dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements Alex  J Salazar-Medina  is a postdoctoral fellow at  González-Aguilar 's lab. This study was financed by the  National Council for Science and Technology (CONACYT)  " Fronteras de la Ciencia 563 ". We also acknowledge the support and facilities obtained through the  "Red Temática de Química Supramolecular" agreement 281251  ( CONACYT ).
Study settings We performed a retrospective survey in 12 mostly urban tertiary hospitals from the following countries in SSA: Côte d'Ivoire (3 sites), Benin, Burkina Faso, Cameroon, Kenya, Senegal, South Africa, Togo, Uganda and Zambia (1 site each), as shown in Fig. 1. All clinics were part of the International epidemiologic Databases to Evaluate AIDS (IeDEA) collaboration, a large international research network of HIV cohorts  [11]. The study received approval from local ethics committees of each of the ten participating countries:.
Study population and data collection Among the total number of patients who started ART between 2010 and 2012, we randomly selected a sample of 100 adults (>18 years) initiating ART per year at each clinic, i. e. up to 300 patients per clinic in total. If the number of patients starting ART was smaller than 100 for a given year, all patients were selected for that year. All demographic and HIV-related clinical data were entered into a local database for clinical care, evaluation and reporting purposes, within the IeDEA framework. As information on viral hepatitis screening, monitoring and treatment were not routinely available in the data collected by IeDEA, we performed a chart review using a standardized data collection protocol in all study sites. In addition to data on screening for HBsAg, anti-HCV antibody and any other available HBV and HCV serological and virological tests, we collected information related to ART history, routinely performed laboratory tests and HCV treatment.
Funding This study was supported by the  National Institute of Allergy and Infectious Diseases (NIAID) , the  National Institute of Child Health and Human Development (NICHD) , the  National Cancer Institute (NCI)  under award numbers  U01AI069924  for  IeDEA-Southern Africa ,  U01AI096299  for  IeDEA-Central Africa ,  U01 AI069911  for  IeDEA-East Africa  and  U01AI069919  for IeDEA-West Africa. GW was supported by an  Ambizione-PROSPER fellowship  from the  Swiss National Science Foundation  ( PZ00P3_154730 ). M. J. V. was supported by the  NIH Fogarty International Center  ( K01TW009998 ). Publication of this article was funded by the  World Health Organization.
Data collection covers three periods: before TSN program implementation, during early TSN program implementation and at late TSN program implementation, in the five TSN projects' territories and in five comparison territories. Three populations will be considered: "TSN-targeted people" (healthcare system users and people having characteristics targeted by the TSN projects), "TSN patient users" (people included in TSN experimentations or using particular services) and "TSN professional users" (healthcare professionals involved in TSN projects). Several samples will be made in each population depending on the objective, axis and stage of the study. Four types of data sources are considered: 1) extractions from the French National Heath Insurance Database (SNIIRAM) and the French Autonomy Personalized Allowance database, 2) Ad hoc surveys collecting information on knowledge of TSN projects, TSN program use, ease of use, satisfaction and understanding, TSN pathway experience and appropriateness of hospital admissions, 3) qualitative analyses using semi-directive interviews and focus groups and document analyses and 4) extractions of TSN implementation indicators from TSN program database.
Design of the study The study is mainly based on a controlled before-after study design (Fig. 1 ). Data collection covers periods before TSN program implementation (2012-2015 or T0 period), during early TSN program implementation (2016, T1 period) and at late TSN program implementation (starting in 2017, T2 period), in the five TSN projects territories and in five comparison territories. Analyses of TSN program implementation and use are based on a before-after design.
Outcomes and measurementsdata sources and data collection Four types of data sources are considered (Table  2 ). Extractions from the French National Heath Insurance Database (SNIIRAM) will be conducted to collect the main set of indicators for axes 2, 3 and 4. This exhaustive nationwide database is at the heart of the financing system of diagnosis, drugs and physicians in ambulatory care settings and of independent practitioners in private hospitals (essentially fee for service). It provides data on claims paid for each patient by the Social Security System and is therefore the main source of information on ambulatory setting activity and associated expenditures. This database contains patient data (age, sex, place of living, long-term and chronic diseases, date of birth, date of death, health insurance scheme, benefit of free complementary insurance for lower-income people), all consultations and visits to GPs and ambulatory care specialists (but nothing about their content), all medical technical procedures, all dispensed medical devices and drugs, all lab and diagnostic tests but not their results, and providers' level data (their activity and sales turnover, geography, prescribing behaviour).
