dataesr/scientific-paragraphs-categorization huggingface dataset:
Overall results Of 73 patients included in our data analysis 18 were male and 55 were female with a mean age of 52 years (15-74 years) at the time of surgery. Forty-nine masses were SA (67. 1%) and 24 NSA (32. 9%). Fifty-nine were benign (80. 8%) and 14 malignant were masses (19. 2%). Of the benign masses, 20 were non-secreting (27. 39%) and the remaining 39 were secreting (53. 42%). Of the malignant masses 4 were non-secreting (5. 47%) and 10 were secreting (13. 69%).
M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 8 In contrast with Pattel et al. [17]  no correlation was found between Tmx or Tmx:Lmx and hormonal status. This result could be explained by the differences in the study population. As already mentioned we analyzed 73 patients with cortisol secreting and non-secreting masses. In their study Pattel  [17]  et al. analyzed 24 patients including bilateral masses. Our data confirms the role and utility of 18 F-FDG PET/CT in the distinction between benign and malignant tumors. The conflicting data pertaining to 18 F-FDG PET/CT uptake in functional/secreting adrenal lesions may relate to the presence of confounding patient or technical factors that may influence 18 F-FDG metabolism. At present the correlation between 18 F-FDG PET/CT uptake and adrenal tumor cortisol secretion is not certain and it should not lead to use of 18 F-FDG for definitive interpretation of the functional status of an adrenal tumor.
Acknowledgments This work was supported by the  German Science Foundation (DFG)  under the  Collaborative Research Center (SFB)   676  " Particles, Strings and the Early Universe " and the  Research Training Group (RTG)   1670  " Mathematics inspired by String Theory and Quantum Field Theory ". The work of SL was also supported by the  ANR  grant  Black-dS-String. The work of PR was also supported by a  James Watt Scholarship. We have benefited from conversations with  Constantin Muranaka  and  Marco Zagermann.
Acknowledgements We gratefully acknowledge the contribution of our partners, engaged and collaborating organizations:  Adam Balen ,  Leeds Teaching Hospitals ,  UK Estifanos Baye, Monash Centre for Health Research and Implementation, Australia Jacqueline Boyle, Monash Centre for Health Research and Implementation ,  Australia Leah Brennan ,  Australian Catholic University, Australia Frank Broekmans, University Medical Centre Utrecht, Netherlands Preeti Dabadghao, Sanjay Gandhi Postgraduate Institute of Medical Sciences, India Luigi Devoto, University of Chile, Faculty of Medicine, Chile Didier Dewailly, University of Lille, France Linda Downes, Monash Centre for Health Research and Implementation, Australia Bart Fauser, University Medical Center Utrecht, Netherlands Stephen Franks, Imperial College, London, UK Rhonda M.
STUDY  FUNDING/COMPETING INTEREST (SThe guideline was primarily funded by the  Australian National Health and Medical Research Council of Australia (NHMRC)  supported by a partnership with  ESHRE  and the  American Society for Reproductive Medicine. Guideline development group members did not receive payment. Travel expenses were covered by the sponsoring organizations. Disclosures of conflicts of interest were declared at the outset and updated throughout the guideline process, aligned with  NHMRC  guideline processes. Full details of named authors,  Dr Costello  has declared shares in Virtus Health and past sponsorship from  Merck Serono  for conference presentations. Prof. Laven  declared grants from  Ferring ,  Euroscreen  and personal fees from  Ferring ,  Euroscreen ,  Danone and Titus Healthcare.
Statistical analysis Continuous variables are expressed as means and standard deviations. Parametric and non-parametric tests were used based on sample size and normality of distribution. The categorical data are expressed as percentages. Fisher exact and chi square were used to identify differences between both groups. All analysis was done using SPSS statistical software (IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22. 0. Armonk, NY: IBM Corp).
Results Our retrospective results showed the following: out of 315 patients, 205 patients had MAPSE >7 mm ('Group I' 65%) and 110 had MAPSE ≤ 7 mm ('Group II' 35%). Table  1  provides the baseline clinical data according to MAPSE. Patients with MAPSE ≤ 7 mm were more symptomatic (NYHA III-IV 66. 6% in Group I and 90. 9% in Group II, p-value 0. 004), otherwise there was no statistical significant difference between both groups as regards to the baseline characteristics. The echocardiographic findings and the clinical outcomes are shown in Table  2. An overview of our summative results of both groups was as follows; 3-months mortality was 0. 6%, the hospital readmission was 1. 3% and improvements of the heart failure symptoms were recorded in 76%. There were no statistically significant differences between the groups regarding improvement of the symptoms.
