dataesr/scientific-paragraphs-categorization huggingface dataset:
Phylogenetic analyses Consensus sequences were constructed from clipped sequences using the CAP contig assembly extension in BioEdit sequence alignment editor  [30]. Reference sequences of the respective region from H. perezi and Hematodinium sp. recovered from a broad range of crustacean hosts were sourced from GenBank at NCBI  [31] : Callinectes sapidus, Chionoecetes angulatus, C. bairdi, C. opilio, C. tanneri, Cancer pagurus, Carcinus maenas, Exopalaemon carinicauda, Hyas coarctatus, H. araneus, Liocarcinus depurator, Lithodes couesi, Munida rugosa, Nephrops norvegicus, Pagurus bernhardus, P. prideaux, Penaeus monodon, Portunus trituberculatus and Scylla paramamosain. Sequences from Amoebophyra species (GenBank: HM483395, HQ658161, HM483394 and MK681270) were used as an outgroup for the trees.
Multiple sequence alignments were performed in CLUSTAL X v. 2  [32]. Evolutionary analyses and reconstructions were carried out in MEGA X  [33]  using the maximum likelihood routine based on the Tamura-Nei model. A consensus tree with the highest log likelihood value (-250. 10) from 1000 bootstrap re-samplings was annotated using iTOL software  [34]. All sequences have been deposited in the GenBank database under the accession numbers MN057783-MN057918 for crab DNA and MN049783-MN049789 for water eDNA (see Additional file 1: Table  S4 ).
Presence of Hematodinium in water samples Of the 69 water samples (36 for Pier, 33 for Dock) screened using the primary Hemat-F-1487/Hemat-R-1654 oligonucleotides, no water sample from the Dock location was positive. In the Pier location, the water samples from the months of November and December, across all 3 replicates, plus 1 replicate from August were positive for Hematodinium. From these positive samples, none amplified successfully using the subsequent 18SF2/ Hem3R or 18SF2/ITSR1 oligonucleotides and so were sequenced using the primary Hemat-F-1487/Hemat-R-1654 oligonucleotides and deposited in the GenBank database under the accession numbers MN049783-MN049789 (Additional file 1: Table  S4 ).
Phylogenetic analyses Of the 162 Hematodinium-positive crab samples using the Hemat-F-1487/Hemat-R-1654 oligonucleotides, 149 were re-amplified successfully for sequencing using the 18SF2/Hem3R and 12 with the 18SF2/ITSR1 oligonucleotides. One sample (Pier 40 April) did not amplify successfully using the 18SF2/Hem3R or 18SF2/ ITSR1 oligonucleotides and was instead sequenced with the Hemat-F-1487/Hemat-R-1654 oligonucleotides. Following quality control, 136 of these sequences (of the ITS1 and partial 18S rRNA gene regions of Hematodinium) were combined with 126 reference sequences for evolutionary analyses (Fig. 7 ) and deposited in the GenBank database under the accession numbers MN057783-MN057918 (Additional file 1: Table  S4 ). A single sequence, namely Pier 24 October, shared considerable similarity (490 bp, 100% coverage, 98.
2% identity) to a H. perezi clone from the harbour crab L. depurator (GenBank: EF065708) by Small et al. [38]. The remaining 135 sequences shared high similarity (> 95% coverage and identity) with the so-called Hematodinium sp. clones retrieved from a plethora of hosts, including shore crabs (Carcinus maenas), edible crabs (Cancer pagurus), tanner crabs (Chionoecetes spp. ) and langoustines (N. norvegicus). The topology of the consensus phylogram revealed two distinct, highly supported, clades of Hematodinium A and B (Fig. 7 ). Clade A consists entirely of H. perezi and forms three clusters with respect to established genotypes (I, L. depurator; II, South-East Asia; III, C. sapidus), which is in good agreement with several previous assessments  [38] [39] [40] [41]. Sequences from Hematodinium-positive crabs across both locations (Pier and Dock) and every month of the year-long survey are distributed within Clade B, thereby suggesting that the parasite most likely infecting C.
