dataesr/scientific-paragraphs-categorization huggingface dataset:
One way to study the effects of historic and non-targeted effects of radiation on ecological field and species within is through mathematical modelling. Once the model is developed, it can be used to test various hypotheses using the available experimental and field data to understand the discrepancy between field and available databases. Furthermore, modelling can help in the dynamic prediction of radiation effects and to adopt or develop appropriate measures to address it, as discussed above. Recently, mathematical modelling techniques  (Powathil et al. , 2016)  have been used to study the role of bystander effects at low dose radiations and the inferences made by the models are subsequently confirmed by experimental studies  (Fernandez-Palomo et al. , 2016). 2. 5. Biomarkers from radiation biology can usefully be applied to individual organisms but population biomarkers of exposure and fitness are needed This insight came from the realisation by the radiobiologists that in radioecology biomarkers of population fitness are needed.
The period between diagnosis of HIV infection and ART initiation, which includes periods from testing to linkage to care and then from inclusion in care to ART initiation, is known as the pre-ART cascade. Throughout the pre-ART period, children are at risk for attrition due to death and loss to follow-up (LTFU). A better understanding of the attrition of children across the pre-ART cascade is crucial to reach the 90-90-90 targets, particularly for the second target, to initiate ART in 90% of HIV-infected children identified, assuming that 90% have been identified among those HIV infected. In 2006, the US National Institutes of Health launched the International Epidemiology Databases to Evaluate AIDS (IeDEA) to describe trends in HIV epidemiology in the context of ART access across regions of the world (https://www.
iedea. org/). Clinics from 7 international regional data centers contribute both retrospective cohort data, prior to 2006, and prospective data since, on care and treatment of HIV to evaluate the outcomes of people living with HIV/ AIDS. To better understand the continuum of care from HIV diagnosis to ART initiation in HIV-infected children and in collaboration with WHO, we performed a multiregional analysis of the pre-ART retention cascade of HIV-infected children from HIV diagnosis to ART initiation within IeDEA from 2004 to 2015.
Study design and population This multiregional analysis was prespecified, except for the sensitivity analysis, in an approved concept plan available in the Supporting Information (S1 Concept Plan). We pooled individual patient data from 6 pediatric cohorts of IeDEA and included clinical care sites from Asia-Pacific, West Africa, East Africa, Central Africa, Southern Africa, and Latin America. We included all HIV-infected children aged 0-19 years at enrollment into any IeDEA-affiliated pediatric care program who were ART-naïve at enrollment (except for exposure to perinatal prevention of mother-to-child transmission prophylaxis) between 1 January 2004 (corresponding to the beginning of the era of ART access in low-income countries) and 31 December 2015. Although each clinic within IeDEA has its own protocol for routine follow-up, HIVinfected children who have not yet been initiated on ART are typically seen at least every 6 months.
The data abstracted for this analysis were from routine care and included region, country, site, patient demographics (sex, date of birth, date of HIV diagnosis if available, and date of enrollment in care), clinical WHO/CDC staging at enrollment and ART initiation, laboratory values and dates (CD4 cell count, CD4 percent), cotrimoxazole start date, date of ART initiation, ART regimen, and date of death, LTFU, or transfer out.
Outcomes and key variable definitions The outcomes of interest were (1) time to ART initiation and (2) missed opportunities for ART initiation, defined as either death or LTFU (last clinical visit >6 months before database closure and whereabouts unknown). Baseline was defined as the date of diagnosis, whether this occurred before or after enrollment in the HIV care program; when this date was unavailable, which was frequent in these contexts, we defined baseline as the date of enrollment in the HIV care program. The follow-up period was defined as the time between baseline and the date of ART initiation, death, LTFU, transfer out, or database closure  (31 December 2015) , whichever came first.
