dataesr/scientific-paragraphs-categorization huggingface dataset:
Synthetic data set We synthetically generate 100 datasets that use the actual footprint positions and epochs of the reduced dataset and simulated topography measurements. This allows the evaluation of the actual measurement geometry in the most direct way. Since the difference between the results of the complete and the reduced datasets is small, conclusions drawn from a reduced synthetic dataset can be applied to the com- Fig. 10  Power spectra of the topography of the Moon from the LOLA SHADR model (red), from the SLDEM complemented with the LDEM (blue), and of one realization of a randomly generated topography from l = 300 to l = 7999 (yellow). The simulated spectrum is constrained by a power law which fits the LOLA SHADR power spectrum from l = 300 to l = 2050, the maximum degree of the spherical harmonic expansion of the LOLA SHADR.
The difference between the LOLA SHADR and SLDEM spectra is barely discernible below l ≈ 1000 plete dataset. Each simulated measurement consists of four parts: 1. A spherical harmonic model of the observed lunar topography up to degree 299 (equivalent to a resolution of 18. 2 km at the equator). We use the LOLA SHADR model exclusively using LOLA data  (Neumann 2009b; Smith et al. 2017). We also calculated a power spectrum from the SLDEM by complementing it with the LDEM (Neumann 2014) at latitudes polewards of 60 •. Their power spectra are very similar up to l ≈ 1000 (Fig. 10 ). 2. A synthetic spherical harmonic model of the lunar topography from degree 300 to 7999 (equivalent to a resolution of 680 m at the equator) according to a power law al b. The coefficients of the power law are chosen as a = 3×10 9 m 2 and b = -2.
The recovery of a constant offset between the two tracks, representing a time-dependent signal, yields a bias which is ∼ 30 times larger in the case without mutual correlation after averaging the results of 100 random real-izations. This supports that the observed retrieval bias from synthetic data is only an artifact of the pessimistically simplified simulation and should not be present in the results obtained from real data. The formal errors of the complete dataset, which indicate the precision of the measurement, always remain smaller than the RMSE obtained from the synthetic data set. Due to the simplified simulation of small-scale topography and neglect of other potential error sources, the RMSE still provides an overly optimistic measure of accuracy of the results obtained from real data.
Discussion Figure  1  compares the result of this study with results from previous studies. The result obtained in this work is in agreement with a previous study using LOLA data and the crossover method  (Mazarico et al. 2014). This demonstrates that the method presented in this study is capable of reproducing results achieved using the same data set. However, neither of the LOLA-based values agree with the result from lunar interior structure models constrained by the Love number k 2 (h 2 = 0. 0424,  Williams et al. 2014)  within their respective error bars. On the one hand, this could indicate deficiencies in the LOLA data or their processing. Unaccounted perturbations due to thermal or instrumental effects acting at the tidal frequency of 27. 2 d may cause biases in the results. While the method presented in this study excels at filtering out random noise due to the large amount of measurements, such systematic errors would bias the result.
2008; Mazarico et al. 2014). Fortunately, this limitation does not affect multi-beam laser altimeters like LOLA. However, the upcoming BepiColombo Laser Altimeter (BELA) at Mercury  (Thomas et al. 2007; Hussmann et al. 2018; Thomas et al. 2019)  and Ganymede Laser Altimeter (GALA,  Kimura et al. 2019; Hussmann et al. 2019 ) will be single-beam laser altimeters. Furthermore, the amount of crossovers of these missions will be limited by larger orbital periods and short mission durations due to hostile radiation environments. In the case of BELA, another limitation is the lower pulse repetition rate of only 10 Hz  (Kallenbach et al. 2013). Using the reduced data set, we have achieved a result that differs by less than 1% when omitting 83. 3% of the data, which is promising with respect to future application of the presented method to experiments which provide smaller data sets than LOLA.
