dataesr/scientific-paragraphs-categorization huggingface dataset:
11435013 , No. 11475187 , No. 11521505 , No. 11575017 , No. 11675166 , No. 11705209 ;  Key Research Program of Frontier Sciences, Chinese Academy of Sciences (CAS) , Grant No. QYZDJ-SSW-SLH011 ; the  CAS Center for Excellence in Particle Physics (CCEPP) ; the  Shanghai Pujiang Program  under Grant No. 18PJ1401000 ; the  Ministry of Education, Youth and Sports of the Czech Republic  under Contract No. LTT17020 ; the  Carl Zeiss Foundation , the  Deutsche Forschungsgemeinschaft , the  Excellence Cluster Universe , and the  VolkswagenStiftung ; the  Department of Science and Technology of India ; the  Istituto Nazionale di Fisica Nucleare of Italy; National Research Foundation (NRF)  of  Korea  Grants No. 2015H1A2A1033649 , No. 2016R1D1A1B01010135 , No. 2016K1A3A7A09005 603 , No. 2016R1D1A1B02012900 , No.
Statistical analysis Quantitative variables were expressed as mean with standard deviation (SD). The comparison of two independent groups were performed with a Student t-test if normality was met and otherwise with a Mann-Whitney test. Comparison of more than two groups were performed with a non-parametric Kolmogorov-Smirnov test. A p<0. 05 value was considered as significant. All statistical analyses were performed with the SPSS software (IBM Corp.
Two old and deeply divergent mitochondrial lineages (C, F) were not differentiated using structure analyses and PCAs of microsatellite data. In contrast, grass snakes from Corsica and Sardinia shared the same mitochondrial lineage. Using microsatellite data for structure analyses, the samples from the two islands were lumped together, but using PCAs, two distinct clusters were obtained. However, this study was compromised by small sample sizes and patchy coverage of the distribution area. One mitochondrial lineage (D) from mainland Italy was represented only by a single previously published GenBank sequence  (Guicking et al. , 2006) , and microsatellite data were available for only two Sardinian and three Corsican snakes. In addition, grass snakes from north-eastern Italy, where N. natrix has been reported to occur  (Lanza, 1983; Lapini, Dall'Asta, Bressi, Dolce, & Pellarini, 1999; Thorpe, 1979) , were not studied by  Kindler and Fritz (2018).
| Sampling Samples of 190 grass snakes were analysed as the core data set of the present study. Of these, 100 samples were newly processed, and data for the remaining 90 grass snakes had been generated in our laboratory for previous studies  (Fritz et al. , 2012; Guicking et al. , 2006; Kindler et al. , 2013 Kindler et al. , , 2017;; Kindler, de Pous, et al. , 2018; Kindler & Fritz, 2018; Pokrant et al. , 2016). One hundred and forty-three samples originated from Italy and Corsica. For some analyses, 47 samples were added from adjacent areas (France, Switzerland, Slovenia, Croatia) to put the Italian samples into a broader framework. In addition, microsatellite data of 28 samples from France, generated by us for a yet unpublished study, were included for some structure and cline analyses, and mtDNA sequences of another 38 samples, representing mitochondrial lineages of grass snakes from outside the study region, were added for phylogenetic tree calculations.
, 2017) , a stepwise hierarchical approach was applied, with increasingly pruned data sets in consecutive structure analyses  (Evanno et al. , 2005; Kalinowski, 2011; Puechmaille, 2016). For each round, the optimal number of clusters was estimated using the ΔK method  (Evanno et al. , 2005)  as implemented in structure harvester  (Earl & vonHoldt, 2012). structure results were visualized in distruct 1. 1  (Rosenberg, 2004). The hybrid status of individual samples of each round was inferred using simulations in hybridlab 1. 0  (Nielsen, Bach, & Kotlicki, 2006). Our first structure analysis included all 160 samples. For hybridlab, 15 pure individuals of the local genetic lineage of each species (Natrix helvetica, N. natrix) that meet in north-eastern Italy were selected as parental genotypes (Table  S1 ).