Statistical analysis We assessed the proportion of children initiating ART and the proportion with missed opportunities for ART initiation (death or LTFU) within the first 24 months after enrollment. This conservative time-point was prespecified to provide a reference analysis in scaling up HIV diagnosis and ART initiation before the treat-all era from 2015 onwards. Baseline categorical data are presented as frequency (percent), and continuous data are presented as median (interquartile range [IQR]). Continuous variables were compared using the Kruskal-Wallis test, and categorical variables using the chi-squared or Fisher's test. Time to ART initiation was estimated using cumulative incidence functions (CIFs): mortality and LTFU were considered competing events to ART initiation  [9] , while those transferred out were right-censored, with the assumption that they remained in HIV care and had similar outcomes as patients still in observation.
To better understand the different patterns of ART initiation, taking into account the evolving WHO eligibility criteria for ART initiation between 2004 and 2015  [4, 5, 10, 11]  Correlates of ART initiation were described in a multivariate competing risks analysis using the Fine and Gray proportional sub-distribution hazards regression model, where time of origin was baseline  [12]. Explanatory variables included sex, age at baseline, region, country income as defined by the World Bank (http://data. worldbank. org/about/country-and-lendinggroups), period of enrollment based on the changing WHO treatment guidelines (before April 2008, April 2008-June 2010, July 2010-May 2013, and after May 2013), and clinical/immunological criteria for ART eligibility at baseline and during follow-up. Model fit was checked graphically, plotting the Schoenfeld-type residuals against time for each of the covariates included in the model; we used the %pshreg SAS macro for this  [13, 14].
There was a high degree of variability of data collection practices across the HIV programs included in IeDEA, and some centers followed up only children who were eligible at baseline to be initiated on ART. It is likely that those programs only recorded data if children initiated ART or were intended to initiate ART before being followed up in a decentralized center. Consequently, incidence of ART initiation among all children in HIV care may be overestimated by these programs. For this reason, we performed an un-prespecified sensitivity analysis excluding the clinics where the ART coverage was >95% of all the patients followed up. Analyses were conducted using the package cmprsk in R statistical software version 2. 11. 1 (R Foundation for Statistical Computing, Vienna, Austria). The adjusted sub-distribution hazard ratios (asHRs) were reported with their 95% confidence intervals (CIs).
9% of the cohort in the Asia-Pacific region but only 28. 1% in Southern Africa. Of all children in the database, 66. 1% did not have a date of confirmed HIV diagnosis in the database: in Latin America and Asia-Pacific, a smaller proportion of children lacked HIV diagnosis date (0. 0% and 2. 8%, respectively), but in Southern Africa this proportion was 78. 4%. Among those with a date of confirmed HIV diagnosis, 39. 3% of children were diagnosed prior to enrollment in IeDEA, ranging from 14. 8% in East Africa to 60. 3% in West Africa (p < 0. 001; Table  1 ); the overall median time to enrollment in care since HIV diagnosis among these children was 1 month (IQR: 0-7).
Third, 24-month incidence of ART initiation and missed opportunities for care were derived from data collected over a 10-year period, during which both national and international guidelines varied over time. Although we adjusted for evolving ART eligibility criteria during follow-up in our final model to address this, further analyses would be necessary to better describe the progress made and treatment gaps on a national level. Fourth, the outcomes of children lost to follow-up were not well known, and the high proportion of children lost to follow-up (20. 4%) includes undocumented mortality, those out of care, and silent transfers. Both death and being out of care represent poor outcomes from missed opportunities, but silent transfers may or may not represent ultimate ART initiation. In the absence of outreach data, it is unclear how these results should be interpreted.
To overcome these limitations, we combined mortality and LTFU as a single outcome (of programmatic failure) in estimating the cumulative rate of missed opportunities for ART initiation after enrollment in an HIV care program. Fifth, HIV programs across and within regions vary. Some programs were specific to children once they started therapy, making it likely that those programs only recorded data if children initiated ART or were intended to initiate ART but not during the pre-ART period. As a result, our analysis may further overestimate the overall proportion of HIV-infected children starting ART as the denominator of all children may have shrunk in these instances. In sensitivity analyses where we excluded such programs from consideration, we observed no significant differences in our results, except in overall and regional ART initiation probabilities, which, as expected, were lower when restricted to sites with ART coverage 95%.
