dataesr/scientific-paragraphs-categorization huggingface dataset:
The household case studies generated many examples where definitions did not match local organisation and situations which were not foreseen in the drawing up of data collection instructions and definitions. They also highlighted contexts in which there were clear problems with translation. Key informant interviews were recorded, transcribed and then coded in an NVivo 5 database. All Tanzanian interviews were undertaken, read multiple times and doublecoded by the two lead authors. Preliminary codes focused on pre-determined themes developed out of our prior experience, the study hypotheses and themes and observations made during the interviews. During the coding process, new themes which emerged from the transcripts were identified and coded. The Tanzanian data informed the key informant interview guides in the subsequent research in the other three countries, as well as the choice of respondents.
Transcripts of these interviews have been entered into a bilingual NVivo data base and coded by research assistants in each country, after a three week workshop to develop a joint understanding of important codes and themes including substantive and analytic codes. The survey and census methodological materials were analysed by repeated reading, comparisons within and between countries, and the development of timelines, which focus on changes in key concepts in the different countries. The process of collecting the household grids allowed the research team to identify real-life ambiguities -in which people really cannot say that a specific person belongs to just one household, or where different people within a household might have contradictory perspectives. Each grid was entered onto an Excel spreadsheet along with detailed notes and a household profile which reflects on the characteristics of the recorded household and ambiguities with respect to the household definitions from the census and surveys.
Thus in Burkina Faso, certain configurations of 'household' could be recorded in three different ways according to the census, the DHS and the Living Conditions survey. The same is true in Uganda, as highlighted by this UBOS statistician: … there is one husband with several wives. So he is a household head everywhere. In the census, he is the household head. but when it comes to other surveys, where he spends the night is where he is the head and in other areas, he is not the head. … so when you come to now running the relationship status. for this household and the same household in another survey you would find different outcomes. (statistician UBOS, Uganda). On the other hand, there is evidence from Senegal that, whatever the official definition used by specific surveys (e. g. DHS), in fact the same local definition is used by Senegalese interviewers in most data collection exercises.
By imposing strict criteria through clear rules to interviewers, these young men should be treated as a separate household and enumerated where they sleep (if they are ever found there). To the extent that such cases, (frequent in Ouagadougou) are properly identified and recorded, data on household characteristics may well be distorted. In reality, though, there remains considerable interviewer power in deciding where to place these dual-habitation young men, whose parents see them as unequivocal members of their household, where they would be situated on both economic and cooking pot criteria. Several informants who had worked on Burkinabe data collection highlighted this problem with the unplanned areas and their unease at prioritising sleeping place over other criteria. Evidence from Burkina suggested that there were often different interpretations of the same instructions, thus giving each interviewer his own way of working.
For surveys dealing with specific topics (e. g. : fertility intentions), it might be worth also recording the interviewers' own preferences and experiences for example. There is increasing availability of household survey datasets for secondary analysis. This means great opportunities for researching the analytic implications of how, and by whom, data are collected. Methodological research on household surveys outside of high income countries remains very limited, with notable exceptions such as the World Bank's Methodological Experiments with the LSMS  (LSMS 2011). This study adds to the methodological research that considers the interview interaction in face-to-face surveys in two important ways. Firstly, it contributes to the very limited research that is published on interview methodology from low-income countries in general, and SSA in particular.
Acknowledgements The research in Tanzania was funded by the ESRC (UK) (RES-175-25-0012) under the Survey Design and Measurement Initiative. The research in Burkina Faso, Senegal and Uganda was financed by ESRC (UK) and ANR (France) under the collaborative research programme (RES 062-33-0007). Some interviews were undertaken with other members of the research team; Fatou-Binetou Dial and Sadio Ba Gning (Senegal), Stephen Wandera, Valerie Golaz and Alex Fanghanel (Uganda) and Bilampoa Gnoumou (Burkina Faso) and we would like to thank them for this contribution, as well as for ideas and input during the project workshops.
