dataesr/scientific-paragraphs-categorization huggingface dataset:
1,3-PDO also has applications in other sectors, such as production of solvents, adhesives, laminates, resins, cosmetics and personal hygiene and cleaning products  [17]. North America is the most prominent region concerning the global market of 1,3-PDO, leading the market and having large growth perspective for the coming years. This is due to the high consumption of PTT and the high demand for polyurethane. The 1,3-PDO market in the year 2020 has been valued at US $ 401. 7 million with an estimated growth of US$ 292. 4 million from 2019 to 2024, presenting a CAGR of 11. 4%, already considering the impact caused by the COVID-19. North America is responsible for 74% of this growth  [23, 24]. 1,3-PDO is not an isolated product in the ITC database, being grouped in SH6 2905. 39, which contains the group of the heaviest diols, such as butanediols, hexanediols, but does not consider ethylene glycol and 1,2-propanediol, which are found in SH6 2905.
Acknowledgements The authors gratefully acknowledge the support of the  RCGI -Research Centre for Gas Innovation , hosted by the  University of São Paulo (USP)  and sponsored by  FAPESP -São Paulo Research Foundation  ( 2014/50279-4 ) and  Shell Brasil. This study was financed in part by the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil  ( CAPES -Personnel Coordination of Improvement of Higher Level) -Finance Code 001. The authors also gratefully acknowledge the financial support of the  FAPESB -Bahia Research Foundation  and  CAPES National Exchange Program  in partnership with  Universidade Federal da Bahia and Universidade de São Paulo.
Results Estimating a vaccine's TBA in the field. To gain insight into how heavily infected naturally blood-fed mosquitoes become, we utilise data collected in two villages (Klesso and Longo) over four collection periods in highly endemic regions of Burkina Faso. In that study, mosquitoes were collected from inside houses and dissected to check for oocysts  21. The oocyst counts observed in the two villages (Fig. 2 ) indicate how difficult it would be for a TBV to block these transmission events. In Klesso, oocysts formed in 18. 5% (364) mosquitoes, with a mean oocyst count in those infected of 11. 5 (Fig. 2A ). In Longo, 7. 5% (179) mosquitoes were positive for oocysts, with a mean oocyst count of 10. 5 (Fig. 2B ). The distribution of oocyst counts in both villages was highly overdispersed, with a number of counts >100 (8 in Klesso, 3 in Longo).
Using the data from each village, we obtain a simple estimate of the vaccine's predicted TBA from the TRA by assuming all oocysts have an equal probability of being blocked (Fig. 2C ). On average, slightly higher oocyst counts were found in Klesso, which results in slightly lower predicted TBA. Though informative this simple, statistically derived estimate of TBA does not provide the full picture of human-mosquito transmission as there is a large body of evidence, which suggests people have a different probability of being infected, and those who are infected are not equally infectious  11. To overcome this, we utilise a transmission dynamics model to facilitate interpretation of field data and predict TBV efficacy. At the end of each of the four collection periods, malaria prevalence in each village was assessed by microscopy.
In this work, we will represent the effect of a TBV through a reduction of values of these parameters in the vaccinated population as follows, c 0 X ¼ c X 1 À TBA X z ð Þ ð Þ ; X ¼ U; A; T; D ð Þ ; 0 ≤ z ≤ 1; ð1Þ where c' X is the human-mosquito transmission probability of a vaccinated individual in state X and, TBA X (z) is the TBA from infections in state X, given a TRA of z. Oocyst counts found in naturally infected mosquitoes in Burkina Faso. We use data on oocyst counts observed in wild-caught Anopheles to assess how heavily infected mosquitoes become after feeding naturally in the wild. Let m X denote the average number of oocysts that successfully develop in a mosquito following a blood meal from a human host in infection state X. We utilised data collected in two villages (Klesso and Longo) close to Bobo-Dioulasso in Burkina Faso, over four time periods in 2014 (making eight data points in total)  21.
Using a mouth aspirator, fed mosquitoes were collected early in the morning from inside houses. All collected mosquitoes were transported to IRSS laboratory where Anopheles females were selected and maintained in an insectary for 7 days before dissection under a microscope to count the number of oocysts. This time period was selected so that all oocysts from previous blood-meals are likely to have burst whilst infections from the last blood meal will remain visible (though see ref. 21. for an alternative method of estimating parasite exposure). Hence, broken oocysts were not included in the analysis here, although they were counted in the original study. At the end of each period of data collection in each site, malaria prevalence in the village was estimated using a sample of 300 people (100 people under 10 years old, 100 people between 10 and 20 years old, and 100 people over 20 years of age).
