dataesr/scientific-paragraphs-categorization huggingface dataset:
The analysis was performed online using a stereology workstation, i. e. , a modified Olympus BX50 fluorescence microscope controlled by the Stereo Investigator software (MBF Bioscience, Williston, VT, USA). Delineations of the regions of interest (Laminae I to III of the dorsal horn, Medial gray region with the DCM, Lateral gray subdivision including the SPN and the ventral horn) were made on the microscopic images displayed on a monitor using 4× objective and adjusted at a higher magnification (10×). Within the delineated areas, the total number of cFos positive cells was evaluated using a 40× objective and the optical fractionator of the Stereo Investigator software by manually counting cFos positive cells. Because of the limited number of sections per region of interest, we did not extrapolate the data into a 2D volume but chose to present results per slice.
Statistics Data were presented as mean ± SEM values. For the evaluation of stimulation parameters, a one-way ANOVA was employed, followed by a Fisher PLSD post hoc test. Histological and behavioral data were analyzed statistically with inter-group comparisons using t-test. All statistical analyses were performed with Sigma Plot 11. 0 version for Windows (Systat Software). P-values lower than 0. 05 were considered statistically significant.
To confirm that the neuronal activation observed in the PAG related to bladder-related functioning and was not triggered by confounding behaviors potentially generated by electrode implantation or electrical stimulation of the urinary bladder, the locomotor behavior was evaluated in the OF. Analysis of the behavioral data in the OF showed that the total distance traveled was not significantly different in stimulated animals compared to sham animals (Sham vs. Stim, 5063. 8 cm ± 135. 9 vs. 5271. 3 cm ± 559. 2, p = 0. 753). Therefore, neither the implantation of the bipolar electrode nor the electrical stimulation of the bladder alters spontaneous locomotor activity. Moreover, animals were tested in the EZM to determine whether anxiety level was affected by the experimental procedure. No significant effect was found between the control and sham conditions (t-test, NS, p > 0.
Background: The study objective was to assess the influence of neutropenia on outcome of critically ill cancer patients by meta-analysis of individual data. Secondary objectives were to assess the influence of neutropenia on outcome of critically ill patients in prespecified subgroups (according to underlying tumor, period of admission, need for mechanical ventilation and use of granulocyte colony stimulating factor (G-CSF)). Methods: Data sources were PubMed and the Cochrane database. Study selection included articles focusing on critically ill cancer patients published in English and studies in humans from May 2005 to May 2015. For study selection, the study eligibility was assessed by two investigators. Individual data from selected studies were obtained from corresponding authors. Results: Overall, 114 studies were identified and authors of 30 studies (26.
Search strategy and eligibility assessment First, public-domain databases including PubMed and the Cochrane database were searched using Exploded Medical Subject Headings and the appropriate corresponding keywords: "NEOPLASM" OR "MALIG-NANCY" OR "CANCER" AND "INTENSIVE CARE UNIT" OR "ICU". The research was restricted to publications in English and studies concerning humans from May 2005 to May 2015. Studies published before 2005 not included to limit heterogeneity across studied populations that may have arisen with regard to prognostic change of both critically ill and critically ill cancer patients  [20, 21]. Abstracts were carefully checked and studies focusing on children or patients aged younger than 18 years old, case reports and studies failing to focus on critically ill patients were excluded.
Data and quality assessment Investigators of selected publications were contacted twice and invited to participate in this study. Authors who agreed to participate were asked to send a file containing individual data including: age, gender, year of admission, underlying malignancy (solid tumor vs hematological malignancy), history of allogeneic stem cell transplantation, severity score, need for organ support (invasive mechanical ventilation, vasopressor use, renal replacement therapy), neutropenia during ICU stay, neutropenia duration, use of granulocyte colony stimulating factor (G-CSF), follow-up and outcome. To enable study comparison, we transformed illness severity scores (Acute Physiology and Chronic Health Evaluation (APACHE) II and APACHE III) into the equivalent Simplified Acute Physiology Score (SAPS) II, using a previously described methodology  [22].
