dataesr/scientific-paragraphs-categorization huggingface dataset:
Genetic diversity The R package poppr  (Kamvar et al. , 2014)  was used to calculate the number of MLHs, the expected number of MLHs based on rarefaction  (Hurlbert, 1971) , the Shannon-Wiener Index  (Shannon, 2001) , the Stoddart and Taylor's Index  (Stoddart and Taylor, 1988) , the Simpson's Index  (Simpson, 1949)  and genotypic evenness  (Grünwald et al. , 2003)  for the populations using the non-clonecorrected dataset, as well as the genetic diversity  (Nei, 1978)  per population using the clone-corrected dataset. The clonal fraction was calculated as in  Barnes et al. (2014b). Furthermore, allelic richness (A R ) and private allelic richness (PA R ) were determined using ADZE  (Szpiech et al. , 2008)  that uses rarefaction to allow for comparisons between populations with varying sample sizes.
Population structure The clone-corrected dataset was used to determine the most likely number of population clusters based on microsatellite allele sizes for all the individuals using STRUCTURE 2. 3. 4  (Falush et al. , 2003). The program assigns individuals to clusters (K) using a Bayesian clustering algorithm. Thirty independent runs of K = 1-20 were performed, with a burn-in value of 100,000 and 500,000 iterations. An admixture model with correlated allele frequencies was selected with no additional priors such as information on the host or location. The optimal number of clusters was estimated with StructureSelector  (Li and Liu, 2018). StructureSelector implements the Evanno method that includes delta (K) and LnP (K)  (Evanno et al. , 2005)  with the additional four Puechmaille methods (MAXMEAK, MAXMEDK, MEDMEDK and MEDMEAK) that provide a more accurate estimate of K in populations with uneven sizes  (Puechmaille, 2016).
In order to implement the Puechmaille methods, countries were assigned as populations in the dataset and the analysis was repeated twice. First a threshold of 0. 5 was selected and second a threshold of 0. 8 was selected to apply more stringent assignment of individuals into clusters. After the optimal K was determined, isolates were assigned into the optimal K clusters with a final STRUCTURE run with 30 independent runs, a burn-in value of 100,000 and 1,000 000 iterations. CLUMPAK  (Kopelman et al. , 2015)  was used to converge all 30 runs of the optimal K and the output was visualized using the DISTRUCT program  (Rosenberg, 2004). Both CLUMPAK and DISTRUCT were implemented using the StructureSelector website (https://lmme. qdio. ac. cn/ StructureSelector/). The adegenet package in R studio  (Jombart and Ahmed, 2011 ) was used to perform discriminant analysis of principal components (DAPC)  (Jombart et al.
, 2010)  to additionally visualize the population genetic structure of the European samples. The find. clusters function was used to determine the optimal number of clusters by assessment of the Bayesian information criterion (BIC). The optimal number of principal components retained in the analysis was determined by cross-validation using the xvalDapc function. An Analysis of Molecular Variance (AMOVA) test was implemented in GENALEX version 6. 5  (Peakall and Smouse, 2012). The test was used to evaluate if there was genetic differentiation among and within groups according to host species, countries and locations. One thousand permutations of the dataset were used to test significance. The null hypothesis of no genetic difference was rejected at p < 0. 05.
05) in the nonclone-corrected dataset, which provides evidence of random mating. The index of association (I A )  (Brown et al. , 1980; Smith et al. , 1993)  and rBarD ( r d )  (Agapow and Burt, 2001)  was used to test for linkage disequilibrium in the 16 microsatellite loci with both datasets using the R-package poppr  (Kamvar et al. , 2014). The null hypothesis of alleles at different loci having no linkage due to sexual mating was rejected when p < 0. 05. R f PA R g H h G i Lambda j E. 5 k D l Belgium 1 1 N/A N/A 16 0 N/A N/A N/A N/A N/A N/A N/A Czech Republic 1 1 N/A N/A 16 0 N/A N/A N/A N/A N/A N/A N/.
