dataesr/scientific-paragraphs-categorization huggingface dataset:
1 x 50 mm Acquity BEH C18 (1. 7 um particle size) UPLC column using the following eluents: A = water + 0. 1% formic acid and B = acetonitrile + 0. 1% formic acid. Eluent flow was 300 µl/min operating in an isocratic mode consisting of 7% B. The column was thermostatted at 40˚C. The UHPLC was a Dionex UniMate 3000 Binary RSLC (Thermo Fisher Scientific). Eluent from the UPLC column was passed to a HESI II electrospray ion source operating in the positive ion mode. Data were acquired on a QExactive Classic HRAM orbitrap mass spectrometer (Thermo Fisher Scientific) in full scan high resolution (70,000 mass resolution) accurate mass mode (HRAM). Accurate mass results were acquired with external calibration. Protonated parent ion current profiles for the targeted parent masses were extracted with a mass tolerance of +/-5 ppm.
Kinetics properties of Xen CPG2 and Ps CPG2 Methotrexate was used for determination of the Michaelis constant (K m ) and the rate of reaction (V max ). Both purified enzymes, Xen CPG2 and Ps CPG2 (2. 12 µg/mL), were assayed at various methotrexate concentration (0. 03 up to 0. 42 mM) in 0. 1 M Tris-HCl pH 7. 3 and 0. 2 mM ZnSO 4 using Nunc 96 plates with UV transparent flat bottom wells. All reactions were carried out at 37˚C for 2 min and the decrease in absorbance at 320 nm was determined using Infinite M200 PRO NanoQuant Plate Reader (TECAN). Apparent K m , V max and K cat values of each protein were determined by fitting to the Michaelis-Menten equation using GraphPad PRISM 6 software. One unit of the enzyme represents the hydrolysis of 1mM of MTX per min at 37˚C. The enzyme activity per ml of extract was calculated by using an extinction coefficient for MTX of 8,300.
is concentration measured in mole/L, the pathlength is measured in cm, and molar ellipticity has units of deg. cm2. dmol -. CD spectra deconvolution method. Protein secondary structure was estimated by CD data deconvolution analysis using the CDNN (version 2. 1) software tool to deconvolute the CD data spectra in the far UV spectral region. Deconvolution of the CD data was done in the 190-260 nm spectral region. In the deconvolution calculations, the number of amino acids/ residues was taken for Xen CPG2 as 392 AAs, with a molecular weight of 41. 76148 kDa and for Ps CPG2; 394 AAs, with 41. 91174, protein conc. (0. 58 mg/mL) and 0. 02 cm light-pathlength of the cuvette was used. Four repeat scans were measured per sample and then averaged. Homology modeling of Xen CPG2. Homology modeling was performed using SWISS-MODEL, a fully automated protein structure homology-modeling server, accessible via the ExPASy web server, or from the program DeepView (Swiss Pdb-Viewer)  [29].
Briefly, we searched for templates matching our target Azo CPG2 sequence. The closest enzyme, carboxypeptidase G2 (PDB ID 1CG2) with a sequence homology of 94% was used to build a model. The model quality evaluation was performed using GMQE (Global Model Quality Estimation). The model was visualized using PyMol. Immunoblotting of Xen CPG2 and Ps CPG2 using anti His and anti Xen CPG2 antibodies. Xen CPG2 or Ps CPG2 were separated by SDS-PAGE and transferred to PVDF membranes overnight at 30 V using a Bio-Rad Mini-Trans-Blot tank and Tris-glycine buffer (25 mM Tris, 192 mM glycine, pH 8. 3). For detection of each CPG2 protein, a polyclonal anti-(His) 6 antibody (Sigma) or a polyclonal anti CPG2 were used separately as the primary antibody (1:3000 dilution), with HRP-conjugated ECL anti-rabbit antibody (GE Healthcare, 1:10,000 dilution) used as the secondary antibody.
Zinc dependence of the native glucarpidase activity The data in Fig  3  indicate that the total protein extract of Pseudomonas lubricans strain SF168 and Xenophilus azovorans SN213 showed no glucarpidase activity towards the substrate methotrexate in the absence of Zn2+ ions. However, when Zn2+ was added to the assay mixture, glucarpidase activity could be measured. We carried out the same experiment in the presence of 10 mM EDTA. No enzyme activity was detected in the presence of EDTA.