Strata analyses were performed using the statistical plan already described for the following strata: patients with ICU admission after 2007 (median ICU admission period in the studied population), patients with hematological malignancy, patients requiring mechanical ventilation and patients receiving G-CSF. All effect sizes with P < 0. 05 were considered significant. All analyses were carried out with software R, version 3. 2. 5. The lme4 and Meta package were used to take into account the random effects and to produce forest plots.
Results Our initial search yielded 1528 citations, of which 38 were excluded due to duplication and 706 were excluded as irrelevant for the scope of this review. All abstracts of the remaining 784 records were carefully checked and 131 full-text articles focusing on critically ill cancer patients' prognosis were scrutinized for further evaluation. Seventeen studies were excluded, including 10 studies with redundancies, five studies lacking neutropenic, non-neutropenic patients or major data required for the analysis, and two studies including only palliative patients. Among the remaining 114 studies, authors of 30 studies (26. 3% of selected studies) agreed to participate in this study, leading to a dataset of 7515 patients, including 1702 neutropenic patients (22. 6%) (Fig. 1 )  [8,.
Discussion This large dataset resulting from systematic review of individual data confirms neutropenia to be independently associated with mortality in critically ill cancer patients. According to our results, the prognostic impact of neutropenia was unchanged when stratifying for malignancy, period of ICU admission or use of mechanical ventilation. However, in patients treated with G-CSF, neutropenia was no longer associated with mortality, suggesting that the use of G-CSF may influence the prognostic impact of neutropenia in this setting. Neutropenia remains an accepted side effect of most treatments administered to hematological patients  [52]. Neutropenia is associated with complications that include severe sepsis  [53] , acute respiratory failure  [54]  and specific conditions such as neutropenic enterocolitis  [55].
Last, only a quarter of identified studies were ultimately included. However, it must be noted that the large dataset, and the analysis adjusting for study influence and modeling of unrecorded data by using a mixed effect model, should have, at least partly, taken these effects into account. In line with this, several arbitrary choices were made during analysis as regards the study inclusion period or cutoff point to define the ICU admission period. The lack of other reliable cutoff points, however, is to be noted when taking into account these limits. Last, the influence of G-CSF was assessed only indirectly, in a subset of patients in whom the use of G-CSF was recorded, without information regarding the rational for its prescription. Thus, our negative results might reflect either a lack of statistical power or an inclusion bias.
Introduction The rapid globalisation of the pharmaceutical production and distribution has not been supported by harmonisation of regulatory systems worldwide. Thus, the supply systems in low-income and middle-income countries (LMICs) remain exposed to the risk of poor-quality medicines. To contribute to estimating this risk in the private sector in LMICs, we assessed the quality assurance system of a convenient sample of local private pharmaceutical distributors. Methods This descriptive study uses secondary data derived from the audits conducted by the QUAMED group at 60 local private pharmaceutical distributors in 13 LMICs. We assessed the distributors' compliance with good distribution practices (GDP), general quality requirements (GQR) and cold chain management (CCM), based on an evaluation tool inspired by the WHO guidelines 'Model Quality Assurance System (MQAS) for procurement agencies'.
Therefore, we also compared the WHO MQAS compliance of local private distributors in SSA versus those in non-SSA, and of those in LICs versus those in middle-income countries (MICs). data collection and sampling Secondary data were derived from the database of QUAMED (Quality Medicines for all; https:// quamed. org/ en/ home. aspx), a partnership that brings together medical non-governmental organisations active in LMICs and some African procurement agencies, and pleads for universal access to quality-assured medicines. 28 34 35 To help partner organisations purchasing quality-assured medicines, QUAMED evaluates the performance of various pharmaceutical suppliers,  28  including local private distributors in the countries where partner organisations run their medical programmes. Suppliers are audited by QUAMED if they (i) are current or potential suppliers of QUAMED partners and (ii) if they voluntarily undergo the audit, under a confidentiality agreement.
