dataesr/scientific-paragraphs-categorization huggingface dataset:
Dogs dataset. The first dataset consists of 28 dogs for which 5615 genes were sequenced to investigate the genetic background of the furnishing phenotype that is a characteristic pattern of a moustache and eyebrows  (Broeckx et al. , 2017). Among the 28 dogs, 16 are standard poodle, thus having a furnishing trait, while 12 are not furnished. Due to the small sample size, the correlation matrix of the test statistics vector may not be positive definite. When needed, it is replaced by the nearest positive definite correlation matrix, computed with the nearPD function of the R package Matrix  (Bates and Maechler, 2018). The p-values are calculated by a Monte-Carlo method with 1,000,000 random permutations for each gene. Due to extremely large computational time, the results for the omnibus test could not be computed.
Crohn's disease dataset. The second application to a genetic association testing issue focuses on the study of Crohn's disease, a type of chronic inflammatory bowel disease for which genetic factors are known to play a major role. In de  Lange et al. (2017) , a list of genes known to be involved in Crohn's disease is given. All the global testing methods listed in Table  1  and the MGF-R procedure are applied to these 73 genes using the WTCCC dataset  (Wellcome Trust Case Control Consortium, 2007). In total, eight genes are significantly detected by at least one method: DENND1B, SCAMP3, MST1, CARD9, ZMIZ1, C10orf55, PTPN2 and SBNO2. The corresponding p-values after a Bonferroni correction are given in Table  5  for all global testing methods, except SKAT which does not detect any significant genes.
, 2013; Lindanger et al. , 2020; Smith et al. , 2016). Given the rate of WWLLN and GLD360 strokes in a 500 km radius around all ASIM TGFs, we estimate the number of strokes associated by chance in a 30 ms interval around all our 14 events to be 0. 05 for WWLLN and 0. 8 for GLD360. For most events we found at least one possible associated stroke in the database of at least one of these networks. The exception is event 6, occurring in an area with low detection efficiency from any network. In other cases  (nos. 1, 9, 10, 12, 13, 14) , lightning activity was detected in the area, but it was not possible to select a specific stroke as the most likely associated candidate. Radio waveforms from the candidate associated strokes were provided by ENTLN. We used them to recognize the type of lightning (intra-cloud, IC, vs.
cloud-to-ground, CG) and the polarity. When MMIA data are available, the waveform is one of the references that we use to correct ASIM's timing. Table  2  provides a list of the possible associated lightning strokes with the available information. The table reports also the peak current and polarity provided by ENTLN and GLD360. The strokes associated to event number 5 was also recorded by one of the authors during a sprite observation campaign. The instrument used is a wideband digital low-frequency radio receiver which can measure electric field strengths from around E 4 Hz to E 400 kHz  (Füllekrug, 2009). The amplitude resolution is around 35 E V and the temporal resolution is 12 ns. The receiver was placed in Klerefontein, South Africa (30. 97°S, 21. 98°E). Finally, the Lightning Imaging Sensor (LIS) on the ISS  (Blakeslee et al.
Meteorological Data The Cloud Top Temperature (CTT) was used to evaluate the severity of the TGF-producing storms and compare their characteristics to the low-latitude ones. Images of CTT were obtained, using the French database ICARE, from the appropriate geostationary satellites: Meteosat, GOES or Himawari. A CTT image is provided every 10 min for GOES, 15 min for Meteosat and 20 min for Himawari. The image closer to the timestamp of the TGF was used. The global lightning flash rate is obtained from the LIS/OTD climatology database  (Albrecht et al. , 2016). The maps offer the monthly flash density per square kilometer over the globe with a resolution of 0. 5° in both latitude and longitude. This is used in the estimate of the TGF-tolightning ratio, with the final goal of comparing the ratio at mid latitude with the one at low latitude.
