dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We sincerely thank the guardians of the cats for allowing their data to be used to help other cats, with special mention to Eric and Carrie, for their commitment to saving their cats. We are immensely grateful to  Os Jarrett  for editorial help and scientific advice in the writing of this paper. SC thanks  Bethany Victoria Smith  for helpful discussions. We are grateful to the office, technical and veterinary staff of  Glasgow University Veterinary Diagnostic Laboratories  for their support. We thank  Dr. Tony Xue  and  Niki Ng  of the  Nantong Mutian Biotechnology  for the donation of pills for the treatment of cats in Households C and E.
Data, materials, and online resources The data and materials are available on OSF at https://osf. io/he5za/. Links to the lab-specific pages of all participating labs are available on OSF at https://osf. io/7zyxj. Data and scripts to re-create the manuscript are available on a companion website at http://git. colling. net. nz/attentional_ snarc/. An archived version of the companion website is available at https://doi. org/10. 5281/zenodo. 3738555. The Supplemental Material (http://journals. sagepub. com/doi/ suppl/10. 1177/2515245920903079) contains detailed results for Models 1 through 5 as well as detailed results for the secondary analyses.
Thus, the experiment was also conducted in German, Dutch, Czech, Spanish, Italian, and Chinese. All materials were translated from English into these other languages and then independently back-translated into English to ensure accuracy. All materials, including the translations, are available at https://osf. io/7zyxj/. To perform analyses, we used R (Version 3. 5. 1; R Core Team, 2018) and the R packages bindrcpp (Version 0. 2. 2; Müller, 2018) , checkmate (Version 1. 8. 5; Lang, 2017) , dplyr (Version 0. 7. 6; Wickham, François, Henry, & Müller, 2018) , forcats (Version 0. 3. 0; Wickham, 2018a) , forestplot (Version 1. 7. 2;  Gordon & Lumley, 2017) , ggplot2 (Version 3. 0. 0; Wickham, 2016) , glue (Version 1. 3. 0; Hester, 2018) , kableExtra (Version 0. 9. 0; Zhu, 2018) , knitr (Version 1.
20;  Xie, 2015) , lme4 (Version 1. 1. 18. 1; Bates, Mächler, Bolker, & Walker, 2015) , magick (Version 1. 9;  Ooms, 2018) , magrittr (Version 1. 5;  Bache & Wickham, 2014) , Matrix (Version 1. 2. 14;  Bates & Maechler, 2018) , nlme (Version 3. 1. 137; Pinheiro, Bates, DebRoy, Sarkar, & R Core Team, 2018), papaja (Version 0. 1. 0. 9842; Aust & Barth, 2018) , purrr (Version 0. 2. 5;  Henry & Wickham, 2018) , pwr (Version 1. 2. 2; Champely, 2018), readr (Version 1. 1. 1; Wickham, Hester, & Francois, 2017) , reticulate (Version 1. 10; Allaire, Ushey, & Tang, 2018), R. matlab (Version 3. 6. 2; Bengtsson, 2018) , stringr (Version 1. 3. 1; Wickham, 2018b) , tibble (Version 1. 4. 2; Müller & Wickham, 2018) , tidyr (Version 0. 8. 1; Wickham & Henry, 2018) , and tidyverse (Version 1. 2. 1; Wickham, 2017).
Procedure We employed an experimental paradigm based on Experiment 2 of Fischer et al. We chose Experiment 2 over Experiment 1 because Experiment 2 had fewer ISI conditions and because the results were statistically significant in a greater proportion of the conditions. Before starting data collection, each lab performed a monitor calibration procedure using a supplied calibration script. This procedure involved measuring the viewing distance from the computer monitor and the size of standard stimuli presented on the screen (see https://osf. io/2m4ad/ for details). After participants provided informed consent, they were seated in front of the monitor with their chin placed in a chin rest that was located a fixed distance from the monitor (set during the calibration procedure), and then data collection commenced.
