dataesr/scientific-paragraphs-categorization huggingface dataset:
This study provides a positive proof-of-concept for detecting P and T waves in arrhythmic ECG beats and brings to light other interesting perspectives that can be investigated. There is a need to investigate P-and T-waves with different morphologies, e. g. , biphasic and inverted to determine if the detector performs efficiently. Additionally, different implementations of the event-related moving average methodology will benefit the analysis of ECG signals on portable, wearable, and battery-operated ECG devices. Even though Brugada's syndrome investigation was not an explicit goal of our study, it is worth noting that most of the recordings of the MIT-BIH database were acquired in patients treated by drugs such as flecainide, procainamide and ajmaline. These therapies may lead to several ECG alterations, in particular PVCs and QRS prolongation, which also appear in patients affected by this pathology, and are correctly managed by our method, as confirmed by our results.
Conclusions There is a limitation when evaluating arrhythmic P and T wave detection algorithms, due to the lack of datasets with annotated P and T waves, thus making it even more difficult to compare standard detection algorithms. Using 10 arrhythmic ECG signals from the MIT-BIH Database, with a total of 21,702 the algorithm was tested and evaluated. Results show promise and are sufficient in demonstrating real-life scenarios of long-recorded arrhythmic ECG signals with different morphologies. Specifically, the algorithm achieved an SE of 98. 05% and a +P of 97. 11% for P waves, and an SE of 99. 86% and a +P of 99. 65% for T waves. Moreover, the proposed algorithm performed efficiently and.
Altogether, an 8-11 year difference in median age was observed between the 2J and EPI classes (Pairwise comparisons using Mann-Whitney Wilcoxin rank sum test, 2J-COL versus EPI, P = 0. 264; 2J-NL versus EPI, P = 0. 099; MJ-CL versus EPI, P = 0. 185; and MJ-SC versus EPI, 0. 959) and between all the integrated versus EPI, the P value is 0. 12. Finally, follow-up data recorded at least 6 months after diagnosis were available for 58 cervical cancer patients. Half were either deceased (14/29), diagnosed with pelvic relapse (10/29) or metastasis (3/29). By comparing the HPV signatures with the disease outcomes, we noted that a poor outcome was more frequently recorded in tumours from the 2J category (17/24) than in the EPI (9/17) and MJ (5/17) categories (Chi-square test: P value integrated versus EPI = 0.
67 million markers for copy-number analysis, with 750,000 SNP probes and 1. 9 million non-polymorphic probes). Analysis was performed using the Chromosome Analysis Suite software (ChAS) version 3. 0 (Affymetrix), as well as the Genome Alteration Print (GAP) software, developed at the Institut Curie. 41 Frequency of Amplicon, Gain and Loss (FrAGL) was performed on the bioinformatics platform (U900, INSERM) of the Institut Curie.
ACKNOWLEDGEMENTS We are indebted to the lab members of the  Nicolas group  for critical discussions;  Gael Millot  for help in statistics; the  NGS  platform at the  Institut Curie ;  Virginie Fourchotte , representing the  Gynecological Tumor Study Group , for providing clinical data;  Odette Mariani  for providing tumour specimens from the  Institute Curie Biological Resources Center ;  David Gentien  and  Cecile Reyes  from the  Department of Translational Research  to perform the CGH array experiments;  Philippe Hupé  and  Philippe de La Rosa  ( U900   INSERM ,  Institut Curie ) for help in CGH data analyses;  Jim Haber  for helpful suggestions and  Dominique Vaur  for critical review of the manuscript. This work was supported in part by the  INCa Integrated Action for the Research Program (PAIR-HPV , No.
EVI1 modulates metabolism in AML cells. To characterize the transcriptional consequences of EVI1 overexpression in AML, we generated an EVI1-dependent gene signature from murine bone marrow Lin Low cells infected with a retroviral construct encoding Evi1 (GSE 34729  16  ). We then used this signature to conduct an open-ended enrichment analysis of 4725 curated gene sets from the c2 collection assembled from various sources such as online pathway databases, publications in PubMed, and knowledge of domain experts available from MSigDB (Figure  1A ). We confirmed the findings of other investigators that gene sets related to cell proliferation and differentiation were enriched in genes whose expression is suppressed by enforced Evi1 overexpression  [16] [17] [18]. Multiple cell metabolism-related gene sets were also significantly enriched in an unbiased search of the complete c2 collection.
