dataesr/scientific-paragraphs-categorization huggingface dataset:
Assessment of meat consumption in the EPIC-Norfolk study Meat consumption and other dietary exposures were assessed with a 7-d diet diary (7dDD) as documented previously  (18). Briefly, on the day of a baseline assessment that included blood sampling, participants were asked to record everything they ate (food types, amounts, brands, recipes, and cooking methods) prospectively for the following 7 consecutive days. The dietary information was then processed into food and nutrient data by programs and databases (DINER and DINERMO) using standard protocols  (18, 19). The meat-related categories were all disaggregated from composite dishes including red meat (unprocessed beef, lamb, pork, veal, rabbit, venison, etc. ),  processed meat (bacon, ham, sausages, etc. , smoked, cured, salted, or chemically preserved), and poultry (chicken, turkey, goose, duck, guinea fowl, pheasant, etc.
Development and validation of metabolite scores of self-reported red meat, processed meat, and poultry consumption In the EPIC-Norfolk study, 781 metabolites were evaluated simultaneously for the prediction of red meat consumption. In the exploratory set, we applied elastic net regression  (20)  with a bootstrap approach  (21, 22)  to select a combination of metabolites for prediction of red meat consumption; and ridge regression  (23)  to estimate penalized weights of these candidate metabolites (Supplemental Methods). We applied the weights of all candidate metabolites and constructed a metabolite score for each individual in both of the derivation and validation data sets. The score was standardized to a mean of 0 and SD of 1 for further analysis. The metabolite scores for processed meat and poultry were derived and tested using the same process.
RCT of meat consumption Given the availability of trial-based data for meat consumption, we further investigated associations of metabolites in the score from the observational EPIC-Norfolk study with red meat consumption in an RCT previously conducted in Lyon, France in 2018 (NCT03354130). The details of this RCT have been reported previously  (24). In brief, 12 healthy adults consumed in random order 5 different foods (fried pork, hot dogs, bacon, salami, and tofu) as part of a controlled diet. For this analysis, we examined the differences in metabolite concentrations between the fried pork (unprocessed red meat) and tofu control arms. In this trial, fasting plasma samples were collected during the morning after the last meal of each test period. Participants provided informed consent and procedures were carried out according to the Declaration of Helsinki.
Then we used paired Welch's t tests to assess whether metabolites were significantly (P < 0. 05) elevated in plasma samples collected after pork intake compared with tofu intake. Second, for metabolites not previously identified in the IARC laboratory, we extracted only those with a coefficient > 1. 0 in the meat intake score from the raw data by formula only to test for their increase in plasma samples after pork intake. Compounds were confirmed by comparison of MS/MS spectra to those in the literature (annotation confidence level 2 or 3)  (25).
Ascertainment of other noncommunicable disease outcomes in the EPIC-Norfolk study We ascertained the incident outcomes of 6 health conditions including cardiovascular diseases (including ischemic heart disease, hemorrhagic stroke, cerebral stroke, heart failure, and atrial fibrillation), gastrointestinal cancers (including colon cancer, rectal cancer, stomach cancer), liver disease, renal disease, fractures, and deaths due to any causes  (16). Outcome data were obtained by linkage to Hospital Episode Statistics, the cancer registry, and the Office of National Statistics. Follow-up ended on 31 March, 2016. Prevalent and incident cases for each disease were identified with the International Classification of Diseases 10th revision as listed in Supplemental Table  1.
Associations of metabolites in the red meat metabolite score with meat intake in an RCT For the metabolites that were part of the metabolite score for red meat intake, we used untargeted plasma metabolomics data from a meat RCT to investigate the differences of metabolite concentrations after a 3-d red meat intervention compared with a nonmeat diet. Of the 50 known metabolites positively associated with self-reported red meat consumption in the observational EPIC-Norfolk study, 11 were identified in the RCT and significantly increased after fried pork (red meat) intake compared with tofu: several glycerophospholipids, 4-hydroxyproline, TMAO, creatine, deoxycarnitine, and stearoylcarnitine (Table  2 , Supplemental Figures  3  and 4 ). Supplemental Figure  5  shows the correlations between these top-ranked metabolites and types of meat consumption in the EPIC-Norfolk study.