BMJ Global Health a simplified evaluation inspired by the MQAS principles and focusing on GDP and quality systems. The 'short audit report' consists of a brief narrative and of a standardised questionnaire, which is a light version of the 'Harmonised MQAS Compliance Self-Assessment Tool'. 36 ince audits are conducted under a confidentiality agreement, the reports are available to the QUAMED partners through a password-protected database. The 'local private distributors' included in the present analysis were extracted from the QUAMED database according to the following criteria (see figure  1 ): 1. Period: conducted between 1 January 2013 and 31 December 2016. 2. Geographical location: all countries where QUAMED has been doing audits, except the 2016 World Bank (WB) region 'Europe and Central-Asia'.
Translation of the data For accurately comparing the audits' findings across distributors, we translated the narrative information contained in the 'short audit reports' (ie, a narrative text, and the open-ended answers of the standardised questionnaire), into quantitative data. In the above-mentioned based on the key activities of the WHO MQAS and used it to measure the MQAS compliance of pharmaceutical procurement agencies undergoing the QUAMED 'full audits'. 28 We adapted this tool, to capture the information collected during the QUAMED 'short audits' of local private pharmaceutical distributors, and we developed an ad hoc 'rating system for local private pharmaceutical distributors' (online supplementary materials). This is based on 12 activities, derived from the WHO MQAS and grouped into five quality assurance (QA) criteria: general quality requirements (GQR), GDP, cold chain management (CCM), selection of sources (SS) and quality control (QC).
Details and examples of such activities and criteria are given in table 1. To analyse our data, we first translated the narrative information contained in the 'short audit reports' into a numerical value (score) per activity. Second, we rated each distributor for each of the 12 activities by attributing a score between 0 and 4, where each number represents a specific level of compliance. Third, we calculated the compliance of each distributor with each of the five QA criteria by calculating the average of the scores of the activities included in each criterion (raw data available as online supplementary materials). For both 'activities' and 'criteria', the score that expresses the level of compliance with the principles of MQAS ranges from non-compliance (0. 00) to consistently compliant (4.
Analysis The quantitative data reported in the 'rating system for local private pharmaceutical distributors' were analysed using Microsoft Excel 2007. The units of analysis were the local private pharmaceutical distributors. The variables were the three QA criteria (GQR, GDP and CCM), treated as ordinal variables. General descriptive statistics Internal consistency of the criteria GQR and GDP was evaluated with Cronbach's α to measure whether the different activities that are meant to measure the same criterion produce similar scores. Primary data were collected by QUAMED under a confidentiality agreement with the audited suppliers; therefore, in order to ensure confidentiality, the identity of the individual distributors will not be disclosed.
resulTs Overall, 154 records of audited distributors were identified through the QUAMED database search. The flow diagram of the distributors' selection process is shown in figure  1. The records of 60 distributors in 13 countries (ie, Burkina Faso, Cameroon, the Democratic Republic of Congo, Ethiopia, Haiti, Ivory Coast, Jordan, Kenya, Lebanon, Myanmar, Niger, the Philippines and Senegal) were eligible for inclusion in the analysis. Out of these 60 distributors, 30 are located in SSA (eight countries) and 30 outside SSA (five countries); 29 are located in LICs (six countries) and 31 in MICs (seven countries).
Compliance with QA criteria in lMICs The compliance with the QA criteria varies strongly across distributors (table  2 ). The internal consistency is high for GDP (α=0. 87) and GQR (α=0. 88). For these criteria, compliance is generally low, with central tendency scores with a median of 1. 13 for GQR (IQR 0. 50-2. 00) and 1. 50 for GDP (IQR 0. 80-2. 25). Out of 60 distributors included in the analysis, only 44 supplied heat-sensitive products and were evaluated for CCM. In general, compliance was better for CCM than for other QA criteria, with a central tendency of 2. 00 (IQR 1. 00-3. 00). However, CCM also showed the highest variability, with scores ranging from non-compliant (0) to fully compliant (4). Coded data by distributor are available in the online supplemental material. The percentage of distributors found to be non-compliant, very low-compliant or low-compliant with the QA criteria is of 70% for GQR, 60% for GDP and 41% for CCM (figure  2 ).