Population structure There was no consensus between different methods of determining the optimal number of clusters in the STRUCTURE analysis. The Evanno ΔK supported nineteen (K = 19) clusters, which indicates that this method failed to detect population structure. LnP (K) suggested K = 10 as the optimal scenario. The four Puechmaille methods suggested that 5-8 clusters are most likely the optimal number of clusters depending on the threshold that was set (Supplementary Figure  S2 ). The STRUCTURE barplots for K = 2 to K = 9 for the major modes are illustrated in Supplementary Figure  S3. The barplots for K = 5-8, together with the geographical distribution of the clusters are represented in Figure  3. In order to conduct the DAPC analysis, the find. clusters function in the adegenet package in R was used and this showed that K resides between 8 and 12.
In isolates from the Czech Republic, Romania and Russia only MAT1-1 individuals were present and in those from Belgium, Hungary, and Slovenia only MAT1-2 individuals were present (Table  3 , Supplementary Table  S2 ). Testing linkage disequilibrium using the clone-corrected dataset, with the index of association and rbarD, provided evidence for sexual recombination only in the population from Spain (p-value of 0. 144). Analysis of the non-clone-corrected dataset also supported evidence of sexual recombination in Serbia (p-values of 0. 281). This result is however not plausible as the data for both Deliblato Sands and Subotica Sands in Serbia were pooled for this analysis and therefore do not reflect that single mating types were observed at each of these locations.
Acknowledgments We acknowledge that this manuscript was a research chapter of the PhD thesis of the leading author, A. van der Nest, and that the thesis will be electronically available in the University of Pretoria library repository as: van der Nest A. 2021. Research chapter 2:  Population structure and diversity of the needle pathogen Dothistroma pini suggests human mediated movement in Europe. In: Species diversity of Lecanosticta and population genetics of Dothistroma species: important needle pathogens of Pinus. PhD(Genetics),  Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Pretoria. Pp 73-115. We thank  Dr Zuzana Janošíková-Hečková  and  Dr Emília Ondrušková  from the  Institute of Forest Ecology Slovak Academy of Sciences in Slovakia  for their assistance in obtaining the large collection of D.
59 3. 48 100 Among Hosts 7 507. 88 72. 55 0. 98 27 0. 01 Among Individuals grouped by hosts 333 1791. 66 5. 38 2. 69 73 Within Individuals 341 0. 00 0. 00 0. 00 0 Total 681 2,299. 54 3. 67 100 a Mating type ratios are indicated per country using the non-clone-corrected dataset. b.
Measures We were allowed to gather survey data in two waves: the month before the start of the interventions and one month after the completion of the training. Of the 290 employees, 178 (61%) responded to both surveys. All items were based on a 7-point Likert scale anchored at 1 = "Completely disagree" and 7 = "Completely agree". Job performance. The company provided objective job performance data of employees' individual service quality from each employee. Service quality was the primary performance measure used internally within the company and the basis for important decisions on the strategic level, such as expansion or termination of client contracts. Employees' individual performance was also the basis for important personnel decisions, such as promotion or termination of employment.
The risk of running analyses with a small sample size is that it can risk to bias Type 1 errors  (Spilke et al. , 2005). To reduce the biases, we follow recommendations by  McNeish (2017)  to use Kenward-Roger correction of the degrees of freedom, which has been highlighted to perform better than the available alternatives (e. g. , Satterthwaite approximation;  Spilke et al. , 2005) , and restricted maximum likelihood (rather than maximum likelihood) for estimating standard errors because of how this is preferred for smaller sample sizes  (Kenward & Roger, 1997). We ran the analysis using the mixed-effects regression function 'lmer' in package 'lme4 0 , version 1. 1-14 in the statistical software RStudio, version 3. 4. 1. To test the mediation hypothesis, we followed the procedures of  Sajons (2020)  to use experimentally randomized instrumental variables (our experimental manipulations) to instrument the mediator effect on the outcome with 2SLS regression.
This procedure has proven effective at countering the problems of inconsistent estimates that are otherwise likely to arise when using OLS to measure the effects of a measured (i. e. , non-randomized, and therefore potentially endogenous) mediator on outcomes. We processed these analyses using the function 'ivreg' in the package 'AER', version 1. 2-9 in RStudio. Because, to the best of our knowledge, the IV estimation is not possible to run with mixed effects regression, we use clusterrobust standard errors for these analyses using the function 'cluster. robust. se' in the package 'ivpack', version 1. 2. We adapted the tools of  Dawson (2014;  see also https://www. jeremydawson. co. uk/slopes. htm) to conduct simple slope and slope difference analyses, as well as to illustrate these slopes graphically.
