dataesr/scientific-paragraphs-categorization huggingface dataset:
All univariate analyses were performed on complete cases. Overall, 23% of patients had missing values for at least one variable, but, of note, only 3 variables are concerned: left ventricular end-diastolic diameter (12. 0%), left ventricular ejection fraction (3. 7%) and serum bilirubin (13. 7%). For the purposes of the multivariable analysis, missing data were handled by multiple imputations using the Markov Chain Monte Carlo method after Little's test had confirmed that they were missing completely at random. Ten imputed datasets were created; results were pooled according to Rubin's rule  (12)  and reported as adjusted odds ratios (ORs) with their 95% confidence intervals (CIs). The absence of multicolinearity in the final model was verified using tolerance and variance inflation factor measurements.
DISCUSSION To the best of our knowledge, this is the largest database on early VAs in LVAD recipients aiming to describe their prevalence, predictors and clinical impact after LVAD implantation. The four main findings are as follows: 1) early VAs are common after LVAD implantation, occurring in 24. 8% of patients; 2) 57. 4% of the patients experience their first early VA during the initial 7 post-operative days; 3) occurrence of VAs prior to LVAD implantation and any surgery combined with LVAD implantation are two independent predictors of early VAs; 4) early VAs associated with electrical storm is the strongest predictor of 30-day post-operative mortality, with a 7-fold increased risk of death.
Then, at day 19 after initiation of tumor growth, mice were sacrificed, and tumors were excised for weight and size analysis. Statistical Analysis: Statistical analyses were performed using GraphPad Prism software (La Jolla, CA, USA). Values are shown as mean ± SEM. Data were analyzed using Mann-Whitney test, *p < 0. 05, **p < 0. 01, and ***p < 0. 01.
Acknowledgements The authors are very grateful to  Séverine Marionneau-Lambot  and  Thibauld Oullier  from  Cancéropôle Grand-Ouest  for their technical assistance for murine 4T1 breast cancer model experiments. The authors are also indebted to  P. Lehn  and  J. -M. Lehn  for their pioneer works related to the synthesis of cationic lipids used in this study and for their stimulating discussions and constant interest in this work. The authors are also very thankful to  Edward Emmott  and  Gray Huffman  from  Northeastern University  for their assistance in correcting this manuscript. This work was supported by special grants from  BPI France financement (Paris, France ) for EMERIT project. T. C. was supported by funds from  IHU-CESTI (French government  financial support managed by the  National Research Agency  " Investment into The Future" program   ANR-10-IBHU-005 , cosupported by  Région Pays de la Loire  and  Nantes Métropole ).
Study Populations and Data Sources Study-Level Data Study-level data including 49 cross-sectional and prospective cohort studies with up to 180 056 participants from the CHARGE-BIG were used (eTable 1 in the Supplement). Descriptions of each participating study are shown in the eAppendix in the Supplement. All participants provided written, informed consent, and ethical approval was granted by local ethics committees for participating studies (eTable 2 in the Supplement). Birth weight was collected by self-reported questionnaires or medical records in each study. Detailed information on the study-specific data collection methods is provided in eTable 2 in the Supplement. Covariates were measured using direct measurement or self-reported using questionnaire data from each study (eTable 2 in the Supplement).
Summary-Level Data Summary-level data from the EGG Consortium,  13, 23  DIAGRAM Consortium,  17  and MAGIC Consortium  [18] [19] [20] [21] [22]  were used. For IV, both the 7-SNP GRS (explained between 0. 32% and 1. 52% of variance in birth weight) (eTable 6 and eTable 7 in the Supplement)  23  and the 43-SNP GRS (explained 2. 0% of variance in birth weight) (eTable 8 and eTable 9 in the Supplement)  13  for birth weight were    17  the Meta-analyses of Glucose and Insulin-Related Traits (MAGIC) Consortium (n = 133 010 participants),  [18] [19] [20] [21] [22]  and the Early Growth Genetics (EGG) Consortium (n = 153 781 participants). 13, 23 HbA 1c indicates hemoglobin A 1c ; and SNP, single-nucleotide polymorphism. a Estimates of 7 SNPs for birth weight were extracted from the EGG Consortium (n = 69 308 participants).
24  validate assumption 1, that the GRS for birth weight was a strong IV for birth weight (eTable 10 in the Supplement), an F statistic for the IV was calculated in the Nurses' Health Study (NHS) and Health Professionals Follow-up Study (HPFS) cohorts as a measure of the strength of IV for prediction of the birth weight, controlling for covariates (age, sex, PCA). An F statistic greater than 10 is evidence of a strong IV. 25  To examine assumption 2, that GRS for birth weight was not associated with potential confounders, the association of the GRS with age, body mass index, smoking, alcohol use, and total energy intake was determined among individuals in the NHS and HPFS cohorts (eTable 11 in the Supplement). Meta-analyses were conducted using study-level data from each study; we then pooled the β coefficients across studies, using random-effects or fixed-effects meta-analysis.