Eye-tracking protocol Code implementing an eye-tracking protocol using an EyeLink 1000 (SR Research, Ottawa, Ontario, Canada) eye tracker was provided to all labs and is available on Github at https://github. com/ljcolling/FischerRRReyetracking. Of the five labs that optionally made use of an eye tracker, one used a different eye tracker; this lab has provided information regarding deviations from the standard protocol on its lab-specific page. The standard nine-point grid was used for calibration and validation at the start of each block and when required during a block. The start of a trial was triggered after the detection of 500 ms of stable fixation within a 2° box centered on the fixation point. If the system could not detect a stable fixation within a 2,000-ms time window, the calibration process was repeated.
Primary analyses Model 1: no moderators. The purpose of Model 1 was to replicate the analysis performed by Fischer et al. , and thus it did not take account of any moderators. Model 1 was estimated on data from 1,105 participants from 17 labs. We summarize the results from Experiment 2 of Fischer et al. along with results from each lab and from Model 1 in Figure  2. The effects we observed both within and across labs were minuscule and incompatible with those observed by  Fischer et al. Specifically, Fischer et al. estimated  an effect of -5. 00 ms in the 250-ms ISI condition, 18. 00 ms in the 500-ms ISI condition, 23. 00 ms in the 750-ms ISI condition, and 11. 00 ms in the 1,000-ms ISI condition. In contrast, Model 1 estimated effects of -0. 05 ms, 1. 06 ms, 0. 19 ms, and 0. 18 ms, respectively, in the four ISI conditions.
Model 3 was estimated on data from 1,014 exclusively left-to-right readers-writers from 17 labs and 76 not exclusively leftto-right readers-writers from 8 labs. Although previous work suggests a weaker or possibly even reversed congruency effect among participants who have experience with languages that are not read and written exclusively from left to right. Figure  3  shows that reading and writing direction had no substantial impact on the results. Specifically, the figure shows a minuscule effect for each reading-and-writing-direction group in each ISI condition and minuscule differences between the congruency effects for the two reading-and-writing direction groups in each ISI condition (see Tables  S4  and S5  in the Supplemental Material for details). Model 4: handedness. Model 4 was estimated on data from 69 left-handed participants from 9 labs and 1,007 right-handed participants from 17 labs.
Discussion The Att-SNARC effect has been used to argue for an early, response-independent, and automatic origin of the SNARC effect. If the SNARC effect is produced by early mechanisms, this would provide good evidence for embodied number representations and support strong claims about the link between number and space (e. g. , a mental number line). We attempted to replicate Experiment 2 of Fischer et al. by collecting data from 1,105 participants at 17 labs. Across all 1,105 participants and four ISI conditions, the proportion of times the congruency effect we observed was positive was. 50. Further, the effects we observed both within and across labs were minuscule and incompatible with those observed by Fischer et al. Given this, we conclude that we failed to replicate the effect reported by Fischer et al.
Insulin-like growth factor-I (IGF-I) and testosterone have been implicated in prostate cancer aetiology. Using data from a large prospective full-cohort with standardised assays and repeat blood measurements, and genetic data from an international consortium, we investigated the associations of circulating IGF-I, sex hormone-binding globulin (SHBG), and total and calculated free testosterone concentrations with prostate cancer incidence and mortality. For prospective analyses, risk was estimated using multivariable-adjusted Cox regression in 199 698 male UK Biobank participants. Hazard ratios (HRs) were corrected for regression dilution bias using repeat hormone measurements from a subsample. Two-sample Mendelian.
MC_UU_12013/1, MC_UU_12013/2, MC_UU_12013/3; Nuffield Department of Population Health, Grant/Award Number: Early Career Research Fellowship; Wellcome Trust, Grant/Award Number: LEAP 205212/ Z/16/Z randomisation (MR) analysis of IGF-I and risk used genetic instruments identified from UK Biobank men and genetic outcome data from the PRACTICAL consortium (79 148 cases and 61 106 controls). We used cis-and all (cis and trans) SNP MR approaches. A total of 5402 men were diagnosed with and 295 died from prostate cancer (mean follow-up 6. 9 years). Higher circulating IGF-I was associated with elevated prostate cancer diagnosis (HR per 5 nmol/L increment = 1. 09, 95% CI 1. 05-1. 12) and mortality (HR per 5 nmol/L increment = 1. 15, 1. 02-1. 29). MR analyses also supported the role of IGF-I in prostate cancer diagnosis (cis-MR odds ratio per 5 nmol/L increment = 1.