1a , Supplementary Data 3). Interestingly, genes affected by APA upon IAV infection significantly overlap with genes reported to be APAed across seven tumor types from tumor-normal tissue pairs in the TCGA dataset  28  (Supplementary Fig. 1c ), suggesting that the IAVinduced APA might correlate mechanistically and/or functionally with APA occurring in cancer. This notion was further supported by the identification of intron-and 3'UTR-located APA events in cancerrelated genes, such as TP53, HEXIM1 and TFAP2A (Supplementary Fig. 1d, e ). Based on this transcriptome-wide analysis, we selected a subset of APA genes as reference markers for IAV-induced APA (Supplementary Fig. 1d ). In order to test for the APA of specific genes, we implemented an RT-qPCR based test to quantify the usage of polyadenylation sites (Fig.
ΔPDUI as a measure of APA status and NS1 transcript abundances are shown alongside mean for 2 separately transfected wells. d Amino acid sequences of IAV NS1 proteins (N = 35,326, NCBI Influenza virus database)), were aligned using Clustal Omega algorithm  90. Normalized entropy (22-letter) as a measure of positional variation at indicated positions is depicted alongside sequence logos. e Indicated NS1 mutant proteins or controls (CTR -Thogotovirus M, vehicletransfection reagent only) were transiently expressed in HEK293T cells via expression plasmid transfection, followed by quantification of transgene expression levels and APA of selected genes. ΔPDUI as a measure of APA status and transgene transcript abundances are shown alongside mean +/-sd for 3 separately transfected wells. Statistics refer to comparisons between indicated samples and gene-matched vehicle controls.
IAV-induced APA of host transcripts occurs independently of viral host shutoff APA of host transcripts and viral host shutoff caused by IAV are mechanistically related events centered on NS1-based perturbation of the CPSF complex. In order to gain better understanding of their delineation, we investigated to what degree they co-occur depending on the nature of amino acids in CPSF4-binding epitopes of NS1. Towards this, we performed full proteome analysis of the human cell line A549 infected with IAV encoding amino acid variations at positions 103/106 and 184 of NS1 (Fig. 2b ). Overall, we detected over 6700 host proteins, over 6000 of which were stably quantified and included in the proteomic analysis (Supplementary Data 6). Dimensionality reduction revealed clear separation of proteome signatures based on the IAV strain used for infection, with an observed tendency for strains interacting with CPSF complex and inducing APA to cluster separately from strains harboring a G184R mutation that abrogates this activity (Fig.
In order to explore the molecular pathways leading to SFV-induced APA, we performed full proteome analysis of mock-and SFV-infected human cell line THP1 (Supplementary Data 7). We stably quantified over 5000 host proteins, including RNAPII complex components and the complexes it interacts with through its CTD encoded by POLR2A. As expected, the abundance of RNAPII complex components, including POLR2A, was reduced, Fig. 6  | APA-deficient IAV strain induced IL-6 secretion is RIG-I and MAVS dependent and can be circumvented by concomitant CPSF4 knock-down. a Mouse embryonic fibroblasts (MEFs) were infected with indicated strains of IAV at various MOIs (x-axis) for 24 h, followed by quantification of (left) secreted IL-6 by ELISA, IL-6 mRNA (middle) and viral transcripts M and NS1 (right) by RT-qPCR for two separately infected wells.
The presented data is representative of 2 independent repeats and shown alongside mean. ΔCt values were calculated relative to the housekeeping gene ACTB. b A549 cells were infected with indicated strains of IAV at MOI 3 for 24 h, followed by quantification of secreted IL-6 by ELISA for three separately infected wells, shown here alongside mean +/-sd. The presented data is representative of 3 independent repeats. c MEFs (3 independent wells) were transfected with shRNA targeting Cpsf4 or Hnrnpc (CTR) 1 day prior to infection with R/SI/R at MOI 3. 24 h post infection, secreted IL-6 was quantified by ELISA (left) and viral transcript M abundance by RT-qPCR, shown here alongside mean +/-sd. Further related data is presented in Supplementary Fig. 4b. The presented data is representative of 2 independent repeats.