We denote the data points as d ab and define S ab as the number of data points at each time point and location. We can then write the likelihood of the data for a given set of parameters θ ¼ m; k ð Þ as L Djθ ð Þ¼ Y 2 b¼1 Y 4 a¼1 Y S ab j¼1 b π ab * d ab j ; θ " # : ð5Þ We introduce prior distributions to ensure that higher oocyst loads are attributed to symptomatic, rather than asymptomatic individuals. Single estimates of m D , m T , m A , m U and k are fit for the entire dataset using the Metropolis-Hastings algorithm. Supplementary Table  1  summarises the posterior distribution obtained. Developing a relationship between TRA and TBA in naturally infected mosquitoes. Estimates of the single-feed infection load experienced by mosquitos feeding on a person of malaria state X, are used to understand the relationship between a vaccine's TRA and TBA in natural infections.
Acknowledgements J. D. C. would like to thank  Ellie Sherrard-Smith  and  Ben Lambert  for useful discussions. J. D. C. , D. F. D. , A. C. and T. S. C. acknowledge funding from the  PATH Malaria Vaccine Initiative. J. D. C. , D. O. M. and T. S. C. acknowledge joint Centre funding from the  UK Medical Research Council (MRC)/UK Department for International Development (DFID)  under the  MRC/DFID Concordat  agreement (grant reference  MR/R015600/1 ). D. O. M. acknowledges funding from the  Wellcome Trust  (grant ref. 215143/Z/18/Z ).
SpacePHARER (CRISPR Spacer Phage-Host Pair Finder) is a sensitive and fast tool for de novo prediction of phage-host relationships via identifying phage genomes that match CRISPR spacers in genomic or metagenomic data. SpacePHARER gains sensitivity by comparing spacers and phages at the protein-level, optimizing its scores for matching very short sequences, and combining evidences from multiple matches, while controlling for false positives. We demonstrate SpacePHARER by searching a comprehensive spacer list against all complete phage genomes. Availability and implementation: SpacePHARER is available as an open-source (GPLv3), user-friendly command-line software for Linux and macOS at spacepharer. soedinglab. org.
Multiple spacers targeting the same invader are not uncommon, due to either multiple infection events or the primed spacer acquisition mechanism identified in some CRISPR subtypes. CRISPR spacers have been previously exploited to identify phage-host relationship  [7, 8, 11]. These methods compare individual CRISPR spacers with phage genomes using BLASTN  [1]  and apply stringent filtering criteria, e. g. allowing only up to two mismatches. They are thus limited to identifying very close matches. However, a higher sensitivity is crucial because phage reference databases are very incomplete and often will not contain phages highly similar to those to be identified. To increase sensitivity, (1) we compare protein instead of nucleotide sequences because phage genomes are mostly coding, and, to evade the CRISPR immune response, they are under pressure to mutate their nucleotides with minimal change on the amino acid level.
We refer to similar q and t as hit, and an identified hostphage relationship Q -T as match. (1) Search all q's against all t's using the fast, sensitive MMseqs2  [10] , with VTML40 substitution matrix  [6] , gap open cost of 16 and extension cost of 2. We optimized a short, spaced k-mer pattern for the prefilter stage (10111011) with six informative ('1') positions. (2) For each q -T pair, compute the P-value for the best hit p bh from first-order statistics. (3) Compute a combined score S comb from best-hit P-values of multiple hits between Q and T using a modified truncated-product method (Supp. Materials). (4) Compute the false discovery rate (FDR = FP /(TP + FP)) and only retain matches with FDR < 0. 05. For that purpose, SpacePHARER is run on a null model database and the fraction of null matches with S comb below a cutoff (empirical P-value) is used to estimate the FDR.
(5) Scan 10 nt upstream and downstream of the phage's protospacer for a putative PAM. Output is a tab-separated text file. Each host-phage match spans two or more lines. The first starts with '#': prokaryote accession, phage accession, S comb , number of hits in the match. Each following line describes an individual hit: spacer accession, phage accession, p bh , spacer start and end, phage start and end, putative 5' PAM|putative 3' PAM. Optionally, the spacer-phage sequence alignment can be included. 1 ) Search all qs against all ts using MMseqs2. (  2 ) For each q -T pair, compute the P-value for the best hit from first-order statistics. (  3 ) Compute score S comb by combining the best-hit P-values from multiple hits between Q and T using a modified truncated-product method. (  4 ) Estimate the FDR by searching a null database.