Cloning and overexpression of the new glucarpidases from Xenophilus azovorans SN213 and Stenotrophomonas sp. SA To clone the CPG2 genes from the newly isolated strains, chromosomal DNA libraries from both isolated strains were prepared and screened for CPG2 producing colonies using media where folate is the only carbon source. The resultant recombinant plasmids were designated pCPGxen16 and pCPGsten8, for CPG2 from Xenophilus azovorans SN213 and Stenotrophomonas sp. SA respectively. Determination of the nucleotide sequence of each fragment in both plasmids revealed the amino acid sequences of each recombinant protein. In each case, the identified open reading frame is 1,176 nucleotides long and encodes proteins with 392 amino acids. Partial nucleotides and amino acid sequence data for CPGxen16 appear in the DDBJ/ Gen-Bank database under accession numbers JX192958 and AGI21725, respectively.
The calculated molecular mass of the deduced amino acid sequence of the CPGxen16 was 41761. 4 Da, which is in close agreement with that of the native enzyme  [12]. A search of the protein database using the program BLAST revealed that the deduced amino acid sequence of the Xen CPG2 exhibits a high identity (94%) to that of CPG2 from the strain of Pseudomonas sp. Strain RS-16  [12, 30]. CPG2sten8 exhibits 99% similarity with CPGxen16. Their amino acid sequence alignment is shown in Fig  4. SDS-PAGE analysis showed that a high level of protein expression (160 mg/L) was observed (Fig  5A ). The CPG2s of both new strains were overexpressed but about 80% of the protein was present as inclusion bodies. The expression of soluble CPG2 did not improve by varying the IPTG concentration (data not shown).
From these results, it was evident that strain SN1 is a new strain and may belong to the family Comamonadaceae. We could not find any strain with identical 16s RNA DNA sequencing. We, therefore, named this isolated strain as Xenophilus azovorans SN213 and the novel 16s RNA was deposited in the GeneBank database with accession number EU650684. 16s rRNA DNA analysis of the second strain indicates that the strain SF3 is a Pseudomonas species, therefore its named was proposed to be Pseudomonas lubricans strain SF168 and was deposited with FJ600733 accession number in the GeneBank database. The 16s rRNA DNA analysis shows that the third strain has 100% homology with Stenotrophomonas sp. AB1 and therefore the third new strain was named Stenotrophomonas sp. SA. Glucarpidase is Zn 2+ -dependent enzyme  [12, 33].
The change in secondary structure components as calculated by the deconvolution analysis software of CD spectra of Xen CPG2 and Ps CPG2 showed a significant increase in the alpha helix calculated component rather than the other secondary structure components, namely β-sheet (both parallel and anti-parallel), β-turn, and random coil of Xen CPG2 (69. 3%) relative to Ps CPG2 (34. 2%) indicating that Xen CPG2 has more folding structure than that of Ps CPG2 (Fig  9 ). We also predicted the structure of Xen CPG2 based on its sequence (Fig  10 ). The best model based on the primary structure of Xen CPG2 we found was carboxypeptidase G2 (PDB ID 1CG2) with a sequence homology of 94%. This was used as a template to predict the tertiary and quaternary structure of Xen CPG2 (Fig 10A  and 10B ). We also did the structural alignment of the predicted structure of Xen CPG2 with CPG2 from Pseudomonas sp.
strain RS-16. (PDB ID 1CG2)  [30]. Not surprisingly we found an overall very good structural match with an RSM = 0. 084. The only significant changes of the alignment were found in the flexible loop, Cand N-terminal regions (Fig 10C ). The kinetic studies of the two CPG2s showed that at the same protein concentration Xen CPG2 has lower K m value (50 µM) in comparison to Ps CPG2 (170 µM), indicating its higher affinity towards MTX than Ps CPG2 and although Xen CPG2 shows a lower turnover number K cat (11. 49 S -1 ) than Ps CPG2 (24. 83 S -1 ), Xen CPG2 shows about double the kinetic efficiency (kinetic perfection) of Ps CPG2 based on their calculated values K cat /K m. The strategy of mutating B-cell epitopes to reduce immunogenicity has also been implemented successfully  [35] [36] [37]  and promises to be a useful technique that has a wide range of applications for recombinant cancer therapy and other diseases.
