dataesr/scientific-paragraphs-categorization huggingface dataset:
Presently, evidence users face three challenges in finding relevant and reliable evidence reviews:  First, evidence reviews themselves may be hidden in the sheer abundance of scientific publications  (Forscher, 1963; Jinha, 2010; Johnson et al. , 2018)  with evidence users often having limited time available to search literature or access to databases to retrieve articles  (Pullin et al. , 2004; Pullin and Knight, 2005). While one of the major justifications for conducting evidence reviews is to collate primary studies for evidence users, as more and more are published, the problem of large volumes of literature extends to evidence reviews themselves  (Gurevitch et al. , 2018; Haddaway et al. , 2015).  Second, evidence users will increasingly have to choose which of the many evidence reviews on the same subject are the most reliable sources of evidence, and recognising strengths and weaknesses of evidence reviews takes time and training  (O'Leary et al.
, 2017 (O'Leary et al. , , 2016;; Woodcock et al. , 2017 Woodcock et al. , , 2014)).  Third, 'synthesis gaps' (i. e. , unaddressed review questions or obsolete syntheses that need updating with new evidence) and 'synthesis gluts' (i. e. , proliferation of similar reviews) are not easily identified, making it difficult to avoid redundancy of evidence reviews  (O'Leary et al. , 2017; Woodcock et al. , 2017). In the health sector, unnecessary duplication of systematic reviews has already become a problem leading to research waste  (Chalmers and Glasziou, 2009; Moher, 2013) , and a similar trend could emerge soon for reviews in the environmental sector  (O'Leary et al. , 2017; Woodcock et al. , 2017). To address these problems and to help overcome some access-related barriers to evidence-informed decision-making, we introduce a new online and freely available evidence service: the CEE Database of Evidence Reviews (CEEDER: www.
environmentalevidence. org/ceeder; Figure  1 ). CEEDER provides an interactive database that facilitates searching for relevant and reliable evidence reviews. CEEDER collates and indexes evidence reviews addressing questions relevant to environmental policy and practice (see eligibility criteria in Text S1), and independently assesses them against the methodological standards using an established assessment tool: CEE Synthesis Assessment Tool (CEESAT; described below). The assessment produces reliability ratings for each question addressed by a review, based on reported methodology. Here, we describe an overview of CEEDER (the evidence service) and the details of CEESAT (the assessment tool), and discuss how CEEDER benefits decision-makers in policy and practice, and supports evidence review production.
Aims and objectives The principal aim of CEEDER is to enable evidence users to locate relevant environmental evidence reviews that have been independently and objectively assessed for their reliability in terms of transparency, level of procedural rigour (susceptibility to bias) and limitations of primary data for synthesis. CEEDER also aims to contribute to improving the conduct and reporting of evidence reviews across the environmental sector. Thus, intended users of the CEEDER evidence service (service users) are researchers, editors and peer-reviewers, as well as decision-makers. The main objectives of CEEDER are: 1. To provide an online, freely available service for evidence users to identify the most robust and reliable reviews of evidence suitable and relevant to their needs (e. g. , for integration into policy and practice); 2.
3. 4). The review questions included in the database therefore vary from broad global issues (e. g. , impact of plastic waste on the marine environment) to precise cause and effect relationships in single species or restricted areas. Note many review articles address multiple questions of impact or effect, some of which may not be eligible for CEEDER and so assessment are made with respect to individual questions (Text S1). In addition to the source review article information, all of the eligible questions addressed in a review article are coded in a standard format for service users to easily find relevant evidence reviews (Section 2. 6). Evidence reviews addressing subjects closely related to environmental management, such as human health and animal veterinary science are included when there is a significant environmental impact or intervention component in the question.
Service platform To provide a user-friendly, functional and useful service platform, we took a usercentred design approach where potential service users were engaged in multiple rounds of scoping and testing for developing the beta version of the platform (www. environmentalevidence. org/ceeder). A CEEDER workshop was held in Cardiff (UK) in July 2019 to engage with potential service users from Welsh Government and Natural Resources Wales. This engagement with stakeholders yielded pertinent information needs they wished to be displayed in the website and suggestions for functionality and visualisation. This beta version website features the evidence review database along with functionality for querying and visualising results. Further, we have invited potential service users from other governmental organisations for an online questionnaire survey via email and website.
Search functionality Service users can use keywords to search the database for: (1) titles, abstracts and keywords of the source article; (2) coded review questions; or (3) a combination of these two search options. Basic search functions such as some Boolean operators (AND and OR), wildcards, parentheses and quotation marks can be used. Returned results feature bibliographic information and coded review question along with visual representation ratings for all 16 assessment criteria of CEESAT v2. Search results can be sorted by the reliability ratings of each of the 16 elements, enabling service users to find the most reliable evidence reviews based on categories they deem of importance to them. Further information about the evidence review, including title, abstract, year, authors and a link to the full text can be obtained.
