dataesr/scientific-paragraphs-categorization huggingface dataset:
Cross-Sensor Comparison of Effective Snow Cover Products The agreement between the mapping of the FSC derived from S-2 imagery and H12 product has been assessed according to the same pixel-to-pixel approach. Like in the assessment of H10 data, the complex topography in mountainous areas affects the consistency between the two analyzed datasets, especially over the Italian Alps, where RMSE values are higher than the other case studies (Figure  17 ). However, RMSE scores are generally lower than 0. 4, except for the same tiles in Italy, in compliance with the product requirements  [105]. Figure  18  shows that during the winter period RMSE values are generally higher than in other seasons. This issue is mainly due to overestimated classifications as full snow cover over the coarser spatial resolution of H12 product with respect to that derived from 20-m S-2 imagery, especially in mountainous regions.
e. , needle-leaved evergreen forest) in Finland, V2vegetation class (i. e. , broadleaved deciduous forest) has a higher impact in Turkey. Figure  19  shows that by filtering out V1-vegetation pixels, the RMSE value in Finland increases with respect to the allpixels analysis. The reduction in consistency between H12 and S-2 snow mapping suggests a good agreement of the two datasets over dense forests mainly due to the long-lasting snow interception over canopy during the winter season. Consistently, in Turkey the resulting RMSE slightly increases when filtering V2-vegetation pixels.
Conclusions This study investigated the potential of using S-2 data to assess moderate-resolution EUMETSAT H-SAF product of snow extent (H10) and FSC (H12) in Finland, the Italian Alps and Turkey. Snow masks derived from S-2 imagery have revealed a significant consistency with both ground-based snow measurements (POD = 0. 82, FAR = 0. 08,) and in-situ webcam photography, revealing a RMSE of about 12%, in terms of FSC. Hence the reliability of assuming this highresolution dataset as a reference for intercomparison purposes. The results obtained in this study reveal that S-2 data can be properly used to continuously assess these medium resolution satellite snow products, which have been commonly validated against in-situ data so far  [53, 56]. However, it is noteworthy to consider that under specific conditions the snow mapping derived from S-2 data can be affected by critical flaws.
Indeed, the analysis of camera images in the Italian Alps has shown that dense cloud cover can undermine the reliability of S-2 snow masks, mainly when patchy snow cover is present. Furthermore, during melting period the widespread presence of meltwater over flat areas may lead to an overestimation of snow cover. The results of the cross-sensor comparison prove that the analyzed H-SAF snow products are highly consistent with S-2 imagery in detecting snow, also in terms of FSC, generally in compliance with the products requirements  [104, 105]. Nevertheless, the analyzed satellite datasets generally reveal a higher agreement over flat/forested areas (PODH10 and RMSEH12 equal to 0. 98 and 0. 15, respectively) than in mountainous regions (over Italian Alps, PODH10 and RMSEH12 equal to 0. 78 and 0.
Conclusions This study investigated the potential of using S-2 data to assess moderate-resolution EUMETSAT H-SAF product of snow extent (H10) and FSC (H12) in Finland, the Italian Alps and Turkey. Snow masks derived from S-2 imagery have revealed a significant consistency with both ground-based snow measurements (POD = 0. 82, FAR = 0. 08,) and in-situ webcam photography, revealing a RMSE of about 12%, in terms of FSC. Hence the reliability of assuming this high-resolution dataset as a reference for intercomparison purposes. The results obtained in this study reveal that S-2 data can be properly used to continuously assess these medium resolution satellite snow products, which have been commonly validated against in-situ data so far  [53, 56]. However, it is noteworthy to consider that under specific conditions the snow mapping derived from S-2 data can be affected by critical flaws.
Indeed, the analysis of camera images in the Italian Alps has shown that dense cloud cover can undermine the reliability of S-2 snow masks, mainly when patchy snow cover is present. Furthermore, during melting period the widespread presence of meltwater over flat areas may lead to an overestimation of snow cover. The results of the cross-sensor comparison prove that the analyzed H-SAF snow products are highly consistent with S-2 imagery in detecting snow, also in terms of FSC, generally in compliance with the products requirements  [104, 105]. Nevertheless, the analyzed satellite datasets generally reveal a higher agreement over flat/forested areas (POD H10 and RMSE H12 equal to 0. 98 and 0. 15, respectively) than in mountainous regions (over Italian Alps, POD H10 and RMSE H12 equal to 0. 78 and 0.