Acknowledgments We acknowledge all of the children and their families followed up in the participating pediatric centers. We also thank the staff from all participating pediatric centers. We warmly thank all the investigators and pediatric coordinators from the IeDEA regions contributing to the project:  Asia-Pacific  ( Annette Sohn  and  Mathew Law ),  CCASAnet-Latin America  ( Jorge Pinto  and  Catherine McGowan ),  Central Africa  ( Marcel Yotebieng  and  Andrew Edmonds ),  East Africa  ( Kara Wools-Kaloustian ),  Southern Africa  ( Mary-Ann Davies ),  West Africa  ( Francois Dabis ), the  IeDEA Pediatric Working Group  ( Rachel Vreeman , Chair), and the  World Health Organization  ( Meg Doherty  and  Martina Penazzato ). For the  IeDEA Pediatric Working Group ,  Annette Sohn  reports funding from  ViiV Healthcare , and  Matthias Egger  is a member of the Editorial Board of PLOS Medicine.
Research reported in this publication was supported by the  US National Institutes of Health (NIAID, NICHD ,  NCI  and  NIMH ) depending of the IeDEA region. Asia-Pacific: The  TREAT Asia Pediatric HIV Observational Database is an initiative of TREAT Asia , a program of amfAR, The Foundation for AIDS Research, with support from the  US National Institutes of Health's National Institute of Allergy and Infectious Diseases , the  Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Cancer Institute, National Institute of Mental Health , and  National Institute on Drug Abuse  as part of the International Epidemiology Databases to Evaluate AIDS (IeDEA;  U01AI069907 ). Caribbean, Central and  South America network for HIV epidemiology (CCASAnet)  is a member cohort of the International Epidemiology Databases to Evaluate AIDS (leDEA) (U01AI069923; CMG is a PI).
Survey methods Telephone. A large telephone survey (N = 1,838) was conducted in June of 2009 by the polling company SurveyUSA using random digit-dialing to contact land-line telephone users in the United States. Volunteer participants answered a series of questions read by a prerecorded female voice program by using their telephone's keypad. Approximately 2. 3% of the 79,014 random digit calls yielded a complete response. This response rate is typical of automated calling surveys and is associated with acceptably low amounts of sampling bias  [22]. All data were collected anonymously. Online. A large sample of respondents (N = 983) was recruited via Amazon Mechanical Turk (MTurk) in July-August of 2011. The listing advertised that respondents would complete a "short survey of your beliefs about psychology.
Weighting to U. S. Census To directly compare both surveys, we weighted each to a nominal nationally representative sample of 750 nationally Americans using the 2010 U. S. Census demographics for sex (male, female), age (< 44, ! 44; based on median age), and race/ethnicity (white, nonwhite). Dichotomous weighting accounts for the over-or under-sampling for each combination of demographics in each polling method and is standard practice in polling and survey methodology. A greater proportion of women and older Americans completed the telephone survey and a greater proportion of younger Americans completed the online survey. Table  1  displays the raw sample sizes and demographic weightings applied to each sample. All data are publicly available (https://osf. io/zkh3e/?view_only=57b247e35eb4496399f40ca20cdf635f).
SCEmNet Architecture Our similarity learning architecture is based on convolutional networks and is illustrated in Figure  1. We rely on the assumption that each cookie c is associated with M sequences (s 1 (c),. , s M (c)) of fixed size. For a given token modality, the sequence of tokens is first processed through a SeqCNN module, similar to the TextCNN architecture introduced in Kim  [3] : given an input sequence of embedded tokens, a convolutional network with one convolution, max-pooling and dropout layer outputs a cookie embedding. We use this module in a siamese network with a pairwise fusion layer to transform the two cookie embeddings into a single pair embedding. erefore, each one of the M modalities yields a distinct pair embedding. e M pair embeddings are combined in the multimodal fusion layer.
