dataesr/scientific-paragraphs-categorization huggingface dataset:
Materials 507 HCV-3a whole genome sequences were obtained from the BOSON clinical trial study that has been described earlier  (24). However, the sequences from the BOSON study were all sampled in a short time span. As a result, these sequences have insufficient temporal signal to conduct a meaningful molecular evolution analysis. To address this, we downloaded previously published HCV sequences in the Los Alamos HCV database  (40). The criterion for choosing the sequences to download are as follow: 1. The sequence has to be categorized as HCV genotype 3a. 2. The sequence has to cover the coding region of the genome. 3. The sequence has to have sampling date information available. 4. When there are multiple sequences sampled from the same patient at different time points, we only include the earliest sample.
We observed that several pairs of sequences on the tips of the tree were very closely related (Sup. Fig. 3 ). Upon further investigation, we found that all these pairs of samples were from the same countries. Furthermore, all these samples were downloaded from Los Almos database, and many of them had little description available in GenBank and were submitted by the same authors (Sup. Table  1 ). This raised the concern that these pairs of closely related samples were obtained from the same patients. To be conservative, we randomly discarded one sample in each of these closely related sample pairs. As a result, 6 more sequences are removed from our dataset.
Host ancestry designation We downloaded unimputed 1000 Genomes Project phase 3 genetic data  (26). Using Plink (v1. 9) (44) , the SNPs in 1000 Genomes Project were pruned by linkage disequilibrium (LD) on all autosomes and the same set of SNPs were also pruned from the host genetic data from the BOSON cohort. Principal components (PCs) were then estimated for both 1000 Genomes Projects and BOSON respectively using Plink (v1. 9)  (44). We then projected the PCs of the BOSON cohort on the PCs of 1000 Genomes Project. By comparing the self-reported ancestries in the BOSON cohort to the individuals of the 1000 Genome Project, we found that South and East Asian were both being reported as "Asian", and some patients that self-reported as "White" were distant from the European ancestry group of the 1000 genome samples in the PC coordinates.
To address this, we trained a random forest classifier as implemented by R package randomForest  (45)  on top 20 PCs from the 1000 Genome Project. We applied this random forest on the PCA data of the BOSON cohort to assign ancestries if the random forest probability was >60%. For individuals where the random forest probabilities were <60%, the "Other/Unknown" ancestry was assigned. For BOSON hosts where genetic data is unavailable (N=19), we used the following scheme for host ancestries designation: We inferred hosts with self-reported ethnicity of "Asian" (N=3) to be of South Asian ancestries as most self-reporting Asians in BOSON are of South Asian ancestries (84%) according to the result of PCA; We inferred hosts self-reporting as "White" to be of European ancestries (N=15); We designated hosts with other self-reported ethnicity as "Other/Unknown" ancestry.
Phylogenetic analysis The molecular clock, demographic change, and Bayesian coalescent-based phylogeny of HCV were estimated using BEAST (v1. 10. 4) (25) , which infers rooted and time-scaled phylogenies using clock models via a Markov chain Monte Carlo (MCMC) algorithm. Because of the large sample size in our dataset, we took a few steps to ensure that the MCMC computation could be finished within a reasonable time frame. We first used FastTree  (42)  to build a ML tree, then used tempest  (43)  to root the tree. Tempest searches for the most likely root by finding the point in the tree that maximizes the likelihood of the tree given the sampling dates of the tips. The rooted maximum likelihood tree was then used with the R package TreeDater  (46)  to estimate a time-scaled tree. The resulting tree was then used as the starting point for the MCMC analysis conducted by BEAST.
2016; Jung et al. 2016). Jennings et al. (2012)  conducted a global study of the impacts of storms on lakes but did not perform a specific standardized assessment of storm-induced temperature changes. Here, we address this gap by conducting a quantitative, comparative, and standardized analysis of wind vs. rainstorm-induced temperature change in lakes across the globe. We used 18 long-term and highfrequency lake and meteorological datasets to assess day-today changes in lake water temperatures in response to storms, and how such temperature responses may be mediated by environmental and lake characteristics.
