dataesr/scientific-paragraphs-categorization huggingface dataset:
The electrothermal technique was found to be more accurate to determine the value of G(0) and will be described below. Voltage versus current V(I) and electrical resistance versus temperature R(T) characteristics were used. The V(I) was measured, using the precision parameter analyzer HP4156B, by applying a sweep varying bias current (I) in the devices (using the two current pads: I+, I-) and by measuring the voltage (V) across it (using the two voltage pads: V+, V-). The R(I) characteristics can be easily calculated from V(I) by dividing V by I. The R(T) characteristics have to be measured independently. By fitting the R(T) data in the vicinity of the temperature of the experiment T 0 using a linear expression, we can deduce the temperature of the bolometer T(R) as function of its electrical resistance R.
Synthetic peptides from RESA-1, RESA-2 and RESA-3 proteins RESA family proteins share high homology sequences. Protein sequence alignments were performed with RESA-1 (accession number: PFA0110W/PF3D7_0102200), RESA-2 (accession number: PF11_0512/PF3D7_1149500) and RESA-3 (accession number: PF11_0509/PF3D7_1149200) using the Geneious software 6. 1. 4. from Biomatters Ltd, by means of the integrated multiple sequence comparison by log-expectation (Muscle) algorithm and the default settings. RESA-1 and -2 (see Additional file 1) shared 38% (333 amino-acids) homology, RESA-1 and -3, 49. 1% (535 amino-acids) and RESA-2 and -3, 38. 9% (361 aminoacids). Thus, cross-reactive antibodies should exist, preventing specific detection of the antigenic protein. To perform ELISA to evaluate the presence of RESA specific antibodies, six synthetic peptides that reproduced putative B cell epitopes of the RESA proteins were used.
Effects of clinical co-factors on severity of malaria Clinical data collected at the time of admission and genetic data were compared between the SM and UM groups. Differences in means were tested by non-parametric Mann-Whitney U test or Kruskal-Wallis test (for more than two groups to be compared), except for age, where the Student's unpaired t test was used as age was normally distributed. A multiple logistic regression was performed to test simultaneously the association between these factors and the severity of malaria. Starting with a complete model including all factors, a stepwise procedure with a backward selection identified the most relevant predictors. The procedure based on the Akaike information criterion (AIC) was implemented in R software. Quantitative independent variables (age, axillary temperature, fever duration, and parasitaemia) were categorized to check if they were linearly related to the log odds.
K-means algorithm aimed to find the best partition of n entities in k clusters (where k is user-defined) so that the total distance between the group members and its centroid is minimized  [46]. Each child was defined by an 8-dimensional vector of binary data (one dimension for each antigen). The similarities between children were measured by the Euclidean which is defined as: where x i and y i represent the response values for children x and y respectively against antigen i, and n is the total number of antigens considered. K-means cluster analysis was successively performed for different numbers of groups (k = 3, 4 and 5). As partition obtained by this method depends on initial random assignment, for a given k, 2,000 iterations of K-means clustering algorithm d x, y = 1 n n i=1 x i -y i 2 were run to ensure to get the optimal clustering solution.
At the end the best partition was chosen considering the number of times the optimal solution was found among the 2,000 iterations and examining the different partitions of children. For partitions with k > 3, k-means algorithm identified small groups of children with less than ten individuals, thus a three-group partition was retained. Visualization of results was done using Java TreeView software  [47]. The relation between the clusters and clinical status was tested including the groups in a multiple logistic regression adjusted on factors that will be found associated with the severity of malaria. In the second analysis, a multiple logistic regression was performed with a backward procedure. The initial model included all the antibody responses (considered as binary variables) and clinical factors associated with SM.
Acknowledgements We thank the participating children and their families as well as  B Ayivi  and the medical staff at the  University Hospital CNHU-HKM. We are grateful to  A Massougbodji  for interceding with local authorities. We thank  Alice Lebreton  for her critical reading of the final manuscript. This work was supported by the  French Agence Nationale de la Recherche  under grant  MIE  ( ANR-08-MIE-031 ) and  CNRS.
