dataesr/scientific-paragraphs-categorization huggingface dataset:
However in  [333]  the authors analysed Horndeski models that still can have non-trivial modifications to GR, possible at the level of linear perturbations and they confirmed the conjecture by  [266]  that (Σ -1)(µ -1) ≥ 0 for surviving models. As an extension beyond this review, DHOST models, as mentioned above, can also provide an interesting phenomenology and are able to evade certain constraints affecting the Horndeski theories. [334, 335]  studied DHOST models that present self-acceleration and  [336] , among others, have studied the astrophysical signatures of these models. However, their theoretical modelling has not been implemented yet in computational tools capable of analyzing the full Planck CMB dataset. Finally,  [337]  performed a cosmological constraint analysis, assuming the form α i = α i,0 a κ on these surviving Horndeski models and using Planck and BICEP2/Keck  [338]  CMB data and galaxy clustering data from SDSS and BOSS, they found that when setting the kineticity to the following value α K = 0.
DMSO at high concentrations is known to inhibit cell growth, therefore, the DMSO tolerance of each cell line was examined in order to determine a maximum final percentage of 10% DMSO in the culture medium (data not shown). Each compound was solubilised in a mix of water/DMSO 5% at different concentrations ranging from 2 mg/L to 5 g/L. The compounds tested were mycosubtilin (M-I), surfactin and fengycin as well as different mixtures such as mycosubtilin/surfactin (M/S-I), fengycin/surfactin. Water and DMSO 5% were used as a control. Nisin and EDTA were used between 100 mg/L and 10 g/L.
Study population ESME-MBC (Data registration: clinicaltrials. gov identifier NCT03275311) is a retrospective national cohort gathering routinely collected real-world data from all consecutive MBC women over 18 who initiated their MBC treatment in one of the 18 FCCCs. Data are updated annually and include patient demographics, histopathology, outcomes, and treatment patterns. The collected information integrates patient data from medical records, multidisciplinary team meeting reports, hospitalization reports, and hospitals' pharmacy records. 8] [19]  For this study, we used data from MBC patients who entered the cohort between 1 January 2008 and 31 December 2016. Data were collected until the cut-off date for data extraction (i. e. 14 April 2020). Male patients and patients whose breast cancer subtype could not be assessed were excluded from the analyses.
Ethics approval and data protection This analysis was approved by an independent ethics committee (Comité De Protection Des Personnes Sud-Est II-2015-79). No formal dedicated informed consent was required, but all patients have been informed about the use of their electronically recorded data and can access, rectify, limit, or require withdrawal of their data on a dedicated web platform at any time. 22 French data protection authority authorized the ESME MBC database in 2013 (registration ID 1704113 and authorization NbrDE-2013. -117). In compliance with the applicable European regulations, a complementary authorization was obtained on 14 October 2019 regarding the ESME research Data Warehouse.
Study population features Among 23 698 patients enrolled in the ESME database since 2008, 20 446 women were eligible for this present study (Figure  1 ), of whom 13 590 (66. 5%), 3919 (19. 2%), and 2937 (14. 3%) had HRþ/HER2e, HER2þ, and TNBC tumours, respectively. Clinical features and patients' characteristics at the date of metastatic diagnosis, for the whole ESME population, are shown in Table  1 , whereas those of each subtype are presented in Supplementary Table  S1 , available at https://doi. org/10. 1016/j. esmoop. 2021. 100114. Most patients and tumours characteristics, as well as the previous receipt of adjuvant treatments, appeared stable over time in the whole population and in subtypes. However, two changes in the HER2þ MBC cohort were significant. First, the proportion of de novo HER2þ MBC significantly increased from 33% in 2008 to over 50% in the most recent years (CochraneArmitage test; P < 0.