Thunderstorm Environment We analyze the thunderstorm environment with data from geostationary satellites (see Section 2. 3). The aim is to have an overview of the evolution of the storm up to the production of the TGF, and of the CTT. CTT is used as a proxy for cloud top altitude (in the troposphere, lower CTT implies higher altitude), since a more precise conversion between the two requires a detailed model of the local atmosphere. Additional information on the storm severity was obtained by tracing the flash activity recorded by the ENTLN, WWLLN and GLD360 lightning detection networks.
Event No. 4: November 7th, 2018 The event occurred on November 7th, 2018 at 05:45:39 UTC over the northern Atlantic Ocean, almost 800 km away from the North American coast. MMIA and LED were active at the time, and both recorded data related to the TGF. Figure  7  shows all these data synchronized between the different instruments and centered around the associated stroke, as recorded by GLD360. MMIA data have been shifted to agree with the sequence of  MAIORANA ET AL.
10. 1029/2020JD034432 13 of 21 strokes detected by GLD360, therefore correcting ASIM's absolute timing uncertainty as described in Section 3. 1; MXGS data were also shifted accordingly. For this event the MXGS-MMIA relative timing accuracy is 80 E   s. Figure  8  is a zoom in around the correlating stroke, to better show the relative position of the TGF and the MMIA optical pulse, and the stepping leader activity in the radio band (the isolated spikes in the waveform on the left of the main stroke). ENTLN reported a stroke which is compatible in position and with a timestamp 30 E s earlier than the GLD360 one; this stroke is also shown in Figure  8. The two strokes align almost perfectly on the waveform, after accounting for the propagation time to the detector, therefore suggesting that they are two reconstruction of the same current pulse.
Meteorological Analysis The 14 events we are presenting are likely following local meteorological patterns, but it is worth noticing that they all happened during the local summer or autumn. None of them happened during the local  winter, which may be linked to a lower frequency of convective activity in the study area, as winter convection tends to be lower above sea level, thus making it even more difficult for a hypothetical TGF to escape the atmosphere. We analyzed the evolution of each parent storm, reconstructed using cloud top temperature data from geostationary satellites. All events except two were produced within isolated and relatively small thunderstorms. The only exceptions are event no. 1, produced in the rainband of hurricane Chris, and event no. 4, associated to extensive multicell systems.
Characteristics of Correlating Lightning Strokes The peak current of the parent strokes, reported in Table  2 , is between 10 and 30 kA for all events except the four ones reported below. Peak current estimation for IC lightning is difficult and imprecise for a variety of reasons  (Cummins & Murphy, 2009) , so global or even long-term datasets of IC characteristics are not available. Cummins and Murphy (2009)  and  Rakov and Uman (2007)  report peak currents of around 1 kA for the K-change phase and even smaller currents for the stepping phase. From the analysis of a sample of positive polarity sferics detected by GLD360 in a 15 E  minutes interval around the trigger times of our sample and 10° radius around ASIM's location we find a median peak current of +6 kA. The peak current of the strokes associated to our sample are significantly higher than that, but, on the other hand, most of them cannot be classified as Energetic In-cloud Pulses (EIP) or Compact Intra-cloud Discharges (CID) either.
Acknowledgments This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement  722337. The authors wish to thank  Earth Networks  (https://www. earthnetworks. com/),WWLLN (http:// wwlln. net)and Vaisala (https://www. vaisala. com/en)for providing lightning data. This study was supported by the  Research Council of Norway  under contracts  208028/F50  and  223252/F50  (CoE). The authors thank the French AERIS/ICARE Data and Services Center which provided MSG/SEVIRI data for cloud top temperature.
Features selection and model building Features were processed for classification and toxicity modeling via a supervised NN approach relying on the NN library in SPSS Statistics Ò (Perceptron Multilayer Network). The detailed protocol is presented in the Supplementary Materials. Three models were incrementally built: using only clinical factors as inputs, adding DVH metrics to clinical factors, and finally considering all available parameters i. e. , clinical, DVH and dose map radiomic features as inputs to the network. The cohort was partitioned into a training set (~70%, n = 117) and a testing set (~30%, n = 50) using the stratified random sampling method  [17]. In the training set, an evaluation of the importance of independent features in the NN determination was performed inside each model.
