dataesr/scientific-paragraphs-categorization huggingface dataset:
D. 7 Random Forests (RF) Decision trees (DT) explained in Section D. 6 suffer from high variance, which means any change in the training dataset can potentially affect the resulting predictions  [46]. One reason is that during the first split, the dataset is roughly divided into two sections  [46, 50]. Hence, if a predictive model is fit to each of the splits, the resulting predictions are not necessarily the same  [46]. One way to address this problem is by decreasing the depth of a DT model  [46]. However, this method increases the bias in the model and consequently decreases its accuracy  [46]. Another solution is to create an ensemble of decision trees using different datasets drawn from a population and averaging the results to decrease the variance  [46]. This notion is the result of the weak law of large numbers  [73].
According to this law, averaging various independent observations decreases variance  [46]. Ideally speaking, by increasing the number of observations to infinity, the variance should diminish  [73]. Nonetheless, this method is also not practical because of the limited access to many training datasets  [46]. Random forests (Figure D. 4) are machine learning methods that try to address the above issue by creating many trees with maximum depth (yielding in low bias but high variance) and averaging the resulting variance through bagging (bootstrap aggregation)  [46, 53]. Bagging is an ensemble method that draws many samples with replacement from a dataset  = ( 1 ,  2 , … ,   )  [51, 53]. The replacement in this process means that one structural element in the training set can appear more than once in the bootstrap dataset  [40].
Then, the RF method fits a decision tree with maximum possible depth to each of the new datasets, creating an ensemble of bagged trees  [46]. Before dividing the feature space at every stage, a random number of  ≈  1/2 ( is the number of predictors in the dataset) independent variables are selected as eligible predictors from which one is picked by the method to split (without replacement)  [46]. The lack of replacement in this process makes sure that the method does not pick a specific predictor repeatedly. This approach is highly advantageous because it makes sure that the bagged trees remain uncorrelated  [46, 51]. Whereas without this limitation, there is a high chance that all the developed trees become highly correlated, which results in a small improvement in the variance of the final model, compared to a single tree  [46].
6  [53]. However, in creating the  -1 decision trees, the AdaBoost method alters the original dataset by weighting observations in the main dataset so that the misclassified observations are weighted higher and the correctly predicted data points are weighted lower  [53]. Hence, the next stage decision tree focuses on those observations with wrong classification in the previous stage  [53]. Finally, the predictions from the ensemble of the decision trees are weighted by the AdaBoost method, so those highly accurate decision trees on the training data are weighted higher than those with the poor performance  [53]. For further details on the AdaBoost method, refer to Section 16. 4 of  [51].
The weights are the unknowns in Eq. (D. 10) and are summarised in Eq. (D. 11)  [53]. In Eq. (D. 10) and Eq. (D. 11),  is the number of independent variables. The goal is to estimate these weights so that the ANN model fits the training dataset well  [53]. Therefore, to guarantee an accurate model, a measure of fit is required to evaluate the quality of the model. The measure of fit is calculated using the squared error or cross-entropy  [53]. For further details about the measure of fit please refer to  [53]. The role of an ANN model is then reiterating two major stages until it reaches a minimum training set error rate. Firstly, estimating the reusability of the building structural elements based on weighted inputs, biases, and a specific activation function in the forward propagation stage.
(D. 12): ()~((), (,  ′ )) Eq. (D. 12)  where () is the mean function, which is commonly and without loss of generality considered to be zero and the kernel function (,  ′ ); where  represents the training datapoint in each dataset of the structural elements. We use the radial basis function (or squared exponential) as the kernel function, see  [52, 78]  for the details of this kernel, including the functional form, and how the hyperparameters (smoothness parameters) of this kernel can be estimated in the light of the observed data. The joint prior distribution of the training outputs, , and the predicted output  * (corresponding to the test input  * ) , according to GP definition given in Eq. Eq. (D. 12) and the properties of multivariate normal distribution, is given by Eq. (D. The mean and covariance of this posterior distribution can be used as an estimate of the predicted value of  * , and uncertainty/sensitivity  [79].