Meteorological and lake temperature data We focused on wind speed (m s -1 ) and daily rain (mm d -1 ) as meteorological factors that can impact lake temperatures by physically mixing the lake (wind) or through delivery of water (directly by rain or indirectly by runoff) with a different temperature than the lake. Meteorological data were recorded across a range of frequencies, from every minute to once per day, depending on the lake and variable. Rain data were typically collected at daily intervals. To standardize the frequency of measurements among lakes, we calculated mean daily wind speed and daily rain (each day being defined from 0:00 to 23:59) for each lake using the package openair in R v3. 6. 0  (Carslaw and Ropkins 2012). We did not use maximum daily wind speed because these observations were not available for all lakes and mean and maximum wind speeds were strongly correlated when data were available for both (r = 0.
Profiles were excluded if >50% of the temperature data from the water column measurement depths were not available or recorded. Four lakes (La Salada, Emerald, Cheney, and Taupo) only had high-frequency lake temperature data from one depth in the epilimnion (Supporting Information Table  S1 ), which we assumed to represent the epilimnion for each of these four lakes. One lake (Harp) only had temperature measurements up to one-third of the station depth from the surface, which were used to calculate epilimnetic temperature. However, Harp had a thermocline depth calculated for 97% of the daily records during the stratified period, thus epilimnetic temperature was still calculated for most daily profiles in the series based on the thermocline depth. We were not able to calculate metalimnetic or hypolimnetic temperature for these five lakes.
2011). For the four lakes with water temperature measurements at only a single, near-surface depth, we determined the thermally stratified period by seasonal epilimnetic temperatures and consultation with the local researchers. All subsequent analyses were based solely on data during the thermally stratified period in each lake. To quantify the effects of storms on water temperature, we compared the cumulative changes in water temperature (as the sum of the day-to-day water temperature changes) during the top 5% of cumulative (summed) mean daily wind speed and cumulative (summed) previous-day total rain amounts with the bottom 95% observations, over 1-to 7-d intervals. For example, if the cumulative mean daily wind speed over days 10-14 was in the top 5% of windstorms for the 4-d interval, then the corresponding cumulative change in epilimnetic temperature (ET) would be: X 13 t = 10 ET t + 1 -ET t ð Þ Likewise, if cumulative rain over the same 4-d interval was in the top 5% of rain events, then the corresponding cumulative change in epilimnetic temperature, accounting for a 1-d lag (see Analysis 1), would be: X 14 t = 11 ET t + 1 -ET t ð Þ The analyses were performed only with epilimnetic temperature changes because metalimnetic or hypolimnetic temperature data were not available for all lakes (Supporting Information Table  S1 ).
Maximum and mean lake depth were strongly correlated (r = 0. 98), so we only included maximum lake depth. All explanatory variables except air temperature were log 10 -transformed to meet assumptions of normality and equal variance with respect to the residuals of the statistical models. The best models were determined through the package MASS in R using AIC, which identifies the best fit models while penalizing for model complexity  (Quinn and Keough 2002; Venables and Ripley 2002). Analysis 4: Water column stability and temperature We tested for significant differences in the percent difference in Schmidt stability and Lake Number (calculated by rLakeAnalyzer) before and after wind and rainstorms for a 1-d interval. We included both the percent difference in Lake Number raw values and the number of observations of Lake Number that were <1 before and after wind and rainstorms.
2012). The amount of energy (from wind) or volume of water (from rain) needed to induce the epilimnion to mix with the meta-and hypolimnion in deep lakes is likely to be large, and infrequent, because deeper lakes tend to have more volume of water on which the atmosphere must act during a storm, compared to shallower lakes (e. g. ,  Eccles 1974). Such reasoning is supported by the greater decrease in PSD 5-95 (which is related to temperature changes) in shallower vs. deeper lakes. Similar storm-induced changes in Schmidt stability was found in previous studies conducted on three of the lakes in our dataset (Feeagh:  Andersen et al. 2020; Calderó-Pascual et al. 2020; Feeagh, Erken, and Müggelsee: Mesman et al. 2020). Internal seiches, upwelling, and internal waves breaking on the shores can also mix colder hypolimnetic water with the warmer epilimnetic water from storms in deeper lakes (i.
95 AE 0. 92 C. Conversely, medium-depth lakes in our dataset had epilimnetic temperatures about 6. 8 AE 2. 9 C greater than their hypolimnetic temperatures. Although mixing a shallower lake is energetically easier than a medium-depth lake, more potential exists in medium-depth lakes for larger epilimnetic temperature changes because of colder hypolimnia with more volume of cold water  (Zhang et al. 2015; Woolway et al. 2020). Maximum depth was the only factor significantly related to epilimnetic temperature change during windstorms, but not from rainstorms. A negative association between maximum depth and epilimnetic temperature change was expected (similar mechanism as above). Shallower lakes are more prone to entrainment of metalimnetic and hypolimnetic waters into the epilimnion leading to a decrease in epilimnetic temperature by wind-driven mixing, upwelling, and internal wave breaking at the shore  (Kirillin and Shatwell 2016; Kasprzak et al.