Taken together, these results show that EVI1 represses RUNX1 expression by direct binding to its promoter. This, in turn, eliminates repressive RUNX1 binding at the CKMT1 promoter and thereby promotes CKMT1 expression. Moreover, we also observed that RUNX1-directed shRNAs attenuated by 17-fold (based on the average bioluminescence across the two hairpins) the reduced disease burden caused by EVI1 depletion (Figure  3M ). Finally, we explored the relationship between EVI1 and RUNX1 expression with CKMT1 mRNA levels in two AML transcriptional datasets (GSE14468 and GSE10358). We divided these cohorts into four subgroups: EVI1 high /RUNX1 low , EVI1 high /RUNX1 high , EVI1 low /RUNX1 low , and EVI1 low /RUNX1 high. Consistent with our mechanistic analysis, AML samples with high CKMT1 levels were significantly more represented within the EVI1 high /RUNX1 low subgroup than in the three other subgroups (Figure  3N ).
Genome Profiling of Evi1-overexpressing Murine Progenitor Cells. eGFP-positive control and Evi1-overexpressing progenitor cells were sorted and total RNA was extracted and profiled using the GeneChip Mouse Gene 1. 0 ST Array from Affymetrix (GSE39103  16  ). Comparative Marker and GSEA Analyses. The  (http://software. broadinstitute. org/gsea/msigdb/). Gene sets with less than 15 genes or more than 500 genes were excluded from the analysis. Gene sets with a nominal p value ≤ 0. 05 and FDR ≤ 0. 25 were considered significant. The gene ranking metric in the weighted enrichment score was the 2-sided SNR, and the p values were calculated using 1,000 permutations of the phenotype.
Gene Expression Profiling of Cyclocreatine Treatment. TF-1, UT-7 and UCSD-AML1 cells were treated in quadruplicate with either vehicle or 3 mM cyclocreatine for 24 hours. Total RNA was extracted and profiled by RNA sequencing (HiSeq, Illumina) at the BioMicroCenter, Massachusetts Institute of Technology (Cambridge, MA, USA). The total number of reads for individual samples ranged from 44 to 48 Mb. Quality control tests for the unmapped reads were performed using the FASTQC software (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). The reads were aligned to the GRCh37 (hg19) GENCODE v17 annotated human reference genome by using TopHat2  50. FPKM scores for genes were computed based on the RSEM R software package  51. Expression data was evaluated as log 2 (FPKM). The significance of the differential expression between the control and cyclocreatine treatment phenotypes was estimated using the EBSeq method implemented in R (Bioconductor v.
3. 3  52  ), based on the significance cutoff 0. 05 for the posterior probability. The aligned RNASeq data is available at the GEO repository(GSE86151). Comparative Marker Analysis. The 24 samples available in the RNAseq data were separated into two groups: 12 =vehicle-treated and 12 =cyclocreatine treated. The Comparative Marker Selection module from GenePattern v. 3. 9. 3  45  was employed to identify individual genes that were differentially expressed between the two conditions. The analysis was performed by applying a 2-sided Signal-to-Noise Ratio (SNR) test followed by 1000 permutations of phenotype labels. The settings for the SNR parameters were log-transformed-data:no, complete:no, balanced:no, smooth p values: yes. A "core" cyclocreatine signature consisting of 54 genes upregulated and of 53 genes downregulated between the vehicle and cyclocreatine-treated samples was defined based on the cut-offs SNR permutation p value ≤ 0.
GSEA analysis and Scatter Plot. The Gene Set Enrichment Analysis (GSEA v2. 1. 0) software  46, 47  was used to identify functional associations of the molecular phenotypes induced by cyclocreatine with a compendia of gene signatures including the MSigDB v5. 0  47, 53  collections c2 of 4,725 curated gene sets and c6 of 189 oncogenic signatures, the DMAP collection of hematopoietic lineage differentiation signatures  53  , and with four AML differentiation gene signatures available from Gene Expression Omnibus (GEO) database: Vitamin D-and PMA-induced differentiation signatures (accession no. GSE995 54 ); JQ1-induced differentiation signature (accession no. GSE29799  55  ), and DOT1L inhibitor-induced differentiation signature (accession no. GSE29828  56  ). Gene sets with less than 15 genes or more than 500 genes were excluded from the analysis.
Samples The in silico test used to determine the proportion of EVI1 high /RUNX1 low , EVI1 high /RUNX1 high , EVI1 low /RUNX1 low , and EVI1 low /RUNX1 high patients exhibiting high CKMT1 versus low CKMT1 expression levels was carried out on the Affymetrix U133 Plus2 gene expression data GSE144688  57  (526 AML samples) and GSE10358  58      Each experiment in D-J was performed at least two independent times.
