dataesr/scientific-paragraphs-categorization huggingface dataset:
To measure the reliability of the reconstruction of the phase information we use F 2. Comparing F 2 to F 1 gives a measurement of the phase reconstruction. These results are also shown in Fig. B2. We find that for both ψ and η, the mean F 2 is at a similar level as F 1 , but the standard deviation of F 2 is much larger than that of F 1 , which suggests that the quality of the phase reconstruction varies dramatically depending on the specific realisation of the sky. Furthermore, we find that the influence of shape noise on F 2 is much less compared to the influence from the mask, as also suggested by Fig. B1. Taken in combination, F 1 and F 2 suggest that considerable information can be inferred about η and ψ, although with much larger uncertainties than for κ. We do not perform further quantitative analyses on these maps, but note that for data sets on areas larger than DES Y1, the reconstruction of these other lensing fields becomes interesting.
ACKNOWLEDGEMENT We thank  Sandrine Pires  for useful discussions. CC was supported in part by the  Kavli Institute for Cosmological Physics at the University of Chicago  through grant  NSF PHY-1125897  and an endowment from  Kavli Foundation  and its founder Fred Kavli. AP acknowledges support from beca  FI  and  2009-SGR-1398  from  Generalitat de Catalunya , project  AYA2012-39620  and  AYA2015-71825  from  MICINN , and from a  European Research Council   Starting Grant  ( LENA-678282 ). Support for DG was provided by  NASA  through  Einstein Postdoctoral Fellowship  grant number  PF5-160138  awarded by the Chandra X-ray Center, which is operated by the  Smithsonian Astrophysical Observatory  for  NASA  under contract  NAS8-03060. BL was supported by  NASA  through  EinsteinPostdoctoral Fellowship Award Number   PF6-170154.
The DES data management system is supported by the  National Science Foundation  under Grant Numbers  AST-1138766  and  AST-1536171. The DES participants from Spanish institutions are partially supported by  MINECO  under grants  AYA2015-71825 ,  ESP2015-88861 ,  FPA2015-68048 ,  SEV-2012-0234 ,  SEV-2016-0597 , and  MDM-2015-0509 , some of which include ERDF funds from the  European Union. IFAE is partially funded by the  CERCA program  of the  Generalitat de Catalunya. Research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Program  (FP7/2007-2013) including  ERC  grant agreements  240672 ,  291329 , and  306478. We acknowledge support from the  Australian Research Council Centre of Excellence for All-sky Astrophysics (CAASTRO) , through project number  CE110001020.
Methods We conducted a systematic literature review across six databases to identify studies (published January 1, 2010 to October 16, 2017) and grey literature reporting meningococcal serogroup data for the years 2010-2016. We performed independent random effects meta-analyses for serogroups A, B, C, W, X, Y, and other. We developed and circulated a questionnaire-based survey to surveillance focal points in countries (N = 95) with known regional bacterial meningitis surveillance programs to assess their surveillance capacity and summarized using descriptive methods.
Data collection and management Two authors (MEP and YL) independently reviewed full text articles and extracted relevant data into a database in Microsoft Access. Any differences in extraction were resolved through discussion. Only one study was chosen for each geographic area and time period. If multiple studies were identified for the same location and time period, the choice between studies was based upon quality, representativeness of geographic area, number of serogroups reported, recent study year, and sample size. We contacted authors of identified publications for any relevant additional information. For several countries in the meningitis belt, multiple studies were identified where the risk of duplicate data could not be reconciled. The two primary reasons included: multiple studies stating the data are reported from the same sources, yet the studies provide different data; and uncertainty about the representativeness of the studies.
Data analysis We conducted meta-analyses per serogroup: NmA, NmB, NmC, NmW, NmX, NmY, and Other Nm (which includes non-groupable, non-encapsulated, NmE, NmE/Z, NmZ, polyagglutinable, and incompletely identified serogroups). All reported serogroups were included in the calculation of the denominator, while unknown or untested samples were excluded. In cases where we could be reasonably certain that a serogroup was tested for, but simply not reported because there were zero cases, we included an assumed zero for that serogroup (Table  S3  in the Online Supplementary Document). If a study reported serogroup data by year, each year was included as a separate datapoint in the analysis. In order to calculate a point estimate for each country, a random effects model using exact binomial confidence intervals and the Freeman-Tukey transformation were utilized.
