dataesr/scientific-paragraphs-categorization huggingface dataset:
| METHODS An online site assessment survey was conducted across HIV care and treatment clinics caring for children within the International Epidemiology Databases to Evaluate AIDS (IeDEA) cohort consortium. IeDEA is an international research consortium established in 2005 by the National Institute of Allergy and Infectious Diseases (www. iedea. org/home/who-we-are). IeDEA collects HIV/AIDS data from seven international regional data centres, including four in Africa, and one each in the Asia-Pacific region, the Caribbean, Central and South America region (CCASA), and North America  [21, 22]. In the present study, focusing on LMICs, the North America region was not included. All IeDEA-participating sites and their corresponding central regional data centres have ethics approvals for the collection of patient-level data within the IeDEA data mergers and for routine surveys of site-level characteristics.
Because the present survey did not include patient-level data from respondents or patients, and was a part of the routine sitelevel surveys, a separate ethics review was not required. A standardized questionnaire was constructed and administered through the web-based REDCap platform (http://projectredcap. org/), as part of a site-level survey about adherence and support services for HIV-infected children and adolescents. The questionnaire was available in English and French. All sites caring for children and still engaged in IeDEA consortium at the time of the survey were included in the study. Coordinators at each site were asked to determine the most appropriate person (one by site) to complete the survey as the person-in-charge of paediatric HIV services at each clinical site. Questions were asked about the site (setting, level, public, size.
Disclosure status was collected every three months in 63% of these sites and every month in 26%. Two-thirds of the sites collecting disclosure status (71%) stored this data electronically; 88% of the sites with electronic disclosure data were from Southern and East Africa. Among the 53 sites (29%) with a formal disclosure protocol, 32 (60%) had designed it locally and 21 (40%) borrowed/ adapted from various external sources, including four from WHO guidelines, five from local national ART treatment guidelines, and 12 from independent sources (Table  2 ). For those sites with a disclosure protocol, the protocol was initiated a median of 4. 6 years prior to the survey (IQR: 1. 4 to 7. 1), with a site from CCASA having a protocol for 16. 7 years. Disclosure counselling was most often provided by counsellors (88% of sites), but also by nurses (78%), physicians (76%), social workers (69%), or other clinicians (66%) (Table  3 ).
F U N D I N G The International Epidemiology Databases to Evaluate AIDS (IeDEA) is supported by the  National Institute of Allergy and Infectious Diseases, Eunice Kennedy Shriver National Institute of Child Health & Human Development, National Institute on Drug Abuse ,  National Cancer Institute , and the  National Institute of Mental Health , in accordance with the regulatory requirements of the  National Institutes of Health  under Award Numbers  U01AI069911  ( East Africa ),  U01AI069919  ( West Africa ),  U01AI096299  ( Central Africa ),  U01AI069924  ( Southern Africa ) and  U01AI069907 (Asia Pacific). The Kirby Institute (data center for IeDEA Asia Pacific)  is funded by the  Australian Government Department of Health  and Ageing and is affiliated with the  Faculty of Medicine, UNSW Sydney.
Samples were analyzed with FACSCalibur flow cytometer (BD 2 Stem Cells International Biosciences). Obtained data were analyzed using FlowJo ver. 7. 6. 5 software (Tree Star) as previously described  [19, 23]. 2. 3. Cytokine Assay. SDF-1, angiopoietin-,1 and angiopoietin-2 plasma levels were quantified by means of commercially available enzyme-linked immunosorbent assays (ELISA, DuoSet, R&D) in 96-well plates. Samples were directly assayed according to manufacturer's instructions. Protein levels in the specimens were calculated from a reference curve generated using appropriate protein standards. Finally, the plates were analyzed with automated light absorbance reader (LEDETEC 96 system). The results were calculated by MicroWin 2000 software. 2. 4. Statistics. Statistical analysis was performed by using GraphPad Prism 6 (GraphPad software).