Access to data For those parts of the project financed by ESRC (Tanzania and Burkina Faso) anonymised transcripts of interviews have been deposited in the ESRC data archive and can be accessed by interested readers. Burkinabe data are at http://store. dataarchive. ac. uk/store/collaborativeCollectionEdit. jsp?collectionPID=archive:730&tabbed Context=collCollection&collectionTitle=null. Tanzanian data are at http://store. dataarchive. ac. uk/store/advancedSearchAction. do?value=%22Dr+Ernestina+Coast%22&co llectionPID=&award=&field=creator. Using the search facility and "Tanzania" or "Burkina Faso" in the Data Archive home page also allows access to the data http://store. data-archive. ac. uk/store/.
Quantitative real-time PCR (qRT-PCR) RNA was extracted from stationary phase promastigotes (approx. 3610 7 ) using Trizol (Invitrogen) according to manufacturer's instruction (1 ml Trizol for a 1 ml promastigote culture). After extraction, precipitation and washing, RNA was resuspended in water (3610 7 parasites in 10 ml) and quantified by spectrophotometry. 0. 5-1 mg of RNA was then used for cDNA synthesis with SuperScript II Reverse Transcriptase (Invitrogen), which was finally purified with a QIAquick PCR purification kit (Qiagen). qRT-PCR was undertaken in a reaction solution of 0. 5 mM primer diluted in SYBR Green Master mix (LightCycler 480 system, Roche). The reaction consisted of an initial denaturation at 95uC for 5 min followed by 40 cycles of amplification: 10 s at 95uC, 10 s at 60uC, 10 s at 72uC and a fluorescence detection step at 78uC to quantify the amplified DNA after each cycle.
The following DNA oligonucleotides (Microsynth, Switzerland) were used: SetA: 59-CTG ACT GGA CGG GGG GTA AT-39 and 59-CAA AAC ACT CCC TTA CGC-39/SetB: 59-GTC TGT TTC GTA CCC GCC G-39 and 59-AAG CTC AGG ATG TGC ATG TTC CA-39/kmp11 specific primers: 59-GCC TGG ATG AGG AGT TCA ACA-39 and 59-GTG CTC CTT CAT CTC GGG-39. SetA and SetB were based on LRV1-4 genome sequence (GenBank accession number: NC003601) and L. major kmp11 gene as described previously  [14]. LRV transcript levels were quantified in triplicate relative to the Leishmania kmp11 housekeeping gene. Analysis and acquisition of data were performed with the LightCycler software 1. 5 (Roche) using the 2 2DDCT method.
Anti-capsid antibody production and immunoblotting The LRV capsid open reading frame was amplified from a cDNA preparation of Lg M5313 and cloned in a pET-28a E. coli expression vector (Merck). Its sequence was found to be highly similar to the capsid sequence of Lg M4147 LRV1-4 (more than 98% identical residues, Genbank accession number: JX313126). Recombinant capsid was purified, using a HIS-tag, then used for rabbit immunization (Covalab, polyclonal antibody identification code: g018d53). Proteins from total parasite extracts were quantified by BCA, 40 mg was loaded and separated on a 10% polyacrylamide denaturing gel, transferred to a nitrocellulose membrane and vizualised by Ponceau Red staining. After a 1 h blocking step in 5% powdered milk diluted in TBS+0. 05% Tween20, the membrane was incubated overnight at 4uC with the g018d53 anti-capsid polyclonal antibody (1:5000 in 1% milk TBS-Tween20).
Peptide arrays on cellulose membranes and epitope mapping For antibody epitope screening, seventy-four 20-mer overlapping peptides (with an overlap of 10 residues) that cover the whole sequence of Lg M4147 LRV1-4 capsid (Genbank accession number: NC003601) were synthesized and attached to cellulose membranes by the Protein and Peptide Chemistry Facility (University of Lausanne). The peptides were synthesized by using Intavis MultiPep synthesizer (Intavis Bioanalytical Instruments AG, Cologne, Germany). The cellulose membrane used was an Amino-PEG500-UC540 sheet (acid-hardened with improved stability). The membrane peptide linker was stable in wide range of aqueous pH (pH 0-pH 14) at ambient temperature for 12 hours. The PEG spacer consisted of 8-10 ethylene glycol units and had free terminal amino groups to start the peptide synthesis.
Cells were then washed 36 in PBS and incubated with goat anti-mouse IgG AlexaFluor 488 (1:1000, Invitrogen) for one hour. After washing again in PBS (36), coverslips were rinsed briefly in water and mounted using Prolong Gold (Invitrogen). Microsocopy was performed using Olympus AX70 microscope and images were obtained using QCapturePro software (Version 5. 1. 1. 14). Image analysis was performed using Image J (1. 45).