RESULTS We identified a total of 7637 articles through literature review and an additional 181 through hand searching. We reviewed 994 articles in full and identified 173 articles meeting our strict eligibility criteria and reporting relevant data (Figure  1 ). We included data from 59 countries, representing, at least partially, all six WHO regions. Study characteristics can be found in Table  S3  in the Online Supplementary Document. Data availability differed substantially between WHO regions (Table  S3  in Online Supplementary Document). The regions with greatest representation included the European (EURO), African (AFRO), and the Americas (AMRO) with 51% (27/53), 36% (17/47), and 26% (9/35) of countries with data included, respectively. Within AFRO, substantially more data were available for the meningitis belt (60%, 15/25) compared to non-meningitis belt (9%, 2/22).
The remaining three WHO regions had substantially VIEWPOINTS PAPERS less representation. Data from only 15% (4/27) of the Western Pacific (WPRO), 10% (2/21) of the Eastern Mediterranean (EMRO), and 9% (1/11) South-East Asian (SEARO) were included in the analysis. Results of country-level meta-analyses (by WHO region) are presented in Table  1. We observed substantial differences in the distribution of meningococcal serogroups by country and region (Table  1 , Figure  2 ). Due in part to the dynamic nature of N. meningitidis serogroup distribution   India was the only country in SEARO included in the analysis, where all reported cases were identified as NmA. Similarly, Sudan was the only country within EMRO included in the analysis. NmA accounted for 90. 0% of all IMD cases. In WPRO, NmB was the primary cause of IMD in both Australia and New Zealand (72.
All participating countries utilized surveillance data to primarily better understand the epidemiology of meningococcal disease within their country. A secondary aim of most countries (71%, 35/49) was to measure the impact of interventions and vaccines. The majority of regions had PCR capabilities for both pathogen detection and serogrouping. Fifty percent of countries within AFRO and SEARO utilized PCR for pathogen detection, while countries within EURO and WPRO utilized PCR more frequently for pathogen detection (100% and 80%, respectively). Use of PCR for genogrouping was high within EURO (75%), while only 30% of countries in AFRO used PCR for genogrouping in-country and 20% utilized a regional reference laboratory for this purpose. The majority of all regions were regularly performing antimicrobial resistance testing.
However, we realize this method of surveillance is the most time-and resource-consuming and, therefore, is not currently feasible for many countries, especially those with limited laboratory capacity. In this case, improving the existing sentinel or passive surveillance systems to promote data completeness and timely reporting should be a priority  [42]. Strengths of this study include the comprehensive systematic literature review and grey literature search to identify meningococcal serogroup data. Many countries are conducting meningococcal surveillance, but only publish reports on their respective websites. We initially planned to search the Chinese literature databases, but during the hand searching process, national data were identified for China. Therefore, the Chinese databases were not included in this search.
7 kV through a nanoelectrospray ion source heated to 275°C. The LTQ Velos MS was used in CID top 10 mode (i. e. 1 full scan MS and the 10 major peaks in the full scan were selected for MS/MS). For raw data processing, Thermo Proteome Discoverer v1. 2 was used with Mascot v2. 3 for database search (http://www. matrixscience. com). The following parameters were considered for the searches: precursor mass tolerance of 1. 5 Da and fragment mass tolerance of 0. 5 Da, a maximum of two missed cleavage sites of trypsin, carbamidomethylation and oxidation set as variable modifications. Protein identification was validated when at least two peptides originating from one protein showed statistically significant identification Mascot scores > 13 (p < 0. 05). Ions score is -10 log(P), where P is the probability that the observed match is a random event.
Individual ions scores > 13 indicate identity or extensive homology. Interrogations were performed against a custom database containing distinct entries corresponding to raw protein sequences and the different predicted mature proteins of L. monocytogenes EGD-e (i. e. DB-Mature-LmoEGDe v2. 0, 5838 sequences) based on putative cleavage sites of the signal peptide (SP)  (Renier et al. , 2015). The subcellular location of identified proteins was determined with the rational secretomics-based strategy for genomic and proteomic analyses developed by  Renier et al. (2012)   (Renier et al. , 2012). Briefly, the flowchart and decision trees designed to predict the subcellular localization use available bioinformatic tools and mimic the biology of protein secretion taking into consideration successively the presence or not of a SP, the type of SP, the secretion system pathway, the export of proteins lacking a SP, the transmembrane domains and conserved cell wall domains.