ACKNOWLEDGEMENTS. Thanks are given to  F. Schulthess  for critically reading the manuscript and correcting the English. This work was supported by funding from the  French ANR (Agence Nationale de la Recherche)  ( Adaptanthrop  project  ANR-09-PEXT-009 ) and the  French Ministry of Foreign Affairs  ( VCI grant ) ( Chargé de Volontaire International   N°04036500 ).
Laboratory diagnosis of IFD Diagnostic procedures for the diagnosis of IFD routinely used galactomannan enzyme immunoassay in serum, bronchoalveolar lavage (BAL), and cerebrospinal fluid (CSF) besides histology, direct examination and culture on clinical specimens according to standard method  [34]. Briefly, identification of fungi was done using classical phenotypically methods until implementation of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS, Microflex, Bruker Daltonics): yeasts were identified using MALDI Biotyper V1. 3 Software (Bruker Daltonics) since April 2013 and filamentous fungi were identified at the species level using an extended reference spectra library  [35]  since October 2014. Mucormycosis diagnosis was carried out using PCR on serum in the Mycology Laboratory of Besancon University Hospital  [36].
Diagnostic strategy and EORTC/MSG 2008 sub group assignment AML patient's characteristics are collected in an ongoing prospective database of the Department of Clinical Hematology and Cell Therapy. The clinical records of patients are evaluated to determine presence or absence of IA, date of diagnosis, date of clinical onset and receipt of antifungal therapies. Date of diagnosis was the earliest date among the day of suggestive computerized tomography (CT) scan or the day of microbiological or histopathological allowing diagnosis for probable and proven IA. The diagnosis of IFD was made in accordance with the revised EORTC/MSG definitions published in 2008  [2]. According to the above definitions, the whole cohort has host factor criteria with recent history of neutropenia (< 0. 5x10 9 neutrophils/L for > 10 days).
Statistical analysis Diagnostic accuracy of serum GM-ODI was estimated by computing and presenting as sensitivity and specificity under the conditions of the serum GM-ODI ICV of 0. 3, 0. 4 and 0. 5 with their 95% confidence interval (CI). We used standard methods for binomial proportions to calculate the CI. The receiver operating characteristic (ROC) curve was calculated and the area under the curves was reported. We estimated association between explanatory variables and IA (binary dependent variable) by performing a multivariable logistic regression. Variables and interaction terms associated (p < 0. 20) with IA status were then included in a multivariate logistic regression model (p < 0. 20). The analyses were performed using SAS 9. 3 (SAS institute Inc, Cary, NC) and R 3. 2. 2 [38].
Water Tracking, Hot-Spot and Pocket Detection The tracking of water molecules and the analysis of water paths, inlets and flow were performed with AQUA-DUCT software  [15]. It is a universal tool that allows for the extraction, analysis and visualisation of the behaviour of solvent molecules during MD simulations. AQUA-DUCT traces molecules that are found in the so-called object, which is usually an area of special research importance. In the present study, the object area was defined as a 4 Å sphere around the centre of geometry of five amino acids from the active site, namely D105, Y154, Y235, D265, and H300. The tracing of solvent molecules outside of the macromolecule is not particularly informative in enzyme studies. AQUA-DUCT allows setting spatial limits in which molecules are traced by allowing the user to define the so-called scope area.
Evolutionary Analysis The Multiple Sequence Alignment (MSA) of 312 sequences of close homologues of soluble epoxide hydrolases deposited with the BALCONY (Better ALignment CONsensus analYsis) package  [38]  was downloaded to perform an evolutionary study. Prior to analysis, the MSA was truncated. The phosphatase domain, as well as terminal leftovers were removed. The remaining part of the MSA was used to calculate entropy values for all the amino acids. We then compared the entropy of the amino acids identified as common hot-spots. Functional and structural perspective as well as higher number of mutations positioned on the surface suggest that variable residues are preferably located on the protein surface  [41] [42] [43]. Indeed, the most variable residues in StEH1 are the hot-spots amino acids positioned at the surface, at the entrance to the TC/M tunnel (L266 and I270) and at the back of cavity I, separating this cavity from its surroundings (R184).