At least, 20,000 events were collected for all samples on an IS MarkII using 488, 405, and 642 nm laser excitations. Cell populations were hierarchically gated for single cells that were in focus and were positive for both Draq5 and p65. Then, based on Draq5 intensity histogram, cells were gated on those that were in G0 and G1/S phase to avoid cells that have a disrupted nuclear membrane. After those gates were applied at least 1,000 cells per group were acquired (follicular: B220 + CD23 + CD21 -, transitional: B220 + CD23 -CD21 -, and MZ: B220 + CD23 -CD21 + ). Following data acquisition, the spatial relationship between the NF-κB and nuclear images was measured using a feature (ratio translocation) calculated in the IDEAS software package. The "Ratio translocation" feature, is the ratio of NF-κB intensity in the nuclear mask over NF-κB intensity in the whole cells (calculated using the brightfield mask) expressed as the percentage.
To assess Notch2 surface expression, we used flow cytometry. We found similar levels of Notch2 expression on WT and DKO bone marrow progenitors (Figures  2A-E ) and recirculating B cells (Figure  2F ) (data not shown). Next, we checked various splenic B cell populations (Figure  2F ). WT and DKO T0, T1, and FO splenic B cells also expressed similar levels. We did note a slight enhancement in Notch2 expression on the DKO T2 cells, and a reduced level on the DKO MZ precursor (MZP) cells. Thus, the reduction in Notch2 target genes noted in the Gαi-deficient B cells cannot be explained by inadequate levels of Notch2 expression at the plasma membrane. To test whether we could restore MZ B cell development by ensuring access to Notch ligands, we used a previously describe in vitro assay of splenic B cell development  (37).
Statistical analysis All data were analyzed in Spike2 (CED, RRID:SCR_000903) and IPython (RRID:SCR_001658;  Perez and Granger, 2007). Statistical tests were conducted using the libraries SciPy v. 0. 19. 1 (RRID: SCR_008058;  Jones et al. , 2001)  and statsmodel v. 0. 8. 0 (SCR_016074;  Seabold and Perktold, 2010). In all cases, results were considered statistically significant if p<0. 05. Data are reported as mean ±SD, along with sample size (number of motoneurons, unless stated otherwise) in every case. No test was performed to detect outliers, and no data points were excluded from analysis. Comparisons between two independent samples were conducted using a two-tailed Mann-Whitney U-test. Contingency tables were analyzed using either the Fisher exact test (for 2 Â 2 tables) or a c 2 test (for 3 Â 2 tables).
Cases where the effect of two independent variables was considered were analyzed using two-way ANOVA. Normality of the residuals was tested using the Shapiro-Wilk test, and homoscedasticity of the datasets was tested using Levene's test. The twitch amplitude was the only parameter that had to be log-transformed to satisfy the assumptions of ANOVA. None of the tests reported here yielded a significant interaction term and therefore only the main effects are reported. Linear regression slopes were compared using ANCOVA.
Acknowledgements This work has benefited from the support and expertise of the microscopy platform and the animal facility of the Saints-Peres faculty of biomedical sciences at  Paris Descartes University. We would like to thank  Dr. Boris Lamotte d'Incamps  for careful reading of the manuscript, and  Martyn Goulding  and his lab for precious help with in situ hybridization. This work was financed by  NIH-NINDS   R01NS077863 ,  TARGET-ALS , the  Association Francaise contre les Myopathies (AFM)  project ' HYPERTOXIC ', and  Thierry Latran Foundation. FR was supported by the  Synapsis Foundation  and by the  Baustein program of Ulm University Medical Faculty.
g. , page limits. As a summary, the contributions of this work are: • An approach to manually annotate the L A T E X source with variability information to automate the derivation of document variants. • An automatic process using machine learning to learn how to meet the constraints using the annotated L A T E X source. • We report on two real usages of VaryL A T E X for a scientific paper and for a curriculum vitae with their corresponding constraints. • We make VaryL A T E X source code and experiments' data publicly available. https://github. com/FAMILIAR-project/varylatex This paper is structured as follows: Section 2 presents VaryL A T E X for generating and learning paper variants that meet constraints. Section 3 describes our implementation. Section 4 reports two experiences. Section 5 presents related work.