2 ). PCR products were purified using the QIAquick Gel Extraction Kit (QIAGEN). For the KisumuP and Acerkis strains, the purified PCR product was directly sequenced. For Acerduplikis, the purified PCR product was cloned using the TOPO TA Cloning ® kit following to the manufacturer instructions (Invitrogen Life Science Technologies), to separate the different duplicated copies, D(S) or D(R). The clones were screened for the presence of the G119S substitution, and at least six clones were sequenced for each copy. Sequencing was conducted on an ABI Prism 310 sequencer (BigDye Terminator Kit, Applied Biosystems, Foster City, CA). Each clone was sequenced using the primers AgEx2dir1 and AgEx7rev2, plus an internal primer due to the fragment length, AgIntdir1 (Supp. Fig. 2 ). Exon 2 to exon 7 sequences of the susceptible single-copy allele from KisumuP (Ag-ace-1 S ), of the resistant single-copy allele from AcerKis (Ag-ace-1 R ) and of the susceptible (Ag-ace-1 D -S) and resistant (Ag-ace-1 D -R) copies of the duplicated allele were deposited in GenBank (accession numbers KM875634, KM875637, KM875635 and KM875636, respectively).
vectorbase. org/) by Real-time quantitative PCR performed with a LC480 Light Cycler (Roche). Two PCRs were performed on each DNA, one specific of ace-1 locus (Agace1qtidir2 and Agace1qtirev2 primers) and the other specific of the reference gene (AgS7Ex5qtidir and AgS7Ex5qtirev primers) (Supp. Table  1 ). 1 ng of each genomic DNA (normalised with the Qubit 2. 0 Fluorometer-Invitrogen) was mixed with 0. 6 μ M or 0. 8 μ M of ace-1 or Rps7 specific primers respectively and 3 μ L of mastermix (LightCycler 480 SYBR Green, Roche). PCR was performed with a 95 °C activation step for 8 min followed by 45 cycles of 95 °C for 4 s, 67 °C for 13 s, and 72 °C for 19 s. Each DNA template was analyzed in four replicates for both genes. The ratio between ace-1 and Rps7 arbitrary concentrations was determined with the Advanced Relative Quantification method of the LightCycler 480 software 1.
Four replicates were performed for each concentration. Six to twelve insecticide concentrations providing a range of mortality from 0 to 100% were used for each insecticide tested. Larval mortality was recorded after a 24 hours exposure. Control bioassays were performed by adding 1 ml of ethanol to 99 ml of distilled water. Temperature was maintained at 27 °C ± 2 °C during bioassays (temperature measured using Waranet technology, Waranet Solutions SAS, Auch, France). The analyses of dose-mortality responses in bioassays were performed using the R software (v. 3. 0. 0). The R script BioRssay (v. 6. 1; 70 ) was used; it is freely available on the website of the Institut des Sciences de l'Evolution de Montpellier. This script computes the doses of insecticide killing 50% and 95% of the tested population or strain (Lethal Concentration 50 and 95, or LC 50 and LC 95 ) and the associated confidence intervals, and tests for the linearity of the dose-mortality response (χ 2 test).
Acknowledgments We are very grateful to  Nicole Pasteur  for her helpful comments on the manuscript. This work was funded partly by  French ANR program  (project " AlterNET " SOC & ENV 2013-2015) and by  Wellcome Trust  (grant  WT093755 ). Assogba S. Benoît was supported by a fellowship from the  IRD. Data used in this work were produced through the technical facilities of the  Environmental Genomic Platform of the Labex Centre Méditerranéen Environnement Biodiversité (CeMEB ): all sequence data were obtained through GenSeq Platform; FISH hybridization was performed through the  Evolutionary Cytogenomic Plateform. Contribution number 201x-xxx of the  Institut des Sciences de l'Evolution de Montpellier  ( UMR CNRS-IRD-UM 5554 ).
Given the current or imminent availability of several long-acting bronchodilators for use in patients with COPD, is it important or necessary to distinguish between them? In this article, we compare the onset and duration of action of long-acting bronchodilators for COPD and consider whether the theoretical 'ideal' profile translates into practical and clinical advantages for patients. Data on onset and duration were obtained from a search of the literature on PubMed for the currently used long-acting bronchodilators in COPD (in English; no date restrictions), with preference given to data from phase III studies using approved doses.