DISCUSSION MBC represents a major social, medical, and economic burden worldwide, with over 620 000 deaths in 2018. 26 everal MBC innovative treatments have been released on the market over the past 12 years, with ranking according to the ESMO Magnitude of Clinical Benefit Scale (MCBS) ranging from 2 to 5. 27  Regarding the high medical need, and also the costs of these medications, it is of utmost importance to carefully scrutinize whether the expected impacts of these drugs are observed in real-life. The ESME-MBC cohort is one of the largest real-world databases of MBC, in a country with universal drug access. It therefore provides an appropriate material for such evaluation. Our study provides two major results: one is the description of the evolution of OS over time, together with evaluation of the independent effect of YOD on outcome in the whole population and among each MBC subtype; the other is the description of the uptake of newly released drugs in the same cohorts of patients classified by YOD.
The main limitations of our study are inherent to its retrospective and observational design. It is very difficult to retrospectively define treatment indications, and treatment choices are made by physicians. Drug exposure could therefore only be assessed 'macroscopically'. Data on quality of life and safety are not available. Patients are recruited by FCCCs, which may not fully represent the French or European general population. In particular, the median age of our cohort is a bit lower than expected due to a slight under-representation of older women (26% of women in ESME are aged !70 years) who are less frequently referred to comprehensive cancer centres. This could lead potentially to a slight overestimation of OS. 42  Performance status, an important predictor of OS, was available in only 40% of the files, and could not be included in the main analyses.
ACKNOWLEDGEMENTS We thank the 18  French Comprehensive Cancer Centres (FCCCs)  for providing the data and each ESME local coordinator for managing the project at the local level. Moreover, we thank the  ESME Scientific Group and Strategic Committee  for their ongoing support. The 18 participating FCCCs are as follows:  I. Curie , Paris/Saint-Cloud;  G. Roussy , Villejuif;  I. Cancérologie  de l'Ouest, Angers/Nantes;  C. F. Baclesse , Caen; ICM Montpellier;  C. L. Bérard , Lyon;  C. G. -F. Leclerc , Dijon;  C. H. Becquerel ,  Rouen; I. C. Regaud , Toulouse;  C. A. Lacassagne ,  Nice ;  Institut de Cancérologie de Lorraine, Nancy ;  C. E. Marquis , Rennes;  I. Paoli-Calmettes ,  Marseille; C. J. Perrin ,  Clermont Ferrand ;  I. Bergonié , Bordeaux; C. P. Strauss, Strasbourg;  I. J. Godinot , Reims; and  C.
monitored during the lockdown period in some regions of China (e. g. the region of Hangzhou), probably due to higher coal consumption for heating  (Wang et al. , 2020). Substantial changes in NO 2 columns measured by the spaceborne TROPOMI instrument have been reported at locations with heavy lockdown measures due to the COVID-19 pandemic. In many Chinese cities, NO 2 column densities decreased by at least 40%  (Bauwens et al. , 2020). In North American and European cities, a decrease of up to 40% was observed  (Bauwens et al. , 2020). In Spain, for example, the average reduction of NO 2 during the lockdown compared with a "businessas-usual" emission scenario was estimated to be close to 40% using machine learning fed by meteorological data  (Petetin et al. , 2020). Disentangling the lockdown effects on NO 2 from the natural variability induced by meteorological conditions is essential to build accurate emissions for air quality modeling  (Goldberg et al.
| DATA AND METHODS Air quality, meteorological conditions and population density data were obtained for the spatial domain under consideration that extends from 11 W to 19 E and from 35 to 60 N. The focus here is on the period March 18-May 18 of 2013-2020. Measured concentrations of regulated pollutants from national monitoring networks were retrieved from the European Air Quality e-Reporting database (AirBase), which provides near-real-time air-quality data for Europe in a standardized format  (AQ e-Reporting, 2020). The data available are hourly concentrations in the case of CO, NO 2 , SO 2 and O 3 , and daily concentrations in the case of PM 10. The domain includes 1,680 AirBase stations. However, data from 1,308 stations were used the present study as stations were only selected with at least 70% of days of the period March 18-May 18 (for each year) filled with at least one hourly value for the respective pollutant.
