dataesr/scientific-paragraphs-categorization huggingface dataset:
Prior to U-Pb dating, a preablation was conducted at 155 μm, 8 Hz, and with a fluence of 4 J/cm 2 in order to reduce potential contamination. For each sample, 40 to 90 ablation spots were analyzed. The NIST614 glass standard was used for the 207 Pb/ 206 Pb calibration, and the WC-1 calcite was used for 238 U/ 206 Pb calibration (254:4 ± 6:4 Ma; Roberts et al. [75] ). Two secondary carbonate standards were also analyzed to validate the quality of the dating procedure (Duff Brown, 64:04 ± 0:67 Ma-dated using the isotope dilution method, Hill et al. [76] ; B6, 42. 99 ± 0. 98 Ma-dated from LA-ICPMS, Pagel et al. [77] ). The uncertainties in brackets correspond to the propagated uncertainties (see Supplementary Materials for analytical details (available here)). Signals were processed with Iolite© software  [78] , and ages were obtained from the conventional Tera-Wasserburg diagram  [79]  using Isoplot© software  [80].
Acknowledgments The authors are grateful to  Thomas Blaise  and  Benjamin Brigaud  ( GEOPS laboratory, Paris-Saclay University ) for fruitful exchanges on U-Pb calcite dating. U-Pb analyses were performed on the HR-ICPMS which is part of the  PANOPLY platform (GEOPS-LSCE ). O. P. benefits from a PhD Grant from the  Paris-Sud University. Valérie Godard  is thanked for thin-section production.
Another study conducted in various virtual environments  3  showed that musical performances of five musicians (violin, oboe, two flutes, baritone singer) presented statistically significant differences, which were perceived by participants of a listening test, induced by the difference in acoustics between the rooms. A further analysis of this data  4  showed that the musicians played a conscious part in their adjustments to room acoustics, as they were able to elicit some aspects in a questionnaire. The adaptation of eleven trumpet players to room acoustics was specifically investigated. 5 Significant correlations were found between certain musical features describing sound level, timbre, dynamics, and tempo, as well as room acoustic parameters related to reverberation time and sound intensity of the room response.
Parameter extraction The room acoustic parameters, calculated from the measured room impulse responses by the ITA-toolbox  13  and custom MATLAB codes, are depicted in Fig. 2(A). The choice of these acoustic parameters is explained in a previous paper. 7 They represent independent perceptual dimensions, described in Ref. 14, namely, reverberance (early decay time; EDT), loudness (early stage support; ST early ), voice clarity (speech transmission index; STI), listener envelopment (late interaural cross-correlation; IACC late ), and bassiness or tonal color (bass ratio; BR). All parameters were averaged over the 500 Hz and the 1 kHz octave bands, except IACC late, which was averaged over the 125 Hz to 4 kHz octave bands, and ST early over the 250 Hz to 2 kHz octave bands, according to the relevant standard ISO-3382-1.
11 This glottal parameter reflects the amount of time during which vocal folds are in contact with each other over a vibratory cycle. Its value depends on laryngeal mechanism (M1 or M2, see Refs. 17 and 12). It varies with the amount of vocal effort, being related to vocal intensity in M1 and to fundamental frequency in M2. 17  3. 1 Statistical analysis Data analysis was conducted in the framework of Linear Mixed Models (LMM). 18 This approach is particularly suited to our dataset-four singers, each performing four musical excerpts in eight rooms-which is structured in hierarchical levels of variance. Voice production parameters were considered as dependent variables that could be explained to a certain extend by room acoustic parameters (fixed effects, independent variables). The various levels of hierarchical data (musical pieces, rooms, and singers when not taken individually) were considered as random effects.
One of them, IACC late , which represents the spatial characteristics of the room response such as the listener envelopment, was the most involved room parameter in the adaptation process of the singers. It suggests that singers were particularly sensitive to the spatial aspect of room responses, which influenced their voice production. These results can be compared to the findings of the recent study that analyzed the same audio dataset in terms of musical features (i. e. , parameters related to musical interpretation) as dependent variables. 6 The singers showed adaptation involving mostly BR and ST early , which relate to timbre and sound level of the room response, respectively. In contrast, the results of the current paper, focused on voice production parameters, involved the room parameter (IACC late ) related to spatial aspects, namely, listener envelopment.
