dataesr/scientific-paragraphs-categorization huggingface dataset:
Single platform flow cytometry technique Blood samples were collected using EDTA-containing tubes and tested for TCD4+ count within 6 hours by flow cytometry using the BD FACScan (Becton Dickinson, San Jose, US), threecolor mAb (TriTEST™ CD3FITC/CD4PE/CD45 PerCP) reagent and BD Trucount tubes according to standard procedures. Briefly, 50 μl of freshly-obtained whole blood were stained with 20 μl of anti-CD45-perCP, anti-CD3-FITC and anti-CD4-PE mAbs for 15 min at room temperature in the presence of a fixed number of fluorochrome-labeled, polystyrene reference beads. Red blood cells were lysed using a FACS Lysing Solution (BDIS) for 15 min at room temperature. Samples were then, analyzed using the FACSCan and the Multiset and Attractor software (BDIS) for calculating absolute values of TCD4+ cells.
Statistical analysis Statistical analyses were performed using the SAS-PC software (version 8. 2, SAS Institute, Cary, North Carolina, USA). The independent variable analyzed was the absolute TCD4+ count. Flow cytometric testing was compared with each Dynabeads method used. Paired data were compared by linear regression, including slopes, intercepts, and Pearson correlation coefficients. Analysis of agreement between the 2 methods was done by the Bland and Altman method, which compares the difference between paired measurements against the mean of the 2 measurements. P-values were the results of two-sided tests and considered as significant if below 5%. Difference between the two techniques was computed on each sample as the subtraction of the FCM result to the Dynabeads result. The correlation coefficient between the two techniques was calculated and a model of regression then applied to the data.
In addition, Dynabeads technology would be implemented in the central laboratories also to face up to the reagents out of stock, breakdown of flow cytometers for the continuing care of HIV patients. A study was demonstrated the feasibility of automation of Dynal T4 Quant in hematology automate as a Point of Care CD4 with the delivering in the same time of hematology parameters and TCD4+ counting  [4]. To improve on throughput using the manual Dynal T4 Quant method, the Sysmex pocH-100i hematological analyzer was used to count stained nuclei and showed 93% concordance with FCM  [4]. The capacity to use the Dynal T4 Quant with hematology automate by the using K-Xpert software was successfully validated  [4]. In April 2002, WHO recommended that when TCD4 cell count is not available or is not affordable to be obtained for affected individuals, a total lymphocyte count of less than 1000-1200 lymphocytes/µl in individuals with stage 2 or stage 3 diseases to be used as an indication to initiate antiretroviral therapy  [24].
2008; Senitza 1989 ). The fifth study site (O1) was located within a Lebanon Cedar plantation at the Ecological Botanical Gardens (EBG) at Bayreuth, Germany. Seed material of these cedar individuals originated from the same province, where the CRF is located and was germinated and cultivated in Germany in 1978. The 2-year-old seedlings were planted in the EBG in 1980  (Messinger et al. 2015). The EBG is characterized by a transition from oceanic to continental (so-called ''sub-continental'') climate with mild-to-cold winters and warm summers without any drought period during the year (Fig. 2 ). MAT is 8. 2 °C (1971 MAT is 8. 2 °C ( -2000, 360 m a. s. l), 360 m a. s. l) , and measured absolute air temperature extremes are -24 and ?36 °C. Mean TAP is 724 mm  (Foken 2007). To determine Leaf Area Index (LAI) of the sites, hemispherical photographs of the forest canopy were taken from the ground and analyzed using HemiView image analysis software (1999 Delta-T Devices Ltd, Cambridge, UK).
