dataesr/scientific-paragraphs-categorization huggingface dataset:
Forearm reactive hyperemia experiment An imaging study was performed on the forearm of a healthy subject undergoing a standard reactive hyperemia protocol. During this protocol an automatic inflatable cuff was wrapped around the subject's left upper arm. A full arterial occlusion was applied by inflating the cuff to 210 mmHg. During the imaging study, the volar forearm was imaged at 50 frames per second for 1 minute of baseline, followed by 3 minutes of arterial occlusion, followed by 3 minutes immediately after the release of the occlusion. Each of the 21,000 frames of data was used to reconstruct 2D maps of µ a , µ s ', and SFI. Regions of interest were analyzed to obtain time course data of µ a , µ s ' and SFI. Studies here were carried out under the UC Irvine IRB approved protocol, and informed consent was obtained from all subjects (HS# 2011-8370).
Acknowledgments The authors gratefully acknowledge funding provided by the  NIH NIBIB Biomedical Technology Research Center LAMMP :  P41EB015890 ,  NIH  (grants:  R21-EB014440 ,  K01-DK-093603 ,  R01-HD065536 ), the  Military Medical Photonics Program, AFOSR  Grant No. FA9550-08-1-0384 , ICube, and the  Arnold and Mabel Beckman Foundation. Michael Ghijsen  was supported by the  BEST IGERT program  funded by the  National Science Foundation   DGE-1144901  and by the  Medical Scientist Training Program  funded by the  National Institute of Health   T32-GM08620.
Angiographic analysis All angiograms were assessed by an independent core laboratory (K. I. C. Co. Ltd, Kanagawa, Japan) using dedicated software (qAngio XA ver. 7. 1, Medis, the Netherlands). Main angiographic parameters at baseline were minimum lumen diameter (MLD) before and after procedure, percentage diameter stenosis (DS%), and acute gain (defined as the change in MLD from baseline to the final procedural angiogram).
Statistical analysis The statistical analysis plan has previously been published  [5]. The CENTURY II randomized trial was powered for non-inferiority of BP-SES compared with PP-EES for the primary endpoint of 9-month TLF. The Kaplan-Meier method was used to estimate event rates for timeto event outcomes, and data were compared with the log-rank test. All analyses were carried out using the SAS software, version 9. 1 (SAS Institute, Inc. , Cary, NC, USA).
Molecular methods A boiled cells suspension of each oxidase positive, Gram negative diplococci (OPGND) isolate was sent to the Department of Zoology at the University of Oxford for molecular typing, where Sanger sequencing was used to characterize gene targets as described in the Supplement. Sequences were assembled using SeqSphere (http://www. ridom. de/seqsphere/) and imported into the isolate's record previously created in a BIGSdb database. 21.
Genetic diversity Sequencing of the variable region of the fetA gene (feta_VR) was used to assess the genetic diversity of the Neisseria identified as described in the Supplementary methods. 28 he sequences were assembled as for the other targets using SeqSphere (http://www. ridom. de/seqsphere/). The trace files of each new allele were manually curated in MEGA version 6. 0,  25  the quality of the sequences were assessed and the modified bases checked in both the forward and reverse trace file. The corresponding protein sequences were also aligned and compared with known sequences stored in the Neisseria sequence definition database of pubMLST  21  before being entered into the allele database and the appropriate isolate record. For isolates for which fetA_VR could not be amplified, an assay identifying the absence of the fetA gene (fetA null, fnl ) was employed, using primers placed on genes on each side of the fetA gene: thdF and fetB as described in the Supplementary methods.
Prevalence of carriage with Neisseria species A total of 4694 of the 46034 pharyngeal swabs collected yielded a Neisseria species, giving a carriage prevalence of 10. 2% [95% CI, 9. 8e10. 5%]: 696 Neisseria were identified out of the 946 OPGND samples received from Chad; 838 out of the 994 received from Ethiopia; 446 out of the 544 received from Ghana; 298 out of the 504 from Mali; 1644 out of 2321 from Niger and 773 out of the 971 received from Senegal. The most frequently isolated species was Nl with a 5. 6% point prevalence [95% CI, 5. 3e5. 8%], followed by Nm at 3. 6% [95% CI, 3. 4e3. 8%]), Np at 0. 6% [95% CI, 0. 5e0. 7%], Nb at 0. 2% [95% CI, 0. 2e0. 3%] and Neisseria subflava (Ns) at 0. 05% [95% CI, 0. 03e0. 1%] (Supplementary Table  1 ). Twenty isolates from Chad were identified as belonging to the Neisseria genus but did not cluster with any known species on the Neighbor-Joining Tree (NJT; data not shown); they were designated Neisseria sp.