Supplemental Figure  3  shows the variation of metabolite intensity after consumption of pork compared with tofu. 3 Supplemental Figure  4  shows the chromatographic tracing of selected metabolites in the blood after consumption of pork compared with tofu. 4 Level of confidence in metabolite identification according to Sumner et al. (25) : level 1, matching of mass, retention time, and mass fragmentation pattern with authentic chemical standard; level 2, matching of accurate mass and mass fragmentation pattern with the corresponding compound in a database; level 3, matching of mass and fragmentation pattern with the corresponding compound in a database-due to the nonspecific fragment, only the functional group, but not the length of each carbon chains can be determined. 14  (8)  17  (10)  11  (7)  17  (10)  54  (8)  A-level 329  (39)  60  (36)  64  (38)  72 (  43 ) 66  (39)  67  (39)  229  (35)  Degree 117 (  14 ) 20  (12)  29  (17)  21 (  12 ) 20  (12)  27  (16)  67  (10)  Smoking Current 112  (13)  15  (9)  17  (10)  19  (11)  27  (16)  34  (20)  79  (12)  Former 328  (39)  53  (31)  59  (35)  63  (37)  69  (41)  84 (  49  37  (22)  51  (30)  42  (25)  50  (29)  290 (44) Moderately inactive 244  (29)  46  (27)  65  (38)  39  (23)  46  (27)  48  (28)  157  (24)  Moderately active 206  (24)  39  (23)  37  (22)  45  (27)  44  (26)  41  (24)  122  (19)  Active 162  (19)  30  (18)  30  (18)  34  (20)  37  (22)  31  (18)  90 (  14 ) BMI, kg/m 2 The associations of the red meat metabolite score and self-reported red meat intake with incident T2D in a nested case-cohort study and exploratory analyses of multiple other health outcomes in the EPIC-Norfolk study.
| The MetCoOp ensemble prediction system -MEPS The MetCoOp Ensemble Prediction System (MEPS) is the primary weather forecasting system for FMI. MetCoOp (Meteorological Cooperation on Operational NWP) includes the national weather services of Estonia, Finland, Norway, and Sweden and is a version of the convection-permitting NWP model HARMONIE-AROME  (Bengtsson et al. , 2017). The forecast domain has a horizontal resolution of 2. 5 km on a 960 Â 1080 grid, covering Scandinavia and several other countries in northern Europe. In the vertical, it uses 65 hybrid levels, with the lowest (highest) level at around 12 m (10 hPa). There are 21 levels in the lowest 1 km in the atmosphere with a discretization of 25-90 m and the vertical resolution decreases with height above this. In HARMONIE-AROME, the effects of the underlying surface are treated by the surface interaction module SURFEX  (Masson et al.
(2017). MEPS is a limited area model (LAM) and needs lateral boundary conditions, which are taken from the IFS global model ECMWF (2021). Specifically, MEPS uses IFS HRES hourly data for the unperturbed control member and the IFS ensemble (IFSENS, IFS, 2020) hourly data for the perturbed members. The unperturbed control member of MEPS is computed four times a day (00, 06, 12, and 18 UTC). Forecasts are available every hour and the first 5 h of the forecast from each cycle have been considered.
The height of urban structures and vegetation is derived from high-resolution laser scanning datasets. PALM depicts urban structures as three-dimensional solid objects via the cell masking method  Briscolini and Santangelo (1989) , whereas trees and other vegetation are modelled as porous media  Auvinen et al. (2020). The LES-generated wind profile dataset used here is obtained from  Auvinen et al. (2020) , which documented a current state-of-the-art LES approach to model the interaction between fully developed UBL turbulence and a coastal urban downtown area. The dataset consists of 11 mean wind speed profiles for the altitude range 0 < z < 256 m within the centre of Helsinki (Figure  1 ). The study considered only neutral to near-neutral meteorological conditions and the initial wind direction was 160.
| LES-lidar comparison The comparison between the LES model and Doppler lidar was made with observations from the WindCube 400S instrument. The 11 LES profiles were averaged to one mean profile representative for the city centre, and a mean Doppler lidar wind profile was retrieved for the corresponding R1 region (Figure  1 ). A schematic of the methodology used for the comparison is presented in Figure  3. First, the lidar dataset was limited to contain only neutral atmospheric conditions, as defined by the in situ observations (Section 2. 2). Second, the comparison dataset was further limited to cases where the wind direction was within the range of 140 -220 , corresponding to situations where PBL turbulence, which has been fully developed over the sea surface, becomes incident on the urban roughness.