( , 2010) )  developed a method for the retrieval of h 2 from laser altimetry data which does not use crossovers directly, but instead solves simultaneously for the global topography and h 2 in a least-squares adjustment. This study advances this method further by implementing 2D splines as basis functions and making the solution strategy more robust. Koch et al. (2008 Koch et al. ( , 2010) )  tested their method only with synthetic data. Here, we apply it for the first time to actual data, choosing the enormous dataset produced by LOLA. The result of h 2 = 0. 0387 ± 0. 0025 agrees with a previous result from the same data set using a crossover method within its standard deviation  (Mazarico et al. 2014) , but is approximately 10% smaller than the probably most reliable value based on a lunar structure model that satisfies the observed value of the k 2 Love number, which is known with an estimated accuracy of 1%  (Williams et al.
Acknowledgements R. Thor is supported by Grant  50 QW 1401  on behalf of the  DLR Space Administration  while preparing his PhD thesis in the framework of the  International Max Planck Research School on Solar System Science at the University of Göttingen. A. Stark was funded by a research grant from the  German Research Foundation  ( HU1286/4-1 ). We thank  H. Hussmann  for helpful discussions and two anonymous reviewers for their comments on an earlier draft of this paper.
The number and quality of seismic stations and networks in Europe continually improves, nevertheless there is always scope to optimize their performance. In this EGU2019 SM5. 2/GI4. 13 session we welcomed contributions from all aspects of seismic network installation, operation and management. This includes site selection; equipment testing and installation; planning and implementing communication paths; policies for redundancy in data acquisition, processing and archiving; and integration of different data sets including GPS and OBS.
This paper introduce a new method for validating the precision of an airborne or a mobile LiDAR data set. The proposed method is based on the knowledge of an a Combined Standard Measurement Uncertainty (CSMU) model which describes LiDAR point covariance matrix and thus uncertainty ellipsoid. The model we consider includes timing errors and most importantly the incidence of the LiDAR beam. After describing the relationship between the beam incidence and other variable uncertainty (especially attitude uncertainty), we show that we can construct a CSMU model giving the covariance of each oint as a function of the relative geometry between the LiDAR beam and the point normal. The validation method we propose consist in comparing the CSMU model (predictive a priori uncertainty) t the Standard Deviation Alog the Surface Normal (SDASN), for all set of quasi planr segments of the point cloud.
INTRODUCTION The validation of LiDAR data is an area of interest for most organization that order surveys and apply quality assurance processes to check the consistency of the data against contractual clauses. The most classical quality assurance procedure consists in comparing LiDAR data on Ground Control Points to check the accuracy of the georeferenced LiDAR data in a geodetic system and with respect to a given projection. This procedure is based on the identification of a geometric target or an intensity sensitive target (photogrammetry like chess mark GCP) and checks the LiDAR georeferenced points on a particular location. However, checking GCP only gives a local accuracy information which may not be representative of the whole survey area. To overcome this difficulty, GCP sampling can be done, but multiplying GCPs significantly affects the survey cost.
Another method can be applied in checking the overlaps between survey strips, like in a strip adjustment approach. Strip adjustment purpose is to correct the platform trajectory and attitude to minimize the strip inconsistencies within the overlap area. However, strip adjustment is an active method that corrects navigation parameter without identifying areas of conflicts within the data set. The method we propose analyses the whole data set and do not rely on the presence of overlaps between LiDAR strip. In comparing two uncertainty models, we can detect areas where abnormal errors occurred without the need for LiDAR data overlap.
Survey data validation methods The use of Ground Control Point is a classical method to detect inconsistencies between the LiDAR data point cloud and a geo-detic point monumented either by an intensity sensitive mark (like in photogrammetry) or a geometrical target. In  [1] , the author defines an hexagonal retro-reflexive target made of 6 reflectors. The target can be identified in the LiDAR point cloud and an accurate estimate of its centre can be done by matching the local point cloud data on a geometric model of the target. In comparing the target centre coordinates with the actual coordinates of the GCP, the local accuracy can be obtained. More recently, in [3], a generalization of GCP analysis by planes intersection was proposed as well as a method to select appropriate planes to get a reliable virtual GCP.
