dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Ten infants who received methylprednisolone IV pulses were identified from the clinical data warehouse. Their antenatal and neonatal characteristics are presented in Table  1. All these infants required mechanical ventilation during their neonatal life. Infants who received pulses of methylprednisolone had their first pulse at a median age (IQ1-3) of 6 months (6-9), specifically at a median age of 6 months (6-6) for those who were on mechanical ventilation or NIV, and 9 months (6-15) for those who were on nasal cannula. Their characteristics at the starting of the methylprednisolone pulse(s) are presented in Table  2. All pulses of methylprednisolone IV were administered at a dose of 300 mg/m2/day over four to eight hours, for three days. The median number of pulses (IQ1-3) administered per patient was 2.
To date, there is a lack of reliable study data on the care for patients with complex respiratory, atopic, and allergic diseases in times of an imminent infection risk from SARS-CoV-2. Type-2-dominant immune reactions, as they are frequently seen in allergic patients, could influence various phases of COVID-19, e. g. , by slowing down the immune reactions. Theoretically, this could have an unfavorable effect in the early phase of a SARS-Cov-2 infection, but also a positive effect during a cytokine storm in the later phase of severe courses. However, since there is currently no evidence for this, all data from patients treated with a biological directed against type 2 immune reactions who develop CO-VID-19 should be collected in registries, and their disease courses documented in order to be able to provide experience-based instructions in the future.
Acknowledgements We thank  Kevin Zahnle ,  Jeff Moore ,  John Wilson ,  Bob Haberle  and  Melinda Kahre  for insightful discussions and stimulating discussions and comparisons between Mars and Pluto. We thank the  NASA New Horizons team  for their excellent work on a fantastic mission and their interest in this research. We thank the  Centre National d'Etudes Spatiales (CNES)  for  its financial support through its "Système Solaire " programme. T. B. was supported for this research by an appointment to the  National Aeronautics and Space Administration (NASA) Post-doctoral Programme at the Ames Research Center  administered by  Universities Space Research Association (USRA)  through a contract with  NASA.
As aerobic fitness is a cardiorespiratory prognostic measure, exercise would be also beneficial, but pediatric publications in non-CF bronchiectasis are limited. 21, 48, 49  Actually, training and frequent revision by a respiratory therapist are mandatory to reach the goal of mobilizing secretions and interrupt the vicious cycle of inflammation and infection, and a personalized plan including clear written advice should be tailored to individual needs. Despite the limited number of evidence-based studies, some key practice points should be kept in mind and recommended (Table  2 ). Finally, as adherence can be suboptimal, novel approaches to measures adherence in children and adolescents should be encouraged (Clinical Trial: NCT02906826).
We await definitive results from the DESKTOP study evaluating the impact of complete surgery on platinum-sensitive recurrences with AGO selection criteria  [31]. Although the results come out significantly in favor of surgery for disease-free survival, impact of overall survival is not yet known. HIPEC in platinum-sensitive recurrence is still being evaluated in randomized trials (CHIPOR in France-NCT01376752 et HORSE in Italy-NCT01539785) and so cannot be recommended outside those trials. In cases of platinum-resistant recurrence, the best reported results were obtained with the association of surgery and complete cytoreduction with HIPEC  [32].
| Materials and Methods The study was approved by the local Ethics Committee of Assistance Publique des Hôpitaux de Marseille (Marseille, France). Flow cytometry provides the leucocyte count and identifies erythroblasts (NRBC%) via the WNR (white and nucleated red blood cells) channel and counts reticulocytes via the RET channel. The IRF% parameter defines the immature reticulocyte fraction. XN-10 RET is used in daily routine in the laboratory, adjustments and calibrations are checked regularly, and controls are analysed each day using internal quality control from Sysmex TM. Consequently, all these mentioned tests (Ferritin, EMA test. ) were not performed for all patients but only according to the call points and the context. If data were not available or if patient were receiving therapy affecting erythropoiesis (Iron therapy, blood transfusion, EPO), they have been excluded from the cohort.