Both LEPS and MEPS show no significant intra-day variation and the overall mean bias error (ME) in wind speed is À0. 83 ± 2. 28 m s À1 for LEPS and 0. 44 ± 1. 60 m s À1 for MEPS. As noted previously, LAPS only considers the daytime cycle from 07 to 15 UTC, hence no conclusions can be drawn for night-time. This arises from a combination of stability measurement availability (from May 2020 to October 2020), daytime length at the measurement site, and failure of the model data retrieval from the database. The overall ME for LAPS is positive (0. 07 ± 3. 13 m s À1 ) but LAPS rather underestimates the wind speed under stable or neutral conditions. This effect is introduced by the imbalanced amount of cases falling in each stability category (see Figure  5 ). ME is a good indicator of the overall skill of the models, but, when assessing the performance in different categories, it is more appropriate to use the normalized mean bias error (NMBE) as this takes into account the fact that the number of cases falling into each category varies substantially.
1. The dataset was separated into four wind direction sectors (NE, 0 -90 ; SE, 90 -180 ; SW, 180 -270 ; and NW 270 -0 ) for each point in the lidar grid domain and for each available height in order to determine the spatial bias. Figure  8  presents the mean horizontal bias between LAPS/LEPS/MEPS and observations at 60 m above ground and shows that, for certain wind directions, the morphology of the city creates a channelling effect on the wind speeds. In order to define the upper range of the discrepancy between the models and the Doppler lidar observations, the mean absolute horizontal bias expressed as the top 5% of the values for each lidar grid point was also calculated (Figure  9 ) rather than using the maximum values to ensure that any systematic error in the modelled or lidar-retrieved winds is minimized.
| DISCUSSION The performance of one NWP model (MEPS) and two analysis systems (LAPS, LEPS) was evaluated through an extended Doppler lidar dataset at two locations in Helsinki, Finland. Monthly mean biases and standard deviations showed that MEPS is the best performing model out of these three, exhibiting the least bias and a good agreement in the vertical profile. MEPS features an extended assimilation of observations and an improved turbulence scheme and, according to  Bengtsson et al. (2017) , the new turbulence scheme has resolved a number of issues present in earlier versions; PBL height and cloud base height being too low being particularly relevant. The wind climatology of the model has also been improved, particularly during near-neutral to convective conditions and during stable conditions (de Rooy & de Vries, 2017).
CONFLICT OF INTEREST The authors declare that although the research was conducted in collaboration with Vaisala through the PoDoCo funding opportunity, the research was performed in the absence of any commercial or financial relationship with them. AUTHOR CONTRIBUTIONS Maria Filioglou: Conceptualization (equal); data curation (equal); formal analysis (lead); funding acquisition (equal); investigation (lead); methodology (lead); project administration (equal); resources (equal); software (equal); supervision (equal); validation (lead); visualization (lead); writingoriginal draft (lead); writingreview and editing (lead). Jana Preissler: Conceptualization (equal); investigation (equal); methodology (equal); resources (equal); supervision (supporting); visualization (supporting); writingreview and editing (equal).
To construct the set of potential correlates we conduct a thorough review of the literature that describes the social and economic factors which contribute to the spread of an epidemic. We identify a total of 28 potential variables that describe a diverse ensemble of factors, including: healthcare infrastructure, societal characteristics, economic performance, demographic structure etc. To investigate the performance of each variable in explaining the coronavirus infections/deaths outcome, we collect a sample of 105 countries, the largest set of countries for which all data were available, and utilize the technique of Bayesian model averaging (BMA). BMA allows us to isolate the most important correlates by calculating the posterior probability that they truly regulate the process. At the same time, BMA provides estimates for the relative impact of the correlates and accounts for the uncertainty in their selection  [12] [13] [14].