Whilst smaller crabs were significantly more likely to display Hematodinium in the Pier location only, this phenomenon has also been observed in N. norvegicus, whereby infection prevalence was highest in smaller individuals  [4]. This pattern is common in many species. For example, medium size and juvenile C. sapidus (≤ 30 mm CW)   S4  for individual numbers), and probed via PCR for Hematodinium diversity. Reference nucleotide sequences for Hematodinium from various crustacean hosts (see methods for full list) were retrieved from GenBank. The spheres/numbers at the nodes indicate bootstrap support (%) received for each partition. The tree was rooted using the corresponding region from Amoebophyra species (GenBank: HM483395, HQ658161, HM483394, MK681270). Postfix: C, China; D, Denmark; G, Greenland have the highest infection prevalence  [51].
Clade A was affiliated with the type-species H. perezi identified by Small et al. [38]  and three distinct genotypes (I, II, III): Genotype I in the English Channel; Genotype II off the east coast of China; and Genotype III along the east coast of the USA  [38, 40]. These genotypes have also been referred to as 'Clades'  [41]. By combining our sequence data with 126 references from GenBank, we provide strong evidence in agreement with previous studies that there are two broad groups of disease-causing Hematodinium: namely (i) H. perezi, which can be separated by distinct host species and geographical locations; and (ii) Hematodinium sp. , which is pervasive. The edible (brown) crab is worth around GBP 50 million per year to the UK and Ireland, and lives alongside shore crabs when in the intertidal zone  [55].
Realism, on the other hand, sees the end of US hegemony and the rise of China as a geostrategic competitor as the trigger. The two theories point to different scenarios for the future of the world economy, with different implications for future IB research. In particular, they suggest that significant aspects of multinational enterprise (MNE) activities under de-globalization will be qualitatively different from what we have seen so far. In what follows, I begin by defining de-globalization and showing data suggesting that de-globalization may already be a reality. I then introduce the two relevant major bodies of theory from political science, liberalism and realism. While further schools of thought exist, the present pair represent the current mainstream. For each theory, I lay out the general mechanisms, the specifics of how the theory would account for (de-)globalization, and the outcomes for a de-globalizing world the theory suggests.
As Figure  6  shows, this measure, too, is consistent with the possibility of ongoing de-globalization, with a considerable drop since its peak in 2011. For de jure FDI globalization, the KOF Globalization Index draws on the investment restrictions variable of the Economic Freedom Index. shows the trend in this variable since 1995, the earliest year for which the variable is available in the dataset (Fraser Institute, 2018 Overall, while these statistics do not constitute conclusive proof, they are consistent with a shift towards lower levels of economic interdependence, and thus with de-globalization. This is particularly remarkable considering that technology has continued to improve, a development that at least in the past and all else equal facilitated higher levels of economic interdependence  (Chase-Dunn et al.
Any decline in the US budget relative to other states is thus likely to be a conservative depiction of actual dynamics. Both metrics paint a clear picture of relative decline of US power. Figure  8  shows US GDP at PPP and constant 2011 international dollars relative to all other countries in the world combined, as well as relative to the respective next largest economy in the world (Japan until 1998, China from 1999 onward). The time series begins in 1990 because the World Development Indicators database used does not report GDP at PPP for earlier years. Relative to the rest of the world, US economic strength in this period peaked in 1999 at 20. 6%. By 2017, that figure had declined to 15. 2% of world GDP, a drop of 26. 2%. The picture is even clearer for US GDP relative to the next largest.
Leading universities attracting top global talent as well as opportunities to generate vast personal wealth are integral parts of this model. China's approach has been different. While US technology incumbents regularly absorb startups, acquisitions by China's three giants -Alibaba, Baidu, and Tencent -seem to reduce the number of independent startups and thus of experiments at a much larger scale  (Economist, 2018). Chinese universities have improved markedly, but, rather than seeking to attract leading global talent, the emphasis seems to be on enticing Chinese nationals educated abroad to return to China. How will these and other differences hinder or help either country gain technology leadership in crucial areas such as artificial intelligence? For instance, will the United States continue to be able to keep, rather than merely to train, top talent from China, which presently accounts for one-third of international students in the United States (Institute of International Education, 2018)? How about other areas of variation in their institutional structures, such as sources and costs of corporate funding (market-driven in the United States, statedetermined for targeted firms in China) (see  Hall & Soskice, 2001; Redding & Witt, 2007; Witt & Redding, 2014 )? How about aspects such as data protection (weak in the United States but even weaker in China)? Given the central role realism ascribes to hard power for the future of (de-)globalization, elucidating these linkages and their local contingencies could be a promising avenue for future IB research.