Optimal cut-off values for each feature/model were determined according to the Youden Index. Quantitative performance evaluation was carried out using balanced accuracy (BAcc) defined as the average of sensitivity (Se) and specificity (Sp), regarding the prediction of each aforementioned toxicity event. To further enhance the robustness of the models and their selected features, the bootstrap resampling method with n = 1000 replications was used relying upon the bagging library implemented in SPSS Modeler Ò. For each sub-sample, a new NN was trained using the previously selected features resulting in an individual performance (assessed using BAcc). Results over all 1000 replicates were then reported as a mean BAcc. Finally, the best trained models for each endpoint prediction were evaluated in the testing set using the BAcc, the AUC and the R 2.
Materials and Methods Looking at the pattern of COVID-19 data of Kuwait, one realises that it consists of two main waves. Each wave lasts approximately three months. We took 100 days both in the first wave and second wave depending on the available data. First wave new cases were considered from 25/02/2020 to 03/06/2020 while second wave new cases were considered from 15/10/2020 to 22/01/2021. For the first wave, we used daily deaths data from 04/04/2020 to 12/07/2020, while for the second wave we used those from 15/10/2020 to 22/01/2021. We calculated the slopes from the log-linear regression analysis using exponential model y = ae bx , where y is the daily number of new cases, x the number of days, b the slope and loga a constant in the log format logy = loga + bx. We also calculated the initial negative autocorrelation slope of the epidemic spread averaged on six days.
Methods We analysed data from two prospective cohort studies in the UK; the English Longitudinal Study of Ageing (ELSA) and the Whitehall II study, to assess sex differences in cognitive performance and cognitive decline by birth cohort (birth year 1930-38, 1939-45, or 1946-55), before and after adjustment for education, and by high and low education level. Memory was assessed using immediate recall, for which data were available from all waves of the ELSA (2002-14) and Whitehall II (1997-2015) studies. Fluency was assessed using a semantic fluency test based on an animal naming task, with data available from all waves of the Whitehall II study and waves one to five (2002-10) and wave seven (2014) of the ELSA study. Cognitive scores were standardised separately in each study based on the mean and SD of the corresponding test among participants aged 50-59 years with secondary education.
] [18]  Considering the complex interplay between sex, level of education, and birth cohort in shaping cognitive ageing trajectories, we used longitudinal data on men and women born between 1930 and 1955 in analyses stratified by birth cohort to examine the role of education in sex differences in memory and fluency performance and decline. The primary hypothesis was that male advantages in cognition would be attenuated in later birth cohorts due to secular changes in access to education.
Data sources and participants We analysed data from two prospective cohort studies in the UK; the English Longitudinal Study of Ageing (ELSA) and the Whitehall II study. ELSA included adults in England aged 50 years and older, recruited from the 1998-2001 Health Survey for England, who have sociodemographic characteristics that are broadly representative of the general population of England. 19 Data collection was done in 2002 (March, 2002, to March, 2003) with followup in 2004 (June, 2004, to July, 2005), 2006 (May, 2006,  to August, 2007), 2008 (June, 2008, to July, 2009), 2010 (July, 2010, to June, 2011), 2012 (May, 2012, to May, 2013), and 2014 (June, 2014, to May, 2015). We used these seven waves of data (2002-14), which comprise participants born in 1930 and thereafter, in our analyses to harmonise the range in birth year cohorts in both studies.
Research in context Evidence before this study We searched PubMed on June 30, 2020, for publications in English from database inception until June 30, 2020, using the Medical Subject Headings search terms "sex factors", "education", "cognition", "Alzheimer's disease", "dementia", "cognitive aging", "aging", "birth cohort", and "neuropsychological test". Previous studies have shown a greater risk of Alzheimer's disease in women than in men, and education is thought to have a causal association with cognitive performance and in delaying the onset of dementia. Although there have been some studies that investigated the role of education in sex differences in cognitive performance and decline, these studies were not sufficiently large to comprehensively assess sex differences in cognitive outcomes and did not examine the role of secular changes in education in men and women.