Meta-analyses were conducted in Stata statistical software version 13. 0 (StataCorp). All P values reported are 2-sided. We assessed heterogeneity with the I 2 statistic. 7] [28]  For the proposed cutoff of I 2 > 0. 25, we found nonnegligible heterogeneity between studies, in particular among the birth weight-T2D associations, but also for the association between GRS and birth weight or T2D (I 2 > 0. 25). As a consequence, we used random-effects meta-analysis throughout. After meta-analysis, we used the IV estimators to quantify the strength of the association of birth weight with risk of T2D. 29  The IV estimator, which is identical to that derived by the widely used 2-stage least-squares method,  30  was calculated as the β of the regression coefficients for GRS-T2D and GRS-birth weight associations (eMethods in the Supplement).
Characteristics of the 49 Participating Studies The characteristics of the 49 participating studies with up to 180 056 participants, including 41 155 patients with T2D, are presented in eTable 1 in the Supplement. Twenty-two studies reported the genetic association between GRS and birth weight, and 33 studies reported the genetic association between GRS and risk of T2D. A total of 41 155 patients with T2D and 80 008 control individuals without T2D provided study-level data. Data from the DIAGRAM Consortium included 34 840 patients with T2D and 114 981 control individuals, overwhelmingly of European descent. 17 The MAGIC Consortium included 133 010 participants,  [18] [19] [20] [21] [22]  and the EGG Consortium included 153 781 participants (Figure  1 ). 13, 23.
34 Using the MR-Egger method, the β coefficient of the MR-Egger regression provides pleiotropycorrected causal estimates and an intercept distinct from the origin provides evidence for pleiotropic effects. 02. 03 For type 2 diabetes, the data were analyzed from 49 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Birth Gene Study where standardized analytic methods were used in individual study. This study included 41 155 patients with T2D and 80 008 controls. Data from the Diabetes Genetics Replication and Metaanalysis (DIAGRAM) Consortium included 34 840 patients with T2D and 114 981 controls, overwhelmingly of European descent. Summary results of 7 single-nucleotide polymorphisms (SNPs) for birth weight identified in genome-wide association studies were extracted from the Early Growth Genetics Consortium.
The impact of insecticide resistance in malaria vectors is poorly understood and quantified. Here a series of geospatial datasets for insecticide resistance in malaria vectors are provided, so that trends in resistance in time and space can be quantified, and the impact of resistance found in wild populations on malaria transmission in Africa can be assessed. Specifically, data have been collated and geopositioned for the prevalence of insecticide resistance, as measured by standard bioassays, in representative samples of individual species or species complexes. Data are provided for the Anopheles gambiae species complex, the Anopheles funestus subgroup, and for nine individual vector species. Data are also given for common genetic markers of resistance to support analyses of whether these markers can improve the ability to monitor resistance in low resource settings.
Background & Summary Current malaria control activities are heavily reliant on vector control using insecticides, which means resistance to these compounds has the potential to derail control efforts 1,2. Studies have started to investigate the impact of resistance in certain situations 3,4 but a full understanding of impact requires comprehensive quantification of resistance. To quantify the factors that influence vector control generally, data from vector populations are required and a number of vector databases are already available for species distributions, infection prevalence, and bionomic parameters  [5] [6] [7] [8] [9] [10] [11] [12]. A database for insecticide resistance in malaria vectors, that allows users to.
download analysis-ready datasets, is vital so that the impact of levels of resistance found in wild populations, on malaria transmission, can be assessed. These datasets are also essential to quantify trends in resistance in space and time, filling the gaps in the available data with robust predictions, to aid resistance management and the deployment of interventions designed to counter resistance  13. Studies of phenotypes in natural populations may be confounded by variation in the environments sampled, including factors linked to climate, land use and malaria control interventions. It is not possible to control for all variables in the natural environment but this issue can, in part, be mitigated by sampling a large number of locations encompassing different combinations of environmental variables.
Large, collated datasets do, however, have potential disadvantages. Collated datasets that are a combination of data points representing different types of sample, different measurement methods, different location types and so on, risk undermining any analysis that is performed  6, 14. Each dataset should be constructed to address a specific question or set of questions, and the data within each set needs to be standardised to allow robust analyses. The goal of the current work was to collate data from multiple studies characterising the insecticide resistance phenotype and genotype in communities of malaria vectors at as many locations and times as possible. The first aim was then to generate standardised datasets designed to address specific questions using geospatial analyses. Namely, what are the trends in resistance in time and space in specific vector assemblages, to then assess whether these trends are associated with trends in malaria transmission.