[13] Here both are nominally constant, so have been disregarded from the analysis. Furthermore, precautionary measures such as carbon coating may be used to reduce beam damage by reducing the loss of volatile species formed during irradiation. [15, 19, 31] Note that this would not necessarily preserve crystallographic structure, but just overall composition. We investigate the relationship between electron dose, quality of STEM-EDX signal, and beam damage for quantitative analysis of a TCDH perovskite as a model beam-sensitive material with a complex chemistry. In particular, we outline five parameters which may be used to quantify STEM-EDX data quality and beam-induced chemical changes: peak detectability, relative measurement error on a pixel-by pixel-basis, absolute measurement error for multiple pixels, HAADF intensity ratio, and elemental peak intensity ratio.
Acknowledgements F. U. K. thanks the  Jardine Foundation and Cambridge Trust  for a doctoral scholarship. S. C. thanks funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowoska-Curie Grant Agreement   N845612. The research leading to these results has received funding from the  European Union   Horizon 2020 Research Infrastructures research and innovation programme  under grant agreement No. 823717 -ESTEEM3. The authors thank  Dr. Mojtaba Abdi-Jalebi  ( Cavendish Laboratory, University of Cambridge, now at Institute of Materials Discovery, University College London ) for PSC fabrication.
A B S T R A C T Post-transplant cyclophosphamide (PTCy) has significantly increased the successful use of haploidentical donors with a relatively low incidence of graft-versus-host disease (GVHD). Given its increasing use, we sought to determine risk factors for GVHD after haploidentical hematopoietic cell transplantation (haplo-HCT) using PTCy. Data from the Center for International Blood and Marrow Transplant Research on adult patients with acute myeloid leukemia, acute lymphoblastic leukemia, myelodysplastic syndrome, or chronic myeloid leukemia who underwent PTCy-based haplo-HCT (2013 to 2016) were analyzed and categorized into 4 groups based on myeloablative (MA) or reduced-intensity conditioning (RIC) and bone marrow (BM) or peripheral blood (PB) graft source. In total, 646 patients were identified (MA-BM = 79, MA-PB = 183, RIC-BM = 192, RIC-PB = 192).
Data Source This was a retrospective analysis using data from the CIBMTR. The CIBMTR is a combined research program of the Medical College of Wisconsin and the National Marrow Donor Program. The CIBMTR comprises a voluntary network of more than 420 transplantation centers worldwide that contribute data on consecutive allogeneic and autologous HCTs to a centralized statistical center. Observational studies conducted by the CIBMTR are performed in compliance with all applicable federal regulations pertaining to the protection of human research participants. Protected health information used in the performance of such research is collected and maintained in the capacity of the CIBMTR as a public health authority under Health Insurance Portability and Accountability Act regulations. The CIBMTR collects data at 2 levels: Transplant-Essential Data (TED) and Comprehensive Report Form (CRF).
8 0 2 (1) 1 (1) (1) To further determine the significance of graft source on chronic GVHD, subset analyses of MAC (n = 262) and RIC (n = 384) cohorts were performed. This confirmed an increased incidence of chronic GVHD with PB compared to BM in both MAC (HR, 1. 81; 95% CI, 1. 00 to 3. 28; P =. 05) and RIC (HR, 1. 72; 95% CI, 1. 10 to 2. 70; P =. 02) groups, but this met the predetermined significance level only in the RIC group. Due to the known differences in age between MAC and RIC groups, an age-adjusted analysis for chronic GVHD was also performed and demonstrated similar results (data not shown). We further analyzed chronic GVHD in a larger cohort of CIBMTR patients receiving a haploidentical transplant (n = 1401) using TED level data and confirmed that in the RIC setting, PB was associated with higher rates of chronic GVHD (P =.
Finally, we included only patients with acute myeloid leukemia, acute lymphoblastic leukemia, chronic myeloid leukemia, and myelodysplastic syndrome, and thus our findings are applicable to only this patient population. In sum, our results show that PB stem cells contribute to an increased risk of chronic GVHD and NRM in RIC PTCy-based haploidentical HCT and that other outcomes such as relapse and OS are similar. These results aid in decision making regarding graft source in RIC PTCy-based haploidentical HCT. Prospective evaluation of PB versus BM using PTCy platforms is needed to confirm these findings. D. W. reviewed design of study, provided data analysis, and critically reviewed the manuscript; GVHD Working Committee members provided review of study design, analysis, and review of manuscript.