LRV detection by a capsid-specific antibody Detection of LRV can also be performed via the recognition of viral proteins  [27]. A high-affinity rabbit polyclonal antibody (g018D53) was raised against the capsid polypeptide of Lg M5313 LRV (. 98% identical to Lg M4147 LRV1-4, Genbank accession number: JX313126) and then tested on control strains by immunoblotting and fluorescence microscopy. With both techniques, LRV detection was achieved in Lg M5313 (and its derivative LRV high clones, Lg 13 and Lg 21; data not shown) as well as in Lg M4147 LRV high , showing a strong staining throughout most of the cytosol of promastigotes (Figures  2A  and 2B ). As expected, no staining was visible in Lg 17 (LRV low derivative clone of Lg M5313), Lg M4147 LRV neg and Lg 1881, but neither in the LRVinfected human isolate Lg 1398 which is probably due to LRV sequence diversity.
Partial Lg 1398 LRV sequencing was performed and surprisingly revealed a high identity of its capsid as compared to Lg M4147 throughout the entire open reading frame (91% identical residues, Genbank accession number: JX313127). Epitopes mapping using a 20-mer peptide arrays representing the complete Lg M4147 LRV capsid sequence showed that g018D53 recognized uniquely Lg M5313 LRV Cterminal capsid sequence, which is poorly conserved in Lg 1398, thus explaining why it is not recognized by g018D53 in this strain (Figure  2C  and 2D ).
Screening for LRV infection in human isolates To assess the applicability of our anti-dsRNA dot blot on field isolates, we used it for LRV screening in human isolates from another Leishmania species that had been previously typed and catalogued as LRV positive  [24]. Five strains were screened, corresponding to L. braziliensis isolated from human lesions (Figure  6 ). As expected, we were able to confirm LRV presence in these isolates. This study suggested that the dot blot method   B ). The fluorescent intensity per cell was assessed using Image J software on Lg M4147 LRV high or LRV neg cells following IFM with the J2 antibody. Cells from phase images were identified and the fluorescent intensity average over the area of the cell was recorded. 108-160 cells from 2 distinct fields were measured, and histogram plots were made using Excel software.
Screening for LRV presence in L. braziliensis isolated from an infected patient To demonstrate that our anti-dsRNA immunodetection approach may be a relevant diagnostic tool in a clinical setting, it was tested on freshly isolated Leishmania parasites obtained from an L. braziliensis infected patient. The subject contracted leishmaniasis in Bolivia, which was later typed by PCR as being L. braziliensis (data not shown). Two parasite samples were taken: Lb 2169 and Lb 2192, derived respectively from a primary cutaneous lesion before treatment, and a secondary/metastatic lesion appearing some time after treatment had started. Parasites from these biopsies were cultivated and directly tested for LRV presence by dot blot using the anti-dsRNA antibody as described above. Lg M4147 LRV high and Lg M4147 LRV neg parasites were used as positive and negative controls respectively.
Figure3. Detection of LRV with a monocolonal anti-dsRNA (J2) antibody by immunofluorescence microscopy. A. Reference strain analysis (protocol A, see ''Material and methods''). Green: dsRNA (J2 Ab). Blue: DAPI (standardized exposure time in all images). B. Phase and immunofluorescent images of Lg M4147 LRV high or LRV neg cells were obtained in the presence or absence of J2 antibody (protocol B). C. Quantitative immunofluorescence (protocol B). The fluorescent intensity per cell was assessed using Image J software on Lg M4147 LRV high or LRV neg cells following IFM with the J2 antibody. Cells from phase images were identified and the fluorescent intensity average over the area of the cell was recorded. 108-160 cells from 2 distinct fields were measured, and histogram plots were made using Excel software.
Acknowledgments The authors are grateful to  J. Patterson  and  Y. T. Ro  for providing the parental lines from which the clonal luciferase-expressing LRV high or LRV neg Lg M4147 strain used here were derived, as well as anti-LRV M4147 capsid antisera. We also thank  Natacha Cossy ,  Tugce Tekozgen ,  Leyder Lozano  and  Tatiana Proust  for technical help,  Catarina Servis  and the  Protein and Peptide Chemistry Facility  ( University of Lausanne ),  Florence Morgenthaler  from the  Cellular Imaging Facility (CIF)  of  Lausanne University in Epalinges  for microscopy imaging and  Habib Zalila  for the input on graphics.