Acknowledgments This project was initiated within the  Global Lake Ecological Observatory Network (GLEON) StormBlitz  working group at the 15 th All Hands' Meeting and was further developed within the  GEISHA  project (https:// www. geisha-stormblitz. fr/),which was jointly supported by the  French Foundation for Research on Biodiversity (FRB)  through its synthesis center CESAB (http://www. cesab. org/),and the  USGS John Wesley Powell Center for Analysis and Synthesis  (https://powellcenter. usgs. gov/). We thank the  U. S. Department of State  and the  Commission Franco-Américaine  for financial support through a  Fulbright Award  to JDS to support this study. We also thank the  University of Savoie Mont-Blanc  and the  Vermont Water Resources and Lake Studies Center  ( G16AP00087 ) for additional funding.
The  Leibniz Institute of Freshwater Ecology and Inland Fisheries  is acknowledged for financial support of the IGB long-term ecological research programme (Lakes Müggelsee and Stechlin). The  German EPA (Umweltbundesamt, UBA)  is thanked for measuring wind and precipitation at Lake Stechlin. Harp Lake data collection was supported by the  Ontario Ministry of the Environment and Climate Change. Collection of Oneida Lake data was supported by the  New York Department of Environmental Conservation  and the  Brown Endowment. Data from  Lake Erken  was made possible by the  Swedish Infrastructure for Ecosystem Science (SITES). Long-term monitoring of Windermere is supported by the  Natural Environment Research Council  award number  NE/R016429/1  as part of the  UK-SCaPE programme delivering National Capability.
Data from Rimov were collected and evaluated with the support of the  Czech Science Foundation , research project  15-13750S. The collection of Lake Washington and Lake Sammamish temperature data was made possible through the support of the  King County Environmental Laboratory. The  Belgian Science Policy Office (BELSPO)  is acknowledged for their support through the research project  EAGLES , ( SD/AR/02A ), which provided the data from  Lake Kivu , collected during the " Biological Baseline of Lake Kivu ", supported by the  Belgian Technical Cooperation. Cheney Reservoir data collection was funded by the  U. S. Geological Survey Cooperative Matching Funds Program  and the  City of Wichita, Kansas. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the  U.
S. Government. RA acknowledges support by the  Mantel-ITN  project ( H2020-MSCA-ITN-2016  and the  LimnoScenES  project;  Belmont Forum-BiodivERsA ). La Salada data collection was supported by grants from the  network  project  PAMPA2  (CONICET),  ANPCyT ,  Universidad Nacional del Sur  ( PGI 24/ G059 ), and the  Inter American Institute for Global Change Research (IAI)   CRN3038  (under  US NSF  Award  GEO 1128040 ). We thank  R. Bruel ,  N. P. M. van Lipzig ,  P. Nõges , and  R. I. Woolway  for statistical assistance and helpful comments on the manuscript.
Collectively, these data showed that canonical NF-κB was constitutively activated in cultured cells but at low levels. Next, we analysed the impact of p65 KD on cancer cell growth in vitro by constructing growth curves using the IncuCyte live-imaging system. Downregulation of p65 did not impair the proliferation of A549 or H1437 cells grown as monolayers in vitro. Analysis of cell apoptosis showed that p65 KD did not affect early or late apoptosis and necrosis (Figure  S2 ). Next, we investigated the role of RelA/65 in human lung tumour cell growth in vivo and its mechanism of action. To this end, we injected control and RelA/p65 KD A549 and H1437 cells into either side of immune-compromised NSG (NOD-SCID-IL2Rgamma) mice and allowed them to grow in vivo as xenografts. RelA/p65 KD human NSCLC cell lines presented significantly smaller tumours compared to their wild-type vector control cells.
Loss of RelA/p65 in human NSCLC cells grown as tumours in vivo lead to the upregulation of CD82/KAI1 and to the downregulation of ROS1 proto-oncogene mRNA levels. The protein expression levels of CD82 in the tumours were also investigated by immunoblotting. Loss of p65 resulted in the upregulation of CD82 protein levels in both human NSCLC lines (Figure  3F, G ). Collectively, these data confirmed that both CD82/KAI1 and ROS1 are RelA/p65 targets.