Adherence/persistence Adherence is poor in COPD, typically around 50% in practice  medication and inhaler  [106,108e111] , and frequency of dosing  [98,102,112e114]. The relative simplicity and convenience of once-daily dosing (compared with multiple daily dosing) may encourage patients' adherence and persistence with their long-term medications. In one retrospective study of a claims database, adherence was strongly correlated with dosing frequency, with 43. 3, 37. 0, 30. 2 and 23. 0 percentage days covered for patient cohorts receiving once, twice, three times or four times daily treatment  [112]. Similarly, in a study to determine the effect of dosing frequency of mometasone on treatment adherence in patients with asthma, mean adherence rates were greater with a once-daily regimen compared with twice daily  [115].
Ex vivo perfused human lung and measurement of alveolar fluid clearance The pulmonary artery and bronchus was cannulated from either the right or left lung and placed in a bioreactor (Harvard Apparatus, Holliston, MA) as previously described  (7). The rate of perfusion, with a solution containing DMEM without Phenol Red + 5% bovine serum albumin and kept at 37°C, was slowly increased to 250 mL/min over 30 min, allowing progressive rewarming of the lung. When the perfusate temperature reached 30°C, ventilation with a large animal respirator (Harvard Apparatus) was started (V T = 300 mL, respiratory rate = 10/min, FiO 2 = 21%, and positive end-expiratory pressure = 5 cmH 2 O, Figure  1A ). Perfusion flow, pulmonary arterial pressure (PAP), pulmonary vascular resistance (PVR), tidal volume (V T ), tracheal pressure, and compliance were recorded in real time using Pulmodyn Data Acquisition software (Harvard Apparatus).
Statistical analyses Results are expressed as mean ± SD if normally distributed and as median [Q1;Q3] if not. Comparisons between several groups were made using the analysis of variance (ANOVA). Comparisons between two groups were made using either ANOVA or the unpaired Student t-test. Comparisons with a sample over time were made by repeated measures of ANOVA using the Bonferroni correction. We used the software GraphPad Prism.
Methods Eligible pts are ≥15 years of age, have stage IIIB/C or IV histologically confirmed MEL that is completely resected, and have been rendered disease free within 12 weeks prior to randomization. Pts having uveal MEL, a history of autoimmune disease, a condition requiring systemic corticosteroids or other immunosuppressive medications, or receiving prior systemic therapy for MEL will be excluded. Pts will be randomized 1:1 to NIVO 3 mg/kg every 2 weeks (Q2W) or IPI 10 mg/kg Q3W for 4 doses then Q12W starting at week 24, and treated until disease recurrence, unacceptable toxicity, or consent withdrawal, for up to 1 year. The primary and secondary objectives are to evaluate RFS and OS, respectively. An estimated 800 pts will be randomized. Clinical Trial registration number: NCT02388906.
Acknowledgements We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies:  BMWFW  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RPF (Cyprus) ;  MoER ,  ERC IUT  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  OTKA  and  NIH (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  CINVESTAV ,  CONACYT ,  SEP , and  UASLP-FAI (Mexico) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom, RAS  and  RFBR (Russia) ;  MESTD (Serbia) ;.
A model of a dimeric human M1 mAChR, which binds the MT-7 toxin was also developed  [41]  and very recently, allosteric modulation was studied using homology model of M1 mAChR based on human β2 adrenoreceptor  [42]. Despite different approaches used in modeling strategies, we may conclude that the fold and transmembrane (TM) helices arrangements of all the discussed M1 mAChR structures were similar. Here, the AutoDock 4. 2 code was used to determine plausible DEET binding sites in M1 and M3 mACh receptors  [43, 44]. Because our in silico docking of ACh molecule led to the same pose as reported earlier, we performed, for the first time, in silico docking of DEET to M1 mAChR model (Fig  4D ) using the same methodology. A cluster of poses (from the range #30-#120) was identified in the vicinity of the extracellular loops ECL2 (Figs  4D  and 5B ) having binding energies (from -4.
