dataesr/scientific-paragraphs-categorization huggingface dataset:
Then, the average error on the total number of datasets per each method is calculated as: e rms = 1 N ∑ e rms ( 18 ) where N is the number of datasets per algorithm to be evaluated. In the case of curve evaluation, N equals three (two real and one simulated datasets), while for speed limits, N equals two (the two real datasets). In France, the speed limit in urban areas is defined as 50 km/h  [24] , which is the maximum speed assigned in the simulations. Other than that, in our datasets, speed limit zones of 30 km/h were identified and considered to adjust vehicle' speed. Regarding curve speeds, they vary from 14 to 36 km/h depending on the sharpness detected for each curve. We analyzed lateral errors produced when speed limits change from 50 to 30 km/h in the UTBM-2 and UTBM-3 datasets (see Table  1 ).
In other words, we analyzed lateral errors in segments of 30 km/h (green zones as seen in Figure  11 ) and compare the results for each lateral control method. The speed limit zone of 30 km/h in UTBM-2 is a straight segment, and even if lateral errors in all methods are similar (15 to 20 cm error), the performance of all methods is improved considering our DSA method (see Figure  12a ). Lateral errors in the UTBM-3 dataset vary more due to its road geometry; therefore, improvements with our DSA method are more noticeable, as shown in Figure  12b. On average, the evaluation of speed limits in segments of 30 km/h improved all of the methods with our DSA, but not more than 5 cm, as seen in Figure  13. It is worth mentioning that the literature has proven that respecting speed limits increases safety  [2] [3] [4] 19].
Autonomous vehicles capable of adjusting automatically speed limits will be preferred, since, in real scenarios, drivers go beyond the authorized speeds, being prone to cause accidents. Now, we will focus our analysis on the most dangerous segments, which are the sharp curves. Tables  2 3 4 show lateral error results obtained by the different steering control algorithms in the detected curves for each path. We will start comparing the simulations performed between paths that contain or not noisy information. Regarding tracking error results obtained in the simulated path (Table  2 ), we can see that most of the lateral errors are the smallest (except for the Alice method) compared to errors obtained in real datasets (Tables  3  and 4 ). A visual representation of this comparison can be seen in Figure  14a.
We can confirm that noise is an important factor for tracking algorithms, and it deserves special attention to reduce it before controlling autonomous cars. In the literature, the authors have combined different information to deal with noise, e. g. , taking into account the dynamics of the car (position, orientation, speed)  [20]. The improvements of each method, with and without considering speed regulations in curves, range from 22% to 88% depending on the path. For example, in the simulated path (see Figure  14b ), tracking error is reduced by 19 cm in the Alice method (22% improvement). In UTBM-2, the method with the largest root mean square error is pure pursuit (1. 2 m), reduced by 72 cm with our DSA (see Figure  14c ). A visual representation of lateral errors on the UTBM-2 dataset with Pure Pursuit method is shown in Figure  15 , which illustrates how our DSA method is able to reduce errors significantly in sharp curves.
At the same time, it is clearly noticeable that the biggest lateral errors are present in sharp curves confirming the need to adapt vehicle' speed. Furthermore, in the UTBM-3 dataset (see Figure  14d ), tracking error is reduced by about 45 cm in the pure pursuit method, while for the Lombard method, it is 69 cm (88% improvement). In general, the performance of our DSA method in sharp curves showed significant (at least 10-cm error) improvements for most of the methods (see Figure  16 ), except for the Stanley method (improvement of around 5 cm). The reason behind this behavior is the definition of its goal point. Since the Stanley method is based on Pure Pursuit (PP) and the goal point is defined with a short distance, it does not suffer greatly from the "cutting corners" effect. As tracking errors for speed limits decrease, but not significantly (1 cm for Stanley, 5 cm for Alice and 3 cm for pure pursuit and Lombard), we will base our final conclusion on the results obtained in sharp curves.