It is measured in constant 2016 international dollars, with PPP adjusted data unavailable. The time series begins in 1988 because this is the earliest year for which the SIPRI database (Stockholm International Peace Research Institute, 2018) contains a budget for the Soviet Union, which during the Cold War was the closest military contender to the United States. Military spending followed a similar pattern to GDP, though peaking earlier. Relative to the rest of the world, US military spending reached its high point in 1992 at 46. 4%.
ACKNOWLEDGEMENTS I thank  Klaus Meyer  for his constructive editorial guidance and the three anonymous reviewers for their insightful feedback. I am further grateful for comments and input received from  Vinold K. Aggarwal ,  Christina L. Davis ,  Geoffrey Jones ,  Marshall Meyer ,  Jonathan Story , and  Douglas Webber. I also thank for their hospitality the  US-Japan Program at Harvard University  and the  Wharton School , where parts of the work on this paper happened. An earlier version of this paper was presented AIB 2018.
It is measured in constant 2016 international dollars, with PPP adjusted data unavailable. The time series begins in 1988 because this is the earliest year for which the SIPRI database (Stockholm International Peace Research Institute, 2018) contains a budget for the Soviet Union, which during the Cold War was the closest military contender to the United States. Military spending followed a similar pattern to GDP, though peaking earlier. Relative to the rest of the world, US military spending reached its high point in 1992 at 46. 4%. 300% 250% 200% 150% 100% 50% 0% By 2017, it had declined to 36. 1%, a drop by 22. 2%. Again, the picture is even clearer relative to the next closest contender: US spending peaked in 1992 at 921. 7% that of the next closest, France. By 2017, it had decreased to 261.
Identification of CRISPR/Cas9 target sequences For the translocation between ADE2 and CAN1, the target sequences CAN. Y and ADE2. Y found in the literature  [29]  were re-used. The new targets formed by the first translocation were targeted to "reverse" the translocation and restore the original junctions. For the ECM34/ SSU1 translocation, specific CRISPR/Cas9 target sequences with minimal off-targets were chosen to overlap the natural recombination site of wine strains with the CRISPOR v4. 3 website (http://crispor. tefor. net) using the reference genome of Saccharomyces cerevisiae (UCSC Apr. 2011 SacCer_Apr2011/sacCer3) and the NGG protospacer adjacent motif. For multiple rearrangements, we identified 39 occurrences of Ty3-LTRs in the latest version of the genome of Saccharomyces cerevisiae S288C (accession number GCF_000146045.
Acknowledgments We are grateful to  Bruce Futcher  and  Gang Zhao  ( Stony Brook University, USA ) for providing the pGZ110 plasmid. We thank our colleagues,  Gianni Liti  ( IRCAN, France ) and  Bertrand Llorente  ( CRCM, France ) for fruitful discussions and constructive suggestions as well as for providing us the wine and SK1 strains. We are also grateful to  Mae ¨lys Born-Bony  and  Allyson Holmes  for their experimental help.
2007; Arnold et al. 2018). Strikingly, IPM built on exploited populations usually integrate population surveys (of alive individuals), catch-at-age data and capture-mark-recapture-recovery (CMRR) data into age-structured population models  (Methot Jr & Wetzel 2013; Arnold et al. 2018; Scheuerell et al. 2019). This state-ofthe-art limited up-to-now the applicability of IPM for two reasons: i) the age of harvested individuals is often not available because its determination is challenging and generally involves expensive and time-demanding analyses. Instead, body mass at capture and/or death are commonly recorded; ii) information on numbers is rarely based on surveys of alive individuals. Instead, the number of individuals removed through exploitation is commonly recorded in many exploited species.
Here, we develop a widely applicable IPM relying on data commonly collected in exploited populations. We illustrate the usefulness of this approach based on the study of a population of an emblematic game species distributed worldwide, the wild boar Sus scrofa. This species has tremendously increased in abundance and distribution over the last decades  (Massei et al. 2015) , leading to important damage to crops and high risk of disease transmission in Europe (see e. g. Schulz et al. 2019). Hunting is commonly assumed to help controlling wild boar expansion. Our model differs from previous IPMs applied to vertebrate populations in two respects: i) typical IPMs integrate data into an age-class structured population model. Instead, we build an IPM that integrates CMRR data and individual measures recorded at death into a body mass-structured population model; ii) both alive and dead individuals are considered in the model allowing us to compare the observed number of individuals shot by hunters in each body mass class to the numbers expected from the IPM.