This database is also linked with the PMSI (programme de médicalisation du système d'information/Information system medicalization program) database providing more detailed information on hospital activity and expenditure, including diagnoses and procedures. Extractions from the SNIIRAM will cover the time period from 2012 to 2017 and will focus on samples from the "TSN targeted people" population, in both TSN projects and control areas. Data will also be extracted from the APA (Allocation personnalisée d'autonomie/personalized autonomy allowance) database. APA is a financial support for the elderly to cover home-care, nursing-care or institutional-care expenses. Ad hoc surveys based on questionnaires will collect information on: -Knowledge of TSN projects assessed at T1 (earlyimplementation observation) and T2 (lateimplementation observation) in a sample of the "TSN targeted people" population (axis 1); -TSN program use, ease of use, satisfaction and understanding assessed at T2 in a sample of the "TSN professional users" population (axis 3); -TSN use satisfaction and TSN pathway experience assessed at T2 in a sample of the "TSN patient users" population (axis 2); -Appropriateness of hospital admissions assessed using the validated French version of the Appropriateness Evaluation Protocol questionnaire (AEP) in most emergency services of the TSN and control areas, twice, before TSN program implementation (T0) and at early TSN program implementation (T1) (axis 2).
Patients, TSN professionals and TSN project developers will receive incentives for their participation to qualitative analyses. The sample size of 3 × 50 participants have been decided to favor heterogeneity of health-care professionals recruited. At each step (T1, T2 and T3), 50 health-care professionals will be recruited from the five TSN territories and from the five health-and social-care professional categories: general practitioner, private-sector specialist physician, private-sector nurse, physicians working at hospital, social care professional. Sample should also be heterogeneous according to TSN program implication. A set of standardised TSN implementation indicators will be requested to TSN projects producers and extracted from each TSN project's database.
Data analysis and interpretation Analysis involves quantitative, qualitative and data visualisation approaches. Quantitative analyses in axes 2, 3 and 4 mostly rely on a set of standardized indicators which represent the outcomes supposedly influenced by the TSN projects in quality and intensity of healthcare consumption or medical practices. These indicators are calculated on an annual or quarterly basis and compared over time (between T0, T1 and T2). Adjustment is done through direct standardization depending on the scope of the indicator: by age and sex for general population-based measures and, to the extent possible, by clinical and social background for indicators focused on particular populations (the elderly, patients witch chronic diseases…). Statistical estimations are then performed to assess the impact of TSN projects outcomes, with inherent limitations.
Causal effects of TSN services can then be assessed if and only if i) services are exclusively intended to specific subgroups of patients, ii) these groups can be routinely identified in SNIIRAM data and iii) there is a sufficient share of actual users of the services within these groups (otherwise, variance of ITT estimate is getting too large). For axis 1 analysis, indicators of TSN use, implementation and of TSN users characteristics will be described. Qualitative analysis of axes 1, 2 and 3 is based on interviews which are recorded, anonymous, integrally transcribed and imported into the NVivo11 software to achieve a thematic content analysis. This method aims "at spotting, in verbal or textual expressions, recurring general themes that appear under various more concrete content"  [35].
The files and audiorecordings are kept in a sealed location of the research centre, with access limited to the qualitative analysis researchers of the EvaTSN research group, and will be destroyed after 15 years. No name will appear on any public documents and no information will be divulged that could allow participants to be identified by a third party. The EvaTSN research group members have access to anonymous quantitative datasets. All the quantitative analyses and quantitative data collection from questionnaire or databases are strictly anonymous. Results will be disseminated as a report to the French Health Ministry. An disseminating plan to participants is in discussion with the French Health Ministry. Authorship of articles is defined according to the Vancouver Convention.