Methods Data from the 3 trials done as part of the phase III program were pooled for this analysis. Pts aged 2-19 yrs with active SJIA were enrolled and received subcutaneous CAN 4 mg/kg or placebo. The post-hoc analysis presented here focuses on SJIA response to CAN therapy in the initial treatment period of a total of 178 CAN-naïve pts. Methodological factors precluded a comparator group, so this analysis is of a descriptive nature.
© 2013 Quartier et al. ; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/2. 0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons. org/publicdomain/zero/1. 0/) applies to the data made available in this article, unless otherwise stated.
Consistently with the results from previous studies, our statistics clearly show that aerosol affects cloud microphysics, decreasing the Cloud Droplet Radius (CDR). The same data indicate a concomitant strong decrease in cloud Liquid Water Path (LWP), which is inconsistent with the hypothesis of aerosol inhibition of precipitation  (Albrecht, 1989). We hypothesise that the observed reduction in LWP is the consequence of dry air entrainment at cloud top. The combined effect of CDR decrease and LWP decrease leads to rather small sensitivity of the Cloud Optical Thickness (COT) to an increase in aerosol concentration. The analysis of MODIS-CALIPSO coincidences also evidences an aerosol enhancement of low cloud cover. Surprisingly, the Cloud Fraction (CLF) response to aerosol invigoration is much stronger when (absorbing) particles are located above cloud top than in cases of physical interaction.
Purpose and strategy Our objective is to use satellite remote-sensing observation from simultaneous MODIS and CALIPSO retrievals of aerosol and cloud properties to provide further observationbased evidence of aerosol-induced effect on microphysics (CDR), optical properties (COT), structure (LWP, CLF) and life cycle (precipitation occurrence) of warm boundary layer clouds. The originality of the work lies in the focus over the South-East Atlantic and the use of CALIPSO data to provide a vertical description of the aerosol and cloud layers, in addition to the horizontal sampling by MODIS. Based on CALIPSO information, the aerosol and cloud layers are assumed to be in direct interaction if their altitudes are very close (within a certain threshold). For such cases, a change in cloud properties with respect to a variation in aerosol concentration is interpreted as the result of an aerosol driven process.
On the other hand, if aerosol and cloud layers are well separated, the observed cloud change is considered to be induced by other causes than direct cloud-aerosol interaction. Thus, although the reduced temporal sampling of the satellite data does not allow a causality assessment from the statistics, the analysis of MODIS-CALIPSO coincidences permits us to isolate (to a certain degree) aerosol-induced effects from meteorology. It is then possible to obtain more reliable estimates of aerosol impact on clouds, than when using relationships based only on vertically integrated measurements. The South-East Atlantic region is particularly well suited to investigate aerosol indirect effects. Large amount of aerosol load, generated by fires in Southern Africa occurring annually  (Ichoku et al. , 2003; Edwards et al.
MODIS retrievals MODIS aerosol retrieval algorithm over ocean  (Tanré et al. , 1997; Kaufman et al. , 1997; Remer et al. , 2009)  uses six spectral channels (0. 55, 0. 66, 0. 86, 1. 24, 1. 64, 2. 12 µm). Level 2 (L2) products are organised into 5 min "granules". Only daytime data are considered for aerosol retrieval and the products are generated at a resolution of 10 × 10 km 2. Kaufman et al. (2005a)  provide an in-depth analysis of error estimates over ocean and calculated that cloud contamination causes a maximum error in MODIS AOD equal to 0. 02 ± 0. 005. Note that aerosol retrievals are only possible in case of clear or broken clouds condition, when MODIS can see between adjacent clouds. MODIS retrievals of cloud effective radius and cloud optical depth L2 products (with a resolution of 1 × 1 km 2 ), are derived using six spectral channels  (King et al.