Conclusions We have proposed a real-time Dynamic Speed Adaptation (DSA) method based on the analysis of speed limits and curvature information. A speed limit database of the traveled paths was created and considered as input for our DSA. Curvature information extraction allowed DSA to identify sharp curves and to compute the recommended speed for each one. The speed limit database and curve geometric information are extracted off-line from GPS paths in order to provide knowledge ahead of the road while traveling in it. The tracking performance using our approach showed significant reduction of lateral errors. This improvement may potentially prevent accidents and reduce severe injuries in real driving scenarios, which is exactly the aim of any autonomous vehicle. The main advantage of our proposed DSA method is its adaptability, since it can be implemented in the vehicle lateral control system independently of the steering wheel angle computation.
Besides, it enables the car to drive with recommended speeds through straight and curved segments respecting traffic regulations. Furthermore, it is smart enough to localize upcoming sharp curves and apply a comfortable deceleration value before arriving at them, reaching their ideal speeds to traverse them. Future works will include the collection and analysis of vehicle dynamics in order to deal with GPS noise. This information will correct noisy points in the path by comparing the vehicle' speed and orientation with the GPS data. Furthermore, we will incorporate the information of a stereo system in order to bring perception capabilities to the autonomous vehicle. The information of this sensor together with a semantic segmentation approach will allow us to: (1) detect the road if GPS is not available; (2) identify speed limit traffic signs in case no speed limit database is accessible; (3) recognize lane markings on the road to guide the algorithm for road boundary detection and shape estimation; and (4) prevent the vehicle from turning if the lane where it is driving has some curved arrows painted.
The Ir slab was composed of three layers, where the bottom layer was held fixed. The dimensions of the cell parallel to the surface are defined by the optB88-vdW calculated lattice constant for graphene (a graphene , optB88-vdW = 2. 465). This functional uses the vdW-DF framework proposed by Dion et al. 50 with a reparameterized version of B88 exchange 51 fitted to the S22 data set. 52 It expands the Ir lattice by 1. 6% relative to the optB88-vdW calculated value of 3. 921 Å. At least 7. 5 Å separates any atom from the top or bottom of the cell. A grid spacing of 0. 1779 Å defines the length of each grid point. Finally, a 2 × 2 k-point mesh was sampled, and all forces were relaxed until they were below 0. 02 eV/Å. Reaction pathways are determined using the AutoNEB code found in ASE with the CI-EB method.
ACKNOWLEDGMENTS We acknowledge financial support from The  Danish Council for Independent Research  (grant no. 0602-02566B  and grant no. 0602-02265B ),  Innovation Fund Denmark (NIAGRA) , The  European Research Council (CoG GRANN) ,  Villum Foundation via Villum Centre of Excellence for Dirac Materials  (Grant no. 11744 ). H. B. acknowledges support by the Director,  Office of Science, Office of Basic Energy Sciences , and by the  Division of Chemical Sciences, Geosciences and Biosciences of the U. S. Department of Energy  at  LBNL  under contract no. DE-AC02-05CH11231. The Advanced Light Source is supported by the Director,  Office of Science, Office of Basic Energy Sciences  of the  U. S. Department of Energy at LBNL  under contract no. DE-AC02-05CH11231.
Patients This retrospective study is based on data from the phase III, multicenter, EORTC 10994/BIG 1-00 trial (p53), which randomised 1856 patients between 2001 and 2006 to either six cycles of anthracycline-based chemotherapy (FEC) or a taxane-based regimen, docetaxel for three cycles followed by epirubicin þ docetaxel for three cycles (T-ET), all given before primary surgery  [4]. The primary hypothesis was the dependence of chemotherapy efficacy (taxanes versus anthracyclines) on TP53 tumour genotype. Eligible patients for the p53 trial were women <71 years with histologically-proven invasive breast cancer candidates for NACT. The primary end-point of the study has been already reported by Bonnefoi H et al. [4, 5]. The trial was registered with ClinicalTrials. gov number NCT00017095 and approved by national and/ or local ethics committees in all participating centres.
Before registration, all patients gave signed informed consent for the trial and for additional research on tumour samples. For the IRF7 signature analysis, only eligible patients for whom a transcriptomic profile has been generated based on the pre-NACT biopsy (n Z 160) were included. These data have previously been reported and raw data are deposited in the National Center for Biotechnology Information Gene Expression Omnibus database with series accession numbers GSE6861. The IRF7 signature score (272 genes due to updates in gene annotation) was calculated as described in Bidwell et al. (2012)  [2]. Signature scores were generated blinded to the clinical database and patient outcome.