The UK Biobank study has standardised measurements of hormones from baseline blood samples collected in the whole cohort (500 000 participants) as well as repeat measurements of the hormones in a subset (20 000). In this article, we aimed to examine the associations of serum concentrations of IGF-I, SHBG, total and free testosterone with prostate cancer incidence and mortality, using observational data from UK Biobank. For IGF-I, we investigated potential causal associations of IGF-I with prostate cancer using MR analyses, with genetic data from UK Biobank and the PRACTICAL consortium (based on 79 000 prostate cancer cases and 61 000 controls). MR analyses of SHBG, total and free testosterone and prostate cancer risk using these datasets have recently been published. 9 MR uses germline genetic variants as proxies of putative risk factors and estimates their associations with disease.
| Exclusion criteria Our analytical dataset included 199 698 men; we excluded 9871 men with prevalent cancer (except C44: non-melanoma skin cancer), 13 509 men who did not have blood data available or who had biomarker measurements that did not pass quality control procedures,  20  1685 participants for whom it was not possible to determine genetic sex or who were identified as being genetically female, 2326 men who reported taking hormone medication at baseline and 758 men who had no body mass index (BMI) data.
6%]), alcohol consumption (non-drinkers, <1-<10, ≥10-<20, ≥20 g ethanol/day, unknown [0. 5%]), and self-reported diabetes (no, yes and unknown [0. 5%]). Adjustment covariates were defined a priori based on previous analyses of UK Biobank data and categories were used to allow for nonlinear associations. 21 ood biomarker measurements were also available for up to 7776 men who attended a repeat assessment clinic a median of 4. 4 years after first blood collection. 18 Measurement error and within person variability using single measures at baseline leads to underestimation of risk (ie, regression dilution bias),  22  to provide more precise and generalisable risk estimates, HRs for trend were estimated per absolute increase in usual hormone concentrations, with correction for regression dilution bias using the McMahon-Peto method.
| Genetic instruments for prostate cancer We used summary statistics for SNP associations with prostate cancer risk that were generated from 79 148 prostate cancer cases and 61 106 controls of European ancestry from the PRACTICAL, CRUK, CAPS, BPC3 and PEGA-SUS consortia. 28, 29 In brief, 44 825 prostate cancer cases and 27 904 controls were genotyped using OncoArray (http://epi. grants. cancer. gov/oncoarray/), and data were also available from several previous prostate cancer GWAS: UK stage 1 and stage 2; CaPS 1 and CaPS 2; BPC3; NCI PEGASUS; and iCOGS. Genotype information was imputed for all samples using the October 2014 release of the 1000 Genomes Project data as the reference panel. Odds ratios (ORs) and SEs were estimated using logistic regression and then metaanalysed using an inverse variance fixed-effect approach.
There is no direct functional evidence for the cis-SNP. However, this SNP is in modest linkage disequilibrium with the SNPs rs17727841, rs10860864, rs5742671, rs1996656 and rs11111250, which are expression quantitative trait loci (eQTLs) associated with IGF1 and WASHC3 expression. 36 PheWAS using published data showed the cis-SNP was associated with measures of lung function and adiposity (Supplementary Table  S5 ). There was a large amount of pleiotropy in the trans genetic instruments, for example, for the top 100 trans-SNPs most strongly associated with IGF-I, there were >2500 published genome-wide significant associations (using the Phe-noScanner resource).
45 Our MR analysis of IGF-I using a cis-SNP is an example of the strongest case for an MR analysis, due to the strong plausibility of a biological link and a reduced likelihood of horizontal pleiotropy  46, 47  ; therefore the association for this cis-SNP indicates that IGF-I may be driving the reported associations with prostate cancer risk. A limitation of the analysis is that prostate tumour stage and grade information are not currently available in the UK Biobank, and only incident prostate cancer data are available in the MR analyses. The UK Biobank participants are predominantly white and healthier than the sampling population; therefore, selection bias may influence the results  48  and risk estimates may not be generalisable,  13  although this is unlikely to affect the direction of the associations.