IG, DJD, and RMS provided patient samples and revised the manuscript. MTH, AP and KS contributed equally to this work as co-senior authors. MTH, AP and KS supervised the study, wrote and revised the manuscript, designed the in vitro and in vivo experiments, analyzed the data, and provided funding for the study. ONLINE METHODS Plasmids and shRNA Constructs Empty and Evi GCDN vectors were a kind gift from Drs. Tomohiko Sato and Mineo Kurokawa (The University of Tokyo, Tōkyō, Japan). Empty and HA-tagged pHAGE-CMV-CKMT1 and pHAGE-CMV-RUNX1 vectors were generated by enzymemediated site-specific recombination between pENTR223-CKMT1 (Harvard PlasmID database, HsCD00372650) and pENTR223-RUNX1 (Harvard PlasmID database, HsCD00378663) donor vectors and pHAGE-CMV-HA destination vector (gift from Dr. J.
ACKNOWLEDGEMENTS We thank  Drs. Tomohiko Sato  and  Mineo Kurokawa  ( The University of Tokyo, Tōkyō, Japan ) for providing plasmid constructs and detailed procedures for Evi1 overexpression in murine hematopoietic cells. We also thank  Dr. Joseph F. Clark  ( University of Cincinnati, Cincinnati, Ohio, USA ) for advice on the use of cyclocreatine in vivo. This research was supported with grants from the  National Cancer Institute  ( NIH   1R35 CA210030-01  ); Stand-up-to-Cancer; the  Bridge  Project, a collaboration between The  Koch Institute for Integrative Cancer Research at MIT  and the  Dana-Farber/Harvard Cancer Center  ( DF/HCC ) (KS and MH) and support from the  Cubans Curing Children's Cancers (4C's Fund)  (KS) and from  Koch Institute Cancer Center Support  (grant  P30-CA14051 ,  NCI ) (MH).
38 least 41 bases of the PCR product were sequenced with an Illumina HiSeq 2000 machine. shRNAswithless than 100 reads in the input sample were excluded from further analysis, and read numbers for each shRNA were normalized to the total read numbers per sample to allow for cross-comparison between samples. We then compared using an EdgeR/RIGER method the hairpin representation between day 0 and day 36 and thus identified genes whose depletion blocked or enhanced cell proliferation. EdgeR (R/Bioconductor v. 3. 3 package38) provided the statistical analysis of count data from the day 0 versus day 36 time-points per cell lines. Next, the RNAi gene enrichment ranking Pooled shRNA Screening A custom library of 361 hairpins directed against 67 human genes involved in glycolysis, Designation Species pentose phosphate pathway, TCA cycle, and related metabolic pathways with at least 3 Accession Number Target Sequence shCKMT1_1 Homo Sapiens NM_020990.
When we added Tx7335 to the outside bath, an increase in channel openings could be observed in a dose-dependent manner both for WT and pmut3 (see representative traces in Fig. 4 , and all-point histograms in Supplementary Fig. S3 ). Mean open-and closed-time analyses of all traces were performed using the maximum interval likelihood (MIL) method with the software package QuB  27  , and results are summarised in Table  1  (data fits are shown in Supplementary Figs  4  and 5  ). It can be noted that the addition of 2. 0 μM Tx7335 induces an ~ 8-fold increase in mean open times for wild-type KcsA and a ~ 13-fold increase for pmut3 KcsA (see Table  1 ). The correlated increases in open probability of about 40-fold for wild-type and about 90-fold for pmut3 channels reveal that there is an increase in the frequency of openings in addition to the increase in open times.
0 (cis) and 4. 0 (trans) were maintained by 10 mM of HEPES and succinate buffers, respectively. The membrane voltage was controlled to +100 mV and the current recorded by an Axopatch 200B amplifier with a Digidata 1322A analog-to-digital converter and Axoclamp software (Molecular Devices)  1, 50. BSA at a concentration of 0. 5 mg/ml was added to all solutions prior to addition of Tx7335 to prevent non-specific binding of the toxin to any surfaces. Toxin stock solutions at 200 μM concentration were prepared from lyophilized HPLC Tx7335 fraction, either in the cis or trans buffers for addition to the respective chambers.
Acknowledgements We thank  Dr. Roderick MacKinnon  for intellectual contributions and material support, as well as for providing the pQE60-KcsA WT and mutant plasmids. This work was supported by  NSF  grant  MCB-1253277  to SFP and a  CUNY Community-College Collaborative Research Grant  as well as several PSC-CUNY grants to IORT and SFP, and an  NIH  grant to  BTC  ( PHS GM103314 ).