The choice of single rather than multiple imputation was primarily to enable analyses stratified by education as each imputed dataset would yield different numbers in each education category, changing the results of stratified analyses between imputations. However, single imputation can potentially underestimate SEs and therefore result in overly precise CIs. Birth cohorts were defined on the basis of sociohistorical events,  15  as follows: the Depression-era cohort (birth year 1930-38), the World War 2 cohort (1939-45), and the post-War cohort (1946-55). Other covariates included age, ethnicity (white and non-white), and practice effect (to account for changes in test performance attributed to increasing familiarity with test instruments and protocols)  15  using a dichotomous indicator of whether or not the measure of cognitive function was the first cognitive assessment for the participant.
Outcomes The primary outcomes were performance and decline in memory and fluency. Memory was assessed using immediate recall. In the ELSA study, participants were read a ten-word list at 2-s intervals by an interviewer. The respondent was then asked to recall aloud as many words as possible within 2 min. In the Whitehall II study, participants listened to a list of 20 words at 2-s intervals on a tape and were then asked to recall in writing as many as possible within 2 min. Data from memory assessments were available at all waves of the ELSA (2002-14) and Whitehall II (1997-2015) studies. Fluency was assessed using a semantic fluency test based on an animal naming task. In ELSA, participants had 1 min to say as many animals as possible aloud to an interviewer. In Whitehall II, participants were given 1 min to write as many animals as possible.
Statistical analysis Participant characteristics by sex and birth cohort were described separately for ELSA and Whitehall II, as well as in the pooled database. Differences between sexes in categorical variables were assessed using Pearson's χ² test and in continuous variables using the t test. The χ² trend test was used to assess birth cohort differences in participant characteristics separately in men and women. To harmonise cognitive tests between the two studies and allow comparison between cognitive domains, cognitive scores were standardised separately in each study on the basis of the mean and SD of the corresponding test among participants aged 50-59 years with secondary education. These data were then pooled for analyses. Linear mixed models were used to assess differences between sexes in cognitive performance and decline.
These models use all available data over the follow-up, handle differences in length of follow-up, and account for the correlation of the measures in each study as well as the correlation of the repeated measures on the same individual. 21 Both the intercept (at the study and individual level) and slope (at the individual level) were fitted as random effects with an unstructured covariance matrix at the individual level, allowing for the consideration of study-specific and individual differences in cognitive performance at baseline and individual differences in rate of cognitive decline in model coefficient estimation. Age was used as the time scale and analyses were centred at age 60 years. Initial models for memory and fluency including effects of sex, sex by age, age, age², age³, ethnicity, birth cohort, and practice were fit to produce summary measures of cognitive performance and decline.
In the ELSA study, results were similar to those in the low education group in the pooled analyses and in the Whitehall II study, results were similar to those in the high education group in the pooled analyses. Neither omi tting respondents with dementia (appendix pp 7-10), limiting years of follow-up to 2002-15 (appendix pp 11-14), nor using multiple imputation rather than single imputation for missing data on level of education (appendix pp 15-16)  sub stantially impacted the results.
Another strength is the long follow-up period, which allowed us to examine cognitive trajectories from midlife to older ages. Our study has some limitations. The ability of linear mixed models to handle incomplete data is dependent on the assumption that data are missing at random, which might not be correct in this study. 15 The effect of attrition on memory scores in the ELSA study was previously examined using joint models and estimates were similar to those using linear mixed models. 15 A paper from the Whitehall II study also found a similar association of socioeconomic factors with cognitive performance and decline when estimates from mixed models were compared with simulations with a missing-not-atrandom assumption. 22 Differences in mean follow-up between men and women in each cohort were small compared with the overall mean follow-up duration (1•3 years difference in Whitehall II and 0•5 years in ELSA, representing at most 10% of the mean follow-up duration of these studies).