ΔCt values were calculated relative to the housekeeping gene ACTB. d RIG-I (left), MAVS (middle) and STING (right) KOs were used alongside respective wt controls and either infected with indicated IAV strains (MOI 3) or treated with indicated stimuli (IVT4 in vitro transcript 4, RIG-I ligand  74  , LPS lipopolysaccharide, TLR4 ligand, dsDNA double-stranded DNA, STING ligand). 24-h post infection/treatment, secreted IL-6 was quantified by ELISA for 3 independently processed wells and shown alongside mean +/-sd. The presented data is representative of 2 independent repeats. Statistics were calculated using two-sided equal variance t-test. n. s. p > 0. 05, *p < 0. 05, **p < 0. 01, ***p < 0. 001. leading us to hypothesize that the degradation of POLR2A might extend to its interacting partners (Fig.
Transcriptomic analysis of IAV-infected A549 cells To evaluate IAV-induced transcriptional and post-transcriptional changes, we performed deep RNA sequencing of A549 cells, infected for 24 h with IAV strain A/Puerto Rico/8/1934 H1N1 (PR8) at MOI 3. The single-end sequencing was performed using Illumina Next-Seq 2000 employing Illumina stranded mRNA Library Prep Kit according to manufacturer's instructions. The raw sequencing data was processed with Trimmomatic version 0. 36  81. Trimmed reads were acquired by removing Illumina TruSeq3 adapters and bases at the start and end of each read, for which the phread score was below 25. Further reads were clipped if the average quality within a sliding window of 10 fell below a phread score of 25. Conclusively reads smaller than 50 bases were removed.
For mapping, the human gene annotation release 29 and the corresponding genome (GRCh38. p12) were derived from the GEN-CODE homepage (https://www. gencodegenes. org/). The virus (A/ Puerto Rico/8/1934, H1N1) segment annotation and reference genome were derived from the following NCBI reference sequences: NC_002016. 1, NC_002017. 1, NC_002018. 1, NC_002019. 1, NC_002020. 1, NC_002021. 1, NC_002022. 1, and NC_2023. 1. A custom genome was generated to include human as well as virus sequences and genes/segments. STAR version 2. 6. 1c  82  was used to map the trimmed sequencing data to the combined reference genome. STAR parameters were adapted from protocol recommendations for STAR  83  and STAR-Fusion  84. For each alignment, the number of accepted multimappings was set to 10 and the number of accepted mismatches to 3.
DESeq2 version 1. 36. 0  85  was used to determine differentially regulated genes between untreated cells and virus infected cells. Genes were determined to be differentially expressed, if the absolute log2 fold change was greater than 2 and the FDR-adjusted p-value was below 0. 01. Analysis of alternative isoform usage. The human reference transcriptome (release 29, GRCh38. p12) was derived from GENCODE. Transcript read count quantification was performed using the pseudo alignment software SALMON v0. 13. 1  86. The quantification mode was set to U (single-end, unstranded) with additional correction for sequence-specific and GC bias. The Bioconductor package IsoformSwitchAnalyzeR version 1. 8. 0  48, 49  was used to identify differential isoform usage between untreated and virus infected samples.
The analysis of MS data sets was performed using R version 4. 0. 2. iBAQ (intensity based absolute quantification intensities, used for analysis of AP-MS experiments and SFV FP-MS experiment) or LFQ (label-free quantification intensities, used for analysis of IAV FP-MS) values were log 2 -transformed and protein groups only identified by site, reverse matches and potential contaminants excluded from the analysis. Additionally, protein groups quantified by a single peptide or not detected in at least allbut-one replicate of at least one condition were excluded from further analysis. Missing values were replaced by sampling from the normal distribution with the following parameters: 0. 3 * standard deviation, mean -1. 8 * standard deviation, where mean and standard deviation relate to the whole AP-MS or FP-MS datasets.
For SFV FP-MS data set, statistical comparison was performed using two-sided equal variance t-test. For other data sets, generalized linear modeling (core function glm) was used along general linear hypothesis testing (multcomp 1. 4 function glht) to quantify protein abundance changes and obtain associated pvalues. For AP-MS, proteins enriched in particular bait over all controls (log2 fold-change > 2 and FDR-adjusted p-value < 0. 05) were considered bait interactors. CPSF4 was identified as unique protein satisfying the following conditions: significant interactor of R/SI/G, SI/R, R/FM/G, FM/G but not SI/R or FM/R. For IAV FP-MS, proteins with absolute log2 fold-change > 0. 75 and FDR-adjusted p-value < 0. 01 were considered significantly changing. For visualization of 1-dimensional density values, R package ggplot2 (version 3.