, Cambridge). Climate data for O1 were derived from the meteorological station at the EBG (360 m a. s. l. , BayCEER). Ts and SWC were measured at a depth of approximately 0. 15 m. Stem temperature (ST) was measured using thermocouples and recorded by data loggers (DL2e). These thermocouples were installed at breast height within the outer stem (N facing) under the bark of three cedar individuals to get a representative mean ST per site. Climate variables were measured at 10-30 min time steps and were later aggregated to daily values (daily mean: Ta mean , maximum: Ta max , minimum: Ta min , and sum: Pp). gR was converted from hourly means into daily sums and expressed as MJ m -2 day -1. In addition, daily means of vapor pressure deficit (VPD, hPa) were calculated from site-specific Ta mean , rH, and air pressure values for all sites.
A temperature threshold was determined after which CA was likely to start within a defined time span (e. g. , 1 week). Therefore, daily means of temperature variables were compared in 0. 1°steps between all sites within a time frame of 2 months before onset of CA. For dSRV-climate analysis, missing Pp and gR data at T2 and T4 were extrapolated using WorldClim and HemiView (Delta-T Devices Ltd. ) data, respectively. An overview about all measured climate variables is given in Table  3.
Radial stem changes were recorded using data loggers (DL2e). To extract stem growth signals from dendrometer records, we used the 'daily mean approach'  (Deslauriers et al. 2007). Hourly dendrometer records were averaged on a daily basis, and daily stem radius variation (dSRV) was calculated as the difference between the mean value of two consecutive days  (Deslauriers et al. 2007; Tardif et al. 2001). Only the main period of radial stem increase (RSI) was considered for dSRV analysis to exclude measurement variability that is higher than radial growth at the beginning and end of growth period (cf. Deslauriers et al. 2007 ). This period is defined by a sharp, almost linear increase of the dendrometer records. When initiation of this linear increase was observed, dendrometer records were set to zero.
(2015). Transverse sections of the microcores were examined and photographed under a Zeiss Axioplan microscope. For each sample, the number of cells in the developing tree ring (cells in the cambial zone, enlargement, cell-wall lignification, and mature tracheids) and the current radial width of the tree ring were determined along three radial files per transverse section and then averaged for further data analyses  (Deslauriers et al. 2003a; Rossi et al. 2007). For results on cell counts, see Gu ¨ney et al. (2015). We determined the beginning of CA as a distinct increase in the number of cambial cells (number of cambium cells [ number of dormant cambium cells) which are considered to be xylem cells in the postcambial growth stage  (Gricar et al. 2006). Critical dates and durations of wood formation were computed from raw cell count data using the R Package CaviaR, based on logistic regressions  (Rathgeber et al.
Statistical analyses To assess climatic sensitivity of dSRV, Pearson correlation coefficients were calculated between dSRV and climate variables (Ta mean , Ta max, and Ta min ; mean Ts, mean ST, Pp sum, mean SWC, mean rH, gR sum, and mean VPD). To assess the overall amount of variability in dSRV explained by climate variables and DOY, we used multiple stepwise regression analysis. To reduce collinearity among variables, collinearity diagnosis was performed. To avoid phases of measurement variability higher than radial growth, only main periods of growth were considered for calculations  (Deslauriers et al. 2007 ). Analyses were performed on both years together via SPSS v20. 0 (SPSS Inc. , Chicago, IL, USA) with a significance level of 0. 05.
Acknowledgements This research was carried out within the project funded by the  German Federal Ministry of Education and Research  ( 01DL12041 ) and in cooperation with the  Southwest Anatolian Forest Research Institute (SAFRI) in Antalya  and the  Ecological Botanical Gardens in Bayreuth (EBG). We would like to thank all who have contributed to this work, with special thanks to Nes ¸at Erkan and the employees of SAFRI as well as to  Gregor Aas  and the employees at the EBG. Furthermore, we would like to thank O ¨zdemir S ¸entu ¨rk,  Serkan Gu  ¨lsoy,  Dieter Schmitt ,  Helmut Dalitz ,  Sabine Remmele , and  Viviana Horna  for their contributions and support.