| Demographic data collection We studied a wild boar population located in the 11,000 ha forest of Châteauvillain-Arc-en-Barrois in North-Eastern France (48°02'N, 4°55'E). Between 1991 and 2016, thanks  to an intensive capture-mark-recapture program, 1,152 females were captured from March to September using traps, marked and released in their environment. For each capture event, we recorded the date of capture and body mass. Between October and February, wild boars were harvested. All females shot by hunters (previously marked or not) were weighed and their date of death was accurately recorded. No information was available for individuals that died from natural causes. As wild boar rut generally begins in mid-December, females are often pregnant when shot during the hunting season. For pregnant females, the number of fetuses present in the uteri was recorded.
Three types of demographic data were thus available: CMRR data, hunting data and reproduction data. i) CMRR data provided individual histories of 1,152 marked females together with the body mass at each capture (alive) and when shot (dead). ii) Hunting data were the number of females shot by hunters in year t, y t together with their body mass at death (n=7,350 over the study period). iii) Reproduction data were the number of pregnant females shot in year t, R t (n = 811 over the study period) together with their body mass as well as the number of fetuses at t, J t (n = 4,344 over the study period). The Integrated Population Model (see Fig. 1  for a schematic representation of the IPM we built) was made of two types of components  (Schaub & Abadi 2011; Zipkin & Saunders 2018 ): i) a population model iterating a population vector, which links demographic rates (e.
| Population model The population vector was considered each year at the end of the hunting season and before reproduction (pre-breeding census). From life history of wild boar and available data (data on body mass, not on age and data on numbers of individuals killed, not on numbers of alive individuals), the population vector considered three classes of body mass (<30 kg (small), between 30 and 50 kg (medium) and >50 kg (large) (see  Gamelon et al. 2012 )) and two states (alive and shot by hunters) for each body mass-class. The population vector had thus 6 components: 3 body mass-classes × 2 states. The stage-structured matrix population model allows stage-specific population sizes at year t+1 to be estimated from stage-specific population sizes at year t and demographic rates  (Caswell 2001).
| Model for hunting data Hunting data consisted of the annual numbers of females shot by hunters by body mass classes (small, medium and large), denoted as y S,t , y M,t and y L,t , respectively. For these data, we used the likelihood of a state-space model  (de Valpine & Hastings 2002) , which consists of a process model (i. e. the population model previously described, that is the population size as a function of demographic rates and the population size the preceding year) and an observation model. The observation model describes the link between the hunting data y S,t , y M,t and y L,t and the true number of females shot by hunters in the population (NH S,t , NH M,t and NH L,t ) (yellow part, Fig. 1 ). For each body mass class j, we assumed that: y j,t ~Normal(NH j,t , τ j,t ) truncated to positive values, with τ j,t = ଵ ఙ ೕ మ ൈ௬ ೕ, మ , where ߪ ଶ is the term we are estimating.
| Model for reproduction data Reproduction data consisted of the annual numbers of pregnant females shot by body mass classes (small, medium and large), denoted as R S,t , R M,t and R L,t , respectively and the annual numbers of fetuses counted by body mass classes, denoted as J S,t , J M,t and J L,t , respectively. We assumed that the number of fetuses per body mass class J j,t is Poisson distributed such as: J j,t ~ Poisson (R j,t ×LS j,t ), where LS j,t is the term we are estimating, that is the litter size for a female belonging to the body mass class j (blue part, Fig. 1 ).
| Model implementation Assuming independence among the datasets, the likelihood of the IPM was the product of the likelihoods of the three different datasets  (Besbeas et al. 2002; Kéry & Schaub 2012) : hunting data, CMRR data and reproduction data. The IPM was fit within the Bayesian framework and we used non-informative priors for all the parameters. We only constrained σ j in the state-space model to be small using a Uniform distribution between 0 and 0. 1, so that the observation errors τ j,t = ଵ ఙ ೕ మ ൈ௬ ೕ, మ can be large. Markov chain Monte Carlo (MCMC) simulation was used for parameter estimation. All the parameters described in Fig. 1  were estimated within the IPM, even parameters difficult to estimate and often assessed from expert opinion such as Spn and piOs (see  Gamelon et al.