In order to assess the effect of government policies regarding social distancing and testing we construct an aggregated government response index. The index quantifies the average daily variation in government responses to the epidemic dynamics. As a measure for the daily variation, we take the Oxford COVID-19 government response index  [29]. The Oxford COVID-19 government response index is a composite measure that combines the daily effect of policies on social distancing, testing and contact tracing in an economy. For each country, we construct a weighted average of the index from all available data since their first registered coronavirus infection, up until the end date, i. e. , the date when the government response index is at its maximum value. This threshold is chosen as a means to capture the moment when a country gains the ability to control and stabilize the propagation of the disease.
Health, social and economic correlates To derive the set of potential health, social and economic correlates of the COVID-19 infection and mortality rates during the first wave of the pandemic we conduct a comprehensive literature review. From the literature review we recognize a total of 28 potential correlates, listed in Table  1. For a detailed description of the potential effect of the correlates we refer to the references given in the same table, and the references therein. We hereby point out that the data for each potential correlate corresponds to the last observed value (the value in 2019). This prevents the possible problem of endogenous independent variables in the specification of the regression. In what follows, we only describe in short, the set of potential correlates on the basis of their characteristics.
In addition, the location of living may greatly affect the way in which the disease is spread  [80, 81]. To express these phenomena, we collect 7 variables. Natural environment: Numerous studies discuss possible correlation between air pollution and COVID-19 infections and mortality rates  [11, 82, 83]. In addition, some authors note that countries where natural sustainability is deteriorated, are also more vulnerable to epidemic outbreak  [10]. On the other hand, healthy natural environments may attract more tourists, which could drive the disease spread  [38]. Finally, weather patterns can also impact the infectiousness of the disease, especially exposure when there are very cold days in winter and very hot days in summer  [84]. We gather the data for 5 variables which capture the essence of this characteristic.
Moreover, with an improved understanding of the dynamics of the coronavirus pandemic, the insights obtained from this analysis can influence the development of appropriate policy recommendations. list of sources together with links to their websites is given in Table  S1. The data used in the analysis are available at https://github. com/pero-jolak/coronavirus-socio-economic-determinants. To reduce the noise from the data we, use only data for countries with population above 1 million. In addition, we only use countries for which there is data on all of the potential correlates. Table  S2  gives the countries for which all of these data was available. Altogether, we end up with data on 28 variables and 105 countries. Table  S3  reports the summary statistics of each variable. We hereby point out that as a measure of the correlate the log of the last observed value is taken (the value in 2019), unless otherwise stated in Table  S3.
Animals All mice used in this work were on an FVB/NJ background and were maintained as inbred homozygous lines. The mice were housed in an AAALAC certified facility, with 12 hours on/off light cycle, temperature control and food and water ad libitum. All procedures were approved by Cornell University's Animal Care and Use Committee (IACUC) and in compliance with the US National Research Council's Guide for the Care and Use of Laboratory Animals (14). Breedings were recorded and lineages tracked with Softmouse Colony Management Software  (15). Euthanasia was performed by CO2 inhalation in accordance with American Veterinary Medical Association approved practice.
RNA-seq experiments were performed in the Transcriptional Regulation & Expression Facility (TREx) at Cornell University. RNA integrity was confirmed on a Fragment Analyzer (Agilent) and RNAseq libraries were prepared using the NEB Ultra II Directional RNA library Prep kit (New England Biolabs), with 1 ug total RNA input and initial poly(A) selection. Illumina sequencing was performed on a NovaSeq to generate at least 20M 2x150 paired end reads per sample. The raw fastq reads were first processed with the Trim Galore package to trim for low quality reads, noisy short fragments, and adapter sequence (24). The filtered reads were then aligned to the GRCm38 reference genome (mm10) with ENSEMBL annotations via STAR using parameters [--outSAMstrandField intronMotif , --outFilterIntronMotifs RemoveNoncanonical, --outSAMtype BAM SortedByCoordinate, --quantMode GeneCounts] (25).