The GP that is briefly explained above, can be used However, since Eq. (D. 15) is non-Gaussian (response is discrete), the above integrals are approximated using the Laplace approximation method  [78]. D. 12 Propositional Rule Learner (PRL) Propositional rule learner (PRL) is a classification machine learning method that finds patterns in each dataset and expresses them in terms of a set of if-then rules  [56]. These rules are then used to classify new structural elements that satisfy a rule condition. The method develops a predictive model in three stages. A PRL method first converts the features in the training dataset into sets of binary features  [56]. Then it constructs the individual rules, each covering a part of the training dataset using a covering method  [56]. At this stage, the method learns a rule that covers a part of the training observations.
Kernel functions quantify the similarity of two observations and can have various forms, including radial, polynomial, hyperbolic, Laplacian, etc. [46]. By replacing the inner product in Eq. (D. 17) with the kernel, the solution function Eq. (D. 17 In this study, a radial kernel is used to expand the feature space, and eventually develop non-linear decision boundaries between the classes. Therefore, Eq. (D. [46]. By increasing the value of , the fit becomes more non-linear  [46]. While this increased non-linearity can decrease the variance on the training dataset, it might increase the chance of overfitting  [46]. Hence, care must be taken while choosing the correct value for   [46]. Another hyperparameter that is required to be selected is known as cost (represented by C)  [46]. This quantity determines the width of the margin in Figure D.
The left-hand panel of Figure D. 3 shows the entire dataset with the class labels and splits. In this figure, the training observations are marked with black circles (reusable) and black plus signs (non-reusable). The complete dataset is the combination of regions  1. 1 ,  1. 2 ,  2. 1 , and  2. 2. Initially, the dataset was split into two regions or leaves,  1 and  2 [46]. Next, to increase the purity of the regions,  1 was divided into  1. 1 and  1. 2 , and  2 was split into  2. 1 and  2. 2 [46]. The DT method then classifies a new observation (shown as a cross) as reusable because it is the most frequent class label in region  1. 1. The right-hand panel of Figure D. 3 shows the process of classifying a new observation using the DT method.
The support vector classifier develops a hyperplane to split the observations in the training dataset into two classes (Figure D. 6) [46]. This classifier depends only on the training observations close to the hyperplane known as the support vectors [46]. In the left-hand panel of Figure D. 6, the left-hand side of the hyperplane represents the circle responses (reusable), and the right-hand side of the decision boundary corresponds to the plus class (non reusable). The dashed lines in this figure are margins for the hyperplane. In Figure D. 6, only the observations on the margin or crossing the margin but on the proper side of the decision boundary are the support vectors [46]. Therefore, training data far from the margins (and the hyperplane) do not play any role in predicting the class-response for a new observation [46].
Acknowledgement The authors would like to thank Coventry University for funding this research project entitled ͡Ά̼͊ϳ̼ΛΉͼ ̮͆ Ά͊ϡμ͊ Ω͔ Ωμφθϡ̼φΉΩ ̮͆ ϡΉΛ͆Ήͼ Ͱ̮φ͊θΉ̮Λμ Ή φΆ͊ Ωφ͊ϲφ Ω͔ φΆ͊ Ήθ̼ϡΛ̮θ Economy. ͢.
𝑝𝑟(𝑟𝑒𝑢𝑠𝑎𝑏𝑖𝑙𝑖𝑡𝑦 = 1│𝑿 = 𝒙) In the conditional probability Eq. (D. 2),  =  ( 1 ,  2 , … ,   ) represents all applicable features in the dataset for every datapoint. If the value of conditional probability given in Eq. (D. 2) is higher than 0. 5, then the Bayes classifier classifies the observation as reusable, otherwise, non-reusable ('' means probability)  [46]. The left-hand panel of Figure D. 1 shows a simplified classification problem with two features ( 1 &  2 ) using the Bayes theorem  [70, 71]. The black dashed line is the Bayes decision boundary. The black circles correspond to reusable training structural elements, and the plus signs represent non-reusable training structural components. For each of the values of  1 and  2 , the probability of an element to be reusable or non-reusable is different.