Using these data, 15 genotypes of each hybrid class (F 1 , F 2 , backcrosses) were modelled. The hybridlab results were then subjected to structure analyses for inferring the threshold for the best identification of pure individuals, F 1 and F 2 hybrids, and backcrosses, resulting in values of 93% and 91% with respect to N. natrix and N. helvetica (Table  S6 ). For a second structure analysis, all samples of N. natrix and of all other grass snakes with genetic impact from N. natrix were excluded, so that only 112 samples of N. helvetica remained. To estimate the hybrid threshold for genetic lineages within N. helvetica, all 14 available Corso-Sardinian grass snake samples served as one parental group and 15 samples of the remaining snakes as the second. For each hybrid class, 15 genotypes were modelled and the simulated data were processed in structure, resulting in a threshold of 73% for snakes from Corsica and Sardinia and 90% for the others (Table  S7 ).
A final exploratory fourth structure analysis included only those 49 samples from mainland Italy and Sicily that did not belong to the N. h. helvetica cluster and that showed no genetic impact from this cluster. In addition to the structure analyses, principal component analyses (PCAs) were performed using the R package adegenet  (Jombart, 2008). structure analyses are based on population genetic presumptions (Hardy-Weinberg equilibrium, linkage disequilibrium;  Pritchard et al. , 2000)  and therefore prone to bias from uneven sample sizes. To validate structure results, it is thus advisable to run additional PCAs that are less sensitive to sample size differences  (Puechmaille, 2016). PCAs were run for the same four data sets as used for structure.
Cluster D comprised six haplotypes with a maximum of four mutations among them. Cluster F had five haplotypes differing by a maximum of two mutations. The star-like cluster E contained 29 haplotypes that differed from each other by a maximum of five steps, even though the majority of haplotypes differed only by one mutation from the central haplotype h1. Cluster B contained seven haplotypes that differed by a maximum of nine mutations. The following haplotypes were newly identified in the present study: a5, a6, b6, b7, c6-c12, d2-d6, f6, f7, and g1 (European Nucleotide Archive accession numbers LR722580-LR722598). Additional information on uncorrected p distances between the haplotype clusters, and with respect to N. natrix from the study region, and information on variable and parsimony-informative sites can be found in the Supporting Information (Table  S11 ).
h. sicula (southern Calabria and Sicily, mitochondrial lineage A), and provisionally N. h. cetti (Sardinia, mitochondrial lineage B) and N. h. corsa (Corsica, mitochondrial lineage B). Only very few samples could be analysed of the latter two subspecies, with weak support for their validity. Since the Sardinian subspecies is imperilled and morphologically distinctive  (Thorpe, 1979) ,  Kindler and Fritz (2018)  refrained from premature taxonomic conclusions that may jeopardize conservation efforts. Lineage D from Apulia was only known by a single previously published GenBank sequence  (Guicking et al. , 2006). Grass snakes harbouring lineages C, D, and F were, despite deep mitochondrial divergence, tentatively assigned to the same subspecies, either because nuclear genomic evidence was unavailable (lineage D) or because analyses of microsatellite loci did not reveal differences (lineages C and F;  Kindler & Fritz, 2018).
Yet, for mtDNA a steeper cline was found, in agreement with the hypothesis of female philopatry combined with population-density-dependent processes such as 'high-density blocking'  (Waters, Fraser, & Hewitt, 2013 ; see below). In contrast to N. natrix, N. helvetica is an old endemic in our study region and shows a remarkable diversity with several deeply divergent mitochondrial lineages. This differentiation has been attributed to the existence of several glacial refugia  (Kindler et al. , 2013; Kindler & Fritz, 2018). In addition to the previously identified lineages, we found a new Italian lineage (clade G), which is phylogenetically sister to clade D, previously known only from a single GenBank sequence derived from a grass snake from southern Apulia  (Guicking et al. , 2006). We confirmed the occurrence of lineage D for this region.