COVID-19 Burden We downloaded reports of COVID-19 cases and deaths from the New York Times 10 for US states and from Our World In Data 11 for countries. We used mortality rate on April 15, 2020, as a rough measure of severity of the pandemic in each of these geographic areas, and subdivided burden into quartiles, based on number of reported deaths per 1 000 000 population, as follows: low (<2. 6), medium (2. 6-13. 8), high (13. 8-53. 7), and very high (>53. 7).
Statistical Methods Analyses were performed using SPSS, version 26. 0 (IBM, Armonk, NY), and R, version 4. 0. 12 To compare categorical variables, we used the chi-square test. To compare continuous variables, we used independent t tests, the Wilcoxon test (paired or unpaired), and Spearman correlation, as appropriate. To assess for an association between practice (multinomial) and COVID-19 burden (ordinal), we used a modified Cochrane-Armitage Test. 13 P values less than. 05 were considered statistically significant.
Element concentrations as measured in moss samples are supposed to represent the accumulated load of the past 2-3 years (Pakarinen and Rinne 1979). Due to this accumulation, element concentrations measured in moss are much higher than in other sample materials, such as precipitation, dust, or other plants, and thus are easier to measure  (Asakawa et al. 2013). The main objective of the present study was to test the statistical approach of the t quantile to compare elements bioaccumulated in P. schreberi collected from Polish national parks (PNP) and to compare the results between lowland and highland/mountain parks. Our study also examined the view that special care should be taken in regional comparisons of bryophyte concentrations of trace elements. A minor objective is to compare the results with earlier data from Poland.
Weak grouping in the data enables application of covariability analysis. To assess the linear covariability between pairs of element concentrations, the quantiles of the t parameter distribution were calculated. In Table  3 , the results of the computations are shown. Symbols describe the covariability character, i. e. , "r" is for random or no linear relationships between concentrations, "p" is for positive covariability and "n" is for negative covariability. For comparison, the commonly used data interpretation Pearson's correlation coefficients r were calculated. The r parameters were tested for a 0 value in the population, at a p-level of 0. 02. For 2D normally distributed data composed of variable pairs representing 30 cases, the critical, absolute |r c | value is 0. 423. In Table  3 , positive correlations bigger than r are in bold and negative correlations less than r are underlined.
Conclusions Moss samples were collected in unpolluted areas of national parks. The distribution of element concentrations was differentiated. Some of the distributions were significantly skewed and in the data outliers are expected. Although their elemental composition was not uniform, clear spatial patterns in concentration distributions were not observed. In the linear covariability estimation, the t quantile approach was used. A number of positive covariabilities were observed. This could be a result of the geochemical characteristics of the local environment, including the crust composition to which soil composition is related. Due to the protection of the national park areas, the influence of pollution sources on the elemental composition of moss is limited. The concentrations of most elements  (As, Ba, Ca, Cd, Cl, Cr, Cu, Mo, Pb, Sr, and Zn)  were higher in mosses collected from highland and mountainous parks than in lowland parks, with the opposite relationship only for Mn.
However, they did not consider the interactions between large-scale flow and microphysics. Keppas et al. (2018)  studied the microphysical properties of a warm front with radar and in situ measurements in clouds. They found that a WCB formed a widespread mixed-phase cloud by producing a significant amount of liquid water, which favoured riming and secondary ice generation. However, they did not formally identify WCB trajectories nor did they confirm that the liquid water was produced within the WCB. There is hence a need to better understand how the strong, coherent ascending motion within WCBs influences precipitation microphysics. To this end, a synergy between remote-sensing and in situ measurements as well as trajectory analyses is needed. In this study, we use data from the International Collaborative Experiments for Pyeongchang 2018 Olympic and Paralympic Winter Games (ICE-POP 2018) campaign to study an extreme snowfall event associated with a WCB over South Korea on 28 February 2018.