Methods Data sources. Data were obtained from published journal articles, published reports, and unpublished datasets. Published journal articles were identified in the Web of Science bibliographic database by using the search terms "insecticide resistance" and "anopheles" together with the name of each malaria endemic country in turn. The Web of Science was chosen because it incorporates many relevant databases including the SciELO Citation Index from 1997 onwards, MEDLINE from 1950 onwards (from the U. S. Library of Medicine), the Data Citation Index from 1993 onwards (provides details of datasets in international data depositories), the BIOSIS Citation Index from 1969 onwards (covers pre-clinical, experimental, and animal research) and the Web of Science's own Core Collection from 1945 to date.
The earliest date was unrestricted, and the search was completed on 31 December 2017. The initial search yielded 3,685 articles published from 1956 to 2017, with the first African paper published in 1957. Data were extracted from each article as outlined below and 342 articles provided data from field samples of mosquitoes collected in malaria endemic African countries for either the insecticide resistance phenotype and/or genotype. If values for some data fields were missing, the authors were contacted. In these instances, either (i) the phenotype/ genotype data was given in the article but supplementary information such as the date of sampling or mosquito identification method was missing, or (ii) the genotype/phenotype data were missing, or had been aggregated across sites or years, so the disaggregated data for each site-year were requested.
In the latter instance, any genotype/phenotype data received from the authors were treated as unpublished. In total, 81 sets of authors were contacted about 114 journal articles. Of these, 56 sets of authors provided further information; 30 sets of authors confirmed details such as the collection dates and 26 sets of authors provided test results that were not published with the original articles, as well as confirming any missing details of their study. In addition, agencies reporting on vector surveillance and groups involved in large studies that had not yet published their results, were asked to provide these reports and unpublished datasets. In total, 48 reports and unpublished datasets from African countries were provided. For all unpublished data, permission to include these data in this release was requested.
Of a total of 11,057 unpublished data points, permission was received to release 10,834. Data aggregation/disaggregation. The aim of this work was to provide measures of insecticide resistance for representative samples of a species population (or a species complex or a subgroup) found at a particular time and place, rather than data at the level of an individual mosquito. Replicates from the same mosquito collection sampled at a single "site" and "collection period" were aggregated. The spatial resolution of a "site" was defined by the original field studies and classified by the current study, as described in the data geo-referencing section below. The temporal resolution of a "collection period" was also defined by the original data generators and the duration of each collection period was recorded in the current dataset, as described below.
If the reported data had been pooled across multiple sites or collection periods, but was originally obtained at a finer resolution, the disaggregated data for each site-period were requested. For example, if mosquitoes were collected from five sites and bioassayed separately, giving a bioassay result for each site, but only a single average result for the region was published, then the five separate results were requested. The purpose of this disaggregation was to avoid imprecise estimates associated with large areas or long time periods wherever possible. Further details on appropriate methods to analyse data at different resolutions is given in the Usage Notes. Datasets were constructed based on mosquito samples that represented either a single species or a species complex or subgroup. Species-level data were entered wherever it was available and aggregated to provide data for the species complex later, using the original species composition.
If insecticide resistance data were provided for each species but the original species composition was not available for that study, the data points for each individual species were included in the species-level datasets (provided they met the inclusion criteria below) but they were not aggregated to provide data for the species complex. Data are provided for individual species within the An. gambiae complex (An. arabiensis, An. coluzzii, An. gambiae, An. melas and An. quadriannulatus) and for species within the An. funestus subgroup (An. funestus and An. parenesis) within datasets 2, 7 and 8. Separately, values for aggregate An. gambiae complex samples and aggregate An. funestus subgroup samples are provided in datasets 1, 3, 4, 5 and 6. The majority of studies provided results for samples of a complex or subgroup, rather than single species (Table  1 ).
If the first control was successful (all of the mosquitoes died) and a baseline mortality rate was obtained from the second control and used in Abbot's formula to correct the mortality for the field sample, this corrected mortality value was entered in the database. Subgroup-, complex-or species-level data on the resistance variants in the voltage-gated sodium channel (Vgsc) gene that were derived from F0 or F1 generations from field collections of Anopheles mosquitoes, provided as either genotype or allele frequencies, were included. Only mosquito samples that were representative of a species complex or subgroup and/or a species were included and any samples that were subject to sub-setting that biased the original sample were excluded. For example, if a mixed species sample was collected but a bioassay result was only reported for the most common species, that bioassay result cannot be considered as representative of the species complex at that time and place.