ACKNOWLEDGEMENTS This research has been conducted using the  UK Biobank Resource  under application number  3282  and data from the  PRACTICAL consortium, CRUK ,  BPC3 ,  CAPS  and  PEGASUS. This work was supported by  Cancer Research UK  (grant numbers  C8221/A19170 ,  C8221/ A20986  and  C8221/A29017 ). ELW was supported by the  Nuffield.
Patients All consecutive patients with a confirmed diagnosis of COVID-19 by PCR and initially admitted to the abovementioned ICU of 31 beds at ZHWU from December 30 2019 to March 12 2020 (n = 57) were included in the study. Patients admitted to this ICU during this period also included 10 consecutive patients for which there was a radiological evidence of viral pneumonia  [14]  while RT-PCR test of throat swabs had remained negative for several times, and these patients were also considered as eligible for the study. Last follow-up of patients was made on April 8 2020, 91 and 34 days after the first and the last admission, respectively. The file of each patient along his/ her hospital course was cautiously reviewed, including whenever the patient was transferred to another hospital. The following data were collected for each patient: age, sex, date of admission and discharge in the hospital as well as the vital status at discharge (dead or alive), date of admission and discharge in the ICU as well as the vital status at discharge (dead or alive), beginning and end dates of mechanical invasive or noninvasive ventilation procedures.
Statistical analyses Data are expressed as mean (95% confidence interval (CI)) or median [interquartile range (IQR)], and represented according to the Kaplan-Meier estimator  [15]. In addition, we examined how the ICU ALOS estimates of COVID-19 patients issued from two estimation methods evolve and compare while the cumulative number of available stays increases along the course of the outbreak. All analyses were made with R statistical software version 3. 6. 1 and censored data were fitted with the use of the flexsurv package. The two methods compared were the following.
The medians of ICU LOS reported  [6-8, 17, 18]  (see Table  1 ) often concern a particular sub-population (e. g. , patients who died, patients who survived), ranged from 4 days (estimate considering six patients who died in the ICU) to 11 days (estimate reported in the same study and based on 12 patients discharged alive from the ICU)  [18] , and raise concerns in terms of the potential bias of the reported estimates (see Table  1 ). These concerns may be then extended to modeling studies  [9, 10, 12, 13, 18]  that will naturally parameterize their forecasts according to the observational data reported (Table  1 ). The data of the series reported here yielded an estimate of ICU ALOS at 21. 1 days (95% CI 17. 5-25. 3) and a median ICU LOS at 17. 4 days [IQR 9. 6-28. 7]. These estimates are well above estimates that were reported in the early phase of the epidemic.
Methods: We assembled an extensive dataset of first records of detection of ANS  across 49 aquatic ecosystems, including the (a) year of first collection, (b) population status and (c) potential pathway(s) of introduction. Data were analysed at global and regional levels to assess patterns of detection rate, richness and transport pathways. Results: An annual mean of 43 (±16 SD) primary detections of ANS occurred-one new detection every 8. 4 days for 50 years. The global rate of detections was relatively stable during 1965-1995, but increased rapidly after this time, peaking at roughly 66 primary detections per year during 2005-2010 and then declining marginally. Detection rates were variable within and across regions through time. Arthropods, molluscs and fishes were the most frequently reported ANS.
| INTRODUC TI ON Analyses of long-term datasets on species introductions can provide insights into past and future trends to inform conservation management  (Seebens et al. , 2017; van Kleunen et al. , 2015) , yet such studies are relatively rare, particularly for aquatic ecosystems where species introductions are often more difficult to detect and study  (Ojaveer et al. , 2015). A recent study examining more than 45,800 records of mainly terrestrial non-indigenous taxa showed a continuous rise in the rate of detection during the time period  and a strong correlation between detection rates and trade values for taxa associated with transport pathways  (Seebens et al. , 2017). Moreover, the growth of trade and worldwide transport of goods by shipping is predicted to lead to a surge in the translocation of both terrestrial taxa (as stowaways in cargo and packing materials) and marine taxa (within ships' ballast water and biofouling)  (Sardain et al.