Smart Home Projects for Elderly Healthcare Services The CASAS (Center for Advanced Studies in Adaptive Systems) smart home project (http://casas. wsu. edu/)  [14] , developed at Washington State University, is a multi-disciplinary research project focused on creating an intelligent home environment by using unobtrusive sensors and actuators. The research areas covered by the CASAS project include assistive technology, artificial intelligence, machine learning, and activity recognition. The benchmark dataset described in reference  [14]  was also made available as part of this project and is widely used to analyze behavior of all types of people including elderly people  [15, 16]. One of the works developed in the CASAS project concerns the design of a lightweight smart home called "smart home in a box"  [17].
In this project, a set of sensors and devices (e. g. , a wrist camera and a Kinect sensor) are used to identify the basic physical activities of elderly people, such as walking, etc. [21]. The E-Health Monitoring Open Data Project (http://www. ehealthmonitor. eu/index. php? content=home)  [22, 23]  provides free online datasets related to the monitoring of dependent people, such as elderly people. Data are generated using simulations or collected from real life platforms using real sensors. Targeted systems are typically smart home-health (or institution-health) monitoring systems. This project also provides a large amount of public datasets researchers can use to conduct further studies in testing and validating innovative E-health services and solutions. In this project, a decision-making system is proposed for patient-physician relationships using a multi-agent organization.
The incoming data is so matched against the previously recorded behavioral pattern to detect a possible anomaly. The approach presented in Reference  [56]  falls into this category. In this work, the authors propose a semi-supervised adapted Hidden Markov Model (HMM) framework, in which usual event models are first learned from a training data, while unusual event models are learned by Bayesian adaptation in an unsupervised manner. In another domain, an approach for recognizing rare events in aerial video is proposed in Reference  [57]. This approach relies on Hidden Markov Models (HMMs) to represent the spatio-temporal relationships between objects, as well as the uncertainty in observations. While it might be possible to build discriminative models of abnormality  [56, 57]   [44]  and applied to elderly people who live alone at home.
In this article, we do not focus on the recognition phase of the human activities. We rather assume that this phase has already been performed and consider available datasets containing the detail of identified activities. Besides, our objective is not to detect sudden changes in the behavior, like the fall of a person, but rather to monitor and analyze the evolution of the behavior over a long period of time using previously labeled activities. Thus, we can detect a slow and silent evolution in the elderly person's routine in order to notify caregivers and/or family members. Moreover, a decision-making system included in our framework can assist them to identify the observed pathology and thus take the appropriate decision. Our proposal belongs to the category of works based on the construction normal behavior patterns, and so follows the profiling strategy introduced in Section 2.
Normal Behavior Pattern for the Elderly Person The first step of any behavior anomaly detection system is to characterize the normal behavior of the person, also called routine behavior or regular behavior. This can be achieved using historical data about the person behavior to capture regularities in every individual activity. The normal behavior consists of the list of activities that a resident performs in her/his house, with the time of the day and duration. Thus, it captures the repetitive daily routines and deviations from the normal behavior may indicate changes of lifestyle or loss of capacity. To build the routine behavior model, we follow several steps: 1. We first use data collected during the previous period which was treated as a baseline behavior period. In our experiments (see Section 4), an important part of the dataset will be dedicated to this step.
Detection of Behavior Change Using a Daily Activity Score Once computed, the normal behavior pattern can be used to detect anomalies by comparing the current behavioral data of an elderly with her/his normal behavior pattern. The basic idea of our behavioral deviation detection system is to estimate the similarity between both patterns using a score. We, therefore, consider three criteria for the activities: the time, duration and chronological order of the activities in the sequence. Intuitively, a particular activity is similar to a pattern if its start time, duration and location are similar to the ones defined by the pattern. The similarity of the time and duration for each activity is estimated by a score. The similarity score of an activity a occurring at day d with respect to the same activity in the normal behavior pattern a n is calculated by the Formula (1).