Global results on proteins identified by the three methods The data included in our results concerned protein identified with at least two unique peptides to ensure MS/MS results and in at least two biological replicates to take into account the variability inside each extraction methods (see the report protein analyses in the supplementary data table). In this way, the total number of proteins identified were 39, 62 and 125 for trypsin shaving, biotinylation and cell fractionation, respectively (Fig. 1 ). They were first subdivided into three broad categories according to their predicted localization: intracellular proteins or cytoproteins, cell envelope proteins and exo-cellular proteins. Despite the precautions taken to avoid cell lysis and using surface-targeted extraction methods, a significant number of intracellular proteins was present in the three methods tested.
Surface proteins identified by the three methods To evaluate the efficiency and specificity of each methods, all cell envelope proteins identified were distributed according to a Venn-diagram (Fig. 3 ). Thanks to the biotinylation protocol, 16 proteins were specifically extracted, while the shaving and cell fractionation methods allow the specific extraction of 2 and 8 proteins, respectively. Seven proteins were extracted and identified by the three methods, which represents a shared core of 18% of all surface proteins identified. Considering the cell fractionation method, 3 proteins were present in the membrane subfraction and 4 in the cell wall subfraction. The analysis of these two subfractions showed that among the 14 other surface proteins identified, only the protein Lmo0392 was identified in both the cell wall and membrane samples (data not shown).
Acknowledgements We thank  Mathilde Cohadon  for her support in biotinylation analysis and  Ingrid Chafsey  for technical assistance in providing the biotinylation protocol. Julia Esbelin  was granted from the  Agence Nationale de la Recherche (ANR) (France)  as part of an  ANR-12-ALID-0005  EcoSec contract. Tiago Santos  is a  Marie Sklodowska-Curie PhD Research Fellow  granted by  ITN ETN List-MAPS.
The InSAR data are fitted by a uniform slip of 28 cm. In addition, 429 earthquakes were relocated with the HypoDD software and the use of a 1-D velocity model. The dip-direction of the fault is not retrievable from InSAR, but a south-dipping plane is clear from seismology and the aftershocks distribution. The spatial distribution of relocated events indicates the activation of one fault with a rupture zone length of about 10 km, a result of the occurrence of off-fault aftershocks along strike the main rupture. A stress inversion using 20 focal mechanisms (M ≥ 3. 6; NOA solutions) indicates that faulting accommodates a N196 • E extension. It is confirmed that moderate (5. 0 ≤ M ≤ 5. 2) shallow events can be traced in InSAR studies and can produce surface displacements that provide useful data in fault inversion.
Introduction In February 2017, a shallow seismic sequence of moderate events hit the region of Gulpinar-Babakale, onshore northwestern Anatolia, Turkey (Table  1 , Figure  1 ). The four main events with magnitudes between 5. 0 ≤ M w ≤ 5. 2 (moment magnitude determination by National Observatory of Athens-NOA), occurred on 6 February 2017 (M w = 5. 2; 03:51 UTC and M w = 5. 0; 10:58 UTC),   [1] ; green after  [2] ) represented with respect to stable Europe. NAT stands for North Aegean Trough. Thin black lines are traces of major faults from the NOAFaults database  [3]. Red star indicates the NOA epicentre of the M6. 3  12 June 2017 earthquake south of Lesvos. Boxes with dashed boundaries indicate the frames of the Sentinel-1 satellite SAR images used (white-ascending, black-descending). Table  1.
Satellite (m-s) Table  2. S1 interferograms produced to estimate the fault parameters. Acquisition times are in the form yyyy-mm-dd-hh-mm. The satellite tracks are shown in Figure  1. The interferograms of Figures  3  and 4  are made with the Gamma Software (  [34] ). In the importing step, a radiometric calibration was applied, using the noise and calibration meta-data provided with the SLC data. The state vectors included in the meta-data are accurate enough to be   The interferograms of Figures  3  and 4  are made with the Gamma Software (  [34] ). In the importing step, a radiometric calibration was applied, using the noise and calibration meta-data provided with the SLC data. The state vectors included in the meta-data are accurate enough to be The interferograms of Figures  3  and 4  are made with the Gamma Software (  [34] ).