Statistical analysis A statistical analysis plan was prospectively defined. Analyses were performed using SAS, version 9. 4 (SAS Institute Inc. , Cary, NC, USA). For the two biomarkers, severe infection and the IRF7 gene signature, different analysis populations were defined. For severe infection, the analysis population excluded patients who did not undergo surgery after NACT, who progressed on NACT or who did not receive six cycles of NACT. These exclusion criteria are motivated by potential confounding of the biological question investigated in this study. That is, patients who did not receive six cycles have a shorter observation period for infection as well as a potential poorer outcome due to reduced therapy exposure. For the IRF7 signature, only patients without an IRF7 signature assessment were excluded from the analyses.
Discussion In this analysis we did not confirm the hypothesis, based on the pre-clinical evidence, that severe infection could activate type I IFN responses triggering an autoimmune response which could improve longer term survival outcomes in breast cancer patients treated with NACT. The strengths of this analysis include the use of a large clinical trial dataset, which results in exceptionally wellannotated clinical characteristics, including the incidence of infection, its severity and long-term clinical outcome. The main weaknesses of our study are that the selected febrile neutropenia cases included different levels of severity which could have a different influence on the end-point. An additional exploratory analysis including only the grade IV cases did not change the conclusion (HR Z 0.
Thus, measurement of IFN signatures in tumour cells may serve as a prognostic marker of innate immunity against cancer and perhaps guide therapeutic interventions aimed at preventing the formation of overt tumours in bone/i. e. dearly intervention. Such hypotheses remain to be further explored and validated in the future. In conclusion, we did not validate in a wellannotated clinical trial dataset the correlation between innate immunity and metastases suppression in breast cancer patients. Whilst the occurrence of a significant infective event during NACT was not associated with decreased DRFI, higher expression of the IRF7 gene signature was associated with reduced bone relapse supporting pre-clinical evidence that a tumour intrinsic innate immunity could be of importance in bone metastases prevention.
Acknowledgements This work was supported by the  European Organisation for Research and Treatment of Cancer (EORTC) Cancer Research fund. Nathan Touati 's fellowship at  EORTC  is supported by the  EORTC Breast Cancer Group. Sherene Loi  is supported by the  John Colebatch Cancer Council Victoria Fellowship  and the  Breast Cancer Research Foundation ,  BCRF NY. Furthermore the authors would like to thank the Fondation pour la lutte contre le cancer et pour des recherches medicobiologiques and  Prof. Richard Iggo  ( INSERM U1218 ) as well as all patients, doctors and nurses involved in the EORTC  10994/BIG 1-00  study.
022 140 857 × 10 23 when it is expressed in the SI unit mol -1   This 2011 text of the proposed new SI is not necessarily the final wording that will be recommended by the CIPM to the CGPM prior to the next CGPM meeting in 2018. The numbers given in the last column (proposed new SI) have been modified according to the CODATA 2014 recommendation  [3]. Final values will be computed by CODATA prior to introduction of the new SI. The amount of substance X, n(X), is given as A (X) (X) / n N N = (5) where N(X) denotes the number of entities X. Equations (  2 ), (  4 ) and (  5 ) are of special importance for chemistry because they connect the mass of an individual entity (microscopic mass) with the mass (usually macroscopic) of an arbitrary number of the same entities, similar to A F N e = (6) and A R N k = (7) which connect the Faraday constant F to the elementary charge e, and the Boltzmann constant k to the universal gas constant R.
The uncertainty of the mass m IPK is zero in the present SI and entered as Y to indicate a number that will be specified before the proposed new SI is implemented, but which can be expected to be 12 × 10 -9 or smaller. (1) At present, the mass of the IPK defines the kilogram and the molar mass constant is defined to be 0. 001 kg mol -1 (exactly). The values of other quantities are measured experimentally or inferred from experiments by making use of well-known equations: all have identical uncertainties to the precision shown. (2) The CGPM has resolved to define the kilogram through a fixed numerical value of h, combined with the present definitions for the second and the metre and to define the mole by giving a fixed value to the numerical value of the Avogadro constant in the SI. Consequently the molar mass constant and the atomic mass constant would have equal relative uncertainties inferred from CODATA 2014  [3] , although a last CODATA adjustment will be made a few months before the redefinitions are adopted.