For a resulting membership function µ R (y), the center of gravity y * can be calculated by Equation (  4 ). The result is a number between 0 and 1 which represents the disease level. y * = 1 0 yµ R (y)dy 1 0 µ R (y)dy = ∑ n k=0 y i µ yi ∑ n k=0 µ yi. ( 4 ) In this section, we described a solution to monitor the behavior of elderly people and notify her/his caregivers about a possible appearing disease. In the following section, we propose an experimental evaluation of our proposal using a real dataset.
Dataset The dataset used for our analysis is provided by Washington State University's CASAS program (http://casas. wsu. edu/) [14]. CASAS (Center for Advanced Studies in Adaptive Systems) aims to provide aid to residents using smart home technology. Real-time data is, therefore, collected from sensors to analyze and monitor residents' health and behavior to improve future smart home living. During our experimentations, we used one of the public datasets (named HH120)  [14]  which was also used in other works like  [16]. This dataset contains sensor data that was collected in the (smart) home of a volunteer adult. It includes one unique subject, covering a total of 63 days. This smart home is equipped with passive infrared (PIR) motion sensors and magnetic door sensors (25 sensors). All the data used in this article was handled in an anonymized way.
For each activity, we use a start time which corresponds to the time when the activity state is equal to begin. Moreover, we use the duration of the activity which is equal to the difference between start time (activity = "begin") and end time (activity = "end"). The datasets do not provide any medical information. For building the normal behavior model, we selected the first month as a data used for profiling, while the rest of the available data is used to test the effectiveness of our proposals.
Learning for Building the Normal Behavior Pattern For building the normal behavior pattern from the first month, we follow the different learning steps presented in Section 3. 1. 2. We apply the DBSCAN algorithm on the first month of dataset for clustering. Then, we eliminate activities out of the identified clusters and marked as noise. DBSCAN has two parameters; one is min_pts which is the minimum number of points in a cluster, and the other is Eps which is the maximum distance between two data points for them to be considered in the same cluster. While learning, data out of Eps would be considered out of clusters and so marked anomalous. Based on the heuristic proposed in reference  [63] , we set min_pts to 4 and Eps to 2. Results obtained for each activity are depicted in Figure  4. 4e  shows that this person once had her/his breakfast for 40 min, which is abnormal compared to the usual behavior.
Illustration of the Decision Support System The objective of our decision support system is to notify family members and/or caregivers about suspected diseases detected using behavior changes. In the following, we illustrate how this process works and how suspected diseases can be identified. Therefore, we consider the identification of a gastrointestinal problem for the elderly person. Even if our objective is to detect diseases in which symptoms are not as obvious as the ones of gastroenteritis, this pathology was easily identifiable for the elderly observed in our dataset. We so decided to exploit this disease to explain how our decision support system works. As we explained in Section 3. 2, our decision support system follows a two-step process. The first one consists in identifying the ADLs impacted by the gastrointestinal problem, called ADL-Gastrointestinal in the following and the fuzzy rules used for decision-making.
The outcome is a pattern of daily activities (e. g. , breakfast takes place between 7:00 a. m. and 9:00 a. m. ). As explained in Section 2. 4, the approach proposed by Caroux et al. [61]  does not consider ADLs, like toileting and leaving home. It is applied to five ADLs: meals preparation, go to bed, get up, taking a shower, and getting dressed. The score of each activity is computed using a specific formula. The daily score is then calculated as the average of activity scores. To compare the effectiveness of both scores for detecting behavior changes, we computed the daily scores for both approaches using the same database (HH120)  [14]  for all common activities (meals preparation, go to bed, get up, and taking a shower). By plotting the evolution of daily scores along time in Figure  13 , we can observe the day-by-day evolution of the elderly person's behavior for both approaches.
However, the ability to compare these clinical trials is limited by differences in their measured outcomes. This lack of standardization relates to the current lack of a core outcome set (COS) that can be used to guide outcome selection and harmonization in AML in current and future trials. The HARMONY (Healthcare Alliance for Resourceful Medicine Offensive against Neoplasms in Hematology) Alliance is a public-private European network established in 2017 and currently includes 53 partners, inter alia six cancer patient umbrella organizations, and 32 associated members from 22 countries. One of HAR-MONY's goal is to use big data to improve understanding and treatment of hematological malignancies. In order to achieve this aim, HARMONY is structured into eight work packages, of which Work Package 2 (WP 2) is focused on defining outcomes that are relevant to each hematological malignancy.