Discussion We combined multiple data sources with a dynamic transmission model to reconstruct unobserved transmission dynamics of ZIKV in Fiji between 2013 and 2017. We found that transmission persisted over multiple years with three consecutive small annual outbreaks between 2015 and 2017, with strong seasonal forcing in transmission resulting in a high risk period of the year for ZIKV introduction. This means there is potential for large, brief flavivirus outbreaks, as well as a period of lower risk where low-level transmission is more likely. We estimated a mean basic reproduction number of 1. 2 (95% CrI: 0. 8-1. 5) for ZIKV, which combined with seasonality in transmission meant there was insufficient infection-and hence acquired immunity-in 2015 or 2016 to prevent re-emergence of the virus in the following year.
Acknowledgements We greatly thank all the participants and community leaders who generously contributed to the study over the three visits. We would like to acknowledge the work of the field teams:  Jessica Paka ,  Amele Ratevono ,  Warren Fong ,  Manisha Prakash ,  Jonetani Bola ,  Mosese Ligani  (2017);  Meredani Taufa ,  Adi Kuini Kadi ,  Jokaveti Vubaya ,  Colin Michel ,  Mereani Koroi ,  Atu Vesikula , and  Josateki Raibevu  (2015);  Dr. Kitione Rawalai ,  Jeremaia Coriakula ,  Ilai Koro ,  Sala Ratulevu ,  Ala Salesi ,  Meredani Taufa , and  Leone Vunileba  (2013). We would also like to thank the staff of the  Ministry of Health clinical services and Mataika House Fiji Center for Diseases Control  for providing the surveillance data and continued support throughout this study. This work was supported by the  Medical Research Council   MR/N013638/1  to A.
Code availability Microsoft Excel (version 16. 44) and R (version 4. 0. 2)  37, 38  were used to clean and process data. All models were implemented in R version 4. 0. 2  37, 38 using the mvtnorm (version 1. 1)  39  , truncnorm (version 1. 0. 8)  42 and deSolve (version 1. 28)  40  packages and parallelised using the doMC (version 1. 3. 6)  41 package. An MCMC sampler was written in R  43  to fit the mathematical model to multiple data sources using tidyverse (version 1. 3. 0)  44. Figures were created with the ggplot2 package (version 3. 3. 2)  45. BEAST and BEAUTi (version 1. 10. 4) were used to analyse sequence data  46. All code used in this analysis are available at https://github. com/a-henderson91/fiji-zikv-model (https://doi. org/10. 5281/zenodo. 4487358)  35.
The sequences from Central Division were recovered from two saliva samples collected in 2015 and a serum sample collected in 2016 (Supplementary Tables  S1  and S2 ). The same sequences retrieved from GenBank in the original study were used in this study, which included 120 ZIKV sequences all from the Asian lineage  10. Modelling seasonal forcing using climate data. We collected daily maximum and minimum temperature data from the Fiji Meteorological Service which covered the study period up to June 2017. We defined a sinusoidal function with variable amplitude and midpoint. We estimated these two parameters by fitting this function to daily average temperature data over the study period by Bayesian Markov Chain Monte Carlo (MCMC) using a normal distribution and likelihood and uninformative priors.
The sequences and. xml file generated with BEAUti for the BEAST analysis are available on GitHub (10. 5281/zenodo. 4487358)  35. Transmission model. We developed a model which had flexibility to consider six possible factors: prior population immunity, accumulation of herd immunity during the outbreak, seasonal variation in climate (Supplementary Fig. S4 ), introduction time (Supplementary Fig. S6 ), interaction between DENV and ZIKV resulting from cross-protection, and inherent viral transmissibility. We excluded antibody-dependent enhancement between DENV and ZIKV from our model as this would result in an increase in reported ZIKV cases and a comparison of our surveillance and serological data showed that very few infections were recorded as cases of ZIKV. We developed a deterministic susceptible-exposed-infectious-recovered compartmental transmission model with a dynamic human population (Supplementary Fig.