NUMERICAL RESULTS We made a test on a dataset acquired at the HIEG-VD with a Microdrones mdLiDAR1000 UAV system. This system comprises a md4-1000 UAV on which is integrated a SICK LiDAR and an Applanix APX15 INS. The CSMU model includes the model of the range uncertainty of the SiCK LiDAR. The uncertainty level of the IMU (attitudes) and of the GNSS (position of the PRP) is determined from the Applanix data sheet. The CSMU model was shown to be consistent with the Microdrones mdLIDAR1000 performance level. Indeed, the (1-σ) precision of individual points measured by this system is about We first segmented the data set from the HEIG-VD building which had the effect of removing all vegetation and irregular shapes. Only quasi planar segment were kept to conduct the survey data validation analysis.
CONCLUSION We presented an new CSMU model which includes timing effect and LiDAR beams incidence angle. We studied a particular UAV LiDAR system (the mdLiDAR1000 from Microdrones) in doing static tests of the LiDAR to provide a Standard Deviation model depending on range and beam angle. A new methodology to validate airborne LiDAR data was proposed. It is based on the comparison of the uncertainty level that we observe from the point cloud and the level of uncertainty we can expect from the system we use (i. e; the predictive CSMU model). Whenever the a priori CSMU model is lower than the observed local uncertainty model, we conclude that the system gathered point with an abnormal level of uncertainty and we tag it as non validated. This approach enabled us to detect an inconsistency in the data set that should not have been detected otherwise.
Moreover, further analysis of non validated point can reveal non regular behavior of the system or, in case of the survey realized with the mdLiDAR1000 non regular features (the underground gutter), as all point were validated. Another possible application of this method can be the detection of the presence of a systematic error within the LiDAR system. Indeed, any calibration error can create local discrepancies between survey lines and thus make the SDASN to be greater than the CSMU. In this case, the validation tool will return unvalidated points in areas where systematic errors are observable. This could facilitate the data analysis for clients of LiDAR surveys in order to validate the relative consistency of point cloud delivered by contractors.
ACKNOWLEDGEMENTS We thank  Microdrones Canada Inc  and  Microdrones Gmbh  for their support in providing data from the mdLiDAR1000 and mdLiDAR3000 LiDAR UAV systems. We also thak the  HEIG-VD  for welcoming us to make mdLiDAR1000 flights in the Campus in the framework of a joint workshop. The author would like to thanks  Rabine Keyetieu  for its final review and help for preparing this manuscript.
27 iven the lack of prospective data, and complexities of the EGFR mutational spectra, numerous real-world studies have assessed the activity of EGFR TKIs in patients with uncommon mutations. Such studies, although not a substitute for prospective data, can play an important complementary role to randomized trials for informing clinical decision making, especially in special populations. 28 The aim of this study was to analyze the available data for afatinib and provide a searchable database. The outcomes of close to 700 patients have been included.
Materials and Methods Details of patients with tumors harboring uncommon mutations treated with afatinib in randomized clinical trials, compassionate-use and expanded-access programs, phase IIIb trials, and noninterventional trials have been collected prospectively since the start of development of afatinib by Boehringer Ingelheim medical information. In addition, a systematic literature review was undertaken on September 19, 2019 (Supplementary Methods). All identified cases with outcome data provided (either time to treatment failure [TTF] or objective response rate [ORR]) were included. Double reporting was ruled out by scrutiny. Central testing was only performed in patients enrolled in the LUX-Lung trials. In all other patients, mutation detection was undertaken locally using different methodologies (Table  1    ).
Only limited details regarding insertion subtype were available in this analysis. However, certain rare exon 20 insertions were documented that were sensitive to afatinib, including A767delinsASVD  39  and A767_S768insSVA. 40 Therefore, afatinib could be a treatment option against some exon 20 insertions in the future, which remains an area of unmet medical need, despite recent progress in the development of specific agents. Osimertinib has demonstrated preclinical activity against a range of exon 20 insertions,  72  and several trials are ongoing (e. g. , NCT03424759, NCT03434418). Poziotinib has shown promising clinical activity in an ongoing phase II trial. 73 ther TKIs specifically targeting exon 20 insertions, such as TAK-788 and JNJ-372, have demonstrated modest activity in clinical trials.