Data source The Job Exposure Matrix was developed using the data from the HILDA survey. This is a longitudinal, nationally representative study of Australian households established in 2001. It collects detailed information annually from over 13,000 working age individuals within over 7,000 households  [24]. The response rate in wave 1 was 66%  [24]. The survey covers a range of dimensions including social, demographic, health and economic conditions using a combination of face-to-face interviews with trained interviewers and a self-completion questionnaire. Although data are collected on each member of the household, interviews are only conducted with those older than 15 years of age. The initial wave of the survey began with a large national probability sample of Australian households occupying private dwellings  [24].
Construction of the Matrix To construct the JEM, we calculated exposure estimates at each intersection between rows (job codes) and columns (job control, job complexity and demands, job security, and fairness of pay). The matrix was developed based on exposures at the four-digit level of the Australian and New Zealand Standard Classification of Occupations (ANZSCO), which represents 358 occupation titles. The classification definitions are based on the skill level and specialisation usually necessary to perform the tasks of the specific occupation, or of most occupations in the group  [29]. Data was weighted so as to provide estimates representative of the Australian working population using the cross-sectional responding person weights generated for the HILDA dataset. These weights were constructed using benchmarks from various Australian Bureau of Statistics (ABS) surveys and adjust the sample according to: sex by broad age group; state by part of state; and state by labour force status, marital status and household composition (number of adults and children)  [30].
Calculation of individual exposures • Self-reported measures of the four psychosocial work factors were used as individual scores for each exposure. Higher scores on each continuous psychosocial job exposure represented high job control, demands and complexity, security, and fairness of pay at each time point. These were calculated for individuals for each wave of data contributed. We used means as the summary measure because we were able to adjust these using the weights available in HILDA to ensure the estimates were representative of the Australian working population. • We then assigned each individual level score to either low or high exposure depending on the population-level medians defined above and generated this as a new variable. At the median and above, exposure was defined as low; while below the median was high exposure for control, security and fairness and the reverse for demands.
Calculation of JEM exposures • Each participant in HILDA could contribute up to 13 annual waves of data (the mean number of employed waves per person was 8. 9), in up to 13 different 4-digit occupational titles. Contributed waves were sorted by 4-digit occupational title and by gender, and mean exposure values by occupational title and gender were calculated. If changes in ANZSCO codes occurred within the 13-year period for a given individual, then the observations for this individual were used to calculate JEM scores for different ANZSCO codes by gender. This resulted in a mean score for each of the four exposures by 4-digit occupational title and gender. • JEM binary exposures were derived from the JEM scores dichotomized at the median thresholds described above. JEM binary exposures were calculated for each ANZSCO 4 digit code and imputed to each observation according to ANZSCO code.
Discussion This study has described the development of a Job Exposure Matrix (JEM) for the measurement of psychosocial job stressors in the Australian context using the HILDA study. We developed this across 358 occupational job titles for four exposures: job control, job demands and complexity, fairness of pay, and job security. We conducted internal validation within the HILDA data set and found that the JEM-based exposures had moderate to acceptable agreement with individually reported exposures. However, the job demands and complexity and job control measure performed better in the validation than did job security and fairness of pay. We would suggest that this indicates that this is because the former are more likely to reliably pattern by occupation code than the latter, thus might be more amenable to measure in a JEM.
Psychosocial job stressors measured using imputation methods such as JEMs have also been used to study physical health outcomes, such as cardiovascular disease  [35]  and all-cause mortality  [17]. This highlights the important range of outcomes that JEM-based measures can be used to study. Understanding the role of job stressors (as measured through a JEM) on a wider range of health outcomes is important in formulating targeted and specific prevention plans. The limitations of our study include the fact that it only provides information on a small number of exposures. For example, we do not have any measure of supervisor or collegial workplace social support. This is because of restrictions in the amount of data and variables available in HILDA. In addition, we have generated a JEM with averaged exposures over a 12-year observation period; with an acknowledged limitation that some psychosocial stressor exposures may have varied by time period.