It may therefore be unwise to assume that IWE principles have the same degree of impact on all Muslims  (Uygur, 2009) , and that there is homogeneity among ME countries simply because they subscribe to Islam as the dominant religion. Finally and most importantly, our study reveals that despite the apparent gap between Islamic principles inherent within the notions of IWE and IL and actual practices observed in certain ME countries, more than half of the positive and negative BSs from the UAE data set are informed by principles of IWE and IL. This shows that IWE and IL exercise significant influence on the personal Implicit Leadership Theories (ILTs) subconsciously held by followers, and consequently their perceptions of managerial behaviors in ME countries. Therefore, ignoring the principles of IWE and IL, as past scholars have tended to do, might be short-sighted, and might curtail our understanding of how to function effectively in ME countries.
The CIT data was collected by Author 2 after having gained permission from the General Manager of M&R. Initially all employees were invited by the HR Department to a meeting wherein they were informed about the objectives of the study, how the data would be collected, and how it would be subsequently used. The meaning of key terms such as 'incident', 'critical incident' and effective and ineffective managerial behavior were also explained. Effective behavior was defined as 'behavior one would wish all managers to adopt if and when faced with similar circumstances', and ineffective behavior was defined as 'behavior which, if it occurred repeatedly or even once in certain circumstances, might cause one to doubt the ability of that particular manager'. Potential participants were informed that the data collected would be anonymous, neither their names nor the name of the company would appear in the final report, and that the company would only be offered an executive summary of the study.
We chose the Egyptian study with which to compare our findings because, to our knowledge, this was the only managerial behavior study of its kind conducted in either a private or public sector organization in ME countries, and it was the 'template' study used to guide our UAE emic replication research. Hence for comparability purposes there is a high degree of 'functional equivalence' across the two BS data sets because both groups of researchers adopted the same research focus, and adhered as closely as possible to the established 'common CIT protocols' for collecting and analyzing the data. Furthermore, as called for by  Kim and Yukl (1995)  for replication studies, there was a degree of central control exercised by Author 3 in both studies in order to ensure consistency in the way the protocols were applied.
Put together, these studies offer two contrasting perceptions of perceived managerial and leadership effectiveness from two ME countries which share considerable similarity in terms of religious affiliations, but are very different from each other on economic and demographic characteristics. 3 https://www. cia. gov/library/publications/the-world-factbook/geos/eg. html) In order to compare the findings emerging from the UAE data with those from the study conducted previously in Egypt, initially Authors 2 and 3 worked independently in search of commonalities and relative empirical generalizations as reflected by evidence of 'sameness', 'similarities' and/or 'congruence' of meaning across the two BS data sets. The definitions of 'sameness', 'similarities' and 'congruence' were the same as elaborated in the previous subsection.
Stage 3 Component (Addressing RQ3) This component of our study involved Author 1 and Author 2 re-interrogating the BSs emerging from the UAE and Egyptian data sets through the conceptual lenses of IWE and IL. They applied open coding  (Flick, 2002)  in search of Islamic concepts in the emergent BSs, and they allowed the richness of the data to emerge through a process of on-going discovery  (Van Maanen, 1983). First, Author 1 independently examined the two sets of BSs at the semantic level to identify Islamic principles of IWE and IL embedded in the textual data. These findings were then shared with Author 2, who, being a follower of Islam and a resident of UAE, was treated as the local expert on the topic.
Minor discrepancies and inconsistencies that arose were resolved through critical examination and discussion to reach a consensus. To ensure the trustworthiness of the Stage 2 research findings in terms of their credibility and dependability a form of 'investigator triangulation' was applied for the comparative analysis (Easterby-Smith,  Thorpe & Lowe, 1991). This involved Authors 2 and 3 first analyzing independently the UAE and Egyptian BS data sets. They then compared and contrasted their analyses in order to arrive at a mutual confirmation of where there was convergence and divergence  (Knafl & Breitmayer, 1991). Overall there was general agreement regarding their respective interpretations. Minor discrepancies and inconsistencies were resolved through critical examination and discussion to reach a consensus.