Although we collected three rectal swab isolates per patient, most extraintestinal K. pneumoniae isolates provided by the clinical lab represented one isolate per site. It is possible that multiple strains may be present at an extraintestinal site but only one isolate was sampled. For wzi sequencing, rectal swab isolates from fewer than 10% of colonized patients were tested. Given 40 unique patients with 43 unique strains, almost every patient was colonized with a different strain. This high level of diversity is unlikely to be maintained in the larger sample set. A limitation of the susceptibility data was that the majority of isolates had no detectable acquired resistance. With a low diversity of resistance phenotypes, we were unable to rigorously test the agreement of susceptibility testing between colonizing isolates and subsequent infecting isolates in the same patient.
32) (38), removing read duplicates using FastUniq (39), and performing error correction using SOAPec (v2. 01)  (40). Preprocessed reads were assembled using VelvetOptimiser (v2. 2. 5). In this process, the reads were assembled by using Velvet (41) and a stepwise Kmer size at a step of 2, from 51 to 149 (for paired-end samples), or from 25 to 51 (for shared-end samples). The assembly with the largest N 50 value was used for subsequent analysis. MLST and cgMLST. The gene sequences corresponding to the international MLST scheme of Institut Pasteur  (19)  were extracted from the genomic assemblies by using the BIGSdb platform (42) and the BLASTN algorithm, and the corresponding allelic number was defined by comparison with the reference nomenclature database (http://bigsdb. pasteur. fr/klebsiella).
cgMLST was performed in the same way using the strict core genome MLST scheme defined by Bialek-Davenet et al. (24). Novel alleles and MLST profiles were submitted to the nomenclature database. MLST profiles were compared using the categorical mismatch method within BioNumerics version 6. 6 (Applied-Maths, Sint-Martens Latem, Belgium). Uncalled alleles were not considered mismatches in pairwise profile comparisons. Antimicrobial susceptibility testing. Antimicrobial susceptibility testing was performed on the Vitek 2 automated system (bioMérieux, Marcy-l'Étoile, France), using AST-GN82 cards loaded per the manufacturer's instructions. Isolates were grown on BAP at 37°C overnight. Colonizing and infecting isolates from the same patient were tested in the same batch. Susceptibility testing was performed on one rectal isolate for each patient.
2 on bivariable tests were eligible for inclusion in the final multiple logistic regression model. This final model of K. pneumoniae infection was constructed via backwards elimination using a likelihood ratio test for variable retention, with a cutoff ␣ of 0. 05. Interactions between variables in the final model were tested and included if significant. Additional model regression assessments included the Hosmer-Lemeshow test for goodness of fit and calculation of the AUROC curve. For interpretation of the results, a P value of 0. 05 was considered statistically significant for all analyses. The analyses were performed using SAS 9. 3 (SAS Institute, Cary, NC) and R 3. 2. 2 (R Foundation for Statistical Computing, Vienna, Austria). Accession number(s). Whole-genome sequencing files have been deposited in the NCBI Sequence Read Archive (PRJNA341404) under accession numbers SAMN05722982, SAMN05722983,.
ACKNOWLEDGMENTS R. M. M. , J. C. , K. R. , P. N. M. , and M. A. B. conceived and designed the study. R. M. M. acquired patient isolates and susceptibility data. J. C. acquired wzi sequence data. J. C. and K. R. acquired patient demographic data. W. W. assembled the sequenced genomes. S. B. and V. P. acquired core genome MLST data. R. M. M. , J. C. , S. B. , V. P. , K. R. , and L. Z. analyzed the data. R. M. M. and J. C. drafted the manuscript. S. B. , V. P. , W. W. , L. Z. , P. N. M. , K. R. and M. A. B. critically revised the manuscript. M. A. B. supervised the study. This work was supported by the  Michigan Institute for Clinical and Health Research (MICHR)  through a grant from the  National Center for Advancing Translational Sciences  of the  National Institutes of Health  ( UL1TR000433 , to M.
A. B. and K. R. ) and the Claude D. Pepper Older Americans Independence Center  under grant no. AG-024824  (K. R. ). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders. We thank the team of curators of the  Institut Pasteur MLST system (Paris, France)  for importing novel alleles, profiles, and/or isolates. M. A. B. thanks  Michelle Hatto ,  Vicki Larson , and  Pratik Shrestha  for assistance with collection and identification of K. pneumoniae isolates.