Acknowledgments: The research has been conducted in the framework of the  EUMETSAT H-SAF  Project, thanks to the collaboration among several partner institutes of the validation cluster of snow products. We wish to thank all the institutions for supporting and promoting these research activities. We are primarily grateful to the  European Cooperation in Science and Technology (COST) , which has funded a  Short Term Scientific Mission (STSM)  of key importance for the success of this collaboration through the  ES1404 Harmosnow Action. The work has been also supported by the  Italian National Department of Civil Protection. The authors would like to acknowledge the  Environmental Protection Agency of Aosta Valley (Italy)  for providing Italian webcam datasets used in this study. We would like to address special thanks to  Edoardo Cremonese  for the fruitful discussions on this matter.
Recordings and statistical analysis During the maturation experiments, each jar containing 100 mg FW callus was considered as an experimental unit and replicated 10 times, and we recorded the mean number of mature somatic embryos per 100 mg FW callus after 12 weeks. During the germination phase, each jar containing 10 mature somatic embryos was considered an experimental unit and each treatment consisted of 10 replications. The frequency of germinated somatic embryos converted into plantlets was calculated after 3 months. The survival rate of plantlets was recorded after 6 months in the glasshouse. Data presented are means + standard deviation (SD). All data were analyzed using analysis of variance (ANOVA). The adopted model was Completely Randomized Design (CRD) and means with significant differences were separated using the Student-Newman-Keuls test (SNK) at p < 0.
2D Monolayer Cell Viability Screening The Hep3B, HCC cell line, was seeded in 384-well plates at a density of 500 cells per well with technical duplicates. The 2D monolayer cells were treated with 60 drugs using a Janus Automated Workstation (PerkinElmer, Waltham, MA). Six days after the drug treatment, cell viability was accessed using an adenosine triphosphate (ATP) assay system based on the firefly luciferase (ATPLite 1step, PerkinElmer, Shelton, CT). Cell viability was measured using an EnVision Multilabel Reader (PerkinElmer, Waltham, MA). Control wells, containing only cells with vehicle (DMSO), were included on each assay plate. These controls were used to calculate the relative cell viability for each plate and to normalize the data on a per plate basis. After normalization, the relative 2D monolayer cell viability was calculated using GraphPad Prism 6.
Drug Library for Efficacy Test We searched for clinical cancer drugs that were undergoing clinical trials and for the standard targeted oncology drugs. Using various drug libraries, we selected 60 drugs whose targets are well known, including the following: epidermal growth factor receptor (EGFR), phosphoinositide 3-kinase (PI3k), mechanistic target of rapamycin (mTOR), vascular endothelial growth factor (VEGFR), c-mesenchymal-epithelial transition factor (c-Met), and fibroblast growth factor receptor (FGFR). Those drugs included were in either phase III or phase IV trials or are approved oncology drugs from public data provided by the U. S. Food and Drug Administration (FDA). We applied 60 drugs to the 3D preformed Hep3B cells to identify possible new indications from existing drugs in drug repositioning for HCC.
9 μM). Previous studies have reported on the efficacy of the seven drugs in HCC  cells. AZD2014 has been used in a phase II trial (ClinicalTrials. gov identifier: NCT03166176) in solid cancers that were TSC 1 or 2 Null or had TSC 1/2 mutations, as well as in liver cancer patients. AZD2014 has also been reported to have efficacy in Hep3B cell lines. 19 AUY922 has been reported to be active against angiogenesis in human HCC. 20 AZD6244 increases the efficacy of sorafenib in the combination treatment of HCC. 21 Bortezomib overcame tumor necrosis factor-related apoptosis-inducing ligand resistance in HCC. 22 Crizotinib induced autophagic cell death in HCC. 23 Panobinostat and carfilzomib increased the efficacy of sorafenib in the combination treatment of HCC. 24, 25 he main targets of the 60 drugs are EGFR, PI3k, mTOR, VEGFR, c-Met, and FGFR.