How CEEDER benefits evidence users CEEDER provides an open-access database of independently assessed evidence reviews from which users can easily find relevant and reliable evidence reviews, and export their search results. The reliability ratings based on the 16 CEESAT criteria enable users to compare the reliability of evidence reviews. Although current licensing agreements do not allow us to provide the full texts of each assessed evidence review, CEEDER provides the necessary information and links for users to navigate to the original publication websites. We believe that using CEEDER would reduce the time required for locating relevant evidence reviews and screening them for rigour in comparison to using web-based search engines (e. g. , Google Scholar, Google) or subscription-based bibliographic platforms (e.
How CEEDER supports evidence review production Another objective of CEEDER is to support review authors, editors and peer-reviewers in producing more reliable evidence reviews. To achieve this, CEEDER's website provides guidance to users on materials and tools for building capacity to collate and synthesise evidence. It has been argued that the importance of formal training in environmental evidence reviews should be recognised in academia  (Kareiva and Marvier, 2012). CEEDER is designed to raise awareness of the formally established evidence synthesis methodology and its value for the research community. CEEDER will support more efficient and effective production of evidence reviews by providing users with a dynamic searchable database of reviews from which they can search for and identify reviews of interest.
Conclusions The CEEDER evidence service supports evidence-informed decision-making in the environmental sector by enabling the identification of pre-screened reliable evidence reviews in a searchable open-access database. CEEDER will also help to identify gaps and gluts in evidence reviews in environmental management and support production of more reliable evidence reviews by providing resources for authors, editors and peerreviewers. We welcome further engagement with the CEEDER evidence service by users and user organisations to facilitate co-production of the service and ensure its relevance to their evidence needs.
CEEDER follows a specific workflow consisting of four key steps: (1) searching; (2) screening and data coding; (3) assessment (rating); and (4) data presentation. Each consists of a series of activities (Figure2; Text S1). It starts with collecting potential evidence reviews by comprehensive searches of multiple bibliographic databases and grey literature followed by eligibility screening, data coding, and assessment of evidence reviews using CEESAT, and indexing evidence reviews in the database. To provide an up-to-date archive of evidence reviews, searches are updated monthly, and records are actively screened. The entire process provided in Figure2is overseen by the Editorial Team and the assessment is conducted by the Editorial Team and Review College members.
ARIA Care Pathways for AIT AIT is a proven therapeutic option for the treatment of AR and/or asthma by sublingual immunotherapy (SLIT) or subcutaneous immunotherapy (SCIT) routes  [24, 50, [52] [53] [54]. The efficacy demonstrated in doubleblind, placebo-controlled, randomized clinical trials (DB-PC-RCT) was confirmed in studies using prescription databases and translates into real life  [55]. In most countries, AIT is more expensive than other medical treatments for AR or asthma and should therefore be considered in patients within a selective medicine approach  [56]. As recommended by the European Academy of Allergy and Clinical Immunology (EAACI) Task Force  [57] , ARIA 2019 has created ICPs for both SCIT and SLIT  [58]. In Kuwait, AIT has to be introduced by allergy specialists in tertiary health care centers to patients with AR who are not controlled on standard treatment or referred from the primary and secondary health care systems due to the same issue.
For the past decades, simulation-based likelihood-free inference methods have enabled researchers to address numerous population genetics problems. As the richness and amount of simulated and real genetic data keep increasing, the field has a strong opportunity to tackle tasks that current methods hardly solve. However, high data dimensionality forces most methods to summarize large genomic datasets into a relatively small number of handcrafted features (summary statistics). Here we propose an alternative to summary statistics, based on the automatic extraction of relevant information using deep learning techniques. Specifically, we design artificial neural networks (ANNs) that take as input single nucleotide polymorphic sites (SNPs) found in individuals sampled from a single population and infer the past effective population size history.
, 2013) ), Approximate Bayesian Computation  (Boitard et al. , 2016b , Navascués et al. , 2017)  and SFS-based approaches  (Bhaskar et al. , 2015, Liu and Fu, 2015)  have shown great results, supporting archaeological evidence and helping to understand species decline or expansion. The study of genetic variation relies primarily on genotyping and sequencing data of very high dimensionality, which is a major difficulty for most inference methods. Some approaches, such as coalescent-HMMs methods  (Spence et al. , 2018) , enable parameter inference using the full dataset by making simplifying assumptions on the underlying models. A few of them can process unphased data  (Terhorst et al. , 2017) , scale to large sample sizes  (Terhorst et al. , 2017)  or to complex models  (Steinrücken et al. , 2019).