Nevertheless, our results are based on an evaluation of 60 different distributors in 13 countries, so the observed poor compliance with QA criteria remains alarming. Some authors have suggested that the private sector could successfully substitute the public sector in the provision of healthcare in LMICs,  30 43  while others suggested on different grounds that the private sector is not a valuable alternative for healthcare and services, and that privatisation may have negative implications for affordability, socioeconomic determinants of use and quality. 30 In the previous study on the QUAMED database, Nebot Giralt et al  28  found that compliance with the MQAS of African pharmaceutical distributors in the public and not-for-profit sector was low. But our study now shows at least comparable weaknesses in the private sector.
Acknowledgements The authors thank all the distributors that accepted to be audited by QUAMED. They thank all QUAMED partners for contributing to the development of the network and making this project possible. They also thank  Christophe Luyckx  and  Vera Melotte , the former coordinators of the  QUAMED  project. Finally, they thank  Dr Matthias Borchert , who coached KVA during the Master in International Health.
Methods Participants. A total of 176 Arizona State University students (88 women and 88 men) were randomly selected from a database managed by the Elinor Ostrom Multi-Method Lab at Arizona State University and recruited by email. The recruitment email indicated that the experimental task will require participants to produce combinations of variously coloured-items and so requested colour-blind recipients to not sign up. The subjects ranged in age from 18 to 26 years (mean 20 years, s. d. 1. 40 years). Participants received $5 for participating and an additional amount ranging from $5 to $20 depending on their own performance. Ethical statement. The study was carried out in accordance with the ethical standards of the Declaration of Helsinki and the Belmont Report. Ethical approval was given by Arizona State University IRB (code: STUDY00003697).
Players were informed that they "might occasionally be provided with successful combinations from other players" but were not aware about the existence of the learning or test phases. Players were told that a stable rule determined whether combinations were successful or not. Finally, participants were informed that their monetary a reward were going to depend on the number of safe boxes they opened. Explanation coding. Rules reported by participants were extracted from the database without any information about which treatment individuals participated in. Rules were independently coded by the two authors as a binary variable. Two reported rules were considered by the two coders as ambiguous so that data associated with the associated individuals were discarded from the analysis investigating the probability of getting the rule (see Supplementary Material for details).
One individual took part in the Social Learning treatment/Low-Difficulty condition, the other one took part in the Social Learning treatment/High-Difficulty condition. Data points associated with these two individuals were included in the analyses investing individuals' success at the trials 30 and 40. Importantly, the analyses of individuals' success at trial 40 are consistent with the analyses of individuals' success at reporting the rule indicating that the removing of two data points in the latter analyses (one per analysis per condition) did not affect our results. Statistical analyses. Models were fitted within a Bayesian framework  34  with weakly informative priors. Data and full details about our models are available as supplementary materials. To estimate the probability of success at trial 30 (i.
Data were then reorganized to investigate the effect of each new successful combinations on learners' probability of inferring the rule. Individual learners who inferred the rule after n successful combinations were assigned n lines in the dataset. The variable "Rule" was set to 0 for successful combinations comprised between 1 to n -1 and was set to 1 for successful combination n. Individual learners who scored n times but did not infer the rule were assigned n lines in the dataset, each line being paired with 0 for the variable "Rule" (see Supplementary Table  1  for details). Individual learners who never scored were removed from the analysis (6 out of 40). For each group of social learners, the data set was cut right after the trial during which the first guesser inferred the rule (see Supplementary Table  2 ).