However, due to computational cost, the global models have to use a limited number of modes or bins to describe the aerosol distribution. In addition, the validation of simulations is often restricted to datasets well documented over the globe, i. e. , surface PM concentrations (without speciation) and aerosol optical depth (AOD), but with a low spatial resolution. CTMs at regional scale simulate the same processes but usually with a more accurate description for the processes involved in the aerosol formation and evolution. At the regional scale, AQ models tend to underestimate PM and the main discrepancies are often attributed to a lack of emissions or difficulties to reproduce stable meteorological conditions during PM episodes  (Bessagnet et al. , 2016; Solazzo et al. , 2012). The chemistry of secondary organic species and deposition is also a source of uncertainties  (Bergström et al.
The EMEP network surface concentrations For the comparisons between observed and modeled concentrations, the background stations measurements performed during campaigns or in a routine way are used. All the stations are listed in Table  1  and displayed in Fig. 1. Depending on each station, several pollutants are measured: O 3 , NO 2 , SO 2 , PM 2. 5 and PM 10. For some stations, the inorganic species sulfate, nitrate and ammonium are used. These measurements are available on the EBAS database (http://ebas. nilu. no/) and are used here on a mean daily basis. Only the background stations are used due to the coarse horizontal resolution of the model. The altitude above sea level (a. s. l. ) is presented for information. The representativity of the station depends on the sub-grid-scale variability of the model cell: the lower the variability, is the more representative the station is.
Site Longitude Latitude Code Country The chemical evolution of gaseous species is calculated using the MELCHIOR2 scheme. The photolysis rates are explicitly calculated using the FastJX radiation module (version 7. 0b)  (Wild et al. , 2000; Bian et al. , 2002). The modeled AOD is calculated by FastJX for the 600 nm wavelength over the whole atmospheric column. A complete analysis of the improvement obtained in the model with this online calculation is fully described in  Mailler et al. (2016a). The way to redistribute the aerosol bins for the FastJX model is extensively described in  Mailler et al. (2016b). At the boundaries of the domain, climatologies from global model simulations are used. In this study, outputs from LMDz-INCA  (Szopa et al. , 2009)  are used for all gaseous and aerosols species, except for mineral dust where the simulations from the GO-CART model are used  (Ginoux et al.
but using the global emission database EDGAR-HTAP annual Table  4. Aerosols emissions with the three modes describing their size distribution: fine, coarse and big. The mean mass median diameter (MMMD) D p is expressed in µm; σ is unitless. "Mo86" refers to the parameterization of  Monahan (1986)  totals as input data. The EDGAR-HTAP project compiled a global emission dataset with annual inventories at the national or regional scale that are likely to be acceptable for policy makers in each region of the world. This compilation of different official inventories from EMEP, UNFCCC, EPA for USA, GAINS for China and REAS was first gap-filled with global emission data  (Janssens-Maenhout et al. , 2012). The version 2 of this emission inventory was available for the year 2010. The available emitted species are listed in Table 4.
PPM corresponds to the non-chemically reactive mass of particulate matter. The "fine" part of H 2 SO 4 corresponds to 1 % of the SO x anthropogenic emissions and thus to primary sulfuric acid. These emissions were already used in this region and for this period in  Menut et al. (2015a). The biogenic emissions are calculated using the MEGAN emissions scheme  (Guenther et al. , 2006) , which provides emission fluxes of nitrogen monoxide, isoprene and monoterpenes. The mineral dust emissions are calculated using new soil and surface databases described in  Menut et al. (2013b)  and with a spatial extension of potentially emitting areas in Europe. The dust production model used is the one of  Alfaro and Gomes (2001). The sea salt emissions are calculated following the  Monahan (1986)  parameterization.
Aerosol optical depth The AOD calculated with CHIMERE does not correspond exactly to the available AERONET data. For the comparison between model and observations, the modeled AOD is interpolated on the AERONET wavelengths. For the region and studied period, the most complete AERONET dataset was found for AOD at λ = 675 nm. The CHIMERE AOD useful for the interpolation are for λ = 600 and 999 nm. First, the AE is estimated as A(λ 1 , λ 2 ) = -log AOD(λ 1 ) AOD(λ 2 ) log λ 1 λ 2 , ( 1 ) where λ 1 and λ 2 are two wavelengths and AOD(λ 1 ) and AOD(λ 2 ) the AOD corresponding to these two wavelengths. In case of this study, λ 1 = 600 nm and λ 2 = 999 nm with CHIMERE. Then, the interpolated AOD is obtained as AOD(λ 3 ) = AOD(λ 2 ) exp(-A(λ 1 , λ 2 ) × log(λ 3 /λ 2 )), (2) with λ 3 = 675 nm for the comparison between CHIMERE and AERONET.