However, no method simultaneously addresses all three. Moreover, handling arbitrarily complex models remains untested (e. g. models with more than three populations) or intractable (e. g. complex spatial models)  (Spence et al. , 2018). Hence, most frameworks solving complex population genetic tasks do not rely on coalescent-HMMs and reduce the data dimension with a pre-processing step during which the dataset is converted into a smaller set of statistics called summary statistics. These statistics can then be used in likelihood and composite likelihood inference frameworks, when the model or statistics are simple enough, or in simulation-based approaches. Among the latter, the widely used Approximate Bayesian Computation (ABC) framework as well as several machine-learning algorithms, including Support Vector Machines (SVM) and random forests, were able to tackle a variety of tasks such as demographic model selection and parameter inference  (Excoffier et al.
A network layer takes as input the outputs of the previous layer(s): each node of the layer performs a linear combination of the inputs, followed by a non-linear transformation, and this value is passed to the next layer. Networks vary in their shape (number of layers and nodes) and in the way nodes are connected. For example a Multi Layer Perceptron (MLP) connects all nodes of a layer to all nodes of the following layer  (Rumelhart et al. , 1986) , while a Convolutional Neural Network (CNN) connects only nodes of similar location  (LeCun et al. , 1995). Despite the differences, any network defines a parameterized function that allows for a complex non-linear mapping from a space to another, and therefore can solve a complex task, when the provided parameters are suitably adjusted. To tune the parameters, the network is trained thanks to a training set consisting of examples of (input, desired output) pairs, by optimizing a criterion (loss function) that expresses how well the network performs on the dataset with its current parameters.
For example, for an object recognition task in images, the input is an image, the output is a probability distribution over possible names of objects, and the loss is the distance between the prediction of the ANN and the expected output (a Dirac peak on the name of the object shown by the image). The parameters of the function are tuned to minimize this loss thanks to an optimization algorithm based on gradient descent and backpropagation. This process usually requires a large training dataset, in order for the network to be able to learn and generalize well, that is, to perform well on data never seen so far. Deep learning has only recently been used to tackle population genetics questions. First, multilayer perceptrons (MLPs) were used to process small SNP windows for population assignment  (Bridges et al.
, 2018 , Flagel et al. , 2018) , selection  (Flagel et al. , 2018 , Torada et al. , 2019) , introgression  (Flagel et al. , 2018 ) and three-step population size histories  (Flagel et al. , 2018). The CNN implemented by  Chan et al. (2018)  and based on Deep Sets  (Zaheer et al. , 2017)  is invariant to haplotype (chromosome) permutation, i. e to the permutation of rows in the SNP matrix, thanks to convolution filters that treat each haplotype in an identical way. The other approaches proposed instead to sort haplotypes by similarity before processing them with filters sensitive to the haplotype order  (Flagel et al. , 2018 , Torada et al. , 2019). More recently, Recurrent Neural Networks (RNN) were applied to estimate the recombination rate along the genome  (Adrion et al. , 2019) , and Generative Adversarial Networks (GAN) to learn the distribution of genomic datasets and generate artificial genomes  (Yelmen et al.
Materials and Methods In this study, we introduce the first deep learning approaches for inferring detailed histories of effective population sizes using genomic data. Based on whole sequences of SNP data of multiple individuals from a single population, we aimed to predict 21 population size parameters, each corresponding to a time step. Our method and the baseline frameworks all relied on large-scale simulated datasets for which the true demographic parameters are known and drawn from prior distributions of population sizes and recombination rates. For each drawn parameter set (i. e. demographic scenario), we simulated 100 independent genomic loci of length 2Mb (i. e. 100 replicates) for 50 haploid individuals using msprime  (Kelleher et al. , 2016). Using this reference panel, we then trained methods based on ABC, deep learning or a combination of both, to predict the demographic parameters (Figure  1 ).
Thus, the spatial information that may exist across these windows for real data is not conserved. Simulations with selection. To investigate the robustness of our approach, an extra set of data was simulated under demographic changes and selective pressure. We used msms (Ewing and Hermisson, 2010) to simulate scenarios including positive selection with additive fitness using varying values of selection coefficient (s in 2Ne units: 100, 200, 400 or 800), selection starting time (T sel : 200, 1000 or 2000 generations ago) and initial frequency of the beneficial allele (f 0 : 0. 1%, 1%, 5%). The SNP under selection was located at the center of the region. The mutation rate was set to 10 -8 , the recombination rate to 5 • 10 -9 , the number of haplotypes to 50 and the region length to 2Mb. We generated 16 × 100 replicates for each of the 36 selection parameter combinations (s, T sel , f 0 ) and 30 × 100 replicates with no selection under three demographic scenarios (constant, declining or expanding size) leading to a total of 181,800 SNP matrices.