A search for a new heavy particle decaying to a pair of vector bosons (WW or WZ) is presented using data from the CMS detector corresponding to an integrated luminosity of 35. 9 fb -1 collected in proton-proton collisions at a centre-of-mass energy of 13 TeV in 2016. One of the bosons is required to be a W boson decaying to eν or µν, while the other boson is required to be reconstructed as a single massive jet with substructure compatible with that of a highly-energetic quark pair from a W or Z boson decay. The search is performed in the resonance mass range between 1. 0 and 4. 4 TeV. The largest deviation from the background-only hypothesis is observed for a mass near 1. 4 TeV and corresponds to a local significance of 2. 5 standard deviations. The result is interpreted as an upper bound on the resonance production cross section.
Signal extraction For this analysis, a novel signal extraction method based on a 2D maximum likelihood fit is introduced. As a cross-check, the prediction obtained with the α method, used in previous versions of this analysis  [20, 25] , is also presented. The signal and background yields are determined through a maximum likelihood fit, performed in the portion of the (m WV , m jet ) plane defined by the event selection described in Section 4. The fit is performed using 2D templates for signal and background processes, starting from simulation and introducing shape uncertainties that model the difference between data and simulation in the full search range. The probability density function (pdf) of X → WV events in the (m WV , m jet ) plane is modelled as: P sig (m WV , m jet |m X ) = P WV (m WV |m X , θ 1 ) P j (m jet |m X , θ 2 ).
Four dried blood spots were also collected on Whatman 903 "Protein Saver" sample cards (GE Healthcare; Cardiff UK), which were dried out of direct sunlight, and stored in cool areas until transfer to refrigerators for subsequent analyses. Geographic coordinates were collected for all participating households, allowing for subsequent mapping of cases and evaluation of clustering at both village and household levels. All data collection instruments were developed in English, translated into Lao, and back-translated to ensure fidelity by a bilingual heath specialist. These were then pilot-tested and revised before implementation. All field data were collected on paper forms, followed by doubledata entry with range checks, by specifically trained staff. Quality assurance (QA) measures included observation of 10% of each enumerator's household interviews by field team leaders, and recollection of a subset of interview questions by team leaders for 30% of all interviews conducted.
We parameterize a model of disease risk as a function of trade structure and biosecurity, controlling for the presence of endemic wild reservoirs. Specifically, we estimate the relation between trade volumes, biosecurity measures, and the existence of wild reservoirs on the probability of disease outbreaks. In the post 2001 regime, application of a compartmental approach to disease management has allowed a significant increase in trade volumes from countries that were previously excluded from the market because of the existence of wild reservoirs. We are countries. We cannot exclude the possibility that there is under-reporting of outbreaks, but we note that low-risk and high-risk countries are both represented in our data (Fig. 1b ). Using the OIE data, we constructed an annual binary new outbreak(s)/no outbreak measure for each country.
It is calculated as a country's aggregate volume of production compared to a base period  (between 2004 and 2006). The third is a measure of the assets that may be destroyed during efforts to control an outbreak-the aggregate standing stock of cattle, sheep, and pigs. Our trade dataset included the volume of imports and exports of all cloven-hoofed animals reported to the Food and Agriculture Organization between 1996 and 2011 (http://faostat3. fao. org/home/E). We grouped imports and exports of countries by their disease status at the OIE: disease-free (no vaccination), disease-free (vaccination), disease-free zones (no vaccination), disease-free zones (vaccination), and not disease-free. The volume of imports captures the direct impact of trade on disease risk, e. g. , the   Aggregate imports from and exports to countries with disease-free zones, no vaccination designation.
Countries that have a high diseasefree designation (low risk of FMD) are likely to behave in ways that maintain that designation. We had expected that countries with high or low disease-free designations would trade only with each other, e. g. , that low (high)-risk countries would trade only with other low (high)-risk countries. In reality, however, there is a large quantity of trade between countries with different disease-free designations  (Figs. 3, 4, 5, and 6). Our biosecurity dataset included proxies for the biosecurity elements thought to affect the likelihood of outbreaks  (Berentsen et al. 1992; Garner and Lack 1995; Schoenbaum and Disney 2003). Unfortunately, direct measures of sanitary capacity are scarce. As a proxy, we included the density of veterinarians registered in each country with the OIE.