Acknowledgments: This work has benefited from fruitful discussions with many bodies and members of IUPAC and outside the Union. In particular, we thank  Professor Ian Mills ,  Dr. Franco Pavese  and  Dr. Barry Wood  for numerous exchanges on this matter. We also thank the  National Research Council of Canada  for the hospitality during our Ottawa meeting. We gratefully remember the late  Professor Paul De Bièvre , who inspired many of us with his enthusiasm and critical view on this matter. Brandt (Belgium); B. Toshev (Bulgaria) ;  L. L. Santibañez  (Chile);  Z. Shuai (China); T. Solomon (Ethiopia); C. S. Reiners (Germany) ;  M. Riedel  (Hungary);  U. Maitra (India) ;  P. E. Childs (Ireland) ;  L. Cardellini (Italy) ;  A. A. Al-Najjar  (Kuwait);  T. -K. Soon  (Malaysia);  R. Adhikari  (Nepal); F.
Historically, the principal resource of BLs was maintained from 2001 at the Lahey Clinic (http://www. lahey. org/Studies/) by George Jacoby and Karen Bush, by assigning new enzyme numbers for a number of representative BL families. From July 2015, this resource was transferred into the Bacterial Antimicrobial Resistance Reference Gene Database (https://www. ncbi. nlm. nih. gov/bioproject/313047/) maintained at the NCBI. Other resources are the Institute Pasteur MLST Database (http://bigsdb. pasteur. fr/ klebsiella/klebsiella. html), the Antibiotic Resistance Genes Database  4  , the Lactamase Engineering Database  5, 6  , the Metallob-Lactamase Engineering Database 7 , the Comprehensive Antibiotic Resistance Database 8 , the b-Lactamase Database 9 , the Comprehensive b-Lactamase Molecular Annotation Resource  10.
However, most of these databases are either not maintained anymore, have a very broad scope or are focused on a few BL families. The aim of our Beta-Lactamase Database (BLDB) is to compile sequence information as well as biochemical and structural data on all the currently known BLs. This comprehensive web-based database, which is updated on a weekly basis, may provide at a glance useful insights in the structure-function relationships of BLs, allowing a better understanding of substrate specificities and key residues involved in substrate recognition and hydrolysis. Altogether, the information provided by BLDB may help to foresee the impact of future mutations on the evolution of BLs.
Implementation details The database is hosted on a dedicated virtual server in the cloud, which allows easy adjustments and evolution of computing resources according to the needs. The core pages are implemented in PHP on a Linux Server under the CentOS 7. 2 operating system, whereas the raw data is stored as tabulated files in order to facilitate the updates. The interactive images showing the list of BL families that are present in the BLDB are generated dynamically in SVG format from the raw data, thus ensuring an updated display at any time. The corresponding URL links are directly embedded in the SVG images. Multiple sequence alignments are automatically generated with Clustal Omega 11 using the default parameters. Phylogenetic trees are processed using Phylip version 3. 695 CONTACT Thierry Naas thierry.
naas@aphp. fr Service de Bact eriologie-Hygi ene, Hôpital de Bicêtre, AP-HP, EA7361, Universit e et Facult ed eM edecine Paris-Sud, LabEx LERMIT, Le Kremlin-Bicêtre, France; Bogdan I. Iorga bogdan. iorga@cnrs. fr Institut de Chimie des Substances Naturelles, CNRS UPR 2301, Universit e Paris-Saclay, LabEx LERMIT, Gif-sur-Yvette, France Supplemental data for this article can be accessed here. (http://evolution. genetics. washington. edu/phylip/) using Clustal Omega's DND output files and represented as SVG images to provide the best quality and minimal file size. The radar charts representing hydrolytic profiles are dynamically built using a modified and personalised version of the D3. js JavaScript library (https://gist. github. com/nbremer/6506614). The BLAST interface is provided by SequenceServer  12  and the BLAST þ binaries are downloaded from NCBI  13.