Quantification of transcript abundance in cell lines by RT-qPCR RNA was isolated using MACHEREY-NAGEL NucleoSpin RNA mini kit according to manufacturer instructions. Reverse transcription was performed using Takara PrimeScript RT reagent kit with gDNA eraser according to manufacturer instructions unless otherwise stated. PowerUp SYBR Green (Thermo Fisher, A25778) was used for RT-qPCR on QuantStudio 3 Real-Time PCR system (Thermo Fisher). Ct values, obtained using QuantStudio Design and Analysis Software v1. 4. 3, were averaged across technical replicates and -ΔCt values as a measure of gene expression were calculated as Ct(RPLP0) -Ct(GOI). -ΔΔCt values as a measure of change in gene expression between conditions were calculated as -ΔCt(KO) -(-ΔCt(NTC)). PDUI (percentage of distal polyA site usage index, Fig.
Western Lightning ECL Pro (PerkinElmer) was used for band detection according to manufacturer instructions. Normalization of band signals was performed using Image Lab Software (Bio-Rad, version 6. 0. 1 build 34). Open Access This article is licensed under a Creative Commons Attribution 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Acknowledgements We want to acknowledge the innate immunity laboratory for critical discussions and suggestions and the  Comparative Experimental Pathology (CEP, School of Medicine, TUM)  for excellent technical support. Work in the author's laboratories was supported by the  Max-Planck Free Floater program , an  ERC Consolidator grant  ( ERC-CoG ProDAP ,  817798 ), the  German research foundation  ( PI 1084/4 ,  PI 1084/5  and  TRR179/TP10  and  TRR237/A07 ),  KA1-Co-02  " COVIPA " ( Helmholtz Association's Initiative and Networking Fund)  to A. P. and  CNRS ,  Fondation pour la Recherche Médicale  ( EQU202003010405 ) and the  European Regional Development Fund  ( FEDER N •  EX016008 TARGET-EX  and  N• EX010381 EUROFéRI ) to B. R. Institute of Molecular Oncology and Functional Genomics, TUM School of Medicine, Technical University of Munich, Munich, Germany.
Data availability The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  89  partner repository with the dataset identifier PXD040456 (https://www. ebi. ac. uk/pride/ archive/projects/PXD040456). Sequencing data is publically available via ENA repository (PRJEB54734, https://www. ebi. ac. uk/ena/browser/ view/PRJEB54734). Source data are provided with this paper.
1 ). We aggregated multiple surveys, resulting in combined 6,701 pelagic and 10,710 benthic BRUVS deployments across all oceans (Fig. 2a ), except in the polar regions, corresponding to 13,402 and 10,710 hours of footage, respectively. This database yielded length measurements for individual sh which were converted to weights using taxa-speci c allometric conversion parameters (Methods, Extended Data Fig. 1 ). We test two competing and mutually exclusive hypotheses regarding the in uence of human footprint on sh size-spectra in the pelagic and benthic habitats. First, we hypothesise a greater human footprint through overexploitation in the pelagic habitat compared to the benthic, since the larger body size of oceanic shes makes them more vulnerable to sheries 6 than benthic species. Therefore, we expect that pelagic sh size-spectra are more sensitive to protection status and proximity to markets than their benthic counterparts.
Fish sampling We collated data from baited videographic surveys to assess size-structure of sh assemblages in pelagic and benthic habitats. The surveys were conducted from January 2006 to May 2020, with varying geographical representation of tropical, subtropical, temperate, and subarctic regions, in the Indian, Paci c and Atlantic Ocean, ranging from 64° N to 40° S. The surveys used standardised stereo baited remote underwater video systems (BRUVS, n = 17,411, Fig. 1a ), described below, deployed in the midwater (n = 6,701) and on the seabed (n = 10,710), from 147 expeditions (66 in the midwater, 81 on the seabed) nested within 80 locations (39 in the midwater, 50 on the seabed, 9 sampled with both). Considerable efforts have already established the strengths and characteristics of BRUVS in accurately sampling sh assemblages, which have repeatedly been shown to be robust in time and space to detect changes in marine ecosystems  65.