12 to -3. 47 kcal/mol). These poses were grouped into three binding regions located between ends of TM1-TM2, TM4-TM5 and TM5-TM6 helices. Since this area, particularly the TM4-TM5 region, was close to the region identified as the MT-7 neurotoxin site in M1 mAChR  [41]  outlined by residues GLU170, TYR179, HIS90, and TRP91, we hypothesized that this could be part of high affinity allosteric M1 mAChR site, located extracellularly (Figs  4D  and 5B ). Alternatively, in silico docking place was located on a second site close to the ACh site from the extracellular side. The amino acids building the DEET binding pocket (orthosteric site) were indicated (Figs  4D  and 5A ). The detailed distances between DEET molecules docked and M1 mAChR residues were presented, (S2 Table ). The in silico docking of DEET to the rat M3 crystallographic structure (pdb code 4DAJ) gave similar results (Fig  4E ).
Patch pipettes had resistances ranging from 1 to 1. 2MO when filled with internal pipette solution (see composition below). The liquid junction potential between extracellular and intracellular solutions was always corrected before the formation of a gigaohm seal (>3GO). Signals were recorded with an Axopatch 200A (Axon instruments, USA). Ionic currents induced by ACh were displayed on a computer with software control pClamp (version 6. 0. 3, Axon Instruments, USA) connected to a 125-kHz labmaster DMA data acquisition system (TL-1; Axon Instruments, USA). DUM neuron somata were voltage-clamped at a steadystate holding potential of -50mV (except when otherwise stated). Experiments were carried out at room temperature. Bath solution superfusing the cells contained (in mM): 200 NaCl; 3. 1 KCl; 5 CaCl 2 ; 4 MgCl 2 ; 10 HEPES; pH was adjusted to 7.
Pharmacological agents such as α-bungarotoxin (α-bgt; 0. 5μM), DEET (used at different concentrations ranging from 10nM to 1μM), caffeine (10mM), TBM-8 (100μM), BW284c51 (100nM), pirenzepine (100nM), 4-DAMP (100nM), MT-7 (30nM), U73122 (10μM), m-3M3FBS (10μM) and propoxur (100nM) were added to external solution. W7 (0. 5mM), BAPTA (10mM), KN-62 (10μM) and calmodulin (50nM) were added in the internal pipette solution immediately before use. Data analysis was performed using the software Prism 5 (Graph Pad Software, San Diego, CA). Data were analysed using one-way ANOVA and data are presented as mean ± S. E. M (Standard Error Mean).
Excitation light was provided by a 75-W integral xenon lamp (Life Science Resources, Cambridge, UK). Excitation wavelengths (340nm and 380nm) were applied using a computer-driven Spectramaster (Life Science Resources). Images were collected with an Olympix digital charge-coupled device (CCD) camera (AstroCam; Life Science Resources), and they were recorded in the computer with Merlin software, version 2. 0 (Life Science Resources). Exposure times at 340nm and 380nm were usually 150ms, and images were collected at various frequencies. Data were expressed as the ratio of emitted fluorescence (340/ 380nm)  [48].
Molecular in silico docking of DEET into human muscarinic acetylcholine receptor The in silico docking of DEET into human Muscarinic acetylcholine receptor M1 (M1 mAChR, P11229 UniProtKB) was performed using the well established AutoDock program (version 4. 2)  [40]. The structure of the receptor was kindly provided by Dr M. Michael Espinoza-Fonesca  [34]. The docked ligand was flexible, the protein was frozen, nonpolar hydrogens were united with carbons and charges were determined using the Gasteiger method. For DEET M1-mAChR system 256 starts of the Lamarckian genetic algorithm were initiated with a maximum number of 2,500,000 energy evaluations and a maximum number of 27,000 generations allowed. Grid maps of increasing density were used to determine precisely the optimum DEET in silico docking place: 70x70x112Å with grid point spacing 1.
0Å or 126 x 126 x 126Å with grid points spacings 0. 408Å and 0. 375Å. The same protocol was used to prepare DEET in silico docking to the rat M3 mAChR crystallographic structure (pdb code 'DAJ  [32] ). Analysis of results and figures were prepared using the VMD 1. 9 code  [41]  and homemade scripts.