The CBD revised Strategic Plan for 2011-2020 proposes that "By 2020, invasive alien species and pathways are identified and prioritized, priority species are controlled or eradicated, and measures are in place to manage pathways to prevent their introduction and establishment"  (UNEP, 2011). New goals for 2021-2030 are currently being drafted, with prevention and pathway management likely to remain important components of the strategy. Despite efforts to establish reporting mechanisms (e. g. Pagad et al. , 2018) , reliable inventories worldwide of non-indigenous species and their pathways are still relatively few in the published literature, especially for aquatic environments. Here, we assembled an extensive dataset of first records of detection of ANS to assess patterns of detection rate, richness, population status and transport pathways during a 50-year time period across 49 global ecosystems, including marine and estuarine habitats as well as the freshwater Laurentian Great Lakes.
| ME THODS We assembled an extensive dataset of primary detection events of ANS (i. e. the first recorded collection of each species in each region) across global (primarily coastal marine, but also estuarine and freshwater) aquatic ecosystems for the period 1965-2015 using online ANS databases as primary data sources, including AquaNIS (http:// www. corpi. ku. lt/datab ases/index. php/aquan is/), GLANSIS (https:// www. glerl. noaa. gov/glans is/), Marine Biosecurity Porthole (https:// www. marin ebios ecuri ty. org. nz/) and NEMESIS (http://invas ions. si. edu/nemes is/). In addition, we conducted a literature search using the Web of Science to locate published datasets not available online, using the search terms "nonindigenous" or "non-indigenous" or "nonnative" or "non-native" or "alien" AND "aquatic" or "marine" AND "database" or "dataset" or "list" or "inventory".
Datasets were included only if dedicated, expert research on ANS had been conducted such that an up-to-date comprehensive and reliable inventory exists; datasets comprised of only a single taxonomic group, or not listing dates of first collection, or covering a shorter time period than this analysis were not included. As the geographic scale of different studies varied, we compiled data at the scale of Large Marine Ecosystem  (Sherman, 1991 ; http://lme. edc. uri. edu), but also included available data for smaller marine ecosystems (i. e. Galápagos Islands and Madeira Archipelago). We excluded records from inland or freshwater habitats but included the Laurentian Great Lakes as a large freshwater ecosystem accessible to global shipping  (Pagnucco et al. , 2015). At least one co-author with extensive regional knowledge of ANS carefully reviewed and edited each regional dataset for accuracy, checking scientific peer-reviewed publications, reports, books and personal collections to confirm: (a) year of first collection; (b) current population status and; (c) potential pathway(s) of introduction (as known up to July 15, 2020; Table  1 ).
We chose the timeframe 1965-2015 with the assumption that there has been increasing awareness and more comprehensive reporting since 1965, with time-lags presumably being smaller compared to reports prior to 1965. In cases of multiple records of the same ANS, the first record within each region was used. The year of first collection was typically recorded as a single year, but when only time ranges were provided (e. g. "2012-2015"), the first year of the range was taken as the earliest possible date. For a small number of records (<0. 01%), the year of first report (the year of publication) was recorded when the date of collection was not provided. Population status was recorded for each introduced species as "established," "extinct," "failed" or "unknown" following definitions used by the NEMESIS database (see Table  1 ) which account for uncertainty associated with limited search effort.
g. sea chests, seawater inlets and inlet grates, anchors and anchor chain) (as per IMO, 2011). Ships' ballast water also included stowaways within ballast sediments as well as solid ballast materials used historically. We considered aquaculture equipment as angling/fishing equipment. Pathway information was initially taken from source online databases and published literature, if stated, and was then critically evaluated for completeness and standardization across regions/data sources based on our own expertise and knowledge. Many references implicate commercial shipping as a possible mechanism of introduction without assessing specific pathways. Therefore, we designated shipping pathways primarily based on our own prior biological surveys of ballast water and ship fouling combined with knowledge of life history characteristics of the species.
Although unassisted pathways, such as water currents, can be an important pathway for the movement of ANS (see Discussion), if the first report of an ANS within an ecosystem was considered solely due to natural spread from a neighbouring ecosystem (e. g. range expansion or range shift), it was excluded from this analysis because unassisted movement was not documented consistently across regional datasets. Transport associated with anthropogenic debris, however, was included (considering  Carlton et al. , 2017). Data were analysed at global and regional levels to explore trends in the rate of detection, ANS richness, population status and pathways of introduction. A ten-year moving average was used in temporal plots to decrease variability across years due to inconsistent search effort (see Discussion).