(  5 ) Scan for putative protospacer adjacent motif (PAM). (B) Performance comparison between SpacePHARER (blue) and BLASTN (red) using inverted phage sequences (solid lines) or eukaryotic viral ORFs as null set (dashed lines) demonstrated by expected number of true positive (TP) predictions at different false discovery rates (FDRs). The inflated FDR when using the eukaryotic null database (dashed blue line) is caused by prokaryotic viruses mis-annotated as eukaryotic (Suppl. Material, section IV).
III. RESULTS Datasets. We split a previously published spacer dataset  [8]  of 363,460 unique spacers from 30,389 prokaryotic genomes randomly into an optimization set (20%, 6,067 genomes) and a test set (80%, 24,322 genomes). The performance of SpacePHARER was evaluated on the spacer test set against a target database of 7,824 phage genomes. We used two null databases: 11,304 eukaryotic viral genomes and the inverted translated sequences of the target database. Viral genomes were downloaded from GenBank in 09/2018. Prediction quality. At FDR = 0. 05, SpacePHARER predicted 319,029 prokaryote-phage matches using the inverted phage sequences as null model and 253,419 using the eukaryotic viruses (Figure  1B ), 4 to 5 times more than BLASTN (65,712 matches using inverted phage sequences and 62,804 using eukaryotic viruses).
e. , S354, D464, and C467) (Fig. 1  and Methods). The resultant model performed better than any previously described cryo-EM structures or models of hASCT2 at enriching for known ligands as compared to a database of ligands and decoys. For example, the model obtained an area under the curve (AUC) of 94. 3 as compared to AUC of 75 for the inward-open structure, suggesting the model represents an alternative ligand binding mode and ASCT2 conformation  (22). Additionally, our previously published proline-based ASCT2 inhibitors were based on the trans configuration of the stereocenter in position 4 of the proline ring  (22, 23). However, we hypothesized that cis isomers would force the linker moiety of the proline-like scaffold to point toward the base of pocket A while maintaining critical polar interactions with hairpin 1 (HP1) and TM8 (e.
Methods SI Appendix includes detailed information on the following: 1) molecular docking with Schrödinger; 2) relative binding affinity prediction; 3) MD simulations; 4) ASCT2 expression and purification; 5) reconstitution into proteoliposomes and transport assays; 6) reconstitution of ASCT2 in nanodiscs; 7) cryo-EM sample preparation and data collection; 8) image processing; 9) cryo-EM model building and validation; 10) cell culture and transfection; 11) cell viability assays; 12) electrophysiological techniques; 13) data analysis; 14) synthesis; and 15) NMR data for major intermediates. Data Availability. Our code is available on the A. S. laboratory GitHub repository: https://github. com/schlessinger-lab/. Details of the MD simulations as well as Protein Data Bank (PDB) files of representative conformations can be found on PLUMED-NEST: https://www.
plumed-nest. org (accession code 20. 015). The three-dimensional cryo-EM density maps of the outward-open ASCT2 in presence of Lc-BPE and the outward-open ASCT2 in presence of ERA-21 have been deposited in the Electron Microscopy Data Bank under accession nos. EMD-12142 and EMD-12143, respectively. The deposition includes the cryo-EM maps, both half-maps, the unmasked and unsharpened refined maps, and the mask used for final Fourier shell correlation (FSC) calculation. Coordinates of three models have been deposited in the PDB. The accession nos. for ASCT2 in the presence of Lc-BPE (position up) and ASCT2 in the presence of Lc-BPE (position down) are 7BCQ and 7BCS, respectively. All other study data are included in the article and/or SI Appendix.
ACKNOWLEDGMENTS. We thank  J. Rheinberger  for help with cryo-EM data acquisition;  M. Punter  for maintenance of the image-processing cluster;  V. Arkhipova  for help with MSP2N2 purification;  T. Postmus  for initial tests in proteoliposomes; and  A. Borowska  for assistance with preparation of ASCT2nanodiscs with ERA-21 for cryo-EM analysis. M. B. would like to acknowledge  PRACE  for awarding access to  Piz Daint  at  CSCS, Switzerland. This study was supported by a grant from the  NIH  ( R01 GM108911 ) to A. S. and C. G. and Grant  T32 CA078207  to R. -A. A. G. ; the  NSF  Grant  1515028  and Grant  R15 GM135843-01  awarded to C. G. ; the  NWO TOP  Grant  714. 018. 003  to D. J. S. ; and the  NWO Veni  Grant  722. 017. 001  and  NWO Start-Up  Grant  740. 018. 016  to C. P. M. B. would like to acknowledge the  INCEPTION  project  ANR-16-CONV-0005.
