dataesr/scientific-paragraphs-categorization huggingface dataset:
2. 2  [44] , each interfaced to Herwig++ 2. 7. 1  [45]  with the UE-EE-5 set of tuned parameters  [46]  and CT10 PDF set. The t t + b b process  [47]  was generated using S 2. 1. 1  [48]  plus OpenLoops  [49]  with the CT10 four-flavor scheme PDF. The V+jets and VV+jets (V = W, Z) processes were generated with S 2. 2. 1 and the CT10 PDF set. Associated production of t t with a boson (t tV) was generated using MG5_aMC 2. 2. 2 combined with P 8. 186  [50] , the NNPDF2. 3LO PDF set  [51]  and the A14 set of tuned parameters  [52]. All predictions, including the t t and tW processes, are normalized to next-to-next-to-leading-order or nextto-leading-order cross-sections  [6, 44, 48, 53, 54]. All samples of simulated data were processed using the full ATLAS detector simulation  [55]  based on G 4  [56].
The rightmost bin of each distribution includes contributions from events beyond the displayed axis limit. between the DR and DS scheme predictions. The size of the dataset leads to statistical uncertainties of up to 20%. Figure  2  presents the differential cross-section observed in data, normalized to the total observed crosssection with this selection. Various predictions are also shown, with uncertainties included from varying the PDF set  [71]  and the renormalization and factorization scales. A χ 2 test statistic is constructed for the various models to assess the level of agreement with the data. Correlations among uncertainties of the unfolded distribution are included, as well as theory uncertainties on the signal predictions. Results of the test are presented in Table  1  as p-values, corresponding to the observed level of agreement over the full distribution as well as the subset m minimax b > 160 GeV where the predicted differences due to interference are largest.
The tW b prediction using the DR scheme gives a better description of the relative normalization of the region m minimax b m t than the DS scheme. However, the DS scheme better models the m minimax b shape over the same range of values. The DR and DS predictions generally bracket the data in the region of large m minimax b , justifying the practice of applying their difference as a systematic uncertainty. The DR2 scheme describes the data well up to the top-quark mass, but significantly underpredicts the data at higher masses5. The calculation from MG5_aMC using the DR scheme is presented alongside the  cross-section compared with theoretical models of the t t + tW b signal with various implementations of interference effects. The uncertainty of each data point includes all statistical and systematic sources, while uncertainties for each of the MC predictions correspond to variations of the PDF set and renormalization and factorization scales.
Dengue patients with comorbidities may be at higher risk of severe dengue and death, however, there have been no large scale studies conducted in Brazil  (Figueiredo et al. 2010 , Teixeira et al. 2015 , Toledo et al. 2016). In a large retrospective cohort study, we determined risk factors for dengue in-hospital mortality using the Brazilian Hospital Information System of the Unified Health System (SIH/SUS) database over an eight year period  (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015)  (Ministério da Saúde -Available from: http://sihd. datasus. gov. br/). Hospital admission records (92 million) from 20,576 hospital departments (e. g. pediatric, clinical) at 5,983 hospitals were included. Patient records from 326,380 hospitalised dengue cases in 9-45-year-old individuals [173,778 female (53.
2%) and 152,602 male (46. 8%)] were analysed. This broad age range includes adolescents, young adults, and adults, thereby excluding the elderly who may have a high prevalence of comorbidities and thus limit the ability to detect differences in CFR. Ethical approval was not required as the database was publically accessible. In-hospital mortality was identified with dengue cases and comorbidities identified directly from the recorded principal and secondary diagnoses for each hospitalised dengue case using the International Classification of Diseases, 10th Revision (ICD 10) codes (WHO 2014). Dengue cases were divided into either non-severe (ICD 10 code = A90; classical dengue) or severe dengue diagnosis codes (ICD 10 code = A91; dengue haemorrhagic fever). In order to exclude potential bias in the analysis, as comorbidities may also be a consequence of severe dengue manifestations, codes considered as severe dengue symptoms and complications were excluded.