The relationship between cancer and coronavirus disease 2019 (COVID-19) infection and severity remains poorly understood. We conducted a population-based cohort study between 1 March and 6 May 2020 describing the associations between cancer and risk of COVID-19 diagnosis, hospitalisation and COVID-19-related death. Data were obtained from the Information System for Research in Primary Care (SIDIAP) database, including primary care electronic health records from 80% of the population in Catalonia, Spain. Cancer was defined as any primary invasive malignancy excluding non-melanoma skin cancer. We estimated adjusted hazard ratios (aHRs) for the risk of COVID-19 (outpatient) clinical diagnosis, hospitalisation (with or without a prior COVID-19 diagnosis) and COVID-19-related death using Cox proportional hazard regressions.
| Study design, setting and data sources We conducted a population-based cohort study from 1 March 2020 until 6 May 2020 (last date of data available), using data from the Information System for Research in Primary Care (SIDIAP; www. sidiap. org), a primary care database from Catalonia, a north-eastern region in Spain. Spain has a universal primary care-based health system, in which general practitioners (GPs) are the first point of contact for care. As a consequence, GPs have diagnosed and managed the majority of COVID-19 cases since the beginning of the pandemic. 14 In addition, because GPs are responsible of issuing sick leaves, patients diagnosed with COVID-19 in other settings (eg, hospital emergency departments) were also bound to contact primary care providers during study follow-up.
The SIDIAP database includes anonymized primary care electronic health records collected since 2006 covering approximately six million people (80% of the population in Catalonia, Spain) and is representative in terms of age, sex and geographic distribution. 15 SIDIAP includes data on demographics, lifestyle information and disease diagnoses, among others and has been linked to SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR) test results and hospital records (both from the public sector), as well as to regional mortality data through unique ID linkage. In addition, SIDIAP has been mapped to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), allowing us to apply common analytical tools developed by the open-science Observational Health Data Sciences and Informatics (OHDSI) network.
| Study participants We included all adults (aged 18 years or older) registered in the SIDIAP database as of 1 March 2020 (index date for all participants) with at least 1 year of prior history observation available. We excluded patients who had a record of a secondary cancer before a record of a primary cancer, patients with a clinical diagnosis or positive test result for COVID-19 prior to index date and patients hospitalised or living in a nursing home at index date (to include only patients representative of the community population).
| Variables The exposure of interest was cancer, which we defined as any diagnosis of a primary invasive solid or haematological cancer, excluding non-melanoma skin cancer, prior to the index date. We used the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) to identify cancer diagnoses: C00 to C96, except C44 (non-melanoma skin cancer) and C77-C79 (secondary cancers). Cancer types by anatomical location were identified using definitions previously validated in the SIDIAP database. 19 To avoid misclassification of primary cancers, we only considered the earliest cancer type registered for each patient. We stratified patients with cancer according to the number of years since the diagnosis to the index date into three groups (<1 year, 1-5 years and ≥5 years), because we lacked information on cancer status (ie, active, in remission) and cancer therapies.
22 To check the proportional hazard assumptions for the variables included in the models, we visually inspected log-log survival curves. Missing data were handled as an additional category. Non-linearity in age and risks of transition was considered by fitting models with age as a linear term, with a polynomial of degree 2 (ie, quadratic), and with restricted cubic splines (with three, four or five knots). 23 We calculated the Bayesian information criterion (BIC) for each of those models, and we selected the models with the lowest BIC values. Second, we estimated the relationship between cancer and COVID-19 outcomes adjusting for age and sex; and adjusting for age, sex, the MEDEA deprivation index and smoking status. Third, we further estimated our main models separately for <1-year, 1 to 5-year and ≥5-year cancer patients and stratified these models by sex (women or men), age (<70 and ≥70 years, 70 years was the median age of patients with cancer) and cancer type (haematological or solid cancer, as well as by solid cancer types).
37stimated aHRs for <1-year cancer patients were similar to ours for death following a COVID-19 diagno-In addition, the proportion of deaths among hospitalised patients was in line with prior studies. 37On the other hand, we cannot discard that some deaths in the general population might have occurred in undiagnosed COVID-19 cases, especially at the beginning of the pandemic. Second, due to the nature of our database, our results are not representative of asymptomatic or pauci-symptomatic COVID-19 cases that did not seek medical care. Third, our data spanned to May 2020, and therefore, our results are generalizable to the first wave of the pandemic. While changes over time might have changed SARS-CoV-2 virulence (eg, the emergence of new variants),.