(2015). Around 10 % of samples were taken during winter needle sampling campaigns (December until March). Sample preparation procedure typically includes separation of needle age classes, drying, milling and chemical analyses for macronutrients, and further drying of a subsample at 105 • C to constant weight for the determination of dry weight. The participating ICP Forests countries harvested and carried out these preprocessing steps and collected the associated metadata. Hg measurements of samples from ICP Forests Level II plots were performed at the University of Basel. Additional foliar Hg concentration data of 1440 samples from the Austrian Bio-Indicator Grid organized by the Austrian Federal Research Center for Forests (German acronym BFW) (Austrian Bio-Indicator Grid, 2016) were included in the analysis.
Please also note that daily foliar Hg uptake rates in this study represent average values over the growing season. The actual daily foliar Hg uptake on a given day might differ from the average value depending on the time period within the growing season (e. g. , early season versus peak season)  (Laacouri et al. , 2013). Needles 1 year of age or older were excluded from calculating daily foliage Hg uptake fluxes since Hg uptake might slow down in physiologically less active older needles  (Wohlgemuth et al. , 2020) , and it is unclear to what extent Hg uptake occurs in older needles in winter and in early spring before the emergence of new foliage. While dates of harvest were available for all samples, we determined the start of the growing season of current-season foliage by combining available data sources with start-of-season modeling.
HOTAIR-induced transcriptome changes depend distinctly on PRC2 and LSD1 To understand molecular mechanisms underlying the altered migratory properties of Epi cells expressing HOTAIR, we studied their transcriptome by RNA sequencing (RNA-seq). First, the transcriptome of Epi-CTR was compared to Epi cells expressing each of HOTAIR variants. We found that the ectopic expression of HOTAIR or its truncated versions in Epi cells induced global changes in mRNA levels, with an overall repressive effect (Fig  2A ). A total of 755 genes showed altered expression in Epi-HOT compared to Epi-CTR, with a fold change (FC) above 2 and adjusted P-value below 0. 05  (Fig 2A and B, Dataset EV1). Of them, 505 were altered only by the full-length HOTAIR and hence dependent on both PRC2-and LSD1-interacting domains.
We referred to this set of genes as HPL-pos (N = 119) and HPL-neg (N = 386) for HOTAIR-PRC2-LSD1 up-and downregulated genes (Fig 2B, Dataset EV2). Some of HPL genes were reported to be EMT markers (Gro ¨ger et al, 2012), while others were previously identified as repressed by HOTAIR-and PRC2-mediated histone H3 lysine 27 methylation  (Gupta et al, 2010)  (Gro ¨ger and Gupta sets, Dataset EV2), including genes involved in proteolysis of extracellular matrix, SERPIN2 and MMP3, and the protocadherin gene family member PCDH18  (Gupta et al, 2010; Xu et al, 2013; Qiu et al, 2014). Gene Ontology (GO) analysis revealed that the HPL genes are enriched for several Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways that are tightly linked to  EMT, cancer, and metastasis (Fig 2C). The downregulated genes were enriched for extracellular matrix (ECM) receptor interactions, focal adhesion, and Hedgehog signaling, whereas the upregulated genes were enriched for Jak-STAT and bladder cancer pathways  (Fig 2C).
Notably, expression of EMT drivers and key EMT transcription factors was not stimulated by HOTAIR overexpression in epithelial cells. On the other hand, 77 and nine protein-coding genes were downand upregulated, respectively, upon expression of any of HOTAIR variants (Fig  2B ). These genes were grouped into a core-set of potential HOTAIR targets that do not require neither PRC2 nor LSD1 (Dataset EV2). According to GO analysis, these genes were involved in developmental processes, cell surface receptor signaling pathways, MAPK cascade, and apoptotic processes. Finally, Epi cells expressing HOTDP or HOTDL variants showed less transcriptomic perturbations (R 2 of 0. 986 and 0. 982, respectively) than cells expressing full-length HOTAIR (R 2 of 0. Source data are available online for this figure.