Another issue is the potential for exposure misclassification within occupational titles (as all persons in a given occupation are assigned the same exposure value, while acknowledging that when measured at the individual level exposures can vary within occupation). At the same time, the use of a JEM helps in the case of missing exposure data. Further, JEM assessments provide an objective measure of exposure information by summing observations across all people having the same occupation, overcoming the potential for individual reporting bias and spurious associations with self-reported outcomes due to common method variance, or dependent misclassification  [36]. Regarding the data source, we would highlight that HILDA is slightly biased towards more educated, and highly skilled workers. However, we adjusted for this potential distortion by using the weights available in the HILDA dataset, making HILDA a large nationally representative survey of the Australian population, pertinent to evaluate psychosocial work exposures at the national level.
Acknowledgments This paper uses unit record data from the  Household, Income and Labour Dynamics in Australia HILDA Survey. The  HILDA Project  was initiated and is funded by the  Australian Government Department of Social Services DSS  and is managed by the  Melbourne Institute of Applied Economic and Social Research Melbourne Institute. The findings and views reported in this paper, however, are those of the authors and should not be attributed to either DSS or the  Melbourne Institute. The data used in this paper was extracted using the  Add-On Package PanelWhiz for Stata. PanelWhiz  (http://www. PanelWhiz. eu) was written by  Dr. John P. Haisken-DeNew  (john@PanelWhiz. eu).
Data were collected with a MultiClamp 700B amplifier (Molecular Devices). Data are presented as mean ± standard error of the mean (SEM). Statistical significance was assessed using a nonparametric test (test of Mann-Whitney, MW) (SigmaStat, v3. 1) and a Kolmogorov-Sminov test for the cumulative probability distributions. A significance of p < 0. 05 was required for rejection of the null hypothesis.
Acknowledgments The authors thank  C. Béclin , and  A. de Chevigny  for critical reading of the manuscript. V. Camosseto ,  P. Delage  and  A. Defays  for expert technical assistance,  B. Gasnier  for anti VGAT antibody. We acknowledge support by the the  France-BioImaging/PICsL infrastructure  ( ANR-10-INSB-04-01 ) and the IBDM animal facilities.
Funding: HC and PP received funding from  Agence National pour la Recherche (ANR BadLampLayers) , and  EG  from  ARC Subvention libre « BAD-LAMP  function in multiple myeloma » and A Ã  MIDEX  project  ANR-11-IDEX-0001-02  funded by the " Investissements d'Avenir" French government program.
Aknowledgements We thank Ilka Brunner, Cesar Gomez, Slava Mukhanov, Giuseppe Policastro and Jan Troost for discussions. T. T. thanks the Philippe Meyer Institute of the École Normale for support during a visit that initiated this project. C. B. thanks the LMU physics department for hospitality during completion of this work. The work of T. T. is partially supported by the European Union Seventh Framework Program (FP7-REGPOT-2012-2013-1) under grant agreement No. 316165.
The anti-invasive outcome of TNKS/2 inhibition is independent of the Wnt pathway The anti-invasive and anti-migratory effects produced by TNKS/2 neutralization were consistent with the working hypothesis that blockade of TNKS/2 activity would blunt Wnt-mediated pro-invasive cues. We therefore analyzed whether this weakened chemotactic response was in fact ascribable to interception of Wnt signaling. First, the TOPflash reporter system was employed to gauge Wnt-dependent transcriptional responses after cell exposure to TNKS/2 inhibitors (for this purpose, we used H322 cells owing to their high amenability to transfection procedures). We found that TNKS/2 inhibition did not affect Wnt transcriptional activity, either basally or upon addition of the canonical Wnt ligand Wnt3a (Additional file 8: Figure  S3A ; raw data in Additional file 9).