Inference methods requiring a fixed input size processed the 400 successive central SNPs (ie 200 before and 200 after the SNP under selection). Summary statistics. For each group of 100 segments corresponding to one scenario, we computed the site frequency spectrum and the linkage disequilibrium as a function of the distance between SNPs averaged over 19 distance bins for a total of 68 summary statistics. Our python script is partly based on the scikit-allel python module  (Miles et al. , 2019). These predefined summary statistics constitute the training, validation and test set for all methods based on summary statistics or on their combination with SNP matrices.
Approximate Bayesian Computation We compared ABC with the simple rejection procedure (i. e. no correction) and three correction methods implemented in the R package 'abc'  (Csilléry et al. , 2012) : local linear regression, ridge regression and non-linear regression based on a single-hidden-layer neural network. Settings were set to default except for the tolerance rate set to six possible values (0. 05, 0. 1, 0. 15, 0. 2, 0. 25 and 0. 3). ABC was run on (a) predefined summary statistics, (b) SPIDNA outputs (i. e. automatically computed summary statistics), or (c) a combination of predefined summary statistics and SPIDNA outputs. We used the median of the posterior distribution as the demographic parameter estimate Θ. Multi-Layer-Perceptron Networks The first MLP is based on summary statistics, has 3 hidden layers, ReLU activation functions and uses batch normalisation.
This network has a total of 131,731 trainable parameters. Flagel network We reused the code associated with the repository of the first paper using a CNN for demographic inference  (Flagel et al. , 2018)  and adapted it to our dataset and task. We trained the network with the exact same architecture as the one published, except that we changed the last layer to allow the prediction of our 21 population size parameters. We parametrized the network with the set of hyperparameters leading to the best performance in the previous work for two different types of SNP encoding (0/255 or -1/1). It is noteworthy that the actual encoding in their code is 0/-1 and not 0/255, thus we kept the same encoding to be able to compare the performance. The networks were trained with the same procedure of 10 epochs with early stopping in case of no progression of the loss after 3 epochs.
Convolution networks to handle data with variable size A major difficulty that arises with genomic data is that the number of SNP varies from one dataset to another, or from one genomic region to another, due to the stochasticity of biological and demographic processes (and of their corresponding genetic simulations). Therefore, we use convolution layers as they can handle data with variable size while keeping the number of network weights constant. A filter can be repeated more or fewer times to cover the whole input entering each layer, letting the network adapts itself to the data. Consequently, the output size of each convolution layer will vary depending on the input size. This prevents the use of fully connected layers directly after a convolution layer as it is often the case with CNNs.
Hyperparameter optimization Compared to other machine learning methods, ANNs have a potentially infinite amount of hyperparameters when including for instance the number of layers, the number of neurons in each of them, the learning rate, weight decay or the batch size. Moreover, a run over a full dataset with enough epochs to reach convergence is time consuming for networks with a complex architecture defined by many learnable parameters. Therefore, the development of deep learning architectures often relies on the experience and intuition of the practitioner in a try-and-repeat process. Grid search and random search are two strategies for exploring the hyperparameter space uniformly. They are commonly used but are limited by the computing resources available. In our study, we used HpBandSter, a package that implements the HyperBand  (Li et al.
Impact of positive selection on SPIDNA and ABC inference We investigated the impact of positive selection on SPIDNA and ABC inference for three illustrative demographic cases (scenarios Medium, Decline and Expansion of Figure  4 ). Because including selection required a change in the genetic simulator (msms instead of msprime), we first ensured that the change of tool to generate the new test dataset had no influence on the prediction accuracy (Figure  S2 ). We then simulated 2Mb regions including a central SNP under positive selection, with varying selection strength, starting time and frequency of the beneficial allele at this time (100 regions for each scenario). We chose a conservative approach in which all 100 regions are under selection (worst case scenario). For each scenario we predicted the population size history using SPIDNA (batch normalization) or ABC (with local linear correction) on summary statistics.
Both ABC and SPIDNA predictive errors varied with the selection coefficient (Figure  S3 ). On average a moderate selective pressure (100-400) did not decrease the performance (Figure  S3  top row). ABC inference for declining population datasets was the only one negatively impacted (increased error for s=200 and 400). In fact, in multiple cases increasing s decreased the prediction error mean. Very strong selection (s = 800) on the other hand led to an increased prediction error mean in all cases except for the declining histories inferred by SPIDNA. In addition, the 95% quantile and standard deviations of the prediction errors tend to increase with s (Figure  S3 ) indicating that the prediction should be taken more cautiously in the presence of strong positive selection. This variance was systematically smaller for SPIDNA than ABC.