RelA/p65, being a regulator of a plethora of genes, may therefore regulate some of these factors and contribute to tumour growth in vivo. Our data is in agreement with the findings in urethane-and the oncogeneinduced mouse NSCLC models wherein expression of an IκBαSR super-repressor  [11, 25]  or deletion of RelA/p65  [12]  lead to impairment of tumour growth. Similarly, conditional deletion of IKKβ reduced cancer cell proliferation and tumour burden of adenocarcinomas in both urethane-  [26]  and KRAS G12D -  [13]  induced mouse NSCLC, suggesting that IKKβ was also required for LUAD development in vivo. Transcriptome analysis of vector control and Rel/p65 KD A549 and H1437 cell tumours identified the proto-oncogene ROS1 and the LGR6 receptor gene amongst the downregulated genes, and the metastasis suppressor CD82 as one of the upregulated genes.
Assessment of Cell Proliferation and Apoptosis of Human Lung Cells Equal number of control vector and p65 KD cells were plated in 24-well plates and monitored by the IncuCyte-ZOOM ® Live-Cell Analysis System (Sartorius, Michigan, MI, USA) for 92 h to construct growth curves using the IncuCyte ® -ZOOM software. Cell apoptosis was assessed by Annexin V-FITC/PI staining kit (Biolegend #640914, San Diego, CA, USA) according to the manufacturer's instructions, and analysed with a fluorescence activated cell sorter (BD FACSAria III, BD Biosciences, San Jose, CA, USA). The percentages of early (Annexin V + ) and late (Annexin V + /PI + ) apoptotic cells in each group were determined and represented in bar graphs.
RNA-Sequencing and Analysis Unbiased RNA-Seq analysis was performed in three biological replicates per each cell line  [26]. Briefly, total RNA quantity of the samples was measured with Nanodrop, while quality and integrity were verified with Agilent bioanalyser RNA 6000 nano kit. RNA-seq libraries were prepared with the Illumina TruSeq RNA v2 kit with 1 µg of total RNA input. QC of the RNA-Seq Libraries was performed with Agilent bioanalyser DNA1000 kit and quantification with Qubit HS. 75-bp single-end reads were generated with the Illumina NextSeq500 sequencer. RNA-seq analysis was performed, using first of all the quality control program FastQC (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/, accessed on 20 July 2018. Quality scores across all bases and across all samples were good.
After checking the quality, we used the tool tophat2 with standard parameters for alignment. Hg19 reference genome was used. Samtools was then used to sort and filter the above, in order to get significant results. Last step to get the differential expression was to use cuffdiff with standard parameters. For GO analysis we used the online databases: David and EnrichR (https://david. ncifcrf. gov, accessed on 14 October 2020; https://maayanlab. cloud/Enrichr/, accessed on 19 October 2020). For GO analysis we used the online databases: David and EnrichR (https://david. ncifcrf. gov, https://maayanlab. cloud/Enrichr/, accessed on 19 October 2020). In R studio we utilised heatmap in order to produce the heatmaps. The Venn diagrams were produced from an online tool called meta-chart (https://www.
, Milwaukee, WI, USA), followed by appropriate horseradish peroxidase (HRP)-conjugated secondary antibodies (Jackson Immunoresearch labs, UK). Immunoblots were developed using Clarity™ Western ECL Substrate (#170-5061, BioRad Labs, Inc. , Hercules, CA, USA). The chemilluminescence signal was captured with a ChemiDoc XRS Molecular Imager and protein quantification was performed using the Quantity One 1-D Analysis Software version 4. 6. 9 (Bio-Rad Labs, Inc. , Hercules, CA, USA). For all panels of Western blots presented, the images of protein expression were copied and pasted on a white background to make composite blots. Brightness was slightly adjusted for uniformity and clarity similarly across all samples. However, there was no image manipulation. The panels in Figure  3F  and in Figure  5D  resulted from the joining of strips of different blots.
Wound Healing For the wound healing assay 5 × 10 4 cells were seeded in each well of a 24-well plate, in serum-free medium. After 24 h linear scratch wounds were made using a sterile pipette tip. Cells were washed with PBS to remove cell debris and incubated for another 24 h in serum-free medium. The wounds were observed under an optical microscope and photographed at the same position at 0 and 24 h with a Nikon camera. The percent of wound closure was calculated with the ImageJ software and the MRI (Montpellier Resources Imagerie) wound healing tool.