The comor-bidity definitions used are summarised in Supplementary data, Table. Analyses were performed using the KNIME  (Berthold et al. 2008 ) analytic platform integrated with KEM ® (Ariana Pharmaceuticals; Liquiere and Sallantin 1998) data mining tools, MySQL database and R v3. 0. 3 statistical software. Data were presented as CFRs and relative mortality ratios (RMR) and 95% confidence intervals (CI) compared to different groups. P values for between group RMRs were determined for statistical significance by Fisher's exact test, whereby p < 0. 05 was considered statistically significant. We first determined whether mortality rates were increased in hospitalised dengue patients with underlying comorbidities compared to those without comorbidities. Of the 326,380 hospitalised dengue cases in 9-45-year-old individuals there were 971 deaths resulting in an overall CFR of 0.
Results HIV-1 Dissemination Among Locations in Ukraine. A total of 448 HIV-1 pol gene sequences were available from the Ukrainian HIV drug-resistance database: Of these, 427; 19; and 2 were identified as subtypes A, B, and circulating recombinant form 03_AB, respectively. Only subtype A sequences were used in our phylogenetic/phylogeographic analyses. These data were collected between 2012 and 2015 from 22 administrative regions of Ukraine, from the capital city of Kyiv, and from the Autonomous Republic of Crimea, representing 24 of the 27 Ukrainian administrative units. For the phylogeographic analysis, we grouped sequences into seven geographic locations: Center, Crimea, East (including Donetsk and Lugansk-two regions in eastern Ukraine involved in military conflict), Kyiv, Odessa, South, and West.
Hereafter, we refer to these groups as "locations" and to the Ukrainian administrative units as "regions. " Full details of the sequences used are provided in Dataset S1, Table  S1  A and B and Supporting Information. An initial maximum-likelihood (ML) phylogenetic tree included 2,199 reference and 427 Ukrainian subtype-A sequences (Fig. 1 ). The tree revealed that the Ukrainian samples formed a distinct monophyletic clade within sequences from countries of the former Soviet Union (FSU). The FSU sequences in turn formed a clade within the global HIV-1 subtype-A dataset, as expected from previous reports  (19, 20). Furthermore, we selected 36 HIV subtype-A sequences sampled in 1986-2014 and added them to the Ukrainian sequences to improve molecular clock calibration (n = 463; combined dataset).
The dataset showed sufficient temporal signal (root-totip correlation coefficient = 0. 5), supporting the use of molecular clock models. Phylodynamic analysis showed that the most recent common ancestor of the sequences in the Ukrainian dataset was 2001 [95% Bayesian credible interval (BCI): 1999-2002], for the FSU countries was 1998 (95% BCI: 1997-2000), and for the combined dataset was 1977 (95% BCI: 1972-1981). The resulting empirical posterior distribution of dated phylogenies was used in subsequent phylogeographic analyses, after the removal of the non-Ukrainian reference sequences (n = 427; Ukrainian dataset). Given the k = 7 sampling locations, we investigated virus dissemination among the k(k -1) = 42 possible pathways of virus spread using discrete phylogeographic models  (21, 22).
More specifically, we investigated the main exporters of viral lineages-i. e. , the locations from which the majority of migration events originated. In our phylogeographic analysis, the East (Donetsk and Lugansk regions) was the main exporter, accounting on average for 92% (95% BCI: 87-96%) of the migration events in the Ukrainian dataset (Fig. 2 , Table  1 , and Dataset S1, Table  S2 ). In particular, viral lineage movements were most frequently observed from the East to the Center, South, Kyiv, and Odessa locations, accounting for 26% (95% BCI: 24-28%), 24% (95% BCI: 21-26%), 22% (95% BCI: 20-24%), and 17% (95% BCI: 16-18%) of migration events, respectively. Very few migration events were observed as originating from the Crimea and West locations (<1% each), but the West received a significant proportion of translocated viral lineages (7% of viral lineage movement events; 95% BCI: 6-8%).
Given that locations were represented by different numbers of sequences, we explored estimates of among-location viral lineage movement in more balanced datasets. To this end, we conducted additional analyses in which the number of sequences per location were randomly down-sampled to match the number of sequences in the third smallest location (Odessa; n = 57). We conducted 10 replicate analyses with different random subsamples (all n = 317) of the dataset and found that the East remained as the main viral lineage export location (accounting on average for 91% of migration events) in 7 of 10 subsampled replicates. For the number of migration events estimated for each subsampled dataset, see Dataset S1, Table  S3. Temporal Change in Virus Lineage Movement After the Beginning of the Conflict.