Results Analysis and Discussion The simulations are performed in the Matlab Simulink (© The MathWorks, Inc. , Natick, MA, USA) environment, aiming to validate the proposed algorithmic approach and highlight the proper interaction between these algorithms while maintaining the bus voltage regulation. Section 4. 1 is dedicated to detailing the microgrid behavior and voltage stability while Section 4. 2 is focused on the load shedding approach. All simulations were made considering the numerical values of parameters presented in Table  2. k L crit = P L /P L_D is the critical load level defined by the end-user. P PV MPP_STC represents the PV generation limit, considering a PV array of 16 PV panels. C REF is the storage capacity, obtained based on the available storage system described in  [2].
For the drugs currently used to treat toxoplasmosis, long courses of therapy are required, and their use is often limited by side effects  (11, 12). Importantly, there is no available drug with efficacy against the slowly growing bradyzoite stage of the parasite, which forms tissue cysts in deep organs, such as the brain, and can reactivate years after initial infection  (13). Despite the seriousness of cryptosporidiosis and toxoplasmosis, interest in the development of new drugs targeting these pathogens has been limited  (14) , as evidenced by the absence of new drugs in clinical trials (www. clinicaltrials. gov). To identify new molecules with activity against Cryptosporidium, we tested several benzoxaborole compounds, building on the known activity against fungal and bacterial aminoacyl-tRNA synthetases (AARSs) of some compounds of this class  (15, 16).
Protein was dialyzed against buffer containing 50 mM HEPES-KOH, 30 mM KCl, 30 mM MgCl 2 , and 5 mM 2-␤-mercaptoethanol, pH 7. 5. Protein solutions were at 50 M, and posttransfer editing analogues were at 10 mM. For the experiment with AN6426 (at 1 mM), AMP was added at 10 mM both in the sample cell and in the syringe. The heat that evolved after each ligand injection was obtained from the integral of the calorimetric signal. The resulting binding isotherms were fitted by nonlinear least squares to a single-site binding model. Analysis of the data was performed using Mi-croCal Origin software (version 7. 0; OriginLab). Experiments were performed at least twice. The variability in the binding experiments was estimated to be 5% in the binding enthalpy and 10% in the binding affinity. Crystallization.
4 M sodium/potassium phosphate, pH 5. 5, and 20% glycerol was used as the cryoprotectant. For the CmLeuRS-2-(L-norvalyl)amino-2-deoxyadenosine (Nva2AA) complex, solutions were prepared with 500 M CmLeuRS and 2. 5 mM posttransfer editing analogue (Nva2AA). Crystals were obtained by mixing 2 l of this solution with 2 l of reservoir solution containing 0. 1 M KNO 3 , pH 6. 9, and 22% (wt/vol) PEG 3350. The crystals were frozen in liquid nitrogen after transfer in the mother liquor, which contained 20% ethylene glycol as the cryoprotectant. Structure determination and refinement. The diffraction data sets of CmLeuRS CP1 complex with AN6426-AMP were collected at the French National Synchrotron Facility (SOLEIL, France), the diffraction data sets of apo-CmLeuRS CP1 and its complex with Nva2AA were collected at the European Synchrotron Radiation Facility (ESRF; France), and the diffraction data sets of CmLeuRS CP1 complex with Ile2AA were collected at the Shanghai Synchrotron Radiation Facility (SSRF; China).
The data sets were integrated and scaled with the XDS suite  (26)  or with the HKL2000 program package  (27). Further data analysis was performed with the CCP4 suite  (28). The structure of CmLeuRS CP1 was initially solved by molecular replacement with the PHASER program (29) using the structure of Candida albicans LeuRS CP1 (PDB accession number 2WFG) as a model. The obtained model was improved by manual adjustments with the Coot program  (30). The structure of the Cm-LeuRS-Ile2AA complex was solved by molecular replacement with PHASER using the apo-CmLeuRS CP1 as a model. All models were refined using REFMAC5  (31)  and/or Phenix (32) software. Structure quality was analyzed with the PDBe validation server (http://wwpdb -validation. wwpdb. org/validservice/) and showed all residues in allowed regions for the different models.
