dataesr/scientific-paragraphs-categorization huggingface dataset:
2017c). In this study we used a larger dataset, and the results showed that the divergence time range of subphyla were 406-430 Mya, classes were 211-383 Mya and orders were 99-323 Mya, which generally agreed to the previous study  (Zhao et al. 2017c). Furthermore, in this study we investigated the divergence time of families within Basidiomycota. The results indicated that families of Agaricomycotina diverged during 27-178 Mya, Pucciniomycotina diverged during 85-222 Mya and those from Ustilaginomycotina 79-177 Mya. Compared with the divergence time of most families, the Cryptobasidiaceae should be raised to that of order due to its older divergence time compared to most current families. While the families Urocystidaceae, Floromycetaceae, Ustilentylomataceae, Microbotryaceae and Leucosporidiaceae could be combined with other families as they have a quite younger divergence time.
Acknowledgements  Rui-Lin Zhao  thanks  National Key R&D Program of China  (Project No. 2018YFD0400200 ), the  National Natural Science Foundation of China  (Project IDs:  31470152 ,  31360014  and  31970010 ) and  Beijing Innovative   Consortium of Agriculture Research System  (Project ID: BAIC05-2019 ). K. D. Hyde  thanks the  Thailand Research  funds for grant  RDG6130001  entitled '' Impact of climate change on fungal diversity and biogeography in the Greater Mekong Subregion '' and  Thailand Science Research and Innovation fund  for the grant  DBG6280009  entitled  Macrofungi diversity  research from the  Lancang-Mekong Watershed  and surrounding areas. Armin Mes  ˇicand  Zdenko Tkalc  ˇec are grateful to  Croatian Science Foundation  for support under the project  ForFungiDNA  ( IP-2018-01-1736 ).
Introduction Delayed prescribing can be a useful strategy to reduce antibiotic prescribing, but it is not clear for whom delayed prescribing might be effective. This protocol outlines an individual patient data (IPD) meta-analysis of randomised controlled trials (RCTs) and observational cohort studies to explore the overall effect of delayed prescribing and identify key patient characteristics that are associated with efficacy of delayed prescribing. Methods and analysis A systematic search of the databases Cochrane Central Register of Controlled Trials, Ovid MEDLINE, Ovid Embase, EBSCO CINAHL Plus and Web of Science was conducted to identify relevant studies from inception to October 2017. Outcomes of interest include duration of illness, severity of illness, complication, reconsultation and patient satisfaction.
Study authors of eligible papers will be contacted and invited to contribute raw IPD data. IPD data will be checked against published data, harmonised and aggregated to create one large IPD database. Multilevel regression will be performed to explore interaction effects between treatment allocation and patient characteristics. The economic evaluation will be conducted based on IPD from the combined trial and observational studies to estimate the differences in costs and effectiveness for delayed prescribing compared with normal practice. A decision model will be developed to assess potential savings and cost-effectiveness in terms of reduced antibiotic usage of delayed prescribing and quality-adjusted life years. Ethics and dissemination Ethical approval was obtained from the University of Southampton Faculty of Medicine Research Ethics Committee (Reference number: 30068).
In an IPD meta-analysis, original data for all participants in each study are obtained and synthesised. This allows sufficient statistical power  18  to explore interactions between treatment groups and patient characteristics. This would allow us to identify key groups in which delayed prescribing or no prescribing might be inadvisable due to longer durations of illness, greater severity of illness or higher risk of complications/reconsultation. These findings may inform GPs decisions on prescribing and increase use of appropriate delayed prescribing which in turn can help to reduce antibiotic consumption and antimicrobial resistance. This protocol describes an IPD meta-analysis of both randomised controlled trials (RCTs) and observational cohort studies of delayed antibiotic prescribing for acute respiratory infections.
search strategy for identification of studies A systematic search of the databases Cochrane Central Register of Controlled Trials, Ovid MEDLINE, Ovid Embase, EBSCO CINAHL Plus and Web of Science will be conducted to identify relevant papers published from inception to October 2017. The search strategy will be based on criteria set out by the Cochrane Collaboration in a recently published systematic review and is available in the online supplementary appendix. 19 The International Standard Randomised Controlled Trial Number Registry, a primary clinical trial registry recognised by WHO and ICMJE that accepts and records all clinical research studies in order to improve the publicly available information about clinical studies, will also be searched for any relevant trials. Literature search results will be exported to EndNote and then uploaded to COVI-DENCE, a web-based software that facilitates collaboration among reviewers during the study selection process.