three haplotypes differing from each other by a maximum of two steps. Cluster B contained a loop and consisted of three haplotypes that differed by a maximum of two mutations. One haplotype from each of the clusters B and F was newly identified in the present study (b3, f2), and two haplotypes from each of the clusters C, D, and G were new (c2, c3, d2, d3, g1, g2; European Nucleotide Archive accession numbers LR721654-LR721659, LR722601, LT900415). A more complex network, with more haplotypes, was obtained for the cyt b gene (Figure2, bottom). The network contained loops for three clusters (A, C, F), indicating alternative mutation pathways. Lineage A constituted the most distinct haplotype cluster that was connected over a minimum of 46 steps with cluster C. The latter cluster differed by a minimum of 20 mutations from cluster D, which in turn was connected by six steps with the only cyt b haplotype of lineage G.
Introduction: Benefit and risk of anticoagulation in cirrhotic patients with portal vein thrombosis (PVT) remain controversial, especially in those with asymptomatic PVT and in non-liver transplant candidates. Furthermore, the predictors of portal vein recanalization and bleeding events after anticoagulation are critical for making clinical decisions, but still unclear. We conducted a meta-analysis to investigate the outcomes of anticoagulation for PVT in liver cirrhosis and explore the predictors of portal vein recanalization and bleeding events after anticoagulation. Methods: All studies regarding anticoagulation for PVT in liver cirrhosis were searched via PubMed, EMBASE, and Cochrane Library databases. Thrombotic outcomes, bleeding events, and survival were compared between anticoagulation and non-anticoagulation groups.
Data Extraction Characteristics of included studies and patients were extracted, including first author, publication year, region, enrollment period, study design, study population, features of PVT, exclusion of hepatocellular carcinoma (HCC), exclusion of cavernous transformation of portal vein (CTPV), exclusion of isolated thrombosis within the superior mesenteric vein (SMV) or splenic vein (SV), exclusion of patients receiving antiplatelet treatment, exclusion of patients receiving thrombolytic treatment, interval between diagnosis of PVT and initiation of anticoagulation, baseline gastroesophageal varices, follow-up duration, and type and dose of anticoagulants. Rates of portal vein recanalization, thrombus progression, re-thrombosis, bleeding events, including overall bleeding, major bleeding, upper gastrointestinal bleeding (UGIB), GEVB, and deaths due to bleeding events, and survival in anticoagulation and non-anticoagulation groups were also extracted.
Subgroup analyses were also performed in terms of the variables mentioned above. Sensitivity analyses were performed to assess the impact of each individual study on the stability of statistical results by removing studies one by one from a meta-analysis. Publication bias was evaluated with the Egger test, and P \ 0. 1 was considered as a statistically significant publication bias. We also drew the scattered plots and performed the Spearman correlation analysis in the IBM SPSS 26. 0 (IBM Corp. , Armonk, NY, USA) to explore the correlation between patient characteristics and portal vein recanalization rate after anticoagulation. Coefficients, which are denoted as r s , were calculated. A two-sided P \ 0. 05 indicates a statistical significance.
Compliance with Ethics Guidelines. This article is based on previously published studies and does not involve any new studies of human or animal subjects performed by any of the authors. Data Availability. Full datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Additional data are available in the supplementary materials. Open Access. This article is licensed under a Creative Commons Attribution-NonCommercial 4. 0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
Samples, antibody diluent 2, primary and secondary antibodies, luminol S-peroxide mix and wash buffer were displaced into Wes 12-230 kDa prefilled microplates (pre-filled with separation matrix 2, stacking matrix 2, split running buffer 2 and matrix removal buffer, SM-W004). The microplate was centrifuged for 5 min at 2500 rpm at room temperature. To start the assays, the capillary cartridge was inserted into the cartridge holder and the microplate placed on the plate holder. To operate Wes and analyze the results, Compass Software for Simple Western was used (version 3. 1. 7, ProteinSimple). Separation time was set to 31 min, stacking loading time to 21 s, sample loading time to 9 s, primary antibodies were incubated for 30 min and for detection the High Dynamic Range (HDR) profile used. For analysis of CLEC-2 signal, platelet lysates were first deglycosylated by Rapid PNGase F (New England Biolabs) according to the manufacturer's instructions.
