ability of the AI image classifier to make accurate identifications and propose a checklist of questions researchers should ask themselves when considering this approach to data generation.

Résumé La pandémie de COVID-19 n’a pas épargné les femmes enceintes. Le tableau clinique et biologique est peu spécifique mais la littérature n’a pas retrouvé de susceptibilité accrue des parturientes, à l’infection virale, notamment aux formes graves, ayant comme principale conséquence une prématurité induite et les complications qui en résultent. La pandémie a fait revoir l’organisation de la prise en charge des femmes enceintes, avec une généralisation de la téléconsultation et une réduction d’accès des d’accompagnants en salle de travail comme en suite de couche. La décision du mode d’accouchement est restée uniquement dépendante de l’évaluation obstétricale dans les formes mineures ou asymptomatiques, de même que le mode d’anesthésie à privilégier pour le travail ou la césarienne est resté l’anesthésie locorégionale périmédullaire, en prenant soins de vérifier l’hémostase du fait de l’impact du virus, notamment sur le taux de plaquettes. D’autres situations spécifiques, comme la prise en charge de la fièvre ou de l’hypertension artérielle du péri-partum, n’ont pas été modifiées par le statut viral des parturientes. Le post-partum est la période qui a fait l’objet du plus grand nombre de modifications avec l’administration plus large d’anticoagulants et une utilisation restreinte des AINS. Le point clé de la prise en charge obstétricale en cette période de pandémie a finalement été la coopération interdisciplinaire, impliquant anesthésiste-réanimateur, gynécologue et pédiatres.

International audience

International audience; Superstructures or self-assembled nanoparticles open the development of new materials with improved and/or novel properties. Here, we present nickel fluoride (NiF2) self-assemblies by successive preparatory methods. Originally, the self-assemblies were obtained by exploiting the water-in-oil microemulsion technique as a result of auto-organization of hydrated NiF2 (NiF2·4H2O) nanoparticles. The nanostructuration of NiF2·4H2O nanoparticles was confirmed by X-ray diffraction (XRD) and transmission electron microscopy (TEM) data. The size and shape of NiF2·4H2O nanoparticles and their subsequent self-assemblies varied slightly as a function of water-to-surfactant and water-to-oil ratios. Scanning electron microscopy (SEM) and TEM characterizations revealed that the nanoparticles are organized into a succession of self-assemblies: from individual nanoparticles assembled into layers to truncated bipyramids, which further auto-organized themselves into almond-shaped superstructures. Anhydrous NiF2 was achieved by heating NiF2·4H2O self-assemblies under the dynamic flow of molecular fluorine (F2) at a moderate temperature (350 °C). Preservation of self-assemblies during the transformation from NiF2·4H2O to NiF2 is successfully achieved. The obtained materials have a specific surface area (SSA) of about 30 m2/g, more than 60% of that of bulk NiF2. The lithium-ion (Li+) storage capacities and the mechanism of the nanostructured samples were tested and compared with the bulk material by galvanostatic cycling and X-ray absorption spectroscopy (XAS). The nanostructured samples show higher capacities (∼650 mAh/g) than the theoretical (554 mAh/g) first discharge capacity due to the concomitant redox conversion mechanism of NiF2 and solid–electrolyte interphase (SEI) formation. The nanostructuration by self-assembly appears to positively influence the lithium diffusion in comparison to the bulk material. Finally, the magnetic properties of nanostructured NiF2·xH2O (x = 0 or 4) have been measured and appear to be very similar to those of the corresponding bulk materials, without any visible size reduction effect. The hydrated samples NiF2·4H2O show an antiferromagnetic ordering at TN = 3.8 K, whereas the dehydrated ones (NiF2) present a canted antiferromagnetic ordering at TN = 74 K.

Machine learning (ML) has great potential to drive scientific discovery by harvesting data from images of herbarium specimens—preserved plant material curated in natural history collections—but ML techniques have only recently been applied to this rich resource. ML has particularly strong prospects for the study of plant phenological events such as growth and reproduction. As a major indicator of climate change, driver of ecological processes, and critical determinant of plant fitness, plant phenology is an important frontier for the application of ML techniques for science and society. In the present article, we describe a generalized, modular ML workflow for extracting phenological data from images of herbarium specimens, and we discuss the advantages, limitations, and potential future improvements of this workflow. Strategic research and investment in specimen-based ML methods, along with the aggregation of herbarium specimen data, may give rise to a better understanding of life on Earth.

Superstructures or self-assembled nanoparticles open the development of new materials with improved and/or novel properties. Here, we present nickel fluoride (NiF

International audience; Premise Weed removal in agriculture is typically achieved using herbicides. The use of autonomous robots to reduce weeds is a promising alternative solution, although their implementation requires the precise detection and identification of crops and weeds to allow an efficient action. Methods We trained and evaluated an instance segmentation convolutional neural network aimed at segmenting and identifying each plant specimen visible in images produced by agricultural robots. The resulting data set comprised field images on which the outlines of 2489 specimens from two crop species and four weed species were manually drawn. We adjusted the hyperparameters of a mask region-based convolutional neural network (R-CNN) to this specific task and evaluated the resulting trained model. Results The probability of detection using the model was quite good but varied significantly depending on the species and size of the plants. In practice, between 10% and 60% of weeds could be removed without too high of a risk of confusion with crop plants. Furthermore, we show that the segmentation of each plant enabled the determination of precise action points such as the barycenter of the plant surface. Discussion Instance segmentation opens many possibilities for optimized weed removal actions. Weed electrification, for instance, could benefit from the targeted adjustment of the voltage, frequency, and location of the electrode to the plant. The results of this work will enable the evaluation of this type of weeding approach in the coming months.