This study evaluates MODIS snow cover characteristics for large number of snowmelt runoff events in 145 catchments from 9 countries in Europe. The analysis is based on open discharge daily time series from the Global Runoff Data Center database and daily MODIS snow cover data. Runoff events are identified by a base flow separation approach. The MODIS snow cover characteristics are derived from Terra 500 m observations (MOD10A1 dataset, V005) in the period 2000-2015 and include snow cover area, cloud coverage, regional snowline elevation (RSLE) and its changes during the snowmelt runoff events. The snowmelt events are identified by using estimated RSLE changes during a runoff event. The results indicate that in the majority of catchments there are between 3 and 6 snowmelt runoff events per year.
g. Paudel and Andersen, 2011; Wang et al. , 2009; Xinghua et al. , 2017) , seasonal snow cover duration  (Dietz et al. , 2012; Gascoin et al. , 2015; Wang et al. , 2015)  or snow cover depletion  (Déry et al. , 2005; Li et al. , 2008; Riboust et al. , 2019). A recent study of  Krajčí et al. (2014)  proposed and evaluated a method to describe the snow cover distribution in catchments by a time varying snow line elevation. The results indicate that spatial patterns of snow cover during snowmelt period closely follow topography and snow line elevation approach can be used to describe snow cover distribution in alpine basins. Most of the previous studies analysed and evaluated MODIS snow cover products at seasonal or monthly time scale. The main objective of this study is to analyse snow cover characteristics derived from MODIS datasets for individual snowmelt runoff events in a large number of European catchments.
DATA The analysis of snowline elevation changes is performed in 145 catchments in 9 countries of Europe. Table  1  lists the number of catchments in each country and the range of their size and gauge elevation. The median of catchment size and gauge elevation is 265. 6 km 2 and 523 m a. s. l. , respectively. The location of the catchments is presented in Figure  3. The selection of catchments is determined by the availability of daily discharge and MODIS snow cover data, by the significance of anthropogenic influence, and by the climate region indicating where snowmelt is one of the important flood generation processes. The core for the selection is the open dataset of the pan-European catalogue of flood events  (Parajka, 2017) , which lists runoff events identified for 1315 gauging stations available in the Global Runoff Data Centre (GRDC) database (http://www.
bafg. de/GRDC/EN/Home/homepage_node. html). Additionally, daily discharge data from Slovenia, Czech Republic and Turkey have been processed using the same approach as applied in the Catalogue of flood peaks. Finally, stations with at least 6 years of daily discharge observations in the period 2000-2015 have been selected. The median data record length of these stations is 12 years and the total number of analysed snowmelt events is presented in Table  1. Regional snow line elevation is processed from daily MODIS snow cover data (MOD10A1, version 005, spatial resolution 500 m) in the period 2000-2015. For each catchment, the corresponding MODIS tile is selected and data are downloaded, processed and re-projected to the national projection of each country. The RSLE is estimated from the SRTM 90m Digital Elevation Model (v 4.
The proportion of snowmelt events increases also with increasing size of the catchments, particularly for catchments in Slovenia (R 2 = 0. 6) and Norway (R 2 = 0. 50). A comparison of larger datasets from Austria and Slovenia shows that the proportion of snowmelt events to all runoff events is generally smaller in Slovenia, but it depends mainly on the maximum elevation of the catchments. While for high altitude catchments with maximum elevation above 2600 m the proportion of snowmelt events is greater than 50% in both countries, for most of the catchments with maximum elevation below 1000m the proportion of snowmelt events is less than 15% and 25% in Slovenia and Austria, respectively. It should be noted, however, that Slovenian dataset includes catchments situated in sub-Mediterranean climate and also two transboundary catchments (Drau, Mura).
Acknowledgement. This work was supported by the  European Commission   FP7  funded research projects " Sharing Waterrelated Information to Tackle Changes in the Hydrosphere -for Operational Needs " (grant agreement number  603587 ), the  European Research Council  under the  ERC Advanced Grant  " Flood Change ", project no. 291152 ) and the  Slovenian Research Agency  (grant  P2-0180 ). The financial support is gratefully acknowledged. We would like also to thank the  Global Runoff Data Centre, Koblenz, Germany and Czech Hydrometeorological Institute  for providing daily runoff data for this work.
T he development of polygenic risk scoring (PRS) has greatly advanced the field of psychiatric genetics. This approach allows for even sub-genome-wide significant threshold results from large genome-wide meta analyses to be leveraged to explore genetic risk in smaller studies  1. The effect sizes at many individual single-nucleotide polymorphisms (SNPs), estimated by large genome-wide association studies (GWAS) on the disorder of interest, are used to calculate an individual level genome-wide PRS in individuals from an independent genetic dataset. The PRS based on the summary statistics of the schizophrenia (SCZ) GWAS by the Psychiatric Genomics Consortium (PGC)  2, 3  has proven to be most powerful in predicting not only SCZ  1, 4  but also other psychiatric disorders  [5] [6] [7]. In addition, updated, more powerful, summary statistics from the Psychiatric Genomics Consortium from the latest GWAS for bipolar disorder (BPD) and major depressive disorder (MDD) are available via the PGC Data Access Portal (https://www.