Bioinformatics Analysis We validated CD82 in the Cancer Genome Atlas (TCGA) data sets, Gene Expression Profiling Interactive Analysis (GEPIA) database (http://gepia. cancer-pku. cn/, accessed on 27 February 2021, and UALCAN, a comprehensive and interactive web-resource for analysing cancer OMICS data (http://ualcan. path. uab. edu/cgi-bin/ualcan-res. pl, accessed on 27 February 2021). The pan-cancer cohort of TCGA was downloaded through cBio-Portal (https://www. cbioportal. org/, accessed on 27 February 2021). Single cell analysis took place using the Single Cell Portal (https://singlecell. broadinstitute. org/single_cell, accessed on 27 February 2021).
Acknowledgments: We are grateful to  Michael Kracht ,  Rudolf Buchheim Institute of Pharmacology, Justus-Liebig University Giessen, Germany , for providing plasmid vectors pSuper-Puro and pSuper-Puro-shp65, and  James. E. Goldman ,  Department of Pathology and Cell Biology, and Irving Comprehensive Cancer Research Center, Medical Center, Columbia University, New York, USA  for CD82 retrovectors and antibodies. We thank  Georgia Papaspirou  and  Antigoni Christodoulou ,  Laboratory of Pathology, University of Ioannina Medical School, Greece , for helping with the immunohistochemistry staining. We also thank  Spyridoula Tsiomita  for helping with the FACS experiments. We thank  Georgios Markopoulos ,  University of Ioannina , for providing luciferase assay protocols and  Periklis Katopodis ,  Brunel University London , for his help with some of the figures.
Funding: This research has been funded by an  Institutional Program Grant  for the Development of Research Institutes "Advanced research activities in biomedical and agro-alimentary technologies,  ARABAT (BITAD)" (MIS5002469) of the operational program  " Competitiveness, Entrepreneurship and Innovation " ( NSRF2014-20 ,  EU-ERDF) (EK) , a research grant in Biomedical Sciences from FON-DATION SANTÉ (EK), a  STAVROS NIARCHOS Foundation-FORTH  Fellowship for PhD candidates of the program ARCHERS: Advancing young researchers' human capital in cutting edge technologies in the preservation of cultural heritage and the tackling of societal challenges (ER, EK), and by internal grants from the  Biomedical Research Division, IMBB-FORTH (EK)  and the  University of Ioannina Research Committee (ER, EK).
Study Design and Inclusion Criteria The study investigators established the Systematic, Collaborative, PFO Closure Evaluation (SCOPE) Consortium to undertake meta-analysis of pooled individual participant data. Study methods adhered to the Preferred Reporting Items for Systematic Review and Meta-Analyses of individual participant data guidelines and the protocol was registered on PROSPERO (CRD42020186537). 16 The collaboration included all randomized phase 3 trials comparing PFO closure vs medical therapy for recurrent stroke prevention published by September 2021. Trial methodology was assessed with the Risk of Bias 2. 0 tool (Cochrane Methods). Investigators were contacted and data were collected and harmonized (for details see eAppendix 1 and eTable 1 in the Supplement). The 3 prespecified study aims were to (1) assess overall efficacy and safety of PFO device closure plus medical therapy vs medical therapy alone for the prevention of recurrent stroke; (2) examine whether the Risk of Paradoxical Embolism (RoPE) Score  17  is associated with differential treatment effect; and (3) examine whether the PFO-Associated Stroke Causal Likelihood (PASCAL) Classification System  18  is associated with differential treatment effect.
The RoPE Score ranges from 0 to 10, with scores of 0 to 3 indicating a negligible likelihood that the stroke is attributable to the PFO and a score of 9 or 10 indicating an approximately 90% probability that the stroke is attributable to the PFO. See eAppendix 2 and eTable 2 in the Supplement for details. b PASCAL combines the RoPE Score with the presence or absence of high-risk PFO features to determine the likelihood that the PFO was causally related to the index stroke. See eAppendix 3 and eFigure 1 in the Supplement for details. Large shunt size was defined in the database as >20 bubbles in the left atrium on transesophageal echo; ASA was defined as Ն10 mm of excursion from midline. 6, 12, 24, and 60 months for each treatment group. After confirming no violation of proportional hazards assumptions (eAppendix 1 and eTable 1 in the Supplement), effects were estimated using Cox proportional hazards regression with a study-specific random effect.