The LSD1-interacting domain of HOTAIR shapes the high-migration transcriptome To define highly expressed genes involved in the maintenance of epithelial properties in low migrating cells (Epi-CTR and -HOTDL) and, in contrast, highly expressed genes associated with the acquisition of migration properties in high migrating cells (Epi-HOT and -HOTDP), we performed anew a differential expression analysis, comparing gene expression between these two phenotypically distinct groups. All genes, except those of the core-set, with upregulated expression (FC > 1. 5 and adj. P-value < 0. 05) in low-migrating cells were assigned as a Low Migration Signature (LMS), whereas genes with upregulated expression in high-migrating cells were assigned as a High Migration Signature (HMS) (Fig  3A  and B , Dataset EV3).
In total, we defined 131 LMS and 75 HMS genes presenting gene expression patterns specific to each phenotypic group (Dataset EV2). Gene Ontology analysis showed that the LMS set is enriched for genes involved in pathways linked to cardiac development (as the integrin family), lipid biosynthesis, and cytokine-cytokine receptor interactions, whereas the HMS set includes genes of ECM-receptor interactions and focal adhesion pathways tightly linked to EMT, cancer, and metastasis (Fig   In conclusion, the conversion of low-to high-migration phenotype is associated with modulation of epithelial transcriptome altering above all focal adhesion and ECM-receptor interactions. These perturbations depend on the presence of the LSD1-interacting domain within HOTAIR supporting the importance of LSD1 in maintaining epithelial identity and its altered activity during HOTAIRinduced EMT.
Wound healing assay Cell suspensions containing 200,000 cells were seeded to each well of the six-well plate and left growing to 95-100% of confluence for 24-36 h. After scratching, each wound was imaged twice at time t = 0 and t = 24 h post-scratch with a Zeiss inverted microscope with a total of 30-48 high-field units (HFU) per cell line in transduced Epi, six HFU per cell line in ASO treated cells, and 30 HFU per cell line for LSD1-OE. The invaded area was calculated as a ratio of migrated cell surface area to total surface area with a web-based image automated analysis software Tscratch  (Geb€ ack et al, 2009). Results were presented as a mean AE SEM. P-values were calculated using the Student's t-test.
Fluorescent microscopy and image analysis For each cell line, three HFUs per well were acquired as stacks composed of twenty slices of 0. 5 µm interval over the height of the cells with a 40X objective on a Zeiss AxioVision microscope equipped with an ApoTome module (PiCT-BDD platform, Institut Curie). All images were captured with identical exposure time for DAPI and Alexa Fluor 594 using AxioVision software and then analyzed using the Fiji software  (Schindelin et al, 2012). Stacks were processed with the "Z-project" tool ("Sum Slices" setting) and an identical minimum threshold value was set for all images. Signal was then quantified using the "Measure" tool and normalized by the number of cells per image. Integrated density per cell was again normalized on overall image background signal to reduce hybridization biases between slides and presented as a mean AE SEM for at least 11 HFUs.
RNA-seq data analysis Reads were mapped allowing three mismatches using TopHat 2. 0. 4  (Trapnell et al, 2009)  and the human genome version hg19. Uniquely mapped reads were assembled using the BedTools suite  (Quinlan & Hall, 2010)  and merged in segments if mapped in the same strand to the GENCODE v15 annotation to extract proteincoding genes and annotated noncoding genes including lncRNA, antisense, sense_intronic, sense-overlapped, and pseudogenes. Differential expression analysis was performed using the DESeq R package  (Anders & Huber, 2010). Only genes with an expression fold change (FC) above 2 and adjusted P-value below 0. 05 were retained as significantly differentially expressed.
CUT&RUN analysis Reads were first aligned to human genome version GRCh38 using bowtie (version 2. 4. 1) (Langmead & Salzberg, 2012) , with options -I 10 -X 700-no-mixed. Reads with MAPQ value ≥ 30 were kept for further analysis. To equilibrate datasets, the larger samples were randomly down-sampled to approximately 9 million of reads. For histone marks (H3K27ac, H3K4me2) and LSD1, peaks were called relative to IgG control using HMCan  (Ashoor et al, 2013)  with punctuate parameters according to (github. com/BoevaLab/HMCan/tree/ master/configurations/HMCan. config. punctuate. txt), keeping duplicate reads, and without correction for ploidy. Only peaks showing at least one nucleotide overlap between two replicates were retained and subsequently merged to common peaks. For further analysis, common peaks between conditions were also merged to peak regions (Dataset EV5).