LC-MS/MS instrumentation and analytical conditions LC analyses were used to determine the presence of duloxetine or catecholamines using the multiple reaction monitoring mode (MRM). Analyses were performed on a Dionex Ultimate 3000 HPLC system (Thermo Scientific) coupled with an Endura triple quadrupole mass spectrometer (Thermo Electron). The system was controlled by Xcalibur version 2. 0 software (Thermo Electron). Dried samples (spinal cord extracts) were suspended in 100 l of H 2 O/formic acid 0. 1% (v/v) and 10 l of the solution was loaded onto an Accucore RP-MS column (ref 17626-102130; 100 ϫ 2. 1 mm 2. 6 m; Thermo Electron) heated at 35°C. For derived samples, 10 l of the derivatization mixture was analyzed. Elutions were performed by applying linear gradient of mobile phase A/B. Mobile phase A corresponded to H 2 O 99.
qRT-PCR To verify the selectivity of the siRNAs, we quantified the expression of the subtypes of the ␣ 2 adrenoceptors' genes (adra2a, adra2b, and adra2c) in lumbar spinal cord by qRT-PCR. Total mRNA was extracted using the RNAeasy kit (Qiagen) and the cDNA was generated using the iScript cDNA synthesis kit from Bio-Rad. qRT-PCR was performed on a Thermocycler MylQ (Bio-Rad) using the Bio-Rad IQ Syber Green Supermix assay. PCR consisted of an initialization step at 95°C for 3 min and the amplification was realized for 40 cycles with denaturation at 95°C for 20 s, followed by annealing at 95°C for 20 s, and an extension at 60°C for 20 s. All experiments were performed in duplicate sample deposits on the amplification plate. The relative abundance of each RNA target gene transcript was normalized using endogenous control genes hypoxanthineguanine phosphoribosyltransferase (HPRT) and glyceraldehyde phosphate dehydrogenase (GAPDH).
Data were analyzed according to the standard curve method. Primers have been designed using Oligo6. 0 and M-fold softwares based on Mus musculus mRNA sequences available in the NCBI database and were as follows: musADRA2afw, CCAAGCTGCAAGATCAACGA and musADRA2arev, TACGCACGTAGACCAGGATC; musADRA2bfw, CAGAAGAAGGGGACTAGTGGG and musADRA2brev, GGCAGT GTTTGGGGTTCACAT; musADRA2cfw, CCTACTGGTACTTCG GGCAA and musADRA2crev, CCAGTAGCGGTCCAGACTAA.
Bioinformatics analyses The bioinformatic processing of RNA-Seq data was done as described previously  (Maussion et al. , 2015; Farmer et al. , 2016; Lutz et al. , 2017)  using the FASTX-Toolkit (http://hannonlab. cshl. edu/fastx_toolkit/ links. html) and Trimmomatic  (Bolger et al. , 2014)  for adapter trimming from raw read sequences; Bowtie2 and TopHat2  (Trapnell et al. , 2009)  for alignment of trimmed reads to the reference mouse genome; HTSeqcount for gene counting; and DESeq2  (Love et al. , 2014)  for differential expression analysis. Alignment. Following the high-throughput sequencing, 50 bp paired-end reads were aligned to the mouse reference genome (mm10) using Bowtie2 and TopHat version 2. 1. 0 (http://ccb. jhu. edu/ software/tophat/index. shtml) with the following parameters: a mate insert distance of 75 bp and a fr-unstranded library type because our library preparation protocol does not conserve information regarding strand (see illumina.
com/documents/products/technotes/ RNASeqAnalysisTopHat. pdf). The reads that reached mapping quality of 50 under the TopHat2 software (i. e. , that map to a unique location in the reference genome) were used for gene quantification. Quantification. Genes were first annotated based on the Ensembl annotation database (Ensembl release 75, GRCm38). To quantify gene expression level, we then used HTSeq-count version 0. 6. 1p1 (http://htseq. readthedocs. io/en/release_0. 10. 0/) to count the number of reads that overlapped the genes identified in the aforementioned annotation  (Anders et al. , 2015). To attribute or not each read to a given gene, including when overlaps with two genes were present, we used HTSeq-count under the intersection-nonempty mode (https://htseq. readthedocs. io/en/master/count.
ncifcrf. gov/), and the Gene Set Enrichment Analysis software (GSEA, version 2. 2. 0, The Broad Institute/Massachusetts Institute of Technology). The DAVID Bioinformatics Resources was one of the earliest bioinformatics tools for functional analyses of large gene lists, which are usually obtained from genome-wide studies  (da Huang et al. , 2009). It relies on the focus on specific sets of genes; that is, groups of genes that are known to share common biological function. However, such analysis is based on the list of genes displaying significant expression changes, which is dependent upon the significance threshold chosen for differential expression (here, p Ͻ 0. 01). GSEA is another analytical method for interpreting gene expression data by using their ranking as a function of fold changes across experimental groups (upregulation/downregulation)  (Subramanian et al.