In the importing step, a radiometric calibration was applied, using the noise and calibration meta-data provided with the SLC data. The state vectors included in the meta-data are accurate enough to be used without further refinement. Overall, it is found that the quality of the state vectors provided in the S1 SLC data products is already high and efficient for co-registration, geocoding and interferometry. However, in the case of anomalous results, concatenated scenes or using advanced interferometric techniques (e. g. , Persistent Scatter Interferometry), which is not the case in the present study, Precise Orbit Determination (POD) precision state vectors should be applied (  [34] ). The co-registration step was carried out in order the slave scenes to obtain the same geometry as the master scene (Table  2 ).
For this purpose, we downloaded forty-nine (49) interferograms of the area produced by the COMET LiCSAR service (http://comet. nerc. ac. uk/COMET-LiCS-portal/), 10 from the ascending track 29, 12 from the ascending track 131, 13 from the descending track 36, 14 from the descending track 109 (Table  4 ). Among them, 40 are co-seismic (with 1, 2, 3 or 4 main events captured), 3 are pre-seismic, and 6 are post-seismic. For the fault inversion we used the LiCSAR data because the LiCSAR archive was complete with no need of any other processing. The LiCSAR interferograms are of very good quality and comparable to those we produced ourselves (Figures  3  and 4 ). The studied area is small and with good coherence, so all interferograms were clear enough to identify fringes (or absence of fringe) with no ambiguity.
Seismology The main shock of 6 February 2017, 03:51 UTC (M L = 5. 0, M W = 5. 2), as well as the aftershock sequence of 546 catalogue events, were relocated (with magnitude 1. 0 < M L < 4. 4; 88% between M L = 2. 1 and 3. 5; Figure  5a ). The phase data were derived from manually picking the arrival time of the P-and S-waves from the recordings of 17 permanent seismological stations of the Hellenic Unified Seismic Network (HUSN) and from the Kandilli Observatory and Earthquake Research Institute (KOERI) Seismological Institute (see Figure  1  for station locations). Excluding one KOERI permanent station (GPRN), all other stations are located at least 40 km away from the Gulpinar sequence. The seismic datasets cover the period 1-28 February 2017. Two location algorithms were used for the initial relocation procedure, namely the HYPO71 earthquake location program (  [36] ) and the double-difference algorithm, HYPODD (  [37] ).
For each phase, a quality value and a time uncertainty in seconds were assigned. A preliminary hypocenter location was performed using the HYPO71 code considering only events having more than three P-and three S-wave arrival-time readings. By removing time-biased data and employing a subset of events located with accuracy better than 1. 5 km and having solution RMS < 0. 2 s, the average Vp/Vs ratio and the velocity model of the area were investigated, using the mean travel-time residuals and location uncertainties (RMS, ERH, ERZ) minimization method. The use of the optimal, as previously described, dataset yielded satisfactory horizontal location uncertainties. More specifically, the majority of the epicentral solutions present ERX (98. 6%) and ERY (97. 4%) less than 1 km (Figure  5d, e ).
Stress Field We analyzed the data set of Table  1  with the open source STRESSINVERSE package. This package runs a joint inversion for stress and fault orientations from focal mechanisms ([39]), using the Michael's method (  [40, 41] ). The objective is to calculate the stress tensor that best explains the seismological observations. The inputs are the orientation and slip direction of a set of fault planes as determined from moment tensor inversion (Table  1 ). The stress inversion used 19 focal mechanisms (Figure  7 ; Mw ≥ 3. 6; period 1-28 February 2017) indicating an extensional stress field (Figure  8 ) with the minimum principal stress axis σ3 oriented NNE-SSW (N196°E) and plunging almost horizontally (2°). A similar result for σ3 (N18°E, 3° plunge) was resolved by the SATSI ([42]) procedure (  [32] ), confirming the stability of our solutions.