In the case of O 3 , the median (P50) of the anomalies is positive (4. 3 μgÁm -3 , or 7%), which means that a majority of the available stations have reported an increase in the average concentration. For PM 10 , the median (50th percentile) of the anomalies is slightly negative (-0. 7 μgÁm -3 , or -4%), and the 75th percentile (P75) is positive (1. 6 μgÁm -3 , or -6%). Note that on March 28, a severe dust event over Southeast Europe led to strong positive PM 10 anomalies for three days  (Menut et al. , 2020). The present authors' confidence in the database to represent accurately the distribution of the concentration anomalies across Europe is high for NO 2 and O 3 because 71% and 68% of the stations provide data (i. e. 1,196 and 1,148 stations, respectively), and to a lesser extent for PM 10 with 53% (i.
e. 895 stations). For CO and SO 2 , the number of monitoring stations is low (12% and 27%). Nevertheless, it can be observed that the median of the anomalies is -13. 5 μgÁm -3 (-5%) for CO and -0. 2 μgÁm -3 (-11%) for SO 2. Note that the 75th and 90th percentiles of CO and SO 2 are positive (Table  1 ), which shows that, in some regions, NO 2 decreased while CO and SO 2 increased. This suggests that traffic decreased while other emission sources such as residential or industrial emissions increased. It can be expected that the strongest reduction in concentrations of primary pollutants happen in areas where human activities are the most intense. In other words, the reduction may be stronger at urban stations than at T A B L E 1 Anomalies of the mean concentrations of carbon monoxide (CO), nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ), ozone (O 3 ) and particulate matter (PM 10 ) in 2020 compared with the average of the previous seven years (2013-2019) for the period March 18-May 18, 2020, measured at 1,308 European Air Quality e-Reporting database (AirBase) stations P10 P25 P50 P75 P90 % availability μgÁm -3 % μgÁm -3 % μgÁm -3 % μgÁm -3 % μgÁm -3 % CO -131.
mean concentration and (b) ozone (O 3 ) mean concentration at European Air Quality e-Reporting database (AirBase) stations in 2020 compared with the previous seven years (2013-2019) for the period March 18-May 18, 2020. Dots are coloured according to the anomalies in concentration (μgÁm -3 ) and sized proportionally to the relative change (%). Black dots correspond to stations with < 30% of available data COVID-19 incidence has been among the highest (COVID-19. who. int), and where the strongest mobility restrictions were imposed. At two stations (see Figure  S2  in Appendix S1 in the additional supporting information), the NO 2 concentrations are surprisingly greater in 2020 than during the seven previous years (anomaly > 10 μgÁm -3 ), which suggests recent increases in local emissions. Anomalies of the 50th percentile (median) and 90th percentile (P90) of daily maximum concentrations show the same spatial patterns (see Figure  S3a  and S3c in Appendix S1 in the additional supporting information).
I G U R E 2 (a) Total cloud cover anomalies from the Copernicus Atmosphere Monitoring Service (CAMS) global forecast. Anomalies of the means in 2020 compared with the previous seven years (2013-2019) for the period March 18-May 18, 2020. (b) Ozone (O 3 ) anomalies against total cloud cover anomalies at European Air Quality e-Reporting database (AirBase) stations. (c) O 3 anomalies against nitrogen dioxide (NO 2 ) anomalies at AirBase stations. Stars in (b, c) represent "urban" stations; dots represent "rural" stations. "Urban" stations are defined by a population density threshold of 1,500 inhabitantsÁkm -2. Dots and stars in (c) are coloured depending on total cloud cover anomalies of (a) at each station. Blue and red lines are obtained by reduced major-axis regression restricted to positive and negative anomalies, respectively.
I G U R E 3 Map of mean total oxidant (O x ) concentration anomalies at European Air Quality e-Reporting database (AirBase) stations in 2020 compared with the previous seven years (2013-2019) for the period March 18-May 18, 2020. Dots are coloured according to the anomalies in concentration (μgÁm -3 ) and sized proportionally to the relative change (%). Black dots correspond to stations with < 30% of available data. Violet squares correspond to the location of the six major European cities for which are presented the daily total clover cloud anomalies (green line) and daily O x mean concentration anomalies compared with the average of the previous seven years at each station (grey lines) and for the average of all stations (black line) in (a) Berlin (Germany), (b) London (UK), (c) Brussels (Belgium), (d) Paris (France), (e) Milan (Italy) and (f) Madrid (Spain).