The results of the Stage 2 comparative analysis were then sent to Author 1 who acted as a confirmatory auditor. To ensure the trustworthiness of the Stage 3 research, Author 1 first independently examined the BSs from the UAE and Egyptian data sets to identify their embeddedness within the tenets of IWE and IL. Subsequently, Author 2 reverted back to Author 1 with his assessment of the degree of embeddedness of the BSs within the tenets of IWE and IL. Since there was almost total agreement between them, the iterative process of discussion and subsequent refinement of the findings was fairly short at this stage.
Stage 2 Component (Addressing RQ2) In this sub-section we present the results of the Stage 2 comparison of the positive and negative BSs obtained from our UAE study and the previously conducted Egyptian study. Numeric details of the BS data sets resulting from these two studies are given in Table  3  below. ---Insert Table  3  about here---The findings of the comparative analysis demonstrate empirically that perceptions of what behaviorally distinguishes effective managers from ineffective managers within a UAE-based multinational company and an Egyptian public sector hospital are highly congruent. The vast majority of the behavioral indicators of perceived managerial and leadership effectiveness identified in both countries are substantially the same and are described in similar terms. We found that 76.
, 2006). Therefore, understandably, respondents from these countries would feel compelled to give responses that show conformity with principles of IWE. To avoid such instances of social desirability bias, we decided to collect data on perceived effectiveness/ineffectiveness of managerial behaviors through CIT interviews, and subsequently infer whether and to what extent these were informed by principles of IWE and IL. As a final methodological limitation, past studies have shown differences between different segments of the same population with regards to their adherence to IWE principles 6. Unlike these studies, our present paper does not investigate such differences, for instance between local and expatriate participants, or between male and female participants. Exploring such differences could have provided a deeper understanding of how individual factors (e.
Sustainability outcome The starting point for fsQCA is defining the outcome of interest, which informs subsequent case selection  (Kaminsky and Jordan, 2017). Rural water program operational status was defined as the extent to which water systems were functioning and water services were available to community members at the time of the study. This definition is adapted from  Carter et al. (1999) : community water supply is sustainable if it continues to work and be used over time  (Carter et al. , 1999). Some argue that composite measures better approximate the multi-dimensional aspects of water supply functionality over time  (Bhandari et al. , 2005; Kleemeier, 2000; Marks et al. , 2013)  or that functionality is a dynamic state between regular breakdowns and repairs  (Fisher et al. , 2015).
Case selection A central aim of fsQCA is to uncover the richest possible combinations of conditions leading to a particular outcome  (Ragin, 2008a, b). To accomplish this end, cases are purposefully identified to maximize variation in outcome and causal condition values  (Kaminsky and Jordan, 2017). Eligible cases for the present study were first gathered through a review of the academic and grey literature. The review consisted of four steps: 1) initial key word search of scientific and development organization databases, 2) removal of duplicate articles, 3) title and abstract scan for relevance, and 4) in-depth review of article details. Articles were limited to those written in English. No restrictions were placed on study design, location, or year of publication. See Supplementary Material for details on the databases searched and key word combinations used.
Fuzzy-set qualitative comparative analysis Analyses of necessary and sufficient conditions were conducted using the fs/QCA software (version 2. 5) developed by Charles Ragin and Sean Davey (University of Arizona). Necessary conditions are those that are nearly always present when the outcome occurs, but may be insufficient on their own to produce the outcome. Identification of necessary conditions depends on two "goodness of fit" measurements: consistency and coverage. Consistency measures how often a causal condition (or combination of conditions) is observed when the outcome occurs. Coverage measures how often a condition (or combination of conditions) is absent when the outcome does not occur. As recommended in the QCA literature, score thresholds for inclusion were 0. 90 and 0. 30 for necessity consistency and coverage, respectively  (Ragin, 2008a, b).