Stress Field We analyzed the data set of Table  1  with the open source STRESSINVERSE package. This package runs a joint inversion for stress and fault orientations from focal mechanisms (  [39] ), using the Michael's method (  [40, 41] ). The objective is to calculate the stress tensor that best explains the seismological observations. The inputs are the orientation and slip direction of a set of fault planes as determined from moment tensor inversion (Table  1 ). The stress inversion used 19 focal mechanisms (Figure  7 ; M w ≥ 3. 6; period 1-28 February 2017) indicating an extensional stress field (Figure  8 ) with the minimum principal stress axis σ 3 oriented NNE-SSW (N196 • E) and plunging almost horizontally (2 • ). A similar result for σ 3 (N18 • E, 3 • plunge) was resolved by the SATSI ([42]) procedure (  [32] ), confirming the stability of our solutions.
1  locations. Relocated events are from this study. A south-dipping plane can be seen on the vertical cross section C1-C2 that shows the foci projections orthogonal to fault's strike. 1  locations. Relocated events are from this study. A south-dipping plane can be seen on the vertical cross section C1-C2 that shows the foci projections orthogonal to fault's strike. [43] ; beachball numbers correspond to events in Table  1 ). Thin black lines are active faults from the NOAFaults database  [3]. [43] ; beachball numbers correspond to events in Table  1 ). Thin black lines are active faults from the NOAFaults database  [3].
1. We used S1 interferograms to map tectonic deformation due to the February 2017 shallow earthquake sequence onshore Biga Peninsula (NW Turkey). The total surface deformation is 2. 2 fringes (or maximum 6. 2 cm along LOS) and it is well visible with InSAR because of the shallow depth of the four main events (6-8 km) and the good coherence of the signal phase. 2. 429 earthquakes were relocated with the HYPODD software. The dip-direction of the fault is not retrievable from InSAR, but a south-dipping plane is clear from seismology and the aftershocks distribution, as seen on a vertical cross-section across strike. The spatial distribution of relocated events indicates the activation of one normal fault with a rupture zone length of about 10 km, an effect of the occurrence of off-fault aftershocks.
Acknowledgments: We are indebted to three anonymous reviewers for constructive comments and suggestions. We thank  ESA Copernicus for Sentinel data, COMET LiCSAR  for interferograms and NOA and KOERI seismic analysis groups for phase data. Data of ASTER GDEM is the property of  METI  and  NASA. Bathymetry data used in figures are from  EMODnet Bathymetry Consortium  ( 2016 ):  EMODnet Digital Bathymetry (DTM). Some figures were prepared with GMT software ( [64]). Acknowledgments: We are indebted to three anonymous reviewers for constructive comments and suggestions. We thank  ESA Copernicus for Sentinel data, COMET LiCSAR  for interferograms and NOA and KOERI seismic analysis groups for phase data. Data of ASTER GDEM is the property of  METI  and  NASA. Bathymetry data used in figures are from  EMODnet Bathymetry Consortium  ( 2016 ):  EMODnet Digital Bathymetry (DTM).
Application of BIM The reconstruction was made using Graphisoft ArchiCAD 18, a BIM package that allows plan, section and perspectival renderings of the complex to be derived from a single file (Boeykens 2011: 4). Inconsistency between renderings is therefore not an issue, as it might be with separately drawn ones. As an additional advantage, the package uses a library of pre-set, adaptable ' objects' or architectural elements, which makes it possible to adapt hypotheses quickly. 11 This approach could be likened to a game of building blocks. However, the use of a pre-set 'vocabulary' -the building blocks -is potentially problematic in historical reconstruction. ArchiCAD's library is oriented towards contemporary design, which means that most of its library objects are not suited to the era, architectural style and construction types of the case at hand.
8 Studies using the Hartmann-Shack wavefront sensor aberrometer showed image aberrations in DED,  9, 10  but ignored the scattering effect, and, thus, may have overestimated optical quality in these patients. 11 ore recently, the optical quality analysis system (OQAS; OQAS II; Visiometrics S. L. , Tarrasa, Spain), a double-pass (DP) system aberrometer, was used to evaluate the aberrations and scattering induced by the optical system and tear film during the interblink period,  [12] [13] [14]  showing good reproducibility. 15, 16 OQAS is a noninvasive instrument that records images of a monochromatic point source after reflection on the retina and a double pass through the ocular media. It was developed to provide an objective evaluation of the optical quality of ocular structures. The tear film analysis program of OQAS records dynamic changes of the objective scatter index (OSI) values and calculates the mean value of the OSI over 20 seconds and its standard deviation (OSI SD).