Methods COS development will follow recommendations of the Core Outcome Measures in Effectiveness Trials (COMET) initiative from the international Core Outcome Set Standards for Development (COS-STAD)  [3, 4]. The Delphi method will be used to achieve a consensus from different stakeholder groups. A prospective study protocol was registered in the COMET database (http://www. comet-initiative. org/studies/details/1347). The protocol was written in accordance with the Core Outcome Set-Standardised Protocol (COS-STAP) recommendations  [5]  in cooperation between WP 2 and WP 6. In Fig. 1 , each step of the study is illustrated.
The measured outcomes will be more easily comparable, meta-analyses of pooled studies will be more meaningful, and the level of evidence of future guidelines will be improved. Funding This work was funded through the Innovative Medicines Initiative 2 joint undertaking and listed under grant agreement 116026. This joint undertaking receives support from the European Union's 2020 research and innovation program and the EFPIA. An additional cash grant was obtained from Bayer AG for conducting the Delphi survey development. The manuscript was written and reviewed by different EFPIA members and members of the HARMONY Alliance, who were also involved in study design. Data analysis and interpretation will be performed with support from Bayer AG.
The work also evidenced that curcumin has recently been classified as both a pan assay interference compound (PAINS) and invalid metabolic panaceas (IMPS) candidate  (Nelson et al. , 2017). Two critical scientific contributions were published in response to both manuscripts  (Kurien et al. , 2011; Bahadori and Demiray, 2017) , with both highlighting that the limited number of cautionary reports that indicate research on curcumin as an "excess of the need" were numerically exceeded by the plethora of scientific manuscripts regularly published on this topic. Indeed, in 2015, a Curcumin Resource Database (CRDB)  (Kumar et al. , 2015)  was set up, covering over 9000 publications, 500 patents, 1186 curcumin analogs, 195 molecular targets, and 176 varieties of C. longa, whose aim was to support the preclinical development of curcuminoids.
In addition, different investigations have suggested curcumin to be a potential chemopreventive and anticancer treatment in human papilloma virus (HPV) infection, as well as in primary and malignant squamous cervical cancer  (Teymouri et al. , 2017). However, high-quality clinical trials are needed to unequivocally confirm the beneficial effects of curcumin in different clinical conditions. Going in that direction, the clinical trial registered with the number NCT02944578 is actually recruiting women with high grade squamous intraepithelial cervix lesion to validate topical application of curcumin in precancer cervical lesions (https://clinicaltrials. gov).
ACKNOWLEDGMENTS This work was supported by  CONICYT   PIA/APOYO CCTE AFB170007. N. Martins  would like to thank the  Portuguese Foundation for Science and Technology (FCT-Portugal)  for the  Strategic  project  ref. UID/BIM/04293/2013  and " NORTE2020 -Northern Regional Operational Program " ( NORTE-01-0145-FEDER-000012 ).
2-5. 4% yr -1 ). In contrast, NPP on the outflow shelves showed a much smaller increase (8%, Baffin) and even a decrease (-15%, East Greenland) in response to much smaller declines in sea-ice cover (0-0. 9% yr -1 )  17. A more recent satellite-based study  18  that was extensively validated using a large in situ database has shown that since reaching the minimum sea-ice extent in 2012, the rate of increase in open-water area in the Arctic Ocean has slowed considerably, decreasing from 88,000 km 2 yr -1 between 1998 and 2012 to only 620 km 2 yr -1 between 2012 and 2018 (Fig. 2a ). In addition, the average ice-free period in the Arctic only increased from 147 to 152 open-water days (five days) between 2012 and 2018. Thus, neither the extent of open-water phytoplankton habitat nor the length of the growing season changed significantly during this time.