ACKNOWLEDGMENTS The authors thank  Gabriele Pfister ,  Antonio Caltabiano  and  Tao Wang  for their valuable comments on the manuscript. The  National Center for Atmospheric Research  is sponsored by the  National Science Foundation. The authors are grateful to the  European Environmental Agency (EEA)  for maintaining and providing the surface concentrations of criteria pollutants over Europe. All data used the study are publicly available at the following repositories: Regulated pollutant concentration measurements "Air Quality e-Reporting" at https://www. eea. europa. eu/data-and-maps/data/aqereporting-8(permanent link: b21a537e763e4ad9ac8ccffe987d6f77). Meteorological variables "ECMWF CAMS" at https://apps. ecmwf. int/datasets/data/cams-nrealtime. Population density " CIESIN/CIAT 2005 Center for International Earth Science Information Network " at https://doi.
F I G U R E 2 (a) Total cloud cover anomalies from the Copernicus Atmosphere Monitoring Service (CAMS) global forecast. Anomalies of the means in 2020 compared with the previous seven years (2013-2019) for the period March 18-May 18, 2020. (b) Ozone (O 3 ) anomalies against total cloud cover anomalies at European Air Quality e-Reporting database (AirBase) stations. (c) O 3 anomalies against nitrogen dioxide (NO 2 ) anomalies at AirBase stations. Stars in (b, c) represent "urban" stations; dots represent "rural" stations. "Urban" stations are defined by a population density threshold of 1,500 inhabitantsÁkm -2. Dots and stars in (c) are coloured depending on total cloud cover anomalies of (a) at each station. Blue and red lines are obtained by reduced major-axis regression restricted to positive and negative anomalies, respectively.
F I G U R E 3 Map of mean total oxidant (O x ) concentration anomalies at European Air Quality e-Reporting database (AirBase) stations in 2020 compared with the previous seven years (2013-2019) for the period March 18-May 18, 2020. Dots are coloured according to the anomalies in concentration (μgÁm -3 ) and sized proportionally to the relative change (%). Black dots correspond to stations with < 30% of available data. Violet squares correspond to the location of the six major European cities for which are presented the daily total clover cloud anomalies (green line) and daily O x mean concentration anomalies compared with the average of the previous seven years at each station (grey lines) and for the average of all stations (black line) in (a) Berlin (Germany), (b) London (UK), (c) Brussels (Belgium), (d) Paris (France), (e) Milan (Italy) and (f) Madrid (Spain).
Sensor Based HAR from sensors consists of using a network of sensors and connected devices, to track a person's activity. They produce data in the form of a time series of state changes or parameter values. The wide range of sensors -contact detectors, RFID, accelerometers, motion sensors, noise sensors, radar. -can be placed directly on a person, on objects or in the environment. Thus the sensor-based solutions can be divided into three categories, respectively : Wearable  [6] , Sensor on Objects  [7]  and Ambient Sensor  [8]. Considering the privacy issues of installing cameras in our personal space, to be less intrusive and more accepted, sensor-based systems have dominated the applications of monitoring our daily activities  [2, 9]. Owing to the development of smart devices and Internet of Things, and the reduction of their prices, ambient sensor-based smart homes have become a viable technical solution which now needs to find human activity algorithms to uncover their potential.
Some of these challenges are common to other areas of pattern recognition (sec. 2) and more recently on automatic features extraction algorithms (sec. 3), such as computer vision and natural language processing, while some are specific to sensor-based activity recognition, and some are even more specific to the smart home domain. This field requires specific methods for real-life applications. The data have a specific temporal structure (sec. 4) that needs to be tackled, and poses challenges in terms of data variability (sec. 5) and availability of datasets (sec. 6) but also specific evaluation methods (sec. 7). The challenges are summarised in fig. 2 ) To carry out our review of the state of the art, we searched the literature for the latest advances in the field. We took the time to reproduce some works to confirm the results of works proposing high classification scores.
Pattern Classification Algorithms for Human Activity Recognition (HAR) in smart homes are first pattern recognition algorithms. The methods found in the literature can be divided into two broad categories: Data-Driven Approaches (DDA) and Knowledge-Driven Approaches (KDA). These two approaches are opposite. DDA uses user-generated data to model and recognize the activity. They are based on data mining and machine learning techniques. KDA uses expert knowledge and rule design. They use prior knowledge of the domain, its modeling and logical reasoning.