Input data is downloaded from NCBI using the "Entrez Direct: E-utilities on the UNIX Command Line"  13  and from the PDB with personalised scripts. Structures are updated semi-automatically on a weekly basis, after each PDB update. New enzymes are added following every update of the Bacterial Antimicrobial Resistance Reference Gene Database. Constant literature survey also provides newly described BLs and synthetic mutants, as well as their hydrolytic profiles. The long-term maintenance of the BLDB is ensured by the collaboration between two academic teams with active interest and experience in the field of BL-mediated antibiotic resistance.
Enzymes The Enzymes tab of the main menu gives access to a list of classes and sub-classes of BLs, together with their corresponding BL families (Figure  S3 ). This is represented as an SVG image that is dynamically generated from the raw data, which always ensures up-to-date information. Each entry of a given BL family contains the class, protein name and eventually alternative names. When the family features several clearly defined sub-families, this information is also present. GenPeptID and GenBankID (with a RefSeq number when provided by NCBI) are also provided, with the corresponding links on the NCBI's website for more detailed information. Bibliographic data (PubMedID, DOI), functional (phenotype, hydrolytic profile) and genetic (natural or acquired type) information and links to the other sections are also provided (Figure  S2 ).
Structures The Structures tab gives access to a table containing all threedimensional structures of BLs reported in the Protein Data Bank  14. Each entry contains the name of BL, together with the class or sub-class to which it belongs, followed by the PDB code and resolution (if applicable). The protein sequence is linked to the corresponding UniProt entry and, if appropriate, the existing mutations (extracted from the PDB file content) are shown. Bibliographic data (PubMedID, DOI) allows an easy retrieval of the original articles associated with the structure through links to PubMed and to the journal website. All ligands, buffer molecules and ions present in the structures are highlighted, together with their interaction mode with the protein (non-covalent, covalent, metal coordination).
Funding This work was supported by the  Laboratory of Excellence in Research on Medication and Innovative Therapeutics (LERMIT)  [grant number  ANR-10-LABX-33 ], by the  JPIAMR transnational project DesInMBL  [grant number  ANR-14-JAMR-0002 ] and by the  R egion Ile-de-France (DIM Malinf ).
, 2011). The reasons for these discrepancies have to be clarified. It is of utmost importance to understand the processes and transport patterns, to identify aerosol sources and quantify their strength in a reliable way. Besides high performance climate models, reliable datasets are needed to constrain these models and to validate their projections. The development of such datasets on a global scale relies on longterm satellite missions able to provide measurements with good temporal and spatial resolution and coverage. For the considered period, several instruments can be used such as the Optical Spectrograph and InfraRed Imaging Sensor (OSIRIS) launched in 2001 onboard ODIN  (Bourassa et al. , 2007) , the Scanning Imaging Absorption spectroMeter for Atmospheric ChartographY (SCIAMACHY) launched onboard  ENVISAT (von Savigny et al.
, 2015). In this work, we make use of the Global Ozone Monitoring by Occultation of Stars (GOMOS) experiment, also launched onboard ENVISAT and providing global measurements over the period  2002-2012. It is the ultimate objective of the Aerosol_cci project  (Popp et al. , 2016) , part of the Climate Change Initiative (CCI) of the European Space Agency (ESA)  (Hollmann et al. , 2013) , to quantify the global aerosol distribution in the atmosphere (particularly in the troposphere and stratosphere) and their variability. The aim of ESA's CCI is to develop Climate Data Records (CDR) from ESA space remote sensing experiments for the main Essential Climate Variables (ECV) identified by the Intergovernmental Panel on Climate Change (IPCC). Based on global observations of the Earth's atmosphere, such CDRs are needed, among other applications, to constrain and validate climate models.
, 2016) , where both tropospheric and stratospheric aspects are addressed. In the present work, the focus is exclusively on the stratospheric aerosol extinction coefficient CDR (we will use the shortcut "aerosol extinction CDR" in the following) derived from the AerGOM algorithm. The retrieval of particle size information and related CDRs, which are still under development, will be the subject of a future publication. The SPARC (Stratosphere-troposphere Processes And their Role in Climate) Community represents the Climate Modelling Community for the stratospheric aspects of Aerosol_cci. The User and Validation teams not only uses the produced data records and benchmarks them against other available datasets and model projections, but also performs the validation tasks, using in-situ and ground-based datasets.