Other types of uncommon mutation that demonstrated sensitivity to afatinib included missense exon 19 mutations (e. g. , L747P, L747S), missense exon 20 mutations (e. g. , Q787Q, R776H, V765M), missense We acknowledge that this study has several limitations. First, 86 of the patients included in the analysis were published case studies, and this may introduce bias because it is more likely that positive cases are published. Second, EGFR mutation detection platforms vary widely, and different testing methods have been used across studies in our analysis; central testing was only performed in the LUX-Lung trials. This may have introduced some unrecognized biases. Third, the database currently only captures treatment of patients with afatinib (versus other EGFR TKIs). Finally, as is the case with most real-world studies, tumor response was based on investigators' report without monitor or audit.
For this reason, TTF was chosen as a pragmatic end point suited to real-world studies because it is not dependent on Response Evaluation Criteria in Solid Tumors classification of tumors; it encompasses the common clinical practice of continuing treatment beyond radiologic progression and can be measured from electronic health records or claims databases. Recently, TTF has been found to correlate with progression-free survival in clinical trials. 78 n conclusion, this study supports the use of afatinib for the treatment of tumors harboring several uncommon mutation categories: (1) major uncommon mutations; (2) compound mutations, especially those including a major uncommon mutation; and (3) perhaps certain uncommon mutations in the "other" category and certain exon 20 insertions. These categories require further clinical evaluation to identify specific mutation subtypes that may be sensitive.
In total, 87 patients with tumors harboring other uncommon EGFR mutations received afatinib. This category was highly heterogeneous and encompassed rare EGFR mutations across exon 18 (n ¼ 19), exon 19 (n ¼ 25), exon 20 (n ¼ 25), exon 21 (n ¼ 14), and noncanonical mutations (n ¼ 4). A searchable database of these outcomes for afatinib reported by EGFR genotype was created for individual genotype-efficacy critique (www. uncommonEGFRmutations. com).
Acknowledgments This study was funded by  Boehringer Ingelheim. The sponsors played a role in the collection, management, analysis, and interpretation of data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication and, as such, are included in the author list. Medical writing assistance, supported financially by  Boehringer Ingelheim Pharmaceuticals Inc. , was provided by  Lynn Pritchard of GeoMed , an  Ashfield Company , part of  UDG Healthcare plc , during the preparation of this article.
193 Phase IIIb open-label trial in 15 Local testing; individual Unpublished (NCT01931306) patients with EGFR TKI-naive institutions' methodology EGFRmþ NSCLC 1200. 51 Compassionate-use program in 57 Local testing; methodology Heigener et al. , 2015 20 EGFR TKI-pretreated patients: not documented German subgroup 1200. 45 United States expanded access 46 Local testing; methodology Kim et al. , 2017 30 program not documented Global Compassionate use program in 294 Local testing; methodology Heigener et al. , 2015 20 ; compassionate EGFR TKI-pretreated patients not documented Cappuzzo et al. , 2018 31 use program 1200. 205 Multicenter German cohort study in 20 Local testing; methodology Brueckl et al. , 2018 32 patients with EGFR TKI-naive not documented EGFRmþ NSCLC Italian cohort Multicenter Italian cohort study in 5 Local testing; direct Passaro et al.
Study population Between 1997 and 2016, 508 patients were included in the Stanford prospective PAH registry. Inclusion criteria included a diagnosis of idiopathic, familial, drug and toxins, or connective tissue disease-related PAH (MPAP ! 25 mmHg and pulmonary arterial wedge pressure (PAWP) 15 mmHg) and complete RHC data as defined below available at inclusion (defined by the date of first RHC). Exclusion criteria included other PAH etiologies (with shunts such as congenital systemic-to-pulmonary shunts or portal hypertension, HIV-related, or non-WHO group 1 causes) or incomplete or unavailable RHC data. A total of 308 patients were included and will be referred to as the ''total cohort. '' Follow-up RHC was available in 187 patients (including 99 patients with more than one followup RHC) who will be referred to as the ''follow-up cohort.
9, 10 he protocols for animal preparation, anesthetic maintenance, and mechanical ventilation have been previously reported. 11, 12 Maximal RVPmax and LVPmax were simultaneously measured using fluid-filled catheters at different conditions. A period of 15 min of hemodynamic stabilization was observed during each condition prior to data acquisition and after return to baseline. Hemodynamic data were measured during short end-expiratory apnea. Pressures were recorded on a dedicated computer. CO was measured by the thermodilution method, total pulmonary resistance was calculated by MPAP (measured using a Swan-Ganz catheter) divided by CO.