Figures were drawn with the PyMOL program (http://www. pymol. org/). Homology model of TgLeuRS and docking of the adduct AN6426-Ade76. The model of Toxoplasma gondii LeuRS (TgLeuRS) was built by using the protein structure homology server SWISS-MODEL  (33)  and the structures of cytosolic apo-CmLeuRS and human LeuRS (PDB accession number 2WFD) as the templates. The sequence similarity between the Toxoplasma and Cryptosporidium editing domains was 52. 6%, and that between the Toxoplasma and Homo sapiens editing domains was 38. 1%. Residues 1 to 312 of TgLeuRS were used to build the homology model, which presents a Qmean Z score of Ϫ3. 43 and a QMean4 score of Ϫ0. 56, indicating a good global-quality model  (34). Docking of AN6426-AMP into the Toxoplasma LeuRS's editing site was done by secondary structure matching of the model of TgLeuRS described above onto the structure of the complex of CmLeuRS with the adduct AN6426-AMP.
EC 50 s were determined by fitting the dose-response curve using GraphPad Prism software. Accession numbers. The atomic coordinates and structure factors have been deposited in the Protein Data Bank (PDB) under the following accession numbers: 5FON for the apo-CmLeuRS structure, 5FOL for the complex of CmLeuRS with Ile2AA, 5FOG for the complex of CmLeuRS with Nva2AA, and 5FOM for the complex of CmLeuRS with the adduct AN6426-AMP.
ACKNOWLEDGMENTS We thank the  EMBL-ESRF-ILL-IBS Partnership for Structural Biology  for access to structural biology instrumentation, notably, the high-throughput crystallization facility and the  ESRF  X-ray beamlines. We are also thankful for the contribution of the SCIMI platform ( UMR 5309 ). The Toxoplasma work was supported by the  European Research Council  (ERC; consolidator grant no. 614880 HostingTOXO  to M. -A. H. ); R. -J. L. was supported by the  Youth Innovation Promotion Association (Chinese Academy of Sciences ), and the work with Cryptosporidium parvum was supported by the  Bill & Melinda Gates Foundation  (to P. J. R. and J. G. ).
Funding Bioinformatics analyzes benefit from the C2BIG computing centre funded by the  Région Ile de France  and  UPMC. This work was partially supported by the  ICAN Institute of Cardiometabolism and Nutrition  ( ANR-10-IAHU-05 ), the  Ligue nationale contre le cancer  ( Labeled team H Raslova ), and the  "Fondation pour la Recherche Médicale FRM " (grant to  PS FDM20150633607 ). We thank  Dr. J. Ghysdael  and  Dr. F. Guidez  for providing the plasmid constructs,  Laboratory of Pr. D. Raoult  (URMITE), microscopy unit (P. Weber),  Dr Paola Ballerini  ( Hopital Trousseau ) for genotyping;  Dr. JC. Bordet  for transmission electron microscopy;  Dr C Chomiene  and  C Dosquet  for the platelet survival assay;  M Crest  for experimental help, the  French Reference Center on Hereditary Platelet Disorders (CRPP)  for patients' clinical exploration.
For the  F2-F6  families, study makes use of whole genome sequencing data and analysis approaches generated by the  NIHR BioResource -Rare Disease BRIDGE Consortium. The  NIHR BioResource -Rare Diseases  is funded by the  National Institute for Health Research of England (NIHR ; award number  RG65966 )). KF is supported by the  Fund for Scientific Research-Flanders (FWO-Vlaanderen ,  Belgium ,  G. 0B17. 13N ] and by the  Research Council of the University of Leuven (BOF KU Leuven‚ Belgium ,  OT/14/098]. ETV6  and platelet formation haematologica | 2017;  102(2 ).