Thus, the effect of attrition on our findings is likely to be small. Participants in the ELSA and Whitehall II studies are mostly white, and the extent to which these results are generalisable to other racial and ethnic groups is unknown. However, the ethnic composition of both studies reflects the population in England for the birth years included in the analyses. 23 x in both studies was measured as declared in administrative documents rather than by gender identity. The effect of education on cognition is likely to arise through expectations of gender roles rather than effects of biological sex, but absence of data on gender leads us to refer to sex rather than gender differences. Detailed analysis of education using years of schooling, or more categories than our four-category measure, is likely to affect findings, perhaps strengthening the attenuation in sex differences after adjustment for level of education.
Contributors SS, AD, JD, and AS-M conceived the idea for the study. SS, AD, and AF designed the study methodology. MK, AS-M, AS, and AB did the investigation. SS and AD accessed and validated the dataset. MB, AD, and SS did the formal data analysis. MB, AD, AF, and SS curated the data. MB and SS prepared the first draft of the manuscript. All authors reviewed and edited the manuscript. MB, AD, and SS were responsible for the figures. SS and AS-M supervised the study. AS-M, MK, and AS acquired the funding for the study.
Acknowledgments The  Whitehall II  study has been supported by grants from the  National Institute on Aging, National Institutes of Health  ( R01AG056477 ,  RF1AG062553 ); the  UK Medical Research Council  ( R024227 ,  S011676 ,  K013351 ); and the  British Heart Foundation  ( RG/16/11/32334 ). ELSA funding has been provided by the  National Institute on Aging, National Institutes of Health  ( R01AG017644 ), and a consortium of  UK Government  departments coordinated by the  National Institute for Health Research.
www. thelancet. com/public-health Vol 6 February 2021 https://www. ucl. ac. uk/epidemiology-health-care/research/epidemiologyand-public-health/research/whitehall-ii/data-sharing. The ELSA data are freely available to researchers through the UK Data Service at: https://beta. ukdataservice. ac. uk/datacatalogue/series/series?id=200011.
To mitigate batch effects, we pooled IAV-challenged and noninfected cells from distinct donors in each library, assigning cells to their condition in silico via genetic barcoding  (20). After stringent quality control where we removed low-quality, dying, and contaminants of the CD14 + monocyte isolation, our final dataset contained 88,559 high-quality cells, among which we predicted >99% monocyte purity at T 0 (Figure  1A ; Supplementary Figures  1  and 2 ). At later time points, a substantial fraction of non-infected cells (up to 70% at T 8 ) were predicted to be macrophage-like, indicating monocyte differentiation over the course of the experiment. For clarity, we refer to cells as monocytes at T 0 and as monocyte-derived cells from T 2 -T 8.
Interplay of Cytokine and Ribosome Networks Drive Cell States Upon Infection To characterize host transcriptional responses over time, we next subsampled each subset (CD16 -/CD16 + ), cell state (unexposed, bystander, infected), and time point in our scRNA-seq data to a uniform number of cells to avoid biases emanating from differences in sample sizes. Limited by the number of CD16 + high IAV-transcribing cells, we randomly sampled 100 cells from each subgroup, while ensuring representation of all donors. We then focused on the 6,669 host genes with average log 2 normalized count >0. 1 in at least one subgroup (Supplementary Data 3A). Overall, CD16 - and CD16 + subsets behaved similarly upon stimulation with changes in gene expression between cell states being strongly correlated among subsets (Pearson r=0.
We used this feature to identify droplets presumed to contain two or more cells originating from the same donor; barcodes with > 5 doublets as nearest-neighbors were excluded from further analysis (Supplementary Figures  1A, B ). Additionally, droplets containing low-quality cells (i. e. damaged, dying) were excluded using the following thresholds: <1500 total counts, <500 genes, or >50% mitochondrial gene content (Supplementary Figure  1C ). This QC resulted in 96,386 single cells. Transcriptomes (i. e. counts) were adjusted for the presence of ambient RNA with SoupX, (https://github. com/constantAmateur/ SoupX, accessed November 28, 2019) (21), using estimated contamination fractions (per 10X library) from SoupOrCell  (53). SoupX-adjusted counts were normalized using pool-based size factors followed by deconvolution as implemented in the scran R package  (54).