Differential gene expression analysis was performed by DESeq2  (26, 27). A heatmap and hierarchical clustering dendrogram of differentially expressed genes was produced using Morpheus  (28). Venn diagrams were produced with BioVenn  (29). Gene ontology classification analysis was performed using DAVID v6. 8, a web-based bioinformatics resource for functional genomics analysis to identify biological processes, molecular functions, and cellular components  (30, 31). 246 uniquely upregulated genes were identified when comparing DKO to WT using an FDR < 0. 05. 238 of these genes were identified in the DAVID database and used for subsequent analysis.
Additionally, we found that 263 genes were upregulated and 140 genes were downregulated in the DKO samples compared to WT samples (p<0. 05). Seventeen of the 22 genes that were significantly upregulated in the P2KO samples were also upregulated in the DKO samples. A comparison of upregulated genes is shown in Figure 7a. The complete lists of differentially expressed genes (DEGs) are shown in Supplementals 3 and 4. Two hundred and forty-six genes that were uniquely upregulated in DKO testes were then subjected to DAVID gene ontology analysis. Of the 246 genes, 238 were identified in the mouse database and these genes were categorized into three domains: Cellular Components, Molecular Functions, and Biological Processes (Supplemental 5). Analysis of the Biological Processes domain finds that a majority of the DE genes are involved in reproductive functions (Figure 7b), thus supporting the phenotypic traits we observed.
We hypothesized (Hypothesis 2) that switching to novel food resources in recently colonized areas influence diet composition. We analysed (1) which climatic and environmental factors affected the diet composition of the jackal. We then examined (2) whether the diet composition differed during the range expansion process between the recently colonized (settled areas by jackals in the last 2-3 decades) and the historic distribution ranges of the species. We predicted that, in the newly colonized northern areas of Europe, the trophic niche of the jackals will be narrower than that in the warmer and drier southern Eurasian areas. To test these hypotheses, we synthesized data from the literature on golden jackal stomach and scat analyses throughout Eurasia.
| Literature compilation We collected golden jackal dietary data from across Eurasia using literature searches in electronic databases (Web of Science, Scopus, Science Direct, Google Scholar) using combinations of the following keyword searches: ('golden jackal' or 'jackal' or 'Canis aureus') and  ('food' or 'diet' or 'feeding' or 'dietary' or 'prey' or 'trophic'). We conducted data compilation up to June 2020. We reviewed and extracted information from a total of 48 studies (25 from Europe and 24 from Asia, with one study from both) concerning the diet of the golden jackal, from which we selected 40 seasonal datasets (Europe 32, Asia 8; see criteria below) from 25 studies for our analysis (Figure  1 ; Table  S1 ). Detailed data of discarded studies and a full list of references are given in Table  S2.
| Variable selection We only used data that separately described the seasonal or periodical diet of jackals (cold and/or warm seasons). We assumed that food abundance varied significantly between these seasons, namely the amount of natural food types important to jackals (small mammals, wild ungulates, fruits) starts to increase in late spring and is high in summer and autumn. We defined the 'cold' season as winter and spring (December-May), or the dry half of the year, and the 'warm' season as summer and autumn (June-November), or the wet half of the year. We averaged data if a study contained separated seasonal (winter, spring, summer and autumn) values. We pooled results from the same study site to avoid pseudo-replication. The samples used were scats or stomach contents. The minimum sample number was n = 20 for seasonal data (20 samples per study site and/or season is typically considered a minimum for statistical power, e.
We limited the number of knots in the GAMMs to six as large numbers of knots can cause over-fitting of the model. We included also season (cold and warm) and distribution range (historic range or recently colonized range) as explanatory variables and material used in the study of jackal diet (scat or stomach) as random effect. Similar GAMM model with seven described above explanatory variables and one random effect was performed to analyse food niche breath variation. All environmental data were calculated within a circle of 10 km radius centred on the study site coordinates (excluding large permanent water bodies). We obtained the annual mean temperature (BIO1) and mean annual precipitation amount (BIO12) from the WorldClim database (1970-2000; 2. 5-min spatial resolution; F I G U R E 1 Golden jackal dietary study sites across Eurasia.