We aimed to investigate whether viral gene flow among regions has changed after the initiation of war, a phenomenon we call hereafter "a phylogeographic transition. " We estimated rates of viral lineage movement using models with time-dependent lineage transition rates. Specifically, we compared the "single-epoch" model (in which lineage migration rates remained constant between 2012-2015) with the "two-epoch" model (in which lineage migration rates were allowed to be different before and after the start of the conflict in 2013). The results of the epoch analysis showed that the two-epoch model fitted the data better than the single-epoch model, as demonstrated by a Bayes factor (BF) support of >30 in favor of the more complex model. Standard terminology defines the strength of support for a particular model as "very strong" for BF >30 (23).
This result was consistent across all 10 replicate datasets generated by random subsampling (Dataset S1, Table  S4 ). Correlation Between Observed Virus Flow and PWID Behaviors. We further investigated whether among-location viral lineage movement was correlated with important epidemiological characteristics of the HIV-1 epidemic in Ukraine. Here, we calculated Spearman correlation coefficients between exporting and importing viral gene flow and all behavioral covariates of interest. In particular, we were interested in the total number of lineage migration events that originated in a given location (exportation events) and the total number of lineage migration events received by a given location (importation events). Behavioral data relating to PWID and regional epidemiological characteristics were aggregated to the same geographic units (locations) as used in the phylogeographic analysis.
The behavioral variables came from (i) the national survey of PWID (Integrated Bio-Behavioral Surveillance; IBBS), (ii) the regional HIV surveillance systems, and (iii) data on the warassociated movement of IDP. We chose the IBBS data that were collected closer to the phylogeographic transition (i. e. , IBBS-2013) as an indicator of behaviors at the time when viral movement was hypothesized to have changed. We found that injecting behaviors of PWID were not associated with the observed viral migration flow. Risky sexual behaviors of PWID, on the contrary, were associated with the number of importation events. Specifically, the higher proportion of PWID who always use condoms with casual and commercial partners was associated with a lower number of importation events [Spearman correlation P = 0.
03 and 0. 05, respectively, after adjustment for multiple comparisons using the false discovery rate method  (24) ]. The number of importation events per location was surprisingly negatively correlated with the proportion of PWID who reported participating in group sex (P = 0. 04). Importantly, we found that the number of exportation events to a location was positively correlated with the HIV prevalence in the general population (P = 0. 05) and the number of IDP relocated to that location (P < 0. 01). At the same time, the number of HIV-infected IDP registered at that location was positively correlated with a higher number of importation events (P = 0. 03). Table  2  shows statistically significant correlations at P ≤ 0. 05; see Dataset S1, Table  S5  for all correlation coefficients.
Discussion Here, we explored HIV dissemination in Ukraine by analyzing geographical patterns of viral genetic flow in recent years. Our results suggest that viral lineages have moved from the regions engaged in the war to the rest of Ukraine: Many strains sampled in Ukraine during 2012-2015 appear to have originated, directly or indirectly, from Donetsk and Lugansk (i. e. , the ancestral sequence location of the Ukrainian dataset was found to be in the East location). The East was the main exporter of viral lineages, and this remained the case when the number of sequences per location was made more balanced. The most intensive viruslineage movement was observed from the eastern to the central and southern regions and to Odessa and Kyiv. The East location is heavily affected by HIV: 24% of all nationally registered HIV cases, and the largest number of HIV-positive PWID (45,000), live or have lived in the Donetsk and Lugansk regions.
Russia Belarus Poland Our data support a transition in the pattern of viral lineage movement around the end of 2013, the time of the beginning of several major events in Ukraine: the Maidan protests in Kyiv, the annexation of Crimea, and an initiation of the war in the east. The movement of people who relocated from the areas not controlled by the Ukrainian government to locations elsewhere corresponds to the observed virus movement patterns. Furthermore, we believe that the observed virus-lineage movement is not a result of recent transmissions, but reflects the redistribution of preexisting infections within the country. Viral gene sequences in the Ukrainian HIV database come from patients who experienced antiretroviral therapy (ART) failure and whose blood samples were sent for HIV drug-resistance testing.