But many HI technology interventions still need to be designed and tested in a variety of settings. Care coordination is the next opportunity and challenge for HI technology. Recently, a call to methodologically robust research on HI technology impact on care coordination was launched by the International Medical Informatics Association (IMIA)  [24]. Our research project is the first one conducted in France which covers such a large perimeter of types of care and disorders or patient conditions. Since complex interventions like HI technology include multiple components that are interrelated or interdependent, it can be challenging to develop, document, evaluate and report on them  [41]. Our project will apply the principles of complex intervention evaluation, combining quantitative and qualitative analyses, conducting impact and process evaluation with multiple evaluation criteria collected from different sources ("TSN targeted people", "TSN patient users", "TSN professional users", TSN developers and stakeholders, French National Heath Insurance Database).
Another methodological challenge is linked to the gap between the evaluation and the TSN project intervention level. Each TSN project is focused on specific conditions or diseases (specific chronic conditions, social vulnerability situations, elderly dependant people, etc. ) and includes samples of patients and people presenting these conditions ("TSN patient users"). Our main quantitative analyses use data from the French National Heath Insurance Database (SNIIRAM database). As it is impossible to identify the TSN patient users individually speaking, we need to select groups of people presenting similar characteristics as those targeted in the TSN projects and living in the TSN territory ("TSN targeted people") using validated algorithms. This lack of specificity in TSN projects evaluation may lower the strength of the association between TSN projects interventions and outcomes.
Acknowledgement We thank all actors of the  EvaTSN Research group  (supplementary file) and TSN projects for their support. The Eva TSN Research Group: General coordination:  Louis-Rachid Salmi  ( Univ. Bordeaux, ISPED, Centre INSERM U1219 Bordeaux population health research center, Bordeaux , France: principal investigator),  Emmanuelle Fourneyron  ( Univ. Bordeaux : project manager),  Sylvie Calvar  ( Univ. Bordeaux : administrative assistant) Group 1: evaluation of use and deployment effectiveness  Louis-Rachid Salmi  ( Univ. Bordeaux, ISPED, Centre INSERM U1219 Bordeaux population health research center, Bordeaux , France: group 1 scientific co-coordinator),  Frantz Thiessard  ( Univ. Bordeaux ,  INSERM U1219  Bordeaux population health research center, Equipe de recherche en informatique appliquée à la santé: group 1 scientific co-coordinator),  Stéphane Pincemail  ( Univ.
| Randomisation and sample size For both studies, randomisation lists were generated by a statistician and transmitted to the assigned clinical packaging organisation for labelling. The RANUNI routine of the Statistical Analysis System (SAS Institute Inc. , Cary, NC, USA) was used for the randomisation lists. The randomisation lists and electronic files were secured in a locked cabinet with restricted access to only the designated personnel directly responsible for labelling and handling the study treatments. No statistical rationale of sample size was necessary for these studies, as no inferential statistics were performed.
Despite the subtotal metabolic response seen on the earlier PET scan, the lymph nodes remained tumoral in pathological analyses and ALK status was confirmed on the resected specimen. Because an active ALK tumor was found after 7 weeks of treatment (same histological aspect in the initial tumor site), we do not yet know the perfect timing for surgery, and a longer duration of neoadjuvant treatment might be more beneficial. After surgery, adjuvant treatment was discussed: there were 2 main options. One was to pursue treatment with a tyrosine-kinase inhibitor (TKI), but there is no data on its potential benefits or the most appropriate duration of treatment. Because cisplatin plus Navelbine have not been shown to be as effective as neoadjuvant, we chose to use 2 cycles of carboplatin plus pemetrexed because some data suggest that pemetrexed has better efficacy on ALK-positive tumors  [2, 9].