Funding The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by a grant from the  Korea Health Technology R&D Project  through the  Korea Health Industry Development Institute (KHIDI) , funded by the  Ministry of Health & Welfare, Republic of Korea  ( HI17C2412 ); by a  National Research Foundation of Korea (NRF)  grant funded by the  Korea government (MSIT)  (no. 2018R1C1B5045068 ); and by the  X-mind Corps program  of the NRF funded by the  Ministry of Science, ICT  ( 2018025568 ).
Study patients Pharmacokinetic analyses were undertaken using plasma concentration data from patients who par-ticipated in either of two completed Phase III trials which have been described previously  [35, 36]. In brief, the first (TRx-237-015) was a 15-month study which recruited 890 patients from 115 sites across 16 countries in EU, North America, Asia, and Russia. Patients had to be <90 years of age and have a diagnosis of mild to moderate probable AD according to National Institute of Aging (NIA) and Alzheimer's Association (AA) criteria  [38]  and a Mini-Mental State Examination (MMSE) score of 14-26 inclusive and with a Clinical Dementia Rating (CDR) total score of 1 or 2. The second study (TRx-237-005) was an 18-month trial which recruited 800 patients from 108 sites in Canada, United States, Australia, and Europe.
MRI scans were undertaken at screening or baseline and at 3-month intervals. MRI acquisition protocol and parameters were standardized across sites and all data were centrally collected, quality-controlled, and analyzed blind by an imaging core lab (Bioclinica). Volumetric data were used to measure changes in whole brain volume (WBV, which provides a measure of the volume of grey and white matter), lateral ventricular volume (LVV), and other regional volumes including hippocampus, although only the global measures are reported in the present study. Baseline volumes were assessed using FreeSurfer 5. 3, and volume change was assessed using Boundary Shift Integral and Tensor Based Morphometry for regional structures. Full details and study protocols for these two trials are available  [35, 36].
Application of the model to the observed parent MT concentrations from the Phase III studies was dependent on the availability of exact date and time of dosing relative to sample collection. This was recorded most accurately for samples collected in clinic on Day 1. Data from post-Day 1 visits were available, but the lack of precise timing of sample collection relative to last dose reduced their accuracy. Although the results were directionally comparable to the more precise Day 1 estimates, only the latter were used for the exposure-response analyses reported here based on the per-subject estimated steady state maximum concentration of the drug (C max,ss ). Renal function, estimated using the Cockcroft-Gault equation for creatinine clearance  [39] , was a significant term in the model; age, weight, body mass index, albumin, and other factors were not significant.
Statistical analyses The exposure-response analyses were conducted independently by ICPD and verified by TauRx. As a first step in the analysis, the concentration-response data were reviewed for ADAS-cog 11 decline at week 65 in all patients receiving LMTM at a dose of 8 mg/day using a sigmoid E max (maximum response) model not requiring assumptions regarding exposure subgroups. The model was fitted using covariates geographical region, CDR at baseline, co-medication status, and baseline ADAS-cog 11. A 90% bootstrap confidence interval was also calculated. Having shown a concentration-dependent response for cognitive decline, a mixed-effects model for repeated measures analysis was used to characterize further the relationship between each efficacy endpoint and parent MT plasma exposure (steady state C max,ss ) according to the following formula: pharmacological activity ∼ plasma-level × visit + co-medication-status × visit + co-medication- status × plasma-level + geographical-region + CDR-at-baseline + baseline-score × visit The following terms were categorical variables in the models: plasma exposure (C max,ss ) (7 levels), visit (5 levels), co-medication status with AD drugs (2 levels), geographical region (2 levels), and CDR at baseline (3 levels).
RESULTS The exposure-response relationship was first examined for decline on the ADAS-cog 11 scale over 65 weeks in patients receiving the 8 mg/day dose using a sigmoid E max analysis in which both the plasma concentration and cognitive decline data sets were included as continuous variables. As can be seen from Fig. 1 , there is a clear relationship between cognitive decline and steady state plasma concentration of the drug at the 8 mg/day dose. This implies that LMTM at a dose of 8 mg/day has a concentration-dependent effect on cognitive decline. In order to examine this relationship further, patients receiving LMTM at a dose of 8 mg/day were categorized on the basis of the C max,ss into a low exposure group defined by the percentage of patients with first dose plasma levels below the level of quantitation of the assay (0.