Patients with multiple changes of drug schemes are prioritized. Thus, a patient failing ART due to apparent adherence issues is unlikely to be prioritized for resistance testing over patients with failures under treatment. Indeed, the percentage of sequences with no resistance mutations at the time of failure did not change significantly over time (17% and 20% before and after the beginning of war, respectively), suggesting that severe adherence issues are not overrepresented in our data (Dataset S1, Table  6B ). More details on the drug-resistance-associated mutations in our sample can be found in Supporting Information. Regrettably, the viral genetic data from patients of the AIDS centers in Ukraine do not have associated risk group information. Thus, we cannot be confident as to what risk population our sample mostly represents.
Methods Data. Sequences sampling and phylogenetic analysis. We used 448 HIV-1 genetic sequences sampled in 2012-2015 from patients of 24 Ukrainian Regional Centers to Prevent and Fight AIDS (AIDS Centers) (52% males) and available from the Ukrainian HIV drug-resistance database. All of the available high-quality (<5% of ambiguous nucleotides) subtype A (n = 427) sequences were used in the analysis. All sequence IDs and accession numbers are in Dataset S1, Table  S1  A and B. We aligned the sequences using MEGA software (Version 7. 0)  (40)  and then manually edited the alignment, deleting 43 codon positions associated with drug resistance (41) (the positions are listed in Supporting Information). The final alignment consisted of 866 codons that corresponded to the 2,253-3,216 positions of the HXB2 HIV reference sequence (pol gene).
We used the REGAv3  (42)  and COMET  (43)  subtyping tools to identify subtypes and recombinant sequences. To explore the position of the 427 sequences from the Ukrainian HIV database within the global subtype-A epidemic, we merged our sequences with a globally representative subtype-A reference dataset, which consisted of 2,199 sequences, using Clustal W  (44). We then reconstructed an ML phylogenetic tree with 2,626 sequences (427 Ukrainian and 2,199 reference) using RAxML  (45)  to check the position of our sequences within the global subtype-A diversity (Fig. 1 ). For further analyses, we chose 36 subtype-A HIV-1 reference sequences sampled in 1987-2013 that were publicly available from the Los Alamos database to improve the molecular clock calibration. Sequences were selected to ensure a balanced geographical and temporal representation.
We used RAxML to build the ML tree of the 427 Ukrainian and 36 reference sequences (Fig. S1 ) (the combined dataset; n = 463). Behavioral data. Data on PWID behaviors came from the IBBS of PWID (n = 9,002) conducted biyearly in 25 administrative regions of Ukraine in 2013. In IBBS-2013, participants were asked about their injecting and sexual behaviors and tested for HIV and hepatitis C virus antibodies by using rapid tests CITO TEST HIV 1/2/07. More information on sampling, phylogenetic analysis, and reference dataset, as well as additional information on the IBBS survey, can be found in Supporting Information and elsewhere  (25).
The maximum clade credibility tree for the Ukrainian dataset was reconstructed in TreeAnnotator and is presented in Fig. 2. Discrete trait analysis. For the discrete trait phylogeographic analysis, we grouped the 427 Ukrainian sequences from 24 different administrative regions into seven geographic locations, as shown in Fig. 2  and Dataset S1, Table  S1B. We grouped the data into seven locations to avoid overparameterization of the model and to ensure that every geographic location had at least 10 sequences. We also made sure that this grouping was appropriate in the cultural and social context of Ukraine. We computed a discrete phylogeographic analysis with the robust counting method as implemented in BEAST, using an empirical tree distribution as described  (48) (49) (50) (51). This approach inferred the expected number of viral migrations for every pathway on a branch-by-branch basis.
The location-exchange process throughout the phylogeny was estimated by using asymmetric (nonreversible) discrete-time Markov chains, which quantified the number of exporting and importing virus-lineage movements for each pair of locations. We estimated viral gene flow as the number of migration events, which is the number of viral-lineage movements from one location to another. In the first step of the analysis, we included the full Ukrainian dataset, n = 427. In an attempt to account for bias that might arise from unequal allocation of sequences per location, we ran additional analyses with reduced numbers of sequences per location. Specifically, we conducted 10 replicate analyses, in which we reduced the number of sequences in each location to the third smallest number in the dataset (n = 57 for Center, East, Kyiv, Odessa, and South; n = 10 for Crimea; and n = 22 for West).