Mortality rates were recorded 24 hours after testing. Data were expressed in nanograms of active ingredient per milligram of mosquito female body weight. Six replicates were done for each tested concentration using different batches and generations of mosquitoes. Data of mortality induced by DEET alone and in combination with propoxur were analyzed through a logistic regression using the proportion of dead mosquitoes among the mosquitoes exposed to chemicals as the response variable. The analysis consisted in logistic regression modeling of the probability of a mosquito to die in relation to explanatory variables: • concentration of DEET, • mean weight of tested mosquitoes (for each replicate), • presence of a dose of propoxur killing 10% of exposed mosquitoes when used alone, • interaction between DEET and propoxur Using these explanatory variables, we performed an analysis of deviance in order to construct the final general linear model.
D) Comparative bar graph summarizing the anticholinesterase effect of the specific inhibitor BW284c51 (100nM) and the carbamate, propoxur (prop) (100nM) measured on the duration of the AChinduced inward currents (measured at 50% of the maximum current amplitudes) obtained in whole-cell voltage-clamp at a steady-state holding potential of -50 mV. E) Concentration-dependent inhibition of the residual AChE activity determined spectrophotometrically induced by propoxur and expressed as percentage of initial activity (i. e. , without propoxur). The curve represents the best fit to the data points according to the Hill equation yielding the corresponding IC 50 (i. e. , the concentration of propoxur that produces 50% inhibition of the AChE enzymatic activity) as illustrated in the comparative bar graph shown in inset.
Enterococcus faecalis is a Gram-positive lactic acid intestinal opportunistic bacterium with virulence potential. For a better understanding of the adapation of this bacterium to the host conditions, we performed a transcriptome analysis of bacteria isolated from an infection site (mouse peritonitis) by RNA-sequencing. We identified a total of 211 genes with significantly higher transcript levels and 157 repressed genes. Our in vivo gene expression database reflects well the infection process since genes encoding important virulence factors like cytolysin, gelatinase or aggregation substance as well as stress response proteins, are significantly induced. Genes encoding metabolic activities are the second most abundant in vivo induced genes demonstrating that the bacteria are metabolically active and adapt to the special nutrient conditions of the host.
This should allow the identification of genes actually differentially expressed in vivo. The recently introduced massively parallel cDNA sequencing (RNA-seq) techniques becomes now the gold standards for the sensitive determination and accurate quantification of transcriptomes  [11] [12] [13]  although hitherto only few studies analysing gene expression of a pathogen during infection of mammalian hosts have been published  [14] [15] [16] [17] [18] [19]. In this work, we will present the first comprehensive in vivo expression database of a clinical isolate of E. faecalis obtained by RNA-seq.
Quality trimming and length filtering was done via fastq_quality_trimmer (parameters:-Q33-v-l 20-t 20) followed by quality filtering with fastq_quality_filter  [20]  (parameters:-Q33-v-q 20-p 85). The remaining high quality sequence reads were mapped to the Enterococcus faecalis reference genome (Gen-Bank accession no. AE016830) and to the human and mouse genomes (GenBank accession no. GCA_000001405. 11 and GCA_000001635. 3) using BWA  [23]. The post-processing was done using Samtools  [24]. The resulting sorted BAM files were the input for the peak detection via MACS  [25]  (parameters:-g 3. 22e+06-shiftsize = 36-nomodel). The coverage of the detected peaks was calculated using a custom perl script (available on request) resulting in a list of coverage per peak. The normalization via TMM  [26]  and the following differential expression analysis was carried out using NOISeq  [27].
Identification of genes induced under infection conditions by RNA sequencing In order to identify genes induced during in vivo infection using RNA-Seq, RNA was prepared from E. faecalis V19 grown to mid-log phase or stationary phase in BHI broth and from cells incubated 24 h in the mice peritoneum and sequenced on the Illumina GAIIx platform. In total, we analysed 14,219,699 sequences (total number of reads). Reads size were between 70 and 100 bp for all conditions, and we mapped 3,102,984; 4,111,728 and 5,136,137 reads to Enterococcus faecalis, for laboratory culture (exponential and stationary growth phases) and in vivo conditions, respectively. In all datasets only few reads were mapped to the human and mouse genomes: 972 (0. 03%), 4,101 (0. 10%) and 587 (0. 01%), respectively. We used these data as a first screen for the identification of differences in gene expression with a special emphasis to identify genes induced during mice peritonitis.