In the BBF; the affected married-in individuals have a higher, though non-significant, polygenic score than affected or unaffected family members but it appears that we observe significant consequences of this in that the offspring of an affected married-in parent collectively show significantly increased SCZ:PRS and BPD:PRS. However, it is puzzling we do not see an effect on offspring of two affected parents (which would include a married-in parent), which could indicate this finding to be of limited statistical robustness. A contribution of the married-in parents to a genetic driven anticipation in age of onset is supported by the increase in SCZ: PRS over generations, although our cross sectional study dataset was less well powered to find an association with age at onset within affected family members.
Finally, we explored the balance of common and rare risk variation through combining our current PRS results with previously performed linkage analyses. We did not find a decrease in potential rare risk allele genotypes over generations contrasting the increase in SCZ:PRS, and PRS profiles for individuals carrying rare risk genotypes are not significantly different. This indicates that these factors separately confer independent disease risk. We recognize the limitations in sample size of our pedigree and therefore the power to draw statistically robust conclusions, especially in the offspring and combined linkage and PRS analyses. Even though the BBF might not be sufficiently powered, our point is to use this dataset to illustrate our approach and emphasize the unique nature of the family enabling the study of patterns of PRS and the balance of common and rare genetic risk for psychiatric disorders conferred within families.
In total 308 interviews were completed, and 5 eligible members declined an interview. In the rare event of discrepancies, two independent psychiatrists reviewed them and a final consensus diagnosis was assigned. All affected and unaffected adult family members that have been included in the genetic study have given informed consent. Minors have given assent, followed by consulted consent by their parents in accordance with accepted practice in both the U. K. and Brazil. The project was approved by the Brazilian National Ethics Committee (CONEP). Table  1  contains the demographics of the subjects used in the current analysis (n = 243 passed genotype quality control procedures described below). The population control dataset (BRA controls) was collected in Sao Paulo, Brazil, as a control dataset in a genetic study of first-episode psychosis  26.
They were volunteers who had no abnormal psychiatric diagnoses (SCID) or family history of psychotic illness. The Research Ethics Committee of Federal University of Sao Paulo (UNI-FESP) approved the research protocol, and all participants gave informed consent (CEP No. 0603/10). Demographics for n = 57 BRA controls can be found in Table  1. Genotype data. Following diagnostic interview, interviewers obtained whole blood in EDTA containing monovettes for adults and lesser amounts or saliva given personal preference or age (DNA Genotek Inc. , Ontario, Canada). Genomic DNA was isolated from whole blood and saliva at UNIFESP using standard procedures. Whole-genome genotype data was generated using the Illumina Infinium PsychArray-24 (http://www. illumina. com/products/psycharray. html) for both the BBF and the BRA control dataset at the in-house BRC BioResource Illumina core lab according to manufacturers protocol.
Samples were excluded when average call rate was <98%, missingness >1% with additional check for excess heterozygosity, sex, family relationships and concordance rates with previous genotyping assays. SNPs were excluded when missingness > 1%, MAF < 0. 01 or HWE < 0. 00001 and if showing Mendelian errors for the BBF dataset in Plink v1. 07  27  and v1. 9  28  or Merlin v1. 1. 2  29. The BBF and BRA control datasets were QC'd separately and then merged, applying the same SNP QC thresholds to the merged dataset as well. This quality control procedure resulted in a dataset of 225,235 SNPs for 243 BBF individuals (197 family members and 46 married-in individuals) and 57 BRA controls. Eigensoft v4. 2  30  was used to check for population differences between the BBF family members, married-in individuals and BRA control sets.