By doing so, we limited our analysis to periods where social learners were provided with social information in an irregular fashion and were still actively searching for solutions. The methodology explained above was then applied to social learners. First guessers who inferred the rule after n successful combinations were assigned n lines in the dataset. The variable "Rule" was set to 0 for successful combinations comprised between 1 to n-1 and was set to 1 for successful combination n. Non-first guessers who scored n times before the first guesser got the rule were assigned n lines in the dataset, each line being paired with 0 for the variable "Rule" (see supplementary table 2 for details). We fitted a mixed effects logistic regression with "Rule" as the outcome variable, "Success Number", "Social Learning" and their interaction as predictor variables and "Player's identity" as random effect (see Supplementary Tables  1  & 2  and Statistical model 2 for details).
To estimate the relationship between non-first guessers' score during the learning phase and their probability of inferring the rule, we fitted a logistic regression with "Rule" as the outcome variable and "Score during the learning phase" as the predictor variable (see Supplementary Table  2. C and Statistical model 3 for details). All analyses were conducted in R 3. 2. 1  35  and models were fitted using map2stan, in the rethinking package  34.
Acknowledgements We thank people from the  Elinor Ostrom Multi-Method Lab at Arizona State University , members of the  Evolutionary Foundations of Human Uniqueness  project, members of the  Adaptation, Behavior, Culture and Society group  and members of the  Institute for Advanced Studies in Toulouse  for helpful discussions. We also thank two anonymous reviewers for their constructive comments that helped to improve this manuscript. This research was made possible through the support of a grant (ID:  48952 ) from the  John Templeton Foundation  to the  Institute of Human Origins at Arizona State University. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the  John Templeton Foundation.
g. Ono et al. 2014)  or in the continuum (e. g. Fujimoto et al. 2016 ). We will show that ∼20% of the sources detected in the survey described in this paper are HST-dark, and strong evidence suggests that they are not spurious detections. The aim of the work presented in this paper is to exploit a 69 arcmin 2 ALMA image reaching a sensitivity of 0. 18 mJy at a resolution of 0 60. We use the leverage of the excellent multiwavelength supporting data in the GOODS-South field: the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey  (Koekemoer et al. 2011; Grogin et al. 2011) , the Spitzer Extended Deep Survey  (Ashby et al. 2013) , the GOODS-Herschel Survey  (Elbaz et al. 2011) , the Chandra Deep Field-South  (Luo et al. 2017 ) and ultra-deep radio imaging with the VLA  (Rujopakarn et al.
2016) , to construct a robust catalogue and derive physical properties of ALMA-detected galaxies. The region covered by ALMA in this survey corresponds to the region with the deepest HST-WFC3 coverage, and has also been chosen for a guaranteed time observation (GTO) program with the James Webb Space Telescope (JWST). This paper is organized as follows: in §2 we describe our ALMA survey, the data reduction, and the multi-wavelength ancillary data which support our studies. In §3, we present the methodology and criteria used to detect sources, we also present the procedures used to compute the completeness and the fidelity of our flux measurements. In §4 we detail the different steps we conducted to construct a catalogue of our detections. In §5 we estimate the differential and cumulative number counts from our detections.
Data reduction All EBs were calibrated with  CASA (McMullin et al. 2007 ) using the scripts provided by the ALMA project. Calibrated visibilities were systematically inspected and few additional flaggings were added to the original calibration scripts. Flux calibrations were validated by verifying the accuracy of our phase and bandpass calibrator flux density estimations. Finally, to reduce computational time for the forthcoming continuum imaging, we time-and frequency-averaged our calibrated EBs over 120 seconds and 8 channels, respectively. Imaging was done in CASA using the multi-frequency synthesis algorithm implemented within the task CLEAN. Submosaics were produced separately and combined subsequently using a weighted mean based on their noise maps. As each sub-mosaic was observed at different epochs and under different weather conditions, they exhibit different synthesized beams and sensitivities (Table  1 ).