For open-ended survey questions, including questions on the nature of changes observed, and participants' perspectives on why changes had occurred, responses were coded into categories. These categories emerged from analysis of participant responses by the researcher who conducted the surveys. Responses that were cited by two or more participants were considered response categories. We analyzed separately the responses of three participant groups: Mexican nationals originally from Loreto; Mexican nationals not originally from Loreto; and foreign nationals. Maps and figures were created using R statistical software (R Core Team 2020) and the R packages ggplot2, ggspatial, rnaturalearth, and wesanderson  (Wickham 2016; South 2017; Karthik and Wickham 2018; Dunnington 2020).
Acknowledgements We thank  Yong Chen ,  Carla Guenther ,  Joshua Stoll , and  Bridie McGreavy  for their feedback on earlier conceptualizations of this paper. We thank  Linda Ramirez  and  Eduardo Murillo  for their help reviewing, translating, and pretesting survey questionnaires. We also thank  Alfredo Baeza  for logistical assistance in the field, and the community members of the  Loreto region  for their participation in this study. We thank  Melissa Britsch  for her helpful feedback, which helped to improve the clarity of the paper. We also thank two anonymous reviewers for their thoughtful and constructive feedback, which significantly improved the paper. Funding was provided by the  US National Science Foundation  (Grant Number  DEB 1632648  to HL). Funding Open access funding provided by  Stockholm University.
Data source The standardized national treatment demand monitoring system (TDI) collecting data on clients entering drug treatment was used for the purposes of this study. TDI constitutes a common European data collection method and monitoring system, which gathers country-level information on an annual basis on the number and profile of clients entering drug treatment in a large variety of treatment programmes based on a standard protocol, following detailed methodological specifications and guidelines. Annually, this system provides data from 30 European countries (28 EU Member States, Norway and Turkey) to the European Monitoring Centre for Drugs and Drug Addiction (EMCDDA). This indicator represents a powerful instrument for policymakers and drug professionals to compare their national situation in drug treatment and drug problems with other EU countries.
Measures The participating countries agreed to share aggregated data from their national monitoring registers for the years 2011 to 2018 in order to perform trend analysis. To ensure harmonization and robust trend data, countries with less comprehensive national coverage or who encountered technical changes over time in their TDI monitoring register were requested to provide data collected among a subgroup of centres that have provided stable and reliable data over the years. Otherwise, the TDI data set providing yearly data for all centres included in their TDI monitoring was used. TDI gathers information on the primary and adjunctive substances of use at treatment admission, as well as demographic data and information concerning the treatment history of the client. Data on the total number of clients in treatment for substance use, number of clients in treatment for cocaine as primary and cocaine as primary or secondary drug were collected.
A distinction was also made between the treatment episodes of the specific substances (powder cocaine and crack) and between patients treated previously or treated for the first time for drug addiction. The proportion of clients entering treatment for cocaine as primary substance or as primary/adjunctive substance between 2011 and 2018 were separately calculated. Proportions were calculated for clients entering treatment for the first time in their life as well as for all clients entering treatment (first-time plus previously treated). The proportion of clients reporting the use of cocaine was used in preference to absolute numbers because for some countries a subset of the total database was used, but also to enable comparisons between countries with variable treatment populations. Annual data from 2011 onwards were included in order to focus only on the most recent trends including the period where important changes were observed in Europe on other cocaine-related indicators (mortality, prevalence, wastewater residues, supply and production).