Results and Discussion General nucleosome architecture around TSSs and ORIs. Our MNase-Seq datasets provide a 50× coverage across the yeast genome, increasing up to 70× across gene bodies and 86× when only the replication origins are considered. We have been able to map the average location of about 67000 nucleosomes, which occupy nearly 80% of the yeast genome. Nucleosome architectures around TSSs and ORIs have been classified based on the width of the NFRs (closed (c) or open (o)) and the positioning of the -1 and + 1 nucleosomes (missed (M), fuzzy (F) or well-positioned (W)), as described earlier  17. Overall, we could classify 81% of TSSs and more than 90% of ORIs nucleosome strings. Figure  1a ,b demonstrate the average nucleosome configurations of three biological replicas collected at G1 phase, which are used for further comparison of nucleosome organization around TSSs and ORIs.
Nucleosome profiles are conserved in more than 87% of TSSs and 60% of ORIs among replicas. Yet, only 47% of TSSs and 24% of ORIs can be classified into the same nucleosome architecture family (i. e. sharing the same -1/ + 1 nucleosome positioning and NFR annotation; see Methods and Sup. Dataset 1 for details). Notably, NFR width is mostly conserved among replicas at both TSSs and ORIs, confirming that NFR is the most stable property of nucleosome architectures  17. Finally, the noise level in nucleosome architectures is higher around ORIs than around TSSs (41. 5% of ORIs show significant replica-dependent changes compared to 23. 9% of TSSs) (Fig. 1e ), confirming loose chromatin-dependent regulation around ORIs. Functional implications of basal nucleosome architecture. We could not detect any significant difference in gene expression at G1 phase between "noisy" and "noiseless" genes, but genes with expression fold-change greater than 1.
As next, we have further categorized the analyzed genes according to their nucleosome architecture variations at TSSs along the cell cycle. We have classified the set of genes with conserved patterns as "stable", those with changes in family annotation as "plastic", and those with substantial profile changes (corr + ICDB) as "mobile" (around 10% of the genes analyzed) (Sup. Dataset 1; see Methods). Accordingly, 1617 genes (representing 49% of total analyzed genes) are "plastic", albeit most of changes detected along the cell cycle in these "plastic" genes are rather small. About 63% of "plastic" genes show nucleosome variations only in one of the cell cycle stage (i. e. nucleosome architecture is maintained in 3 of the 4 stages). These nucleosome changes mostly take place at S or M phases, where there is an increase in nucleosome fuzziness (Fig.
28 to infinite; Benjamini & Hochberg (BH) adjusted p-values between 10 -3 and 10 -6 ; Fig. 4e  and Dataset 2 ). On the contrary, mobile genes have higher variability in the gene expression along the cell cycle and are largely enriched in cell division processes such as conjugation (OR around 10, BH p value < 10 -8 ), sexual reproduction and pheromone response (OR between 3-8, BH p-value between 10 -3 and 10 -5 ) (Fig. 4f  and Dataset 2). A focused analysis on individual cell cycle transitions reveals that mobile genes in G1→ S or M→ G1 transitions typically correlate with higher expression changes at these transitions, demonstrating that nucleosome fluctuations at the entry into (M→ G1) or the exit from (G1→ S) G1 stage have the strongest impact on gene expression (Fig. 4c ). In general, the increase in expression in one phase is associated to a decrease in nucleosome coverage and positioning at the corresponding phase compared to the adjacent one (Fig.