This approach allowed us to test for a change in viral gene flow after a specified time point. For the two-epoch model, we chose the year 2013 as the transition time between the two epochs. For this analysis, we merged the sequences from two locations: East (Donetsk and Lugansk) and Other (all other sequences). We created 10 subsamples, down-sampling the number of sequences in the "Other" location to meet the number of sequences in the East. We used path-sampling (53) and stepping-stone-sampling (54) marginal likelihood estimators to determine which epoch model fit the data best. The results of the epoch analysis showed that the two-epoch model fit the data better than the single-epoch model. This result was consistent across all 10 replicate datasets generated by random subsampling (Dataset S1, Table  S4 ).
ACKNOWLEDGMENTS. We thank  Louis du Plessis  ( Department of Zoology, University of Oxford ) for assistance. T. I. V. is supported by the  Clarendon Fund and Hertford College of the University of Oxford ; S. R. F. is supported by  NIH National Institute on Drug Abuse  Grant  DP1DA034989 ; G. M. is supported by  Medical Research Council   Clinician Scientist Fellowship   MR/K010565/1 ; and N. R. F. is supported by the  Medical Research Council/Wellcome Trust/Newton Fund  (Grant  MC_PC_15100/ZK/16-078 ). IBBS data collection was administered by the  Ukrainian Institute for Social Research (UISR) after O. Yaremenko (UISR)  under  International Charitable Foundation Contract "Alliance for Public Health. ".
In 1 month old recruits on dead Porites skeletons, RSM increased from 4. 33 ± 0. 49% after 3 months Figure  2. Substratum-dependent microborer colonization of coral recruits. Euendolithic filaments were detected by photonic and/or scanning electron microscopy in skeletons of P. damicornis (2 to 9 replicates per life stage and substrate type). One month old recruits developed on clean glass had 25% microborer prevalence. Data for 3 months old recruits on dead Porites are missing because full colonization was already reached on these substrates in 1 month old recruits (100% prevalence) and -given the limited availability of larval materialfocus was given to the colonization process. of exposure (n = 191 measurements) to 10. 95 ± 1. 81% after 7 months (n = 66 measurements). In 3 months old recruits on calcite spar, RSM increased slowly, from 1.
The molecular aspects of our study confirm that microboring communities in living corals are reservoirs of new Ostreobium diversity. Indeed, 9 new Ostreobium rbcL clades (OTU99%) were detected in branching Pocillopora damicornis (type beta  21  ) from long-term aquarium cultures, P. verrucosa from Eilat reef (Haplotype A and E  36  ) and Pocillopora sp. from a New Caledonian reef, of which only clade K (OTU99%) was previously known from a shallow water massive Porites coral from the Red Sea. These results provide new information on the diversification of the genus Ostreobium, adding to the growing datasets of operational taxonomical units, which have recently been reported from the skeleton of mostly massive scleractinian corals, using the rbcL gene as well as complementary gene markers  3, 11, 13, 14.
Vertical and horizontal extensions of juveniles were determined on thin sections (with ImageJ software, N. I. H. , USA) allowing the calculation of the ratio DP, i. e. 'penetration depth of microborers/height of coral skeleton (in the vertical axis)'. A relative DP of ~1 indicated a complete penetration of microboring filaments from the settlement substrate up to coral tissue vicinity while a relative DP of ~0 indicated a limited penetration of filaments into coral skeletons.
Amplified fragments were visualized in 1% agarose gels with SYBRGold, and purified (NucleoSpin ® gel and PCR clean-up kit, Macherey-Nagel, France). Reducing the number of cycles did not yield enough DNA for visualization. To increase the recovered diversity, amplicons were pooled from 2 to 6 independent positive PCR reactions, before cloning into pGEM-T easy vector plasmids and competent Escherichia coli JM109 cells (Invitrogen, France). DNA plasmids of insert-containing colonies were extracted using Wizard Plus SV Minipreps (Promega, France) and 4 to 11 clones per amplicon were Sanger-sequenced in one