114 × 10 -5 ), and PC1 and PC2 to BPD:PRS (PC1 r = 0. 189, p = 9. 710 × 10 -4 ; PC2 r = -0. 123, p = 0. 033). The principal components were not used in subsequent analyses. Polygenic risk scores. Polygenic risk scores for each family member (n = 243) and population control (n = 57) were generated in the same run using the PRSice v1. 25 software  31  with the publically available PGC schizophrenia GWAS 2 as a base dataset (36,989 SCZ cases, 113,075 controls), in addition to MDD (51,865 MDD cases, 112,200 controls, not including 23andme individuals) and BPD (20,352 BPD cases, 31,358 controls) summary statistics from the latest PGC meta analyses (unpublished data  18, 19  ). We performed p-value-informed clumping on the genotype data with a cut-off of r 2 = 0. 25 within a 200-kb window, excluding the MHC region on chromosome 6 because of its complex linkage disequilibrium structure.
Acknowledging the possibility of over-fitting, we selected the PRS thresholds most predictive in discriminating affected from unaffected family members through linear regression in PRSice for SCZ:PRS (p < 0. 00055, 1218 SNPs), MDD:PRS (p < Linkage analysis. The main linkage analyses identifying rare genetic risk variation were performed as part of a previous paper on the BBF 23 using the Affymetrix 10k linkage genotyping array. In order to explore the balance between common and rare risk variation, we selected the strongest signal for affected versus unaffected family members on chr2p23 (chr2:30000001-36600000, LOD = 3. 83). Following the strategy described by Rioux et al. 32  , we performed a transmission disequilibrium test on the 25 markers in this linkage region in an attempt identify "linkage positive" individuals in n = 300 family members with one or both types of genotype array data.
N = 155 individuals overlap with the current study and based on exploration of patterns of PRS in the current study we attempted to answer two questions: (1) with an increase of common risk variation, does rare risk variation become less important over generations, (2) do linkage positive individuals carrying the presumed risk allele show differences in PRS. Statistical testing. All PRS were standardized mean = 0 and SD = 1. Linear mixed model analyses were selected to be able to model covariates and relatedness within this complicated dataset. The analyses were performed using the Wald conditional F-test  33  in ASReml-R software  34  with one of the categories of mood disorders or family status as dependent variable and PRS as the independent variable (Supplementary Methods). Age (except for the generation analysis) and sex were fitted as fixed effects in the models.
For 7 individuals in the BBF age at collection was missing and imputed to be the mean age of the relevant generation. To account for relatedness in within-family comparisons, an additive genetic relationship matrix was fitted as a random effect. The relationship matrix was constructed using LDAK software  35  with weighted predictors and LD correction parameters suited for pedigree data, resulting in pairwise relatedness estimates and inbreeding coefficients on the diagonal. The variance explained by each PRS was calculated using: (var(x × β))/var(y), where x was the standardized PRS, β was the corresponding regression coefficient, and y was the phenotype  36. For the analysis of offspring, we defined four spouse pair categories ("both unaffected", "married-in parent affected", "family parent affected", "both affected").
Demographics of the Brazilian bipolar family members and the Brazilian population control dataset (BRA controls) in the current studyThe first column contains the number of individuals affected with the disorder. A breakdown of gender, age, age at onset (with ± sd; standard deviation) is given in the next columns. The married-in column contains the number of individuals in each diagnostic category married-in to the family. The last column contains counts of individuals in each category who have experienced a psychotic episode during their lifetime Diagnostic categories are BP1 bipolar I, BPII bipolar II, BPNOS bipolar not otherwise specified, rMDD recurrent major depressive disorder, MDD major depressive disorder, SADB schizoaffective disorder, schizophrenia, cyclothymia and dysthymia 0. 0165, 715 SNPs) and BPD:PRS (p < 0.
Acknowledgements We would like to thank the family members for their enthusiastic participation. We thank our ethics consultant  Prof. Barbara Prainsack  for insightful discussions. This paper represents independent research part-funded by  FAPESP  ( 2014/50830-2 ;  2010/08968-6 ), the  Marie Curie International Research Staff Exchange  ( FP7-PEOPLE-2011-IRSES/ 295192 ), and the  National Institute for Health Research (NIHR) Biomedical Research.
gayana, we identified loci putatively under selection from a dataset of 1,709 SNPs produced using restriction site-associated DNA sequencing (RAD-seq). Significant and positive relationships between local estimates of genetic and species diversities (α-SGDCs) were only found with the putatively neutral loci datasets and with species richness, confirming that neutral processes were primarily driving the correlations and that the involved processes differentially influenced local species diversity components (i. e. , richness and evenness). In contrast, SGDCs based on genetic and community dissimilarities (β-SGDCs) were only significant with the putative non-neutral datasets. This suggests that selective processes influencing C. gayana genetic diversity were involved in the detected correlations.