The location of Pyeongchang on a peninsula at mid-latitudes offers an interesting setting to study the interplay between synoptic circulation, orographic effects and microphysics. First, the surrounding Yellow Sea and East Sea provide nearby sources of moisture for precipitation, which is particularly relevant for wintertime WCBs  (Pfahl et al. , 2014). Secondly, WCBs play a crucial role for precipitation over the Korean Peninsula: between 80 % and 90 % of extreme precipitation is associated with WCBs in South Korea according to a climatological study by  Pfahl et al. (2014). The ICE-POP 2018 data set includes, among other data, multiple-frequency radar measurements and highresolution snowflake photographs. We also make use of the Integrated Forecast System (IFS; ECMWF, 2017) model from the European Centre for Medium-Range Weather Forecasts to identify WCB trajectories associated with this event.
To understand the role of the WCB during this intense precipitation event in Korea, we address the following questions: -What was the synoptic situation leading to this intense snowfall event? -Which microphysical processes were involved? -How did the specific flow conditions in the WCB influence the observed microphysics? The paper is structured as follows. We first introduce the measurement campaign and data sets in Sect. 2. The synoptic situation is presented in Sect. 3. Section 4 shows the evolution of the event over Pyeongchang. An analysis of the microphysics observed by radar and snowflake images during succeeding periods of interest is presented in Sect. 5. We then summarise the key findings of this study with a conceptual model in Sect. 6 before concluding in Sect. 7. 2 Measurement campaign and data set ICE-POP 2018 was a measurement campaign organised by the Korea Meteorological Administration and supported by the World Meteorological Organisation.
Warm conveyor belt trajectory computation We define the large-scale WCB ascent over the Korean Peninsula from a Lagrangian perspective as a coherent ensemble of trajectories with an ascent rate of at least 600 hPa in 48 h  (Wernli and Davies, 1997; Madonna et al. , 2014). The 48 h trajectories were computed with the Lagrangian analysis tool LAGRANTO  (Wernli and Davies, 1997; Sprenger and Wernli, 2015)  based on the 1-hourly 3D wind field of the hydrostatic model IFS (model version Cy43r3, operational from July 2017 to June 2018; ECMWF, 2017). The details of the microphysics scheme can be found in  Forbes et al. (2011). The IFS is run with a spatial resolution of O1280 (approximately 9 km) and 137 vertical hybrid pressure-sigma levels (ECMWF, 2017). The IFS data set combines operational analyses at 00:00, 06:00, 12:00 and 18:00 UTC with hourly short-term forecasts in between.
Acknowledgements. The authors are greatly appreciative to the participants of the  World Weather Research Programme Research and Development and Forecast Demonstration Project International Collaborative Experiments for Pyeongchang 2018 Olympic and Paralympic Winter Games  ( ICE-POP 2018 ), hosted by the  Korea Meteorological Administration. In particular, we are thankful to the  High Impact Weather Research Center  of the  Korea Meteorological Administration  for providing us with radiosonde data and giving us access to their facilities at  Gangneung-Wonju National University. We would also like to thank  Christophe Praz  and  Jacques Grandjean  for their help during the deployment of the instruments. Special thanks go to  KwangDeuk Ahn  from the  Korea Meteorological Administration  for his support during the campaign and to  Kwonil Kim  for his help with some instruments.
J. Gehring et al. : Microphysics and dynamics of snowfall associated with a warm conveyor belt Code and data availability. The trajectories were computed with the Lagrangian analysis tool LAGRANTO (Sprenger  and Wernli, 2015). We used functions from the Python libraries Py-ART  (Helmus and Collis, 2016)  and MetPy (https://doi. org/10. 5065/D6WW7G29; May et al. , 2008). Other codes and data are available upon request from the corresponding author.
The proposed model uses an optimal KD operator parameterized with weight coefficients to capture weak and blurred boundaries of inhomogeneous objects in images. Combined global and local image statistics were computed and added to the proposed energy function to increase the proposed model's sensitivity. The segmentation time complexity of the proposed model was calculated and compared with previous state-ofthe-art active contour methods. The results demonstrated the significant superiority of the proposed model over other methods. Furthermore, a quantitative analysis was performed using the mini-MIAS database. Despite the presence of complex inhomogeneity, the proposed model demonstrated the highest segmentation accuracy when compared to other methods.