IMPLEMENTATION VaryL A T E X is implemented in Java and integrated with different specific languages for instrumenting the whole derivation and learning process. As presented in the introduction, Section 1, the source code among exemplary and experimental data is available online  3. In this section, we go through the different libraries and languages used to develop VaryL A T E X. Variability annotations. → For annotating L A T E X source files, we rely on Mustache 4  , a template language. We give some examples of the syntax in Figure  1. It is possible to write conditional directives directly in L A T E X (e. g. , using T E X language or off-the-shelf packages). From our experience, the use of an external template language fits our purpose. In particular, we can seamlessly annotate any construct of a L A T E X file (e.
g. , size of a figure or bibliographic reference). For resolving variability within templates, we use Trimou 5  a templating engine implementation written in Java. Trimou can activate or deactivate some portions of texts or instantiate a specific value within the text. Variability modeling and sampling. → For specifying constraints among configuration options or defining the range of possible values of numerical options, we rely on an extension of FAMILIAR  [2]. It allows users to define attributed feature models with a textual language. For reasoning about variability models, we translate them into MiniZinc  6  , an open-source constraint modeling language. A MiniZinc model can be imported in a wide range of solvers. We use this translation to generate a sample of valid configurations out of a variability model.
We use the Choco solver  7  for randomly choosing some values for each option. Generating and Learning. → We generate Bash scripts to 1) instrument the compilation of L A T E X source files into PDF; 2) measure each PDF paper; 3) build a CSV file for serializing the configuration matrix. We use R language and the rpart package for producing classification trees out of the CSV. We have also implemented a procedure in Java for extracting constraints out of decision trees. Usage. → Our tool expects as inputs annotated L A T E X source files and a variability model. The rest of the process is automatic and can be launched through Java. Meanwhile, users can override and control many technical steps of the tool such as the size of the random sampling or the scripts in charge of compiling the files.
RELATED WORK We can consider a technical document as an artifact intended for a human to consume. Human-centered product lines have received attention from the research community as a paradigm to adapt Human-Computer Interaction (HCI) artifacts (e. g. , user interfaces) to specific constraints, needs or simply to optimize aesthetic or usability preferences  [11]. We are aware of the substantial difference between HCI artifacts and textual documents. However, there are also commonalities: non functional properties such as readability and aesthetic considerations remain as important factors. There are tools such as FeatureIDE  [7] , pure::variants  [4]  and Gears  [9]  that provide variability management functionalities targeting not only source code but also other artifacts including Microsoft Word text documents.
ACKNOWLEDGEMENTS This work benefited from the support of the project  ANR-17-CE25-0010-01  VaryVary. This work was supported, in part, by the  European Commission (FEDER) , by the  Spanish government under BELi  ( TIN2015-70560-R ) project, by the  Andalusian government  under the  COPAS  ( TIC-1867 ) project and by the  ITEA3 15010 REVaMP 2  project: FUI the  Île-de-France region and BPI in France.
Experimental test bench Imaging was performed by epifluorescence microscopy (Olympus, BX60) with a built-in 100 W mercury lamp (Osram, HBO 103W/2). A standard FITC (fluorescein isothiocyanate) filter cube (Olympus, U-MSWB2) was used to detect fluorescent beads. Imaging was performed with a monochrome fluorescence CCD camera (Olympus, XM10). Sequences of images were superimposed and analyzed with ImageJ software to visualize the trajectory of particles flowing in the DLD channel. Fluids were actuated by a pressure-based flow controller (Fluigent, MFCS-EZ) with input pressures from 50 mbar to 1 bar according to the DLD design in order to obtain a flow rate of about 100 μL/min in the pillar array. The flow rate was chosen to obtain a Stokes flow and avoid flow stream mixing. In the Stokes regime, the flow rate does not influence the DLD separation of particles.
The oil flow rate Q o is then determined from the water flow rate Q 2 (denoted Q w in the following sections). The relationship between Q o and Q w was first determined from a simplified T-junction geometry that has the same geometry as the T-junction of platform 3, but without DLD modules. The oil flow rate Q o was experimentally determined (by measuring the weight loss of the oil tube during injection) to obtain the fitting data in the simplified geometry. A linear relationship was found between the droplet length ratio L w = and the flow rate ratio Q w = Q o Q w þQ o (Fig 8a) , with the slope 1. 3. 10 -  ± 0. 2. 10 -9 (m 3 /s) and the intercept 0. 9 ± 0. 1. This linear trend is in agreement with the model of Zhang et al. [43]  Our model was obtained with the same oil and water solutions as for DLD experiments in order to get the correct fitting pre-factor.