05. Hazard ratios are presented per standard deviation of the considered continuous variables to correct for variations of units. Results were considered significant when two-sided p-values < 0. 05. Statistical analyses were performed using SPSS statistical software (SPSS v. 25, Inc. , Chicago, IL) and MedCalc v. 18. In the experimental cohort, hemodynamic data were presented as median and interquartile range. Values were compared using the non-parametric Mann-Whitney test for unpaired values and the Wilcoxon rank test for paired values using GraphPad Prism (8. 3. 0). Results were considered significant when two-sided p-values < 0. 05.
This provides ''real-life'' data complementary to the analysis of clinical trials by Ventetuolo et al. 20  The second limitation of the longitudinal analysis, as in any study relying on follow-up data, is the risk of survival bias, as the follow-up analysis only includes survivors at time of the second RHC. The third limitation originates from the use of the indirect Fick or thermodilution methods to estimate CO in our retrospective study. As discussed previously, this might have added imprecision in the estimation of the CO but it reflects the reality of most catheterization laboratories in which use of direct Fick can be cumbersome for clinical workflow. Overall, this highlights the need for novel metrics independent of CO estimation. Finally, it should be acknowledged that the incremental value of MPAP/MAP or MPAP was evaluated using a basic hemodynamic model, instead of recent more established risk scores such as the REVEAL score (as not all its variables were available for all patients in close proximity to the RHC).
Funding This study was supported by the  Vera Moulton Wall Center of Pulmonary Hypertension at Stanford  (2016 Young Investigator Seed Grant-M. A. ),  French National Research Agency  ( ANR-15-RHUS-0002 -O. M. ), and  Actelion-Janssen  ( Roadmap to Right Heart Phenotyping in Pulmonary Hypertension Research Grant -F. H. ).
For both PCRs, the following protocol was used: 94°C for 5 min, 35 cycles at 94°C for 30 s, 56°C for 30 s and 72°C for 90 s, and a final elongation at 72°C for 5 min. PCR products were purified using AMPure (Agencourt) with a Biomek 4000, and Sanger sequencing reactions were conducted (96°C for 5 min, 30 cycles of 96°C for 15 s, 50°C for 10 s and 60°C for 4 min). Sequencing products were purified using CleanSEQ (Agencourt) with a Biomek 4000 and analysed with capillary electrophoresis. In total, 92 samples were sequenced and the sequences are publicly available on GenBank (accession nos. : MK722526-MK722617, Table  S3 ).
| Phylogenetic analyses of CpDV diversification Sequences were aligned using mafft version 7. 273  (Katoh & Standley, 2013)  and lack of recombination was verified using rdp4 software  (Martin, Murrell, Golden, Khoosal, & Muhire, 2015). Two samples (Elmanar_10_2011 MK722527 and Fontaine_80_2011 MK722526) were excluded from the analyses because of the presence of a large indel. jmodeltest was used for model selection  (Posada, 2008). Phylogenetic analyses were conducted in beast 1. 10. 5  (Suchard et al. , 2018)   All sequences were considered isochronous and a strict molecular clock with a rate fixed to 1 was set. The evolutionary process was reconstructed under a constant population size model and an HKY85 + I nucleotide substitution model  (Altinli, Lequime, et al. , 2019; Hasegawa, Kishino, & Yano, 1985; Yang, 1994).
| Statistical analyses All data analyses were performed in r version 3. 3. 1. To analyse the effects of sampling site, sampling year  (2010) (2011)  and Wolbachia groups (wPip-I, wPip-IV) on CpDV prevalence, we first fitted a generalized linear model from the binomial family. Likelihood ratio tests of the full model against the model without a given effect were used to obtain deviance and p-values. A subset of the data was then created to include only those sites where both Wolbachia strains coexisted and where at least one sample was CpDV positive. Here in each sampling site tested, the probability of a given sample being infected by CpDV was assumed to be independent from the sampling sites where samples were collected. Hence, a binomial generalized linear model was used to test the effect of sampling year (2010 or 2011) and Wolbachia groups (wPip-I, wPip-IV) on CpDV prevalence.