Acknowledgements M. A. was supported by a  European Union 's  Horizon 2020 Marie Sklodowska-Curie grant  (no. 746748 ). This work represents a contribution to the  Sorbonne Université  and  Stanford University. We thank  M. Gosselin ,  M. Poulin  and  E. Leu  for sharing their expertize on phytoplankton ecology and taxonomy;  M. Nicolaus  and  W. Meier  for sharing their expertize on sea-ice; and  L. Oziel  and the whole Arrigo's laboratory for insightful comments on the Review.
In addition, it opens the possibility to analyze a variety of functional complexes with mRNA and ribosomal factors and provide insights into the mechanism of inhibition of the human ribosome induced by ligands. Based on these structures, it becomes possible to perform computational analysis of the complexes to perform structure-guided drug design. In 2020, more than 10,000 cryo-EM maps have been released including 911 ribosome maps, containing 239 eukaryotic ribosomes, with some of them bound to a ligand (Electron microscopy databank source). Even though there are currently no drugs that were discovered based on cryo-EM structures, one can expect that the growing evidence might guide medicinal chemists towards the synthesis of new bioactive molecules. Some examples include the 2. 5 Å resolution structure of the Leishmania donovani ribosome in complex with the antibiotic paromycin  [121]  and the 3.
Funding: This work was supported by  Inserm ,  CNRS ,  Association pour la Recherche sur le Cancer (ARC) ,  Institut National du Cancer (INCa) , the  Fondation pour la Recherche Médicale (FRM) ,  Ligue nationale contre le cancer (LNCC) ,  Agence National pour la Recherche (ANR)  and  USIAS of the University of Strasbourg  ( USIAS-2018-012 ). The electron microscope facility was supported by the Alsace Region and by the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01 , and by  Instruct-ERIC.
Cross Sections Uncertainty Treatment In the preparation of data for the uncertainty sampling procedure, a normal distribution of changes in the initial nuclear data is assumed. The basic (mean) values were computed using APOLLO2, while relevant standard deviations were derived directly from the data distributed by the UAM-LWR team (UAM-XS, 2014) and computed with SCALE6. 2b3 and the XS library ENDF-B. VII. The XS have been modified within relevant intervals of uncertainties presuming the last ones normally distributed. The mean values of the XS were taken as non-perturbed ones, interpolated using CRONOS as mesh-wise matrices. The absolute values of the standard deviations were computed using the relative standard deviations provided in the UAM benchmark. We used pre-evaluated relative standard deviations by the following reasons: 1) there is not yet in APOLLO2 a robust procedure to compute covariance matrices for a few group XS; 2) we tried keeping methodological affinity with other participants of UAM; and, last but not least, 3) it is wellknown that Nuclear Data (ND) and their uncertainties have different nature being fundamentally independent1 (M.
Contributions: A. P. , F. F. and F. -L. C. conceptualized the manuscript. A. P. wrote the initial draft and revised subsequent drafts, F. F. and F. -L. C. reviewed and edited the manuscript prior to submission. All authors have read and agreed to the published version of the manuscript. Funding: Our work is supported by the French "Agence Nationale de la Recherche sur le SIDA et les hépatites virales" (ANRS) and the LabEx Ecofect (ANR-11-LABX-0048) of the "Université de Lyon", within the program "Investissements d'Avenir" (ANR-11-IDEX-0007) operated by the French National Research Agency (ANR). AP is supported by a fellowship of the ENS Lyon.
Author Contributions: A. P. , F. F. and F. -L. C. conceptualized the manuscript. A. P. wrote the initial draft and revised subsequent drafts, F. F. and F. -L. C. reviewed and edited the manuscript prior to submission. All authors have read and agreed to the published version of the manuscript. Funding: Our work is supported by the French "Agence Nationale de la Recherche sur le SIDA et les hépatites virales" (ANRS) and the LabEx Ecofect (ANR-11-LABX-0048) of the "Université de Lyon", within the program "Investissements d'Avenir" (ANR-11-IDEX-0007) operated by the French National Research Agency (ANR). AP is supported by a fellowship of the ENS Lyon.