This work was supported by a  CFR CEA  grant for Ph. D. funding,  FET  Proactive  VIRUSCAN H2020  project (No. 731868 ) and BactiDIAG grant funded by  BPI France  under the  FUI framework  ( FUI-AAP18 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Immunofluorescence and Microscopy Confocal images were acquired using a spinning disc confocal microscope (Quorum Technologies) consisting of an inverted fluorescence microscope (DMI6000B; Leica) equipped with an ORCA-R2 camera and spinning disc confocal scan head, an ASI motorized XY stage and a Piezo Focus Drive (Quorum Technologies). Images were acquired using MetaMorph software (Molecular Devices). Data processing and analysis was performed using Volocity software (PerkinElmer) and images were prepared using Adobe Photoshop and Illustrator (Adobe Systems, Inc. ). Where indicated images acquired as described above were deconvolved using calculated point spread function in Volocity. Unless stated otherwise, for all fluorescence images shown, the main panels are merged z-stacks and the associated higher magnifications are single confocal planes.
Quantification and Statistical Analysis Unless stated otherwise, all data shown are mean ± SEM from three independent experiments. Bacterial attachment to cells following inhibitor treatments was normalized to vehicle treated cells, for each replicate, expressed as 100%. Quantitative analysis of western blots was performed using ImageJ (NIH). All other quantifications were performed using fluorescence microscopy. Statistical analysis was performed using two-tailed Student's t-test when comparing means between 2 groups. Means between 3 or more groups were compared using one-way ANOVA with Tukey's multiple comparison tests (Graph Pad Prism software). Ninety-five percentage confidence interval was used to determine statistical significance.
ACKNOWLEDGMENTS We thank  Dr. H. Shuman  for generously providing the plasmids for vipA deletion and vipA-1 strain and anti-VipA antibodies. We thank  Dr. Sergio Grinstein  for providing the Rac1-GFP, RhoA-GFP, PAK-PBD-GFP and rGBD-GFP plasmids,  Dr. Katalin Szaszi  for sharing the Cdc42-GFP plasmid and  Dr. Andras Kapus  for sharing the FH1/FH2 constructs. We would like to thank  A. Ensminger  for sharing the Lp02 dotA strain and  A. K. Brassinga  for providing the KB288 plasmid. We would also like to thank the  Centre for Neurobiology of Stress  at the  University of Toronto at Scarborough  for the use of the imaging facilities. This work was supported by  NSERC   Discovery grant  and  Ontario Lung Association/Pfizer Canada Research Award  to MT.
Methods A structured search was performed on 10 June 2018 by the first author (K. A. ), using 'pregnancy and acetaminophen/ paracetamol' and 'fetal ductus' or 'fetal ductus closure, or constriction' as keywords, in the electronic resources PubMed, Web of Science and Google Scholar, without language restrictions. The hits were subsequently screened on the title and, when needed, the abstract or full report. Only cases with acetaminophen/paracetamol in monotherapy (no simultaneous intake of other drugs known to be associated with fetal ductus arteriosus closure, such as NSAIDs) were retained. All retrieved documents were subsequently screened for potential relevant references and citations, using the same search engines. In the cases retained, causality between maternal paracetamol intake and fetal ductus arteriosus constriction or closure was graded independent by two authors (K.
Discussion In this case series analysis, we retrieved and discussed 25 cases of fetal ductus arteriosus constriction or closure, and thereby provided evidence on the likely causal relationship with maternal paracetamol intake. Although robust data on its incidence are absent, fetal ductus arteriosus constriction or closure is rare, with two recent retrospective case series on, respectively, 45 and 22 consecutive cases in datasets of 26 000 (0. 17%) and 1602 (1. 4%) referrals for fetal echocardiography  [20, 27]. The specific setting of fetal physiology necessitates patency of the fetal ductus arteriosus to bypass fetal blood flow from the pulmonary artery to the systemic circulation under low pressure and low resistance settings. Fetal ductus arteriosus constriction or closure will result in high pressure, transpulmonary blood flow with subsequent right ventricular hypertrophy, disturbed lung blood vessel development, and cardiac decompensation with hydrops  [20, 27].