The objects description FS CMa. A peculiar spectrum of FS CMa was discovered over 120 years ago  (Pickering et al. 1898 ) and classified as that of a Be star  (Merill et al. 1925). Later a large IR excess due to radiation of circumstellar dust was found, and it became a prototype of objects with the B[e] phenomenon  (Allen & Swings 1976). It was also considered a Herbig Ae/Be star (e. g. ,  Sitko et al. 1995) , but it belongs to no star forming region. Miroshnichenko (2007)  included it in the B[e] subgroup of FS CMa objects, which are thought to be post mass-transfer binary systems. Its binarity has been suspected from spectro-astrometric data  (Baines et al. 2006 ) but unconfirmed by other techniques. FS CMa shows long-term brightness variations, which are large in the visual range (V=6.
Conclusions Both photometric and spectroscopic data show that FS CMa is a highly variable object, while variations of MO Cam are much smaller (Fig. 1 , left panel). Brightness variations of both objects contain no periodic component. There is no clear evidence for secondary companions in the properties of both objects, but the properties are very unusual for single stars. FS CMa may undergo a strong mass transfer phase, while MO Cam might have already finished this phase. A more frequent monitoring at timescales of days to weeks may help understanding the reasons for the objects' variability. Spectroscopic distances to both objects are consistent GAIA parallaxes (0. 62±0. 02 kpc for FS CMa and 3. 0±0. 3 kpc for MO Cam, GAIA DR2, 2018).
Acknowledgements. A. M. and S. Z. acknowledge support from  DGAPA/PAPIIT  Project  IN100614. The work was carried out within the framework of the Project No  BR05236322  " Studies of physical processes in extragalactic and galactic objects and their subsystems " funded by the  Ministry of Education and Science of the Republic of Kazakhstan. The results are partially based on observations obtained at the  Canada-France-Hawaii Telescope (CFHT)  which is operated by the  National Research Council of Canada , the  Institut National des Sciences de l'Univers of the Centre National de la Recherche Scientifique de France , and the  University of Hawaii ;  South African Large Telescope , and  Himalayan Chandra Telescope. We thank the staff of  IAO, Hanle , and  CREST, Hosakote , that made these observations possible.
All cultures were then incubated at 37 °C for 24 h. From one to eight colonies of different morphologies were collected in each medium and reisolated on Trypticase Soy Agar (TSA, BioMerieux SA, France). Each of these colonies was subcultured 3 additional times to obtain a monoculture. Isolates were then identified by matrix assisted laser desorption/ionization, time of flight mass spectrometry (MALDI-TOF MS) and MALDI-Biotyper 3. 0 software (Bruker Daltonics, Bremen, Germany) as described previously  [18]. Bacteria are efficiently identified at the genus and species level when score values are ranging from 2. 3 to 3. 0 and at the secure genus identification and probable species identification when score values are ranged from 2. 0 to 2. 3  [18].
The nucleotide sequences obtained were compared with those from the NCBI database by BlastN analysis. Multilocus sequence typing (MLST) was performed for five ESBL-producing E. coli isolates using genes from both the Warwick (adk, fumC, gyrB, icd, mdh, purA, and recA) (http://mlst. warwi ck. ac. uk/ mlst/dbs/Ecoli ) and Pasteur Institute (icd, pabB, polB, putP, trpA, and trpB) (http://www. paste ur. fr/reche rche/genop ole/PF8/mlst/) schemes. The ST not found in the database was submitted to the Pasteur MLST database to define the new profile.
Acknowledgements This work was presented at the  Twenty-Ninth European Congress of Clinical Microbiology and Infectious Diseases, Amsterdam ,  The Netherlands ,  2019  ( P2161 ). We thank  Lorant Hatvani  and  Guillermo Quindo  ´s for kindly supplying 10 C. fabianii and 2 C. africana isolates, respectively. Cidara Therapeutics, Inc. provided rezafungin and purchased the comparator echinocandins (but did not provide any direct financial support for the study).