Phenology–the timing of life-history events–is a key trait for understanding responses of organisms to climate. The digitization and online mobilization of herbarium specimens is rapidly advancing our understanding of plant phenological response to climate and climatic change. The current practice of manually harvesting data from individual specimens, however, greatly restricts our ability to scale-up data collection. Recent investigations have demonstrated that machine-learning approaches can facilitate this effort. However, present attempts have focused largely on simplistic binary coding of reproductive phenology (e.g., presence/absence of flowers). Here, we use crowd-sourced phenological data of buds, flowers, and fruits from>3000 specimens of six common wildflower species of the eastern United States (Anemone canadensisL.,A. hepaticaL.,A. quinquefoliaL.,Trillium erectumL.,T. grandiflorum(Michx.) Salisb., andT. undulatumWild.) to train models using Mask R-CNN to segment and count phenological features. A single global model was able to automate the binary coding of each of the three reproductive stages with>87% accuracy. We also successfully estimated the relative abundance of each reproductive structure on a specimen with ≥ 90% accuracy. Precise counting of features was also successful, but accuracy varied with phenological stage and taxon. Specifically, counting flowers was significantly less accurate than buds or fruits likely due to their morphological variability on pressed specimens. Moreover, our Mask R-CNN model provided more reliable data than non-expert crowd-sourcers but not botanical experts, highlighting the importance of high-quality human training data. Finally, we also demonstrated the transferability of our model to automated phenophase detection and counting of the threeTrilliumspecies, which have large and conspicuously-shaped reproductive organs. These results highlight the promise of our two-phase crowd-sourcing and machine-learning pipeline to segment and count reproductive features of herbarium specimens, thus providing high-quality data with which to investigate plant response to ongoing climatic change.

International audience; No abstract available

International audience; Herbarium specimens represent an outstanding source of material with which to study plant phenological changes in response to climate change. The fine‐scale phenological annotation of such specimens is nevertheless highly time consuming and requires substantial human investment and expertise, which are difficult to rapidly mobilize.Methods: We trained and evaluated new deep learning models to automate the detection, segmentation, and classification of four reproductive structures of Streptanthus tortuosus (flower buds, flowers, immature fruits, and mature fruits). We used a training data set of 21 digitized herbarium sheets for which the position and outlines of 1036 reproductive structures were annotated manually. We adjusted the hyperparameters of a mask R‐CNN (regional convolutional neural network) to this specific task and evaluated the resulting trained models for their ability to count reproductive structures and estimate their size.Results: The main outcome of our study is that the performance of detection and segmentation can vary significantly with: (i) the type of annotations used for training, (ii) the type of reproductive structures, and (iii) the size of the reproductive structures. In the case of Streptanthus tortuosus , the method can provide quite accurate estimates (77.9% of cases) of the number of reproductive structures, which is better estimated for flowers than for immature fruits and buds. The size estimation results are also encouraging, showing a difference of only a few millimeters between the predicted and actual sizes of buds and flowers.Discussion: This method has great potential for automating the analysis of reproductive structures in high‐resolution images of herbarium sheets. Deeper investigations regarding the taxonomic scalability of this approach and its potential improvement will be conducted in future work.

Transfusion is a major therapy for severe postpartum hemorrhage but there are few population-based descriptions of practice. The objective of this retrospective French population-based study was to describe transfusion practices in women with severe postpartum hemorrhage and the compliance with guidelines.Study data were sourced from a prior prospective population-based study of 182 309 deliveries in France between 2012 and 2013. Transfusion practices and compliance with French national guidelines were described for all women with severe postpartum hemorrhage who had been transfused with red blood cells.In 1495 women with severe postpartum hemorrhage (0.8% of all deliveries), 35.1% were not transfused, 50.0% were transfused during active bleeding and 14.9% exclusively after control of bleeding. Among 697 women with a hemoglobin level7 g/dL, 21.4% were not transfused. In 747 women transfused during active bleeding, 68.8% also received fresh frozen plasma (fresh frozen plasma to red blood cell ratio between 0.5 and 1 in 80.4%). Forty-four percent received fibrinogen concentrate (including 37.4% with a plasma fibrinogen level2 g/L) and 8.6% had a massive transfusion. Among 223 women transfused after bleeding was controlled, 5.4% received fresh frozen plasma and 13% had a hemoglobin level7 g/dL.One in five women with severe postpartum hemorrhage and a low hemoglobin concentration did not receive blood transfusion, which does not comply with French national recommendations. Over-transfusion occurred in women in whom bleeding had been controlled. The use of tools to help clinicians with transfusion decision-making should be developed.

International audience; Anaphylaxis in pregnancy is a rare but severe complication for both mother and infant. Population-based data on anaphylaxis in pregnancy are lacking from mainland European countries. This multinational study presents the incidence, causative agents, management and maternal and infant outcomes of anaphylaxis in pregnancy. This descriptive multinational study used a combination of retrospective (Finnish medical registries) and prospective population-based studies (UK, France, Belgium and the Netherlands) to identify cases of anaphylaxis. Sixty-five cases were identified among 4,446,120 maternities (1.5 per 100,000 maternities; 95%CI 1.1-1.9). The incidence did not vary between countries. Approximately three-quarters of reactions occurred at the time of delivery. The most common causes were antibiotics in 27 women (43%), and anaesthetic agents in 11 women (17%; including neuromuscular blocking drugs, 7), which varied between countries. Anaphylaxis had very poor outcomes for one in seven mothers and one in seven babies; the maternal case fatality rate was 3.2% (95%CI 0.4-11.0) and the neonatal encephalopathy rate was 14.3% (95%CI 4.8-30.3). Across Europe, anaphylaxis related to pregnancy is rare despite having a multitude of causative agents and different antibiotic prophylaxis protocols.