As a complementary approach, expression of established Wnt target genes was assessed by RT-qPCR in the whole panel of lung adenocarcinoma cell lines tested in the cell invasion experiments. As shown in Additional file 8: Figure  S3B  (raw data in Additional file 9), stimulation with Wnt3a led to increased expression of at least some of the target genes, with variable levels of induction in the various cell lines (likely due to cell typespecific differences). Also in this experimental setting, and consistent with the TOPflash assay, cell line-dependent transcription of Wnt3a target genes was not detectably influenced by treatment with TNKS/2 inhibitors (Additional file 8: Figure  S3B ; raw data in Additional file 9). Finally, Transwell assays demonstrated that cell invasion was not evidently fostered by Wnt3a (Additional file 8: Figure  S3C ; raw data in Additional file 9), further supporting the irrelevance of Wnt signaling to TNKS/2-related migratory phenotypes in our cellular models.
APC-decorated protrusions were counted by dividing the number of cortical APC events by the total number of lamellipodia. To detect polymerized tubulin (i. e. , intact microtubules), we used custom-made image analysis algorithms written in MATLAB. Images were filtered with a linear rotating kernel filter  [48]  and then processed with a multiscale vessel enhancement filter  [49]. To quantify the differences in tubulin staining distribution and morphology we measured the total area occupied by microtubules (a measure of the amount of polymerized tubulin) relative to the total cell area.
Time-lapse videomicroscopy Confluent A549 cells were seeded on glass bottom dishes (Willcowells Amsterdam, The Netherlands), scratched with a pipette tip, and treated with HGF with or without TNKS/2 inhibitors. Phase-contrast images were taken every 12 s for 60 min with a 20× objective using a Leica AF6000LX workstation equipped with a thermostatic and CO 2 -controlled chamber. Movies were generated by the LAS AF Leica Application Suite software (Leica) and compressed to 20 frames per second.
Acknowledgments We thank our friends of the  Laboratory of Molecular Pharmacology  for insightful comments;  Noemi Cavalera ,  Marika Pinnelli ,  Nadia Ducano ,  Jessica Erriquez ,  Paolo Luraghi , and  Guido Serini  for help with experiments and discussion; and  Antonella Cignetto ,  Daniela Gramaglia , and  Francesca Natale  for secretarial assistance. FS was the recipient of a ' Fondazione Umberto Veronesi  Fellowship'. Work in the authors' laboratory was supported by  Janssen Pharmaceutica NV  (to LT and PMC);  AIRC ,  Associazione Italiana per la Ricerca sul Cancro , Investigator Grants  10116  and  14205  (to LT),  15571  (to AB), and  11852  (to PMC);  Fondazione Piemontese per la Ricerca sul Cancro-ONLUS , 5x1000  Ministero della Salute 2011  (to LT).
Acknowledgements Rock material for this study was collected during palaeomagnetic expeditions of the  Institute of Geophysics ,  Polish Academy of Sciences , to Hornsund in 1999 (KM), 2002 (KM and RS) 2004 (KM and  J. Bednarek ). This work is partially financed by funds from the  Leading National Research Centre (Krajowy Naukowy Os ´rodek Wioda ˛cy)  to the  Centre for Polar Studies, Poland , for the period 2014Á18, and also partially supported within statutory activities no. 3841/E-41/S/ 2016  of the  Ministry of Science and Higher Education of Poland. We are grateful to reviewers  K. Piepjohn  and  M. Maffione  for constructive comments that helped improve the paper.
LBNO demonstrator A crucial step towards a future realization of a large, tens-of-kton-scale liquid argon detector is the construction and operation a LBNO detector demonstrator (LBNO-Demo) proposed by the WA105 collaboration  [9]. This double-phase liquid argon TPC will have an active volume dimensions of 6x6x6 m 3 (6 m drift and 6x6 m 2 charge readout plane). The detector will be constructed at CERN where it will be exposed to a charged Exposure (/1e20 POT)  particle beam of energies ranging from 0. 5 to 20 GeV. The technical goal of the LBNO-Demo is to demonstrate the feasibility of a large scale DLAr TPC detector design developed by the LAGUNA-LBNO study. In addition, the controlled data set from a charged particle beams would allow to develop and validate the automatic event reconstruction, characterize particle identification and detector performance, and study systematically the calorimetric response of liquid argon to electromagnetic and hadronic showers.