20 21 The "Risk Of Bias In Non-randomised Studies -of Interventions" (ROBINS-I) tool will be used to assess quality of each observational study. The ROBINS-I tool contains items on bias due to confounding, selection bias, bias due to deviations from intended intervention, bias due to missing data and selective reporting. 22 We will use GRADE to rate the overall certainty (quality) of evidence that includes the evaluation of risk of bias, inconsistency, indirectness, imprecision and publication factors. 23 ta extraction and database creation Authors of eligible trials and observational studies will be contacted via an email, or where this is not possible by a letter, outlining the study aims. They will be invited to collaborate and share their data in a format of their choice. A data sharing agreement may be provided on request.
A complete database (containing data on all available measures) rather than key variables used in the publication will be requested. If resource usage and quality of life data were collected in separate forms, such information should be provided too. This will allow us to recalculate the primary outcomes where necessary to ensure a consistent approach has been used across studies with the aim of reducing the level of between-study heterogeneity. Collaborators will be able to provide the data in a format of their choice. Study data will be considered unavailable in the event that none of the authors have responded to multiple contact attempts or if all contacted study authors indicate they no longer have access to the data. Data will be checked by comparing key variables (eg, size of sample, descriptive statistics of demographic and outcome measures) with published data.
We will aim to reproduce individual study results to ensure the current analysis is consistent with what has been done in each study. Collaborators will be contacted if important discrepancies are identified and asked for clarification. The level of missing data within each included study will be assessed and discussed with study collaborators. If less than 5% of data is found to be missing on key baseline characteristics and outcome measures, the data will be analysed on a complete cases basis. However, if there is substantial missing data, the pattern and nature of the missingness will be explored and multiple imputation by chained equations will be used if appropriate. A copy of the data to be included in the IPD meta-analysis will be converted for analysis in STATA V. 14 or above. A database will be created containing study ID, patient ID, outcomes measures, subgroups of interest, antibiotic prescribing approach and propensity scores, where necessary.
Outcome measures from the different studies will be harmonised once data from all study authors have been received. In the case that coding for an outcome in a Open access study differs greatly from other studies, it may be possible to simplify measures to allow the study to contribute data or exclude the study from certain analyses. Any difficulties with harmonisation will be discussed with the full collaborators group to decide on the most meaningful approach. Harmonised data from the different studies will be aggregated (using STATA) into a single large IPD database, with an indicator variable to identify patients from the same trial. The IPD database will be rechecked for accuracy. The key resource information on medication, primary care consultation, hospital inpatients, hospital day cases, outpatient visits, accident and emergency (A&E) attendance from individual studies will be recorded if such information were collected and length of usage and frequency of attendance will be adjusted to the same time periods.
Analysis Study and patient level characteristics will be described for all studies that contribute IPD. Characteristics of any studies that declined or were unable to provide data will be considered in order to determine the extent to which the included studies are a representative sample. A traditional meta-analysis may be performed, using data from published papers, to test for differences between studies included in the IPD meta-analysis and those that could not provide data. 24 eterogeneity of eligible studies will be summarised using an I 2 statistic (tested by Higgins I 2 test). A substantial statistical heterogeneity will be considered if I² statistic is >50%. 21 Sources of heterogeneity will be explored and reported and a random effects model will be used for all analyses. IPD meta-analysis IPD meta-analysis will be conducted using a one-stage approach.
The one-stage approach combines all the data in a single meta-analysis based on a regression model stratified by study. 25 One-stage analysis is often more appropriate for exploring treatment-covariate interactions as it has increased power and is less likely to suffer from aggregation bias. 26 The aggregated IPD will be examined using multilevel regressions clustering on the individual study level to take into account any heterogeneity between studies. Using the aggregated IPD dataset, we will calculate the key outcome measures on a consistent basis. The model selected will be one that is appropriate to the outcome measure of interest. For example, assuming the underlying assumptions of the model are met, a linear regression will be used to model the severity of symptoms, a proportional-hazards model will be used to assess the duration of illness, or a suitable count model where time to event data is not available, and logistic regression models will be used for the complications/ reconsultation outcome, side effects and patient satisfaction.
Symptom trajectories in patients who do not take antibiotics will also be explored. For symptoms recorded in symptom diaries, we will calculate the median and IQR of the reported time until all symptoms have settled completely, in those patients who did not receive an antibiotic prescription or who received a delayed prescription but did not fill it (where these data are available). As sensitivity analyses, we will also explore duration of moderately severe of symptoms. It is possible that the use of multiple outcomes (symptom severity, development of complications, patient satisfaction) to determine clinical effectiveness may result in varying conclusions. We will report the results for each outcome, though the discussion and recommendations will be based primarily on the primary outcome-symptom severity.