MODIS-CALIPSO coincidences Cloud parameters are obtained from MODIS Level 2 cloud product of collection C005 (MYD06 L2. C5) at 1 km resolution for Cloud Optical Thickness, Cloud Water Path, Cloud Effective Radius and 5 km resolution for Cloud Top Pressure and Cloud Fraction. Aerosol Effective Optical Depth Best Ocean (0. 55 µm) and Angstrom Exponent 1 Ocean (0. 55/0. 86 µm), from MODIS Level 2 aerosol product of collection C005 (MYD04 L2. C5) at 10 km resolution, are used to estimate Aerosol Index. Cloud and aerosol layer altitudes are taken from CALIPSO Level 2 products. We make use of Number Layers Found, Layer Top Altitude and Layer Base Altitude, at 5 km resolution for both aerosol and clouds. MODIS and CALIPSO datasets are summarised in Table  1. We use data acquired from June 2006 to December 2010.
Meteorology Figure  13  shows that mixed and unmixed case retrievals are fairly homogeneously distributed over the same area, the central part of South-East Atlantic. We, therefore, argue that these two populations are affected by similar, if not equal, climatological conditions, over the whole time-period. A further check would be the analysis of the data per season, but the relatively low number of MODIS-CALIPSO coincidences does not allow a further binning of the input data. To address this issue, future work should make use of additional, more recent, observations. On the other hand, the existence of two populations over a given region demonstrates the effect of meteorology and its temporal variability. One shall question whether meteorology, and not aerosol, may explain the observed differences between mixed and unmixed relationships.
034) as cloud-aerosol pixel distance increases from 2 to 13. 5 km (coherent with the blueing effect), while no significant variation is shown in case of aerosol above cloud top. In our dataset, however, the average distance between coincident aerosol-cloud pixels ranges approximately between 10 and 20 km, which is mostly out of the adjacency effect radius of influence. We then argue that Rayleigh enhancement of MODIS retrieved radiance in L. Costantino and F. -M. Bréon: Aerosol indirect effect on warm clouds over South-East Atlantic cloud-free pixel is not expected to be a primary source of error in CLF-AI relationship.
shows cloud fraction estimates averaged over constant bin of cloud top pressure, from MODIS L3 daily product. The data are sorted from clean to polluted into six samples based on the AI by step of 0. 05. The mean AI value of each subset is reported in the figure, indicated with the same colour of the respective symbols. CLF correlates well with cloud top pressure, which is a proxy to roughly estimate cloud vertical development. Lower top pressure indicates L. Costantino and F. -M. Bréon: Aerosol indirect effect on warm clouds over South-East Atlantic imary effect is to increase the horizontal extension rather than produce taller and ective clouds. CTP relationships from MODIS daily products, at 1 degree resolution, in the region within [4N-30S; he whole 2005-2010 dataset is sorted by AI, from little to high polluted atmosphere, by step of 0.
The parasite transcriptomes showed large differences in gene expression, including expression of host effectors, which correlated with differences in expression of transcriptional regulators such as the Apetala-2 (AP2) factors. Combining genome and transcriptome data also identified that some strains had large structural variations that had an impact on gene expression levels. Analysis of the host cell transcriptomes revealed activation of several signaling pathways in a strain-specific manner. For example, only a few atypical strains induce macrophage type I interferon production, which was associated with parasite killing, independent of interferon gamma, that leads to the release of parasite ligands that trigger intracellular Toll-like receptors and cytoplasmic nucleic acid sensors. This resource provides insight into the host response to a large variety of Toxoplasma strains and, together with the SNPs dataset we recently published for these strains  [18] , can form the basis for the discovery of novel Toxoplasma effectors.
Specifically, we found that a small group of atypical strains are able to induce production of type I interferons, which are immunomodulatory cytokines. Interferon production is a result of the elimination of internalized parasites through a novel killing mechanism. The dataset we generated is a valuable tool for identification of host cell targets of Toxoplasma secreted effectors and can contribute to our understanding of why certain Toxoplasma strains are more prone to cause severe disease in humans. were significantly enriched for transcription factor binding sites (TFBS) indicating that these clusters represent differential modulation of host/parasite signaling pathways by the different Toxoplasma strains. The induction of host genes in some clusters correlated with strain type: for example, cluster 1 is induced by all type 12 strains, cluster 5 is mainly induced by type II strains or strains that have part of the type II genome and cluster 7 and 9 by type 6 strains, possibly indicating host cells responding to genetic differences between strain types.