If available, data collection included 8 years up to 2018. Data for both substances separately (powder cocaine and crack) as well as for the grouped category were used. A distinction was made between primary and adjunctive substance for the client entering treatment. The primary substance was defined as the substance that caused the most negative (social and health) consequences at the start of treatment and principally led to the treatment demand by the client. This was usually based on client self-report and (or) on the diagnosis made by a therapist, commonly using international standard instruments or clinical assessment. Adjunctive substances were defined as those substances used in addition to the primary substance, leading to additional health and/or social consequences for the client that may change the nature of the problem as assessed by the client and the therapist  [22].
Analyses First, in order to provide a global overview of the trend in the 10 countries together, the annual percentage of change in proportions was calculated for every country and weighted to obtain the European average, using the total number of cocaine treatment entrants in every country as weighting factor. A negative value for this annual percentage of change was considered a downwards trend, whereas a positive value indicated an upwards trend. Secondly, evolution in time was examined using Joinpoint regression analysis  [24]  (Joinpoint Trend Analysis Software version 4. 7. 0. 0) which describes the direction and the magnitude of trends fitting linear segments.
Data set description Table  1  describes the content of the data set received and the specifics for every participating country. In total, for all reported years and for all countries, the data set included 2390152 treatment demands. Three countries (Belgium, England and Switzerland) stated that a subset of data provided a more robust measurement of trends whereas the other countries provided the full national data set. Data sets between 7 and 8 years were available for most of the countries except the Netherlands, where 5 years of data were provided up to 2015. For some countries the distinction between powder cocaine and crack was not (Germany, Luxembourg) or only partially (the Netherlands, Belgium, Ireland) possible. There were also differences among countries in the way adjunctive substances were assessed, particularly in the number of adjunctive substances reported.
Figures on cocaine use and treatment demand In total more than 700 000 treatment demand data for cocaine as primary drug were collected over the whole period. Table  2  shows the large variability among the 10 European countries regarding cocaine figures in the most recent common year (2017 or 2015 for the Netherlands): In England, Spain and Italy, approximately one client out of two who entered treatment reported problems with cocaine as primary/adjunctive substance, whereas in France only one in seven reported cocaine. Two countries had a share of cocaine as primary substance among all treatment entrants lower than 10% (France, Germany), three countries between 10 and 20% (Ireland, England, Switzerland), three countries between 20 and 30% (Belgium, Luxembourg, the Netherlands) and two countries above 30% (Italy, Spain).
Total carbon and total nitrogen concentrations ([C] and [N] ) of soil samples were determined using an elementary analyzer (Flash EA, Thermo Electron Corporation, Bremen, Germany). In spring 2005, the soil bulk density was determined for 27 soil profiles established in all treatments at the three depths: 0-30, 30-60 and 60-90 cm, whereas in 2014, a bulk density of 0-30 cm depth soil was measured in 112 points. For the 2005 data, we used the means per depth. For 2014 data, a mean of bulk density was calculated for each treatment with 2016 data after verifying that the data did not present a spatial residual structure (fit. gstatModel function of package gstat). As the bulk density values were not significantly different from the ones recorded for the same treatments in 2005, we did not make any correction for equivalent mass.
This may be correct for our site, as it has been under similar management for 100 years. We did not expect significant bulk density changes on the decadal timescale of the experiment. For 30-60 and 60-90 cm 2014 data, we used the bulk density measurements of 2005. The soil bulk density (d; g cm -3 ) was the weight of the solid phase (Ms, dried soil at 105 • C) over the cylinder total volume (V) of sampled soil: d = Ms/V (1) Soil C stocks (t ha -1 ) were calculated with Equation (  2 ): C stock = [C] × p × d (2) where [C] is the soil C concentration (mg g -1 ), p is with the depth of the layer (cm) and d is the soil bulk density (g cm -3 ; Equation (  1 ))  [28]. The plots' spatial patterns of carbon stock in 2005 and 2014 (C 2005 stock and C 2014 stock) were estimated using data from each sampling location coupled with a regression co-kriging method  [29].