Outlines To summarise, KDA propose to model activities following expert engineering knowledge, which is time consuming and difficult to maintain in case of evolution. DDA seems to yield good recognition levels and promises to be more adaptive to evolution and new situations. However, the DDA only yield good performance when given well-designed features as inputs. DDA needs more data and computation time than KDA, but the increasing number of datasets and the increasing computation power minimises these difficulties and allows today even more complex models to be trained, such as Deep Learning(DL) models which can overcome the dependency on input features.
Handcrafted Features In order to recognize the activities of daily life in smart homes, researchers first used manual methods. These handcrafted features are made after segmentation of the dataset into explicit activity sequences or windows. In order to provide efficient activity recognition systems, researchers have studied different features  [31]. Initially Krishann et al. [32]  and Yala et al. [33]  proposed several feature vector extraction methods described below: baseline, time dependency, sensor dependency and sensor dependency extension. These features are then used by classification algorithms such as SVM or Random Forest to perform the final classification. Inspired by previous work, more recently Aminikhanghahi et al. [34]  evaluate different types of sensor flow segmentations.
The Baseline Method This consists of extracting a feature vector from each window. It contains the time of the first and last sensor events in the window, the duration of the window and a simple count of the different sensor events within the window. The size of the feature vector depends on the number of sensors in the datasets. For instance, if the dataset contains 34 sensors, the vector size will be 34 + 3. From this baseline reserchers upgrade the method to overcome different problems or challenges.
Automatic Features In the aforementioned works, machine learning methods for the recognition of human activity make use of handcrafted features. However, these extracted features are carefully designed and heuristic. There is no universal or systematic approach for feature extraction to effectively capture the distinctive features of human activities. Cook et al. [32]  introduce few years ago an unsupervised method of discovering activities from sensor data based on a traditional machine learning algorithm. The algorithm searches for a sequence pattern that best compresses the input dataset. After many iterations the it reports the best patterns. These patterns are then clustered and given to a classifier to perform the final classification. In recent years, deep learning has flourished remarkably by modelling high-level abstractions from complex data  [35]  in many fields such as computer vision, natural language processing, and speech processing  [6].
Deep learning models have the end-to-end learning capability to automatically learn high-level features from raw signals without the guidance of human experts, which facilitates their wide applications. Thus, researchers used Multi Layer Perceptron (MLP) in order to carry out the classification of the activities  [36, 37]. However, the key point of deep learning algorithms is their ability to learn features directly from the raw data in a hierarchical manner, eliminating the problem of crafty approximations of features. They can also perform the classification task directly from their own features. Wang et al. [12]  presented a large study on deep learning techniques applied to HAR with the sensor-based approach. Here only the methods applied to smart homes are discussed.
The feature vector size is the number of sensors in the dataset. They compared two features forms: binary representation and numerical representation. The numerical representation method, records the number of firing of a sensor during the time window, while the binary representation method sets to one the sensor value if this one fired in the time window. Wang et al. then use a dense layer on top of the SDAE to fine-tune this layer with the labeled data to perform the classification. Their method outperforms machine learning algorithms on the Van Kasteren Dataset  [43]  with the two features representations. Ghods et al. [44]  proposed a method, Activity2Vec to learn an activity Embedding from sensor data. They used a Sequence-to-Sequence model (Seq2Seq)  [45]  to encode and extract automatic features from sensors.
The model trained as an Autoencoder, to reconstruct the initial input sequence in output. Ghods et al. validate the method with two datasets from HAR domain, one was composed of accelerometer and gyroscope signals from a smartphone and another one that contained smart sensor events. Their experiment shows that the Activity2Vec method generates good automatic features. They measured the intra-class similarities with handcrafted and Activity2Vec features. It appears that for the first dataset (smartphone HAR) intra-class similarities are smallest with the Activity2Vec encoding. Conversely, for the second dataset (smart sensors events), the intra-class similarities are smallest with handcrafted features.