Work on cross design synthesis by the Program and Methodology Division of the US General Accounting Office (1992) has provided a theoretical framework for combining database data and RCT data. Drawing on this work, the current review will apply a similar approach to the synthesis of observational cohort and RCT data. 28 ynthesis of observational studies will additionally use inverse probability weighting by propensity score analysis to adjust for confounding by indication on measured covariates. This serves to balance observational studies on key covariates) so that the distribution of baseline covariates is similar between treated and untreated subjects, making observational studies more like those in a RCT. 29 30 otential covariates to be included in the propensity score include: demographic factors (eg, age, sex), comorbid health conditions and signs and symptoms at baseline consultation.
Economic analysis The economic evaluation will be taken from the NHS and PSS perspective, as most of the studies in this area have collected such data. The NHS and PSS perspective is the most commonly used approach to economic evaluations conducted in the UK. It covers medication, primary care consultation, walking in centre, NHS telephone service, outpatient attendance, A&E visit and hospital admission. Outcomes will be differences in cost, costs per antibiotic prescription avoided and costs per quality of life gained. Individual patient resource usage data such as medication use, GP consultations, outpatient visits, A&E attendance and use of secondary care service from each individual trial or observational study will be costed using UK published data sources (BNF, national reference costs and PSSRU).
The model will simulate individual treatment pathways with defined baseline characteristics and will capture the time spent at different levels of disease severity, time to recovery and recurrence rates. The probabilities of staying or transitioning to another state will be derived from the statistical analyses. The clinical analyses based on all data will provide more robust estimates to populate the input values for the data. The costs and cost-effectiveness of subgroups will be explored in the decision model. Quality of life questionnaire will be translated into utility scores based on the UK tariff appropriate to the instruments used in each study. The different instruments (such as EQ5D, SF6D or Health Utilities Index (HUI)) will be recorded and weighted in the aggregated analyses. Utility scores at each measurement point will be estimated, and a mean utility score for symptom severity will be calculated.
For studies where no quality of life data were collected, we will apply the utility scores estimated from the other studies according to symptom severity. QALYs will be calculated based on an area under the curve approach. Due to the potentially skewed distribution in costs, a generalised linear mixed model will be employed to analyse costs and QALYs, controlling for baseline patient characteristics, length of follow-up and disease severity. The data will be hierarchical, with individual patients nested within studies, with study modelled as a random effect. Incremental costs and cost-effectiveness and associated CIs will be estimated by bootstrapping. The economic analyses will follow the same approach as the statistical analyses with calculations undertaken using a one-stage approach.
Sensitivity analyses All analyses will be repeated using a two-step approach. The two-stage approach involves calculating the effect for each study separately and then combining the results using traditional meta-analysis techniques. Results using the one-stage and two-stage approach will be compared and any discrepancies explored. This will also allow us to include and explore the impact of the aggregate results from trials for which we are unable to obtain data, as aggregate results can only be included in a two-step approach. Aggregated data from observational studies will not be included because in the absence of full data, it will not be possible to control for confounding by indication. Sensitivity analysis will explore whether there are differences in inferences based on differences in the approach taken to delayed prescribing and whether there are any differences if studies at high risk of bias are excluded.
Ethics and dissemination There will be no identifiable patient data in any of the datasets, and data will be stored in a password-restricted folder on the university server. This protocol has been prepared in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol guidelines. 34 he results of this work will provide the best possible evidence regarding when and for whom delayed prescribing is appropriate. The results, disseminated through academic and trade journals, as well as conferences, should help GPs in their decision-making about Open access prescribing during consultations. The results will also be shared with patients and members of the public through our PPI collaborations. There is potential for PPI to help address the issue of patients seeking antibiotics inappropriately which may help reduce unnecessary prescribing and in turn, antimicrobial resistance.
Apache Spark is a Big Data framework for working on large distributed datasets. Although widely used in the industry, it remains rather limited in the academic community or often restricted to software engineers. The goal of this paper is to show with practical uses-cases that the technology is mature enough to be used without excessive programming skills by astronomers or cosmologists in order to perform standard analyses over large datasets, as those originating from future galaxy surveys. To demonstrate it, we start from a realistic simulation corresponding to 10 years of LSST data taking (6 billions of galaxies). Then, we design, optimize and benchmark a set of Spark python algorithms in order to perform standard operations as adding photometric redshift errors, measuring the selection function or computing power spectra over tomographic bins.