V. QUANTITATIVE COMPARISON The quantitative comparison is performed by measuring the Accuracy, Dice Index and Sensitivity for the mini-MIAS  [34]  database of mammograms, which include ROIs of different shapes and breast tumor X-rays. At first, experiments using the proposed method are performed with color images to determine accuracy; the experiments' results were compared with those of previous models. The following metric is used for segmentation accuracy, Dice Index, and Sensitivity metrics are defined: Accuracy = T P + T N T P + T N + F P + F N , (40) DiceIndex = 2 × T P 2 × T P + F P + F N , (41) and Sensitivity = T P T P + F N , (42) where T P defines the true-positive segmented regions, T N defines the true-negative unsegmented regions, F P defines the false positives inaccurately considered true, and F N defines false negatives, i.
The average processing time and average number of iterations are presented in Table  4. Figure  12  illustrates the comparison outcomes of the images. The segmented results of different inhomogeneous intensity images corroborate the accuracy of the proposed method with fewer iterations comparatively to all other best practice level set approaches. The efficiency of deep learning image segmentation models, based on the training sample data set, also has more massive and more extensive data sets that tend to produce better accuracy and segmentation efficiency. Comparatively, supervised learning tends to produce inferior results on small datasets. However, the unsupervised level set methods perform segmentation using preselected parameters based on ROI. The proposed method sets the effective parameters to capture the ROI in images.
Statistical analysis Distributions of all variables were analyzed for normality using the Kolmogorov Smirnov test. Normally distributed variables are expressed as means AE SD. Between-group differences in normally distributed variables were analyzed using Student's t-test. Differences in dichotomous variables were analyzed by Fisher's exact test. Spearman's correlation coefficients were calculated to evaluate relationships between variables. Differences were considered statistically significant at p < 0. 05. The statistical software employed was Statistica (StatSoft, Tulsa, OK, USA).
Lake size and area inventory using satellite imagery Remote sensing data utilized for this study include Landsat-8 medium resolution (MR) and GeoEye-1 ultra-high resolution (UHR) imagery; imagery were used to map distribution of lakes throughout the BZT territory (Supporting Information Fig. S1b ). Landsat-8 images were taken in the late July through early August 2017 and 2018. GeoEye-1 images were collected in the summer months of 2011 through 2017. These summer periods, when the lakes are ice-free, correspond to minimum coverage of the territory by lakes and minimum seasonal change in water level. A combination of Landsat-8 images over 2 yr was necessary as observation over the course of 1 yr could not provide full coverage of the territory. For the entire BZT territory affected by permafrost (195,000 km 2 ), we used a mosaic of 50 Landsat-8 MR images.
2007; Juutinen et al. 2009; Laurion et al. 2010; Elder et al. 2018 ). The gas transfer coefficient was taken from  Cole and Caraco (1998) : k 600 = 2:07 + 0:215 Á U 1:7 10 ð1Þ where U 10 is the wind speed taken at 10 m height, without explicitly accounting for lake size and shape (i. e. ,  Vachon and Prairie 2013). Average daily wind speed was retrieved from official data of the nearest weather station (Naryan-Mar) as published by Rosgidromet for the day of sampling. Further, 8 and 15 waterbodies were used for measuring emissions of CO 2 and CH 4 , respectively, using floating chambers in order to validate modeled wind-based fluxes. The direct CO 2 fluxes were measured using small (30 cm diameter) floating chambers equipped with nondispersive infrared CO 2 logger (ELG, SenseAir;  Alin et al.
Statistical treatment of the data A nonparameric Mann-Whitney U-test was taken for treating the data due to the short series of observation, nonnormal distribution of number of parameters, and some amount of outliers. Regressions were used to examine the relationships between DOC, CO 2 , CH 4 concentrations, and GHG fluxes to the atmosphere of the lake area. The normality of DOC, GHG concentration, and fluxes was assessed via the Shapiro-Wilk test. Because the data were not normally distributed, we used nonparametric statistics. Thus, the median, 1 st , and 3 rd quartiles were used to trace the dependence of DOC, CO 2 , CH 4 concentration, and GHG fluxes to the atmosphere on the area of the waterbody, season, and the year of sampling. This is consistent with other studies on GHG in lakes when dealing with non-normal data distribution  (Erkkilä et al.