This may include reanalysis of the existing cohorts with cases of unexplained ductus closure  [27]  and exploration of existing pharmacovigilance databases to search for confirmation or rejection of the current signal, and putting these findings into clinical perspective. This clinical perspective is relevant, as analgesia during pregnancy and during the peripartum period is of obvious importance, and alternative analgesics, such as opioids or other NSAIDs, also have side effects.
Acknowledgements We would like to thank  Yang-Ting Chien ,  Andrew Larkoski , and  Ian Moult  for useful discussions, as well as  Mrinal Dasgupta  and  Simone Marzani  for comments on the manuscript. FD is supported by the  SNF  grant  P2SKP2 165039. FD and JT are supported by the  Office of High Energy Physics of the U. S. Department of Energy (DOE)  under grant  DE-SC-0012567. GS's work is supported in part by the  French Agence Nationale de la Recherche , under grant  ANR-15-CE31-0016. LN's work is supported by the  DOE  under Award Number  DE-SC-0011632 , and the  Sherman Fairchild  fellowship.
Dimensionality reduction on large textual datasets is computationally quite expensive. A set like the Hathi Trust books may consist of 10 million distinct tokens across 15 million individual books; SVD on a matrix with hundreds of trillions of entries is difficult to perform. This has led researchers to propose sampling techniques which could mitigate the difficulty at the expense of introducing random fluctuations. 7 cond, it is difficult to project out-of-domain documents into the space from a standard projection. The greater the difference between out-of-domain documents and a reference corpus, the more problematic out-of-domain projection becomes. Features that are collinear in one set may not be in another: for instance, "bank" and "river" might be highly collinear in texts about geology, but quite different in texts about economic geography.
It differs from SRP by hashing each word to a single position in the output vector of length N, while SRP places each word into each vector. This creates great advantages in computation, particularly when working with small texts, because a document with only a few dozen texts can be represented and stored with only a few dozen calculations. 27 fective use of the hashing trick therefore involves output vectors of quite high dimensionality (Weinberger et al. test in the range of 1 million to 100 million buckets). 28 In the cases where the hashing trick is most frequently used, parsing texts such as e-mails, the sparsity of the output vectors means that the output vectors can be quite small: in a dataset of 2 million usenet posts, each post has on average 165 distinct tokens, which would take 1.
Choosing dimensionality One notable feature of SRP space is that anyone creating SRP vectors can increase the resolution to an arbitrary level. For certain tasks such as linear language classification reasonably good results can be obtained in as few as ten dimensions; more complicated tasks such multilingual subject classification take at least a few hundred. This paper releases 1280-dimensional SRP vectors for the HathiTrust. This number is chosen because it creates an output file size of about 64 gigabytes; any larger begins to approach the original documents in size. A number of smaller files are also included at resolutions of 50, 320, and 640; the fifty-dimensional vectors, at a little under 3GB, can be loaded into memory on many laptops or downloaded over a wireless connection in a reasonable period of time.
The PCA step was introduced because it would require expensive hardware to compute LargeVis on a 1280 by 13 million matrix. Unfortunately, it likely also means the clustering captures little information aside from for English or other more common languages. of the full Hathi set illustrates how a random projection of the Hathi Trust can capture many different types of textual similarity and difference. The static image above shows six successive zoom levels of this single reduction. At the farthest approach (Panel 1), the visualization is separated by language with the largest language, English, at the center. Languages are relatively distinct, although there are a number of subclusters that include, for example, bilingual works. Panel 2 shows Library of Congress classification headings for the Englishlanguage cluster in panel 1.
It is impractical to visualize the entire Hathi Trust collection at once. (There are more volumes in the collection than pixels on a typical computer monitor. ) Hovering over a point with the mouse displays basic bibliographic information, and clicking links to the volume's full page on hathitrust. org. By choosing any arbitrary point and zooming in, the reader can see what kinds of volumes are present; interactive controls make it possible to filter by subject, date, and title metadata. This visualization can serve as a kind of guide to some of types of textual attributes that the SRP dataset can be used to analyze. If a cluster is coherent in the visualization, then it also exists in some sense in the higher-dimensional SRP space; relations that do not exist in the visualization may exist in the underlying data, or may be lost.