Introduction In 2002 Google released the mapReduce framework (see e. g,  Dean and Ghemawat, 2008)  a new parallel programming model exploiting efficiently many data centre hardware. In 2006 its open source implementation and many other tools related to the growing field of "Big data" emerged within the Hadoop ecosystem  1. In 2009 a research project started at UC. Berkeley  (Zaharia et al. , 2012 (Zaharia et al. , , 2010) )  to overcome some limitations met by Hadoop. In the next years, it captured many companies attention due to order of magnitude better performances on huge distributed data sets. It is today a very famous and active open source project named Spark owned by the Apache foundation 2  and used by more than 1000 companies worldwide. Spark was developed essentially in scala, a multi-paradigm language that appeared in 2004 within the java ecosystem, that revived functional programming (FP)  3.
FP deals with some old concepts related to λcalculus where functions become the central parts of programming. This approach allows the lazy evaluation mechanism which will be discussed in Sect. 2. No knowledge of FP or scala is necessary to use Spark. Its API exposes efficient bindings to the python or R languages. While under the influence of business and web companies, Big Data technologies were emerging, the data trend in astronomy was also rising rapidly. Typical surveys today reach Ter-abytes of raw data and Petabytes in a very near future (e. g. ,  Zhang and Zhao, 2015). On the cosmological side, even after the reduction of images to user catalogues the BOSS experiment 4  already imaged millions of galaxies. While the next generation of spectroscopic survey DESI 5  is planned to image tens of millions of galaxies, the DES photometric survey 6  already provides such a data volume.
Machine Learning Figure  1 : Apache Spark provides four different modules. The SQL module focuses on the manipulating structured data such as tables. The Machine Learning module provides tools to perform distributed machine learning. The Streaming module allows to write streaming application. The GraphX module is the part of the API for graphs and graph-parallel computation. In this paper, we focus only on the Apache Spark SQL module. braries. For this paper we will focus essentially on the SQL part, allowing users to perform efficient queries and analyses on catalogs. But Spark also offers machine-learning possibilities (MLLIB module  Meng et al. (2015) ) which can be complemented by external libraries 8. A first application of the graph analysis module (Graph-X) to investigate the topology of cosmological simulations was recently presented in  Hong et al.
(2019) , while Streaming possibilities open exciting perspectives for the real-time treatment of alerts by brokers in astronomy 9. An introduction on using these different modules can be found in  Zečević, Petar and Bonaći, Marko (2016). Apache Spark was also used to perform distributed processing of astronomical images  (Zhang et al. , 2015; Wiley et al. , 2011)  or to produce mock catalogues from huge N-body simulations  (Carretero et al. , 2017). This illustrates the potential of the framework when used for fundamental sciences. Our goal here is not to report the impressive results obtained by Spark experts, but to address the needs of end-users (physicists) who have little or no knowledge about Spark. We will not elaborate on the theory that can be found in many articles or textbooks (a classical introduction being  Karau et al.
, 2015)  but try to show with concrete examples how this framework offers a simple way to achieve excellent results for analysing today's and upcoming catalogs in astronomy on computing clusters. Today's main language for astronomical data analysis being python, we will focus on that language. We will use some familiar concepts as dataframes and emphasise how external python codes can be reused. The use-case we develop is the following: one obtains a (large) set of astronomical files, not 8 see for instance https://maxpumperla. com/elephas. 9 as in https://fink-broker. readthedocs. io/en/latest/ necessarily produced by the user, and would like to have a look at some of the variables and perform some simple analysis over the entire dataset. As an example, we choose to use a catalogue of 6 billion of galaxies generated with a fast simulation and show how to perform easily an interactive tomographic analysis in a very reasonable time.
Since a very common format in astronomy is FITS, we first developed an efficient connector named spark-fits 10  described in  Peloton et al. (2018) , hereafter called SparkFITS18. After presenting in a physicist's language what Spark is and what its advantages are for data analysis in Sect. 2, we describe how to start using Spark in python in Sect. 3. Then we design and optimize several Spark commands in Sect. 4 with increasing complexity leading to a full tomographic analysis over a simulated set of 10-years of LSST-like galactic data  (Sect. 4. 3. 7). Several options are explored and performances discussed. AppendixA presents a more realistic treatment of photometric uncertainties.
What is Spark and why to use it The main trend for analysing large astronomical datasets in the last two decades was to use High Performance Computing (HPC) approaches on super-computers. The challenge in using such a framework consists in optimising the arithmetic efficiency (number of data moves over number of operations) on more and more complex architectures and requires a high degree of skills. This can be performed with the Message-Passing-Interface (MPI,  Gropp et al. (1996) ) but requires a careful tuning of the code for data exchanges between the nodes. Starting with mapReduce, a new approach to address these problems has focused on High Through-put Computing (HTC) in data centres. Accent is put interacting with nodes that are "close" to the data and minimizing the traffic between them.