Acknowledgments We are grateful to the  European Commission  for the financial support of the project through the  FP7 Design Studies LAGUNA  (Project Number  212343 ) and LAGUNA-LBNO (Project Number  284518 ). We would also like to acknowledge the financial supports of the  Lyon Institute of Origins LabEx program  ( ANR-10-LABEX-66 ) and the  UnivEarthS LabEx program at Sorbonne Paris Cité  ( ANR-10-LABX-0023  and  ANR-11-IDEX-0005-02 ).
Study population Consecutive medical critically ill patients treated between June 1 2011 and June 29 2012 was included. Patients that stayed in the ICU less than 24 h and hospital readmissions were excluded from the analysis. Critical care survivors were interviewed at one year follow up after hospital release. Patient characteristics, acute illness severity scores  [13] [14] [15]  , diagnostic and therapeutic interventions along with costs were prospectively recorded. Data were recorded in a predefined paper form and afterwards entered in a specially constructed database.
Economic evaluation Recommendations of the Panel of Cost-effectiveness in Health and Medicine adjusted for critical care were followed for economic evaluation  [18]. With primary data obtained, a full economic evaluation in the form of cost utility analysis of intensive care of critically ill patients compared to hospital ward treatment from the perspective of the health care system was performed. The analytical horizon was the life time of patients. Decision analytic modeling was used to construct a decision tree (Figure  1 ), which served to calculate the ICER.
Systematic review A systematic review of the available literature was performed in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines  [19]. A comprehensive search strategy was developed in order to identify the studies that dealt with outcomes of the treatment of adult critically ill patients in and out of an ICU. The following electronic databases were searched: PubMed, EBSCO and Web of science. The Cubro H et al. Economic evaluation of critical care in a low resource setting the exclusion criteria (n = 369) were excluded. A total of 118 articles were screened based on the title and abstract and among them full texts of 18 studies were identified and reviewed. Secondarily, the studies that showed highly increased mortality among ICU treated critically ill were excluded from the selection, since it was very unlikely that such outcome would be possible in the case of the Clinical Center University of Sarajevo.
A total of 14 studies were included [12,20-32] (Figure  3  and Table  1 ). The following data were recorded: The year of publication, country of origin, patient population, reasons for ICU refusal, and mortality of critically ill patients treated within the ICU and outside of an ICU. Data were extracted in a previously defined form. Data from the above mentioned studies were used for the calculation of an Odds Ratio for mortality of critically ill patients when treated on the general hospital wards instead of the ICU. Similar calculations were previously done by Ridley et al  [8]  , further details in the Electronic data supplement. For discounting of costs and health effects an annual rate of 3% was used, with sensitivity analysis for 0% and 6%. Uncertainty was controlled by the use of sensitivity analysis of the key model parameters: The attributable risk reduction of death for ICU treatment, the daily ICU cost, the daily standard care general ward cost, the discount rate, and the quality of life after critical illness, the quality of life index calculation method, the adjustment for excess mortality after critical illness (Post-ICU survival rate, PICUS).
The Health Insurance institute of Federation of Bosnia and Herzegovina is the main financier of the Clinical center University of Sarajevo. Patient variable costs were prospectively recorded search targeted original articles, published in English on adult patient population (≥ 19 years) in the date range from the year 1990 to May 31, 2012. The following terms were used: Critical OR acute AND ill AND rationing OR selection OR withholding treatment OR refusal to treat OR triage OR utilization AND mortality OR outcome OR statistics OR numerical data OR economics. In addition we searched through related articles on PubMed and also found additional articles from the reference lists of retrieved articles. Comparative, validation, evaluation, observational studies, clinical trials and controlled trials were included in the study screening process.
Review articles, letters to the editor, commentaries, expert opinions and studies for which the full text was unavailable were excluded. The search process is presented on Figure  2  (PRISMA flow diagram). The first search returned 501 studies or 487 after the duplicates were removed. Studies that did not fulfill the inclusion criteria or the ones that met. The total ICU-stay cost of all 148 patients was used to calculate the daily unit cost for the ICU. The daily post ICU ward cost was calculated as the average daily cost for standard care on internal medical clinics in the Feredation of Bosnia and Herzegovina. This data was also published by the Federal Health Insurance Institute  [9]. The comparator cohort cost was calculated as the product of the average ward daily cost and the hospital length of stay of the real patient cohort.