In this example, the data were included in the species-level datasets released here, but these values were not included in the datasets for species complexes. Similarly, if a mixed species sample was collected and then the F1 generation was sorted into single species by identifying the mother of each egg batch, those results cannot be considered representative of the species complex at that time and place. Furthermore, if the allele frequency was calculated for mosquitoes that survived a bioassay, and dead mosquitoes were not tested, this result cannot be considered as representative for either the species complex, or the individual species, and was not included in any dataset. If a mixture of dead and alive mosquitoes from a bioassay were tested to obtain an allele frequency, but the ratio of dead:alive was not representative of the original sample, for example 80% died in the bioassay but the sample tested was 50:50 then these data were also excluded.
Individual data files. The full database was used to generate eight individual data files (Table  1 ) that address specific questions for defined sets of mosquitoes. The aim in creating Data Files 1 and 2 was to provide a set of comparable results for each insecticide from bioassays that had used the same insecticide concentration and exposure duration, however, the recommended concentrations and durations vary with WHO protocol versions  [18] [19] [20] [21] [22] [23] [24] [25] [26]. The protocol version used to define the standard insecticide concentrations and exposure durations in Data Files 1 and 2 was the 1998 WHO test procedures, because the highest volumes of data across all years were available for the concentrations and durations specified by this protocol version  25. Insecticides that were not covered by the 1998 protocol version were specified in the 2013 version so this later protocol was also used to set the standard values for Data Files 1 and 2  26.
Number Title No. data points or instances where all mosquitoes in the original sample were found to be one species after the bioassay was performed. For Data File 4, an additional identifier (the "matched set ID") is included to allow results from bioassays that used the same mosquito collection and exactly the same bioassay conditions, with or without a synergist, to be identified. The same approach was used for Data File 5 where the "matched set ID" allows results from bioassays that used the same mosquito collection and exactly the same bioassay conditions, but with differing insecticide concentrations and/or exposure durations, to be identified. In total, 453 matched sets are provided in the synergist dataset, 463 in the intensity assay data file, and 148 for Vgsc allele frequencies in paired dead and alive subsamples.
Identification method Category The molecular method used to identify individual species in the original sample. This field is duplicated to record instances where two methods were used. Table  5. Species identification data fields. These data fields are included in Data Files 2, 4, 7 and 8. or other geographical features, and so on. If the data source provided coordinates, then these were converted to decimal degrees. If no coordinates were provided, the site name was searched in at least two online gazetteers (Google Maps, GeoNames, OpenStreetMap, WikiMapia and so on). All options identified by this search were cross-checked against the contextual information. If only one option matched the contextual information, the coordinates were extracted from the online gazeteer and added to the database.
Percent mortality Decimal number The percent of mosquitoes that died across all replicates, adjusted using Abbot's formula if applicable. Table  6. WHO and CDC bioassay data fields. There data fields are included in Data Files 1-5. and administrative level for that unit, were extracted and entered in the database. If an administrative unit within GAUL could not be identified, no code was entered. If an individual site could not be located, or could not be precisely located within a 2. 5 arc-minute grid cell, then the data point was linked to the second order administrative division that the site falls within. The administrative division was identified using the same method as for polygons above. If multiple point locations were sampled and the mosquitoes were pooled before being tested (or only the pooled results were available), the site type was classified as a 'multi-point' and the coordinates for all of the individual point locations were linked to the test result.
Data duplication. The data extracted came from several hundred different sources, which introduced the possibility that individual results had been entered into the database more than once. To identify duplicates the following data fields were used: original sample; species tested; date fields; no. mosquitoes tested; no. mosquitoes dead; percent mortality; site name; coordinates. Fuzzy matching was used for all fields to identify duplicates where different levels of aggregation had been used, or different data values were missing, or names were spelled differently. All partial matches were examined to identify genuine duplicates. Duplicate data points were removed, and the source details linked to the single data point that was retained. In total, 3,483 duplicated data points were removed.
Data Records The data are available for download from the Dryad Digital Repository  28. The spatial and temporal distributions of Data File 1, standard WHO susceptibility test results for the Anopheles gambiae complex and Anopheles funestus subgroup, are shown in Fig. 1. The spatial and temporal distributions of Data File 7, Vgsc allele frequencies for individual species, are shown in Fig. 2. Data File 1 is the largest dataset but all eight have similar spatial distributions with clustered sampling in the east and west of Africa and sparse data points in the centre and southwest. They also share similar temporal distributions with phenotypic data volumes increasing throughout the time period particularly from 2008 onwards, and the genotypic data volumes peaking in 2005 and 2010. The genotype data were almost exclusively extracted from published papers and there is typically a lag of around two years between mosquito collection and the publication of a paper containing the test results.