A, 16:Taillis. B, 17:Tronçais, 18 :Vieilles Forêts des Pyrénées, 19:Vosges 2. 1. Rapid forest habitat assessment All the stands were characterized by the same observer on a 1-ha circular plot centered on the sampling point where taxonomic data were recorded. The observers followed the protocol for the Index of Biodiversity Potential (IBP;  Larrieu and Gonin, 2008) , which includes attentive observation while crisscrossing the stand. Nine of the ten IBP factors belong to three broad categories: (i) living vegetation: number of native tree-species and number of vertical strata; (ii) deadwood and TreMs: number of large snags, large lying deadwood items, very large trees and living TreM-bearing trees; (iii) associated features: open areas, number of aquatic macrohabitat types and rocky macrohabitat types.
Compositional analysis First, we explored the congruence between the between-plot dissimilarity matrix based on pooled habitat variables (see the set of metrics in Table  1 ) and the between-plot dissimilarity matrix based on species composition (for each of the nine taxa). To do so, we performed a Procrustes rotation on non-metric multi-dimensional scaling (NMDS) ordinations (Peres-Neto and Jackson 2001) of the Bray-Curtis dissimilarity matrix for habitat variables and the Jaccard dissimilarity matrix for species occurrence data. Permutation tests associated to the Procrustes method (protest function, R-package Vegan, with the m² Procrustes statistic; number of runs=1,000) revealed the non-randomness of the congruence in fit between multivariate data tables. In order to account for the nested structure of our dataset, permutations were conditioned upon forested area, which was a stratifying variable.
Next, we performed a Canonical Analysis of Principal Coordinates (CAP,  Anderson and Willis 2003;  capscale function, R-package Vegan) to investigate the effects of each habitat feature on variations in species composition for each taxonomic assemblage. Inertia was partitioned from Jaccard's distance matrices (vegdist function, R-package Vegan) on all explanatory habitat variables, since co-linearity among predictor variables is not a problem in CAP  (Anderson and Willis 2003). After partialling out the geographical effect (i. e. forested area), both in the full CAP combining all the habitat variables and in the individual CAPs performed for each habitat variable, we calculated the proportion of inertia cumulatively explained by all the habitat variables, the relative total contribution (intrinsic + co-explained) of each habitat variable to the total inertia and the statistical significance of this contribution (by means of permutation tests -999 runs).
Species richness analysis We used multi-model averaged estimates  (Burnham and Anderson 2002)  to determine the relationship between each of the 14 taxonomic variables for species richness and each of the environmental variables. For each response variable (count data), we generated Poisson Generalized Linear Mixed Models (GLMM, glmer function, R-package lme4). The mixed models enabled us to include forested area as a random variable. Since co-linearity among predictor variables may lead to unreliable parameter estimates, we followed the strategy suggested by  Zuur et al. (2010)  to address the multicolinearity problem before model averaging. First, we sequentially dropped the covariate with the highest variance inflation factor (VIF; vif. mer function from Frank 2011), then recalculated the VIF; we repeated this process until all VIFs were below the cutoff value of 3 suggested by  Zuur et al.
(2010). Then, we generated models with all the combinations of the remaining variables and calculated the differences in the Akaike information criterion (AICc,  Burnham and Anderson 2002)  scores between each model and the null model (dredge function, R-package MuMIn). We also calculated the Akaike weights for each model (model. avg function, R-package MuMIn). Finally, we selected the best two-variable models. Since our aim was to highlight habitat variables as bioindicators and not to explain taxonomic communities, we did not account for a priori ecological links between the taxon and the habitat variables. Furthermore, we checked the significant relationships (at p<0. 01) between habitat variables and species richness in the GLMM for thresholds. Threshold values were calculated by recursive partitioning and derived from estimates of breakpoints by means of maximally selected two-sample statistics  (Hothorn and Hornik 2006 ; ctree function, R-package party).
This method provides a conditional inference tree with p-values for one or more critical thresholds. When using the ctree function, the model is constrained and does not allow any co-variables or random effects to be taken into account. To deal with this issue, we kept only the most significant results: only primary nodes with highly significant (p < 0. 001) thresholds were selected, and only if the number of plots was greater than 30 on either side of the threshold. Then, 95% confidence intervals (bootstrap percentile interval) were calculated for all the selected thresholds, based on 1,000 bootstrap samples (boot. ci function, R-package boot). In order to standardize sampling effort and to account for missing data for saproxylic beetles, ground beetles and hoverflies (traps destroyed by animals, wind, rain or humans), we used the log of a corrected factor based on the number of undisturbed sampling months as an offset in our models.