RNA isolation and real-time PCR Total mRNA were extracted using TRIzol reagent (Thermofisher), isolated from other cellular materials by chloroform/isoamyl alcohol (24:1) precipitation before centrifugation (12,000 × g, 4°C, 15 min), as described elsewhere  [34]. 250 ng total mRNA were reversetranscribed using VERSO cDNA kit (Thermofisher) according to the manufacturer instructions. Real-time PCR was then performed using an Absolute SYBR Green Rox mix (Thermofisher) and a CFX 96 real time PCR detection system (Bio-Rad, Hercules, CA, USA). The cycle threshold (Ct) values were recorded using Bio-Rad CFX Manager 3. 0 software (Bio-Rad)  [34]. PCR primers were synthesized by Eurogentec (Liege, Belgium) as follow (5'-3'): for LRP1: GCTATCGACGCCCCTAAGAC and CGCCAGCCCTTTGAGATACA; for ITGB1: CA TCTGCGAGTGTGGTGTCT and GGGGTAATTTGTC CCGACTT; for RS18: GCAGAATCCACGCCAGTACAA and GCCAGTGGTCTTGGTGTGCT; for RPL32: CATTGGTTATGGAAGCAACAAA and TTCTTGGA GGAAACATTGTGAG.
Microscopy Following plating on gelatin-coated Lab-Tek I chambers, cells were maintained in phenol red-deprived DMEM-F12 (supplemented with low serum growth supplement and 10% FBS) for epifluorescence video microscopy experiments. For bright field video microscopy, cells were observed over a 30 min period using a Zeiss Axiovert 200M equipped with a Coolsnap camera (Roper Scientific, Trenton, NJ, USA). Cells were maintained at 37°C in 5% CO 2 atmosphere using a temperature-controlled chamber (PECON, Erbach, Germany). Data were acquired using MetaMorph software  (Roper Scientific). Timespan between image capture was 4 s. Confocal microscopy images of fixed cells were collected with a Zeiss (Oberkochen, Germany) LSM710 Meta confocal microscope using either a ×63 Plan Apochromat objective (oil immersion, 1.
Image processing Isosurface representations were realized using the AMIRA software program (v6. 2; Visage Imaging, Berlin, Germany) as previously detailed in  [28]. Acquired confocal stacks for vesicle (endosome/ lysosome) labeling were analyzed with a custom made Matlab script in order to determine the number of integrin-containing vesicles and to estimate the corresponding integrin amount from pixel intensity. Briefly, the script integrates data from a pair of stacks (vesicle labeling and integrin labeling). Images were first thresholded and then the fluorescence channels that correspond to vesicle immunostaining (i. e. Rab5a from endosome or LAMP-1 for lysosome) were analyzed to determine the pixels that are in close contact to each other (26-connected neighborhood) and therefore define the volume of each vesicle considered separately.
Any vesicles <7 voxels were considered as artefact and removed from data analysis accordingly. The contour of remaining vesicles was then smoothed and compared to the channels corresponding to integrin labeling. The enabled, number/volume/shape of each vesicle to be measured, as well as the amount of labeled integrins contained within these vesicles. The script was automated in order to analyze wide collections of stacks therefore increasing the number of measurements. For video microscopy experiments, β1-integrin DsRed 2 (DSR-β1-ITG) cluster trajectories were manually determined using the Point Picker ImageJ plugin.
ACKNOWLEDGMENTS We acknowledge the expert technical support of  C. Terryn  ( Cellular and Tissular Imaging Platform, Reims, France ) for video microscopy and confocal microscopy experiments. We are grateful to  Dr. D. K. Strickland  ( Department of Surgery, University of Maryland School of Medicine, Baltimore, MD, USA ) for kindly providing us with the R2629 blocking antibody. The authors acknowledge  N. J. Ward  for editorial assistance.
The likelihood of the data was then calculated by assuming that all data points were independent, treating the variance of the normal error as an additional nuisance parameter to be estimated (separately for each treatment). Bayesian methods were used to estimate log-transformed values of model parameters, assuming uninformative improper priors in all cases. The joint posterior of the parameters was estimated using the freely available function MCMCpack  (Martin et al. , 2011)  in R, taking 1 000 000 samples after discarding the first 20 000 as burn in. Proper convergence was assessed by visual inspection of trace plots and by checking that results were unaffected by chain initial conditions. Credible intervals for the mean response of WT and pyr-to light in terms of ETR were calculated-at each value of the independent variable-by considering the distribution formed by taking 10 000 samples from the joint posterior distribution and using them in Equation  2 , and then transforming the predicted ϕ II values to ETR as described above.