ACKNOWLEDGEMENTS The authors would like to acknowledge the patients who suffered from or died of this devastating disease, and their families and carers. They would also like to thank the healthcare professionals involved in the management of COVID-19 during these challenging times, from primary care to intensive care units in the Catalan healthcare system. The analysis has made use of a range of open-source, free available tools provided by the  OHDSI community. The phenotypes used in our study were developed or adapted from work performed during the OHDSI COVID-19 studyathon. The mapping of data to the OMOP CDM has been supported by a taskforce from EHDEN.
Funding information This project was funded by the  Health Department  from the  Generalitat de Catalunya  with a grant for research projects on SARS-CoV-2 and COVID-19 disease organised by the  Direcci o General de Recerca i Innovaci o en Salut. This project has also received support from the  European Health Data and Evidence Network (EHDEN)  project. EHDEN received funding from the  Innovative Medicines Initiative 2 Joint Undertaking (JU)  under grant agreement No  806968. The JU receives support from the  European Union 's  Horizon 2020 research and innovation programme and EFPIA. The  University of Oxford  received a grant related to this work from the  Bill & Melinda Gates Foundation  ( Investment ID INV-016201 ), and partial support from the  UK National Institute for Health Research (NIHR) Oxford Biomedical Research Centre.
37stimated aHRs for <1-year cancer patients were similar to ours for death following a COVID-19 diagno-In addition, the proportion of deaths among hospitalised patients was in line with prior studies. 37On the other hand, we cannot discard that some deaths in the general population might have occurred in undiagnosed COVID-19 cases, especially at the beginning of the pandemic. Second, due to the nature of our database, our results are not representative of asymptomatic or pauci-symptomatic COVID-19 cases that did not seek medical care. Third, our data spanned to May 2020, and therefore, our results are generalizable to the first wave of the pandemic. While changes over time might have changed SARS-CoV-2 virulence (eg, the emergence of new variants), sis, with an aHR of 1. 72 (1. 50-1. 96) (vs 1.
Data collection Data and blood samples were collected during the baseline visit of the SFS. All measurements of the clinical indicators were performed in the laboratory of the Centre for Preventive Medicine (CMP) in Vandoeuvre lès Nancy, France. Blood count was performed using standard methodology in the laboratory of the CMP. WC was taken at the midpoint between the lower margin of the last palpable rib and the top of the iliac crest (hip bone). Trained professionals recorded measurements to the nearest 0. 1 cm. BMI was calculated by the Quetelet index formula as weight (kg) divided by height 2 (m) 2. Data collection has been previously described  [17, 18]. Obesity was defined as BMI ≥ 30 kg/m 2 for adults and for children BMI ≥ 97th percentile for age and sex calculated on French BMI curves  [19].
DNA methylation assay and quality control Blood samples were taken in the morning between 8 and 9 a. m. following an overnight fast. Whole blood DNA was extracted by the Miller technique  [20]  and was stored at -80 °C. Genome-wide DNA methylation profiling was performed using the Infinium HumanMethyla-tion450 BeadChip (Illumina). Methylation ratio (referred to as beta value by Ilumina's software), is the proportion of methylated by all CpGs (methylated / (methylated + unmethylated) CpGs). Methylation arrays were analyzed and visualized using the R package minfi (version 1. 16. 1) [21]. Detection p-value was generated for every CpG in all samples, indicating the quality of the signal. Poor quality probes were excluded from the analysis using a detection p-value cutoff (˃0. 05). Probes, missing in more than 5% of samples were excluded from all samples.
Statistical analysis Waist-to-hip ratio and WC were not normally distributed and were therefore transformed to the e-log scale. Individual analyses have been performed for the adult population, the children population and the combined population (children and adults together). A linear mixed-effects model was used to analyze the association between methylation levels at each probe and log-transformed phenotypes. The model used included sex, age, BMI, family structure, and individual white cell counts (neutrophils, lymphocytes, monocytes, eosinophils and basophils) as covariates and chip array as random effect. Bonferroni correction and false discovery rate (FDR) methods (< 0. 05) were used for the correction of the results for multiple testing. The association analyses of methylation values and the assessed phenotypes were performed by using the package CpGassoc in R  [24].