4d ). Moreover, the specific analysis of MNase-seq coverage revealed that mobile genes with the peak expression at G1 phase have lower coverage and phasing at G1, comparing to adjacent S and M phases (Fig. 4h ). GO analysis of this set of genes with higher expression at G1 phase shows that they are enriched in cell division functions (Fig. 4g  and Dataset 2). As noted above, "plastic" or "mobile" genes are often "noisy" at G1 phase, i. e. nucleosome re-arrangements (at TSSs) along the cell cycle take place typically in genes that are intrinsically dynamic at G1 phase. However, the nucleosome architectures of a small subset of "mobile" genes (24) are noiseless at G1 phase. These genes mostly show alterations in nucleosome architecture at the entry into (G2→ M) or the exit from (M→ G1) mitosis, and are enriched in a variety of functions related to the cell cycle (OR often above 100; associate BH p-values around 10 -4 ) (Dataset 3).
The integrity and size distribution of digested fragments were determined using the microfluidics-based platform Bioanalyzer (Agilent) prior to DNA sequencing. Typically, samples containing > 80% mononucleosomal fragments, digested with 0,08 U, were sent for sequencing. Cleaved DNA samples were sequenced with Illumina HiSeq 2000, obtaining between 50 M and 90 M paired reads per experiment. Sequencing experiments were done in triplicates to minimize potential artifacts and biases. Raw reads are available at the ENA-SRA website (http://www. ebi. ac. uk/ena) with accession number PRJEB6970.
RNA Isolation and gene-expression arrays. Cells were collected at the same intervals as nucleosomal DNA samples in icy-water and harvested by spinning for 3-4 min at 6000 rpm, frozen in liquid nitrogen and stored at -80 °C. Total cellular RNA was extracted using the RNeasy kit (Qiagen), following the manufacturer's instructions with the spheroplasting protocol (0. 5 mg/ml zymolase for 30′ ). The total RNA was hybridized to Affymetrix GeneChip Yeast Genome 2. 0 arrays for gene-expression analysis. Transcript abundance was measured as the median log 2 ratio of data from all probes fully contained within the length of the transcript, which identified 4,815 coding transcripts. We defined the fold-change expression for a particular gene in two different cycle phases as the power of unsigned difference between their log2 expression ratios.
Raw files available in ArrayExpress under accession number E-MTAB-2839 and E-MEXP-3702. Nucleosome mapping and calling. MNase-seq short reads were aligned to the SacCer3 genome (UCSC, downloaded from http://hgdownload-test. cse. ucsc. edu/goldenPath/sacCer3/ bigZips) using Bowtie aligner  58  allowing up to two mismatches per read. Paired-end reads were matched and coverage was calculated using R/ Bioconductor  59  and nucleR library  60  , yielding to a fold-coverage between 35x-80x. 93. 5% of the genome was covered by at least one read and nucleosome calls covered 79. 7% of the yeast genome, with an average of 67. 000 calls. In order to improve the signal-to-noise ratio, reads were trimmed to their central 50 bases easing the identification of individual peaks in the plots. In order to assess the fuzziness of different peaks, we used the calling score reported by nucleR.
Additionally, we considered only the genes that are also covered by the expression array, yielding to a set of 3279 genes with information about nucleosome structure and gene expression. ORI annotation. We used the annotation of experimentally validated ORIs from Eaton et al. 16  Position of reported ORIs and ACS were updated to SacCer3 genome using liftOver tool from UCSC (https://genome. ucsc. edu/cgi-bin/hgLiftOver). Replication times for different ORIs were obtained by crossing this dataset with the one provided by Yabuki et al. 61. Measure of changes in nucleosome architecture. In order to assess the variations in nucleosome organization between two samples we used two different measures. We considered a qualitative change when the clustering group of a given locus for two samples varies, which also gives information about the directionality of the change.
7 or lCDB > 0. 8) as "mobile". In a similar manner, variations in the same stage of the cell cycle between different replicas are defined as "noisy". Gene Ontology (GO) analysis. Hypergeometric tests for GO-term enrichment in different gene sets were made using GOstats library  62. Significance of GO-analysis was assessed by adjusted p-values using Benjamini & Hochberg (BH) corrections for multiple testing. When applicable, data reduction was done using ReviGO web service  63  , selecting those GO terms with relevance score > 0. 5.