Experimental design and statistical analysis The global design of all experiments is described in Figure  1. Data were collected in a blinded manner to avoid bias. Minimum sample sizes were estimated from previously published datasets with similar experimental parameters. All statistical tests, except for genomic analyses, were performed using STATISTICA 12 software (Statsoft). Sigma-Plot 12. 5 software (Systat Software) was used to generate the graphs. Results in all graphs are presented as mean Ϯ SEM. Individual data were overlapped on bar graphs concerning duloxetine dosage (see Fig. 6 ) and immunoblotting (see  Figs. 8, 9, 11). Behavioral experiments. Experiments were conducted either in males only (see Figs. 2, 3, 4A-D, 5 B, C, 6A, 8D-F, 9C, 10A-E, H ) or in an equal number of males and females per experimental group (see Figs.
RNA-Seq experiments. Neuropathic pain was induced by inserting a cuff around the main branch of the sciatic nerve. Chronic oral duloxetine treatment started at least 2 weeks after surgery and lasted for 3 weeks. The presence and relief of mechanical allodynia were tested using von Frey filaments. L4, L5, and L6 dorsal root ganglia were collected for molecular analysis. RNA sequencing was performed on n ϭ 3 biological replicates per group. A differential expression analysis was performed using the DESeq2 generalized linear model to conduct pairwise comparisons with significance level set at p Ͻ 0. 01. Statistics for functional analyses were conducted using the DAVID website and using GSEA software and included the multiple testing corrections implemented by each type of software.
7, p ϭ 0. 034) (Fig. 8C  and Fig. 8   In mice, right PWTs were assessed using von Frey filaments and neuropathic pain was induced by inserting a cuff around the main branch of the sciatic nerve at least 2 weeks before the start of duloxetine treatment. Dosage in the clinical setting was done on neuropathic pain patients chronically treated with duloxetine (Cymbalta, 60 mg daily in one intake) and reporting relief of at least 30% of their neuropathic pain since the initiation of the treatment. Duloxetine plasma concentrations were determined using mass spectrometry. A, Comparison of mean plasma concentration-time profile and behavioral data for acute duloxetine oral administration in mice (30 mg/kg, p. o. ; n ϭ 7 or 8 per each time point). The insert shows the correlation between duloxetine plasma levels and PWT for animals with detectable duloxetine plasma levels (time points from 1 to 24 h, n ϭ 38 total).
B, Mean plasma concentration of duloxetine 2 h after acute oral administration (30 mg/kg, p. o. ) and after a chronic oral treatment (200 g/ml, 3 weeks) in mice, as well as 6 h (peak) after the daily intake of the chronic treatment (60 mg per day) or in the morning (residual) just before duloxetine intake, in patients with neuropathic pain. Data are expressed as mean Ϯ SEM. n values are given in brackets. ND, Nondetectable. Log2 Fold Change Impact of nerve injury and chronic duloxetine treatment on dorsal root ganglia transcriptome. Neuropathic pain was induced by inserting a cuff around the main branch of the sciatic nerve. Chronic oral duloxetine treatment started at least 2 weeks postsurgery and lasted for 3 weeks. The presence and relief of mechanical allodynia were tested using von Frey filaments.
C, Representation of genes significantly upregulated (red) and downregulated (blue) in dorsal root ganglia following long-term duloxetine treatment. The numbers above the graphs indicate the number of genes that showed significant changes in expression. D, Based on DAVID analysis, the table shows the top functional changes concerning gene expression in the dorsal root ganglia following long-term duloxetine treatment in mice with neuropathic pain. E, At the gene expression level, peripheral nerve injury induced an upregulation of the TNF-␣ signaling via NF-B, whereas chronic duloxetine downregulated this signalization, as shown by GSEA. In these graphs, the x-axis represents the distribution of genes, from the most strongly upregulated to the most strongly downregulated ("gene ranking"). The distribution of the black lines below represents the genes (each line) in the list of genes belonging to the TNF-␣ signaling via NF-B according to GSEA software.