Acknowledgements We would like to thank  Mark Heinnickel  and  Shai Saroussi  for helpful discussions on the JTS-10 data,  Ruben Alvarez  for sharing his R scripts for analysing Chl fluorescence imaging data,  Francis-André Wollman  for providing antibodies,  Madeline Mitchell  for commenting on the manuscript, and  Jeremy Skepper  and  Lyn Carter  for electron microscopy support. Further, we wish to gratefully acknowledge financial support to ODC by  Wolfson College , the  Cambridge Philosophical Society , and the  TH Middleton Fund (Department of Plant Sciences ) toward research-related travel, which was crucial to enable this collaborative study. This work was supported by the  Biotechnology and Biological Sciences Research Council  (PhD studentship  1090746  to ODC and  BB/M007693/1  to MTM and HG).
We hypothesize that (1) the pollen stoichiometry of single species imposes a limiting stoichiometric mismatch on bees, with differing mismatches among various taxa of pollen; (2) different castes and sexes of honeybee experience different mismatches; and (3) polyfloral pollen is needed to build the honeybee imago because such pollen allows stoichiometric balance. The study consisted of two research tasks. Task (1) was planned as a field experiment in which we measured the concentrations of 12 elements in the bodies of 3 castes and sexes of honeybees (queens, drones and workers), in the nectar consumed by bees, and in the pollen pellets (pollen loads) collected by bees, which were composed of various species of pollen. Task (2) was based on data in the literature. This approach allowed the study of elemental composition and stoichiometry of various species of pollen that were collected worldwide (data collection of a scope that would be impossible within a single study).
We collected published data on the elemental composition of the pollen of various plant species. Based on these data, we tested whether pollen stoichiometry may limit the development of bees by calculating the stoichiometric mismatches imposed on the bees by taxonomically different pollen. If various pollen species are scarce in different elements, they should impose different limitations on bee development. If they are also rich in different elements, combinations of various pollen species should allow for a stoichiometrically balanced diet. Thus, appropriate compositions and diversities of flora may promote bee development regardless of the quantity of pollen and nectar produced. If so, intervention strategies aimed at providing nutritional support for bees should consider not only the quantity of pollen and nectar produced by plants but also the quality of pollen (which may be reflected as pollen stoichiometry).
If ðC : XÞ i ! TER x ð2Þ then element x may become a limiting factor for growth at trophic level i+1. The gross growth efficiencies for any given element can be experimentally measured by feeding trials in growing animals. However, such data are extremely scarce for elements other than N and P. In practical terms, the TER index for invertebrates can only be estimated using arbitrary assumptions  [8, 32, 33]. Therefore, we use TSR, a simpler index based on readily available data  [29] , to enable the detection of possibly limiting stoichiometric mismatches between trophic levels and comparisons between various taxa, habitats and foods. We propose the following simplified approach, rewriting condition (2) ðC : XÞ i ! ðGGE x =GGE C Þ Â ðC : XÞ iþ1 to obtain ðC : XÞ i =ðC : XÞ iþ1 ! GGE x =GGE C or TSR x ! GGE x =GGE C Even without precise information regarding the elemental conversion efficiencies for a given organism, we can safely assume that GGE x >GGE C because carbon is always partially lost in CO 2.
A second concomitant breast cancer was present in 15 cases, 1 ipsi-lateral in a different quadrant, and 14 contra-lateral. The position and size of these tumours was also established. These data were included in the analysis of the distribution of the cancer locations in the breast, but for the dose-response analyses, only the dose estimation of the largest invasive cancer was included. For the 170 cases from the UK, we had less information on the radiation treatments deliv-ered for HL, only a general description of the type of field or target areas, the dose and the year of treatment, and no simulation radiographs. One of the field types allocated to the Dutch patients was then chosen based on the information available and the clinical experience of NR who has clinical experience of treating HL patients both in the UK and the Netherlands from the 1980's, and who also discussed earlier treatment fields with older colleagues.