2012 ). The analyses were implemented using JAGS  (Plummer 2003)  version 4. 3. 0 called from R version 3. 4. 3 (R Development Core Team 2017) with package rjags  (Plummer 2016). The JAGS code for fitting the IPM is available in Appendix S3. To assess convergence, we ran three independent chains for 230,000 MCMC iterations, with an adaptation of 180,000 iterations thinning every 100th observation resulting in 1,500 posterior samples. We used the Brooks and Gelman diagnostic R to assess the convergence of the simulations and used the rule R <1. 2 to determine whether convergence has been reached  (Brooks & Gelman 1998). Convergence was reached for most of the nodes except for four years  (1996) (1997) (1998) (1999)  in which not all parameters had converged. To ensure that the priors for initial population sizes do not influence estimates of demographic rates and body mass-specific numbers the first year of the study  (i.
ACKNOWLEDGMENTS Wild boar data collection was performed and granted by the French National Agency for Wildlife and Hunting (ONCFS). We are grateful to all those who helped marking and collecting harvested wild boar, in particular Serge Brandt, Eveline Nivois and Cyril Rousset, to the Office National des Forêts and to F. Jehlé who allowed us to work on the study area. number of fetuses (J) in each body mass class j). Estimated parameters are the body mass-specific recapture probability p j , recovery probability r j , natural mortality NM j , hunting mortality MH j , litter size LS j , numbers of females alive N i , numbers of females shot by hunters NH j , breeding proportions BP j , post-natal survival Spn (from birth to weaning), observation error ߪ ଶ and transitions between body mass classes (growth) with piOs the probability for newborn remaining in the small body mass class, pSS the probability of small females remaining in this class during the year, pSM the probability of small females entering the medium class, pSL the probability of small females entering the large class, pMM the probability of medium females remaining in this class during the year and pML the probability of medium females entering the large class.
Sample Treatment for Cell Density and DNA Extraction A set of three panels was used for flow cytometry and another set for DNA extraction for each condition. Each panel was scraped with a sterile scalpel, and biofilms were fixed in 10 mL solution of artificial seawater (ASW; Sigma-Aldrich) containing 0. 25% glutaraldehyde (Sigma-Aldrich). Fixed biofilms for flow cytometry and scraped biofilm for DNA extraction were kept at -80 • C until analysis. Raw flow cytometry datasets are available on request. It was not possible to assess the percentage of cell detachment by the scalpel, but low standard errors between panels in biological replicates highlight the reproducibility of the technique, as presented elsewhere  (Briand et al. , 2017). Flow cytometry measurements for cell count, and DNA extraction with PowerBiofilm DNA isolation kit (Qiagen) have been described previously  (Pollet et al.
, 2018). Seawater (1 L) was successively filtered at 3 and 0. 2 µm before DNA extraction  (Pollet et al. , 2018). 16S rRNA gene was amplified with the 515F-Y/926R primers set  (Parada et al. , 2016)  by the method described previously  (Pollet et al. , 2018). When no amplification was obtained from all three replicates, samples were pooled before amplification (all surfaces from dynamic and cyclic immersions). Equimolar mixes of amplicons were sequenced using MiSeq Illumina 2 × 250 pb chemistry and generated over 1. 2 million raw reads. After quality control all samples were rarefied to 10,850 reads for further analysis. Sequences assessed in this study have been submitted to NCBI under the accession number PRJNA504753.
Data Analysis Raw sequences were cleaned with QIIME and Prinseq-lite  (Schmieder and Edwards, 2011)  and further analyses were performed for OTU clustering at 97%. Taxonomy assignment and alpha-diversity indexes were calculated with QIIME (version 1. 9. 1), and Silva database version 132  (Caporaso et al. , 2010). The BIOM table after conversion was exported to R and treated with vegan, Tax4Fun, ggplot2, reshape and dependent packages for graphical representation and statistical analysis. Total bacteria count, α-diversity and predicted functions were compared between surfaces and/or time or site with one or two-way ANOVA with R (version 3. 4. 2). Due to the absence of replicates for dynamic incubation, comparison was performed between surfaces assuming different times as replicates. Chao1, Simpson and Faith's phylogenetic diversity (PD)  (Faith and Baker, 2007)  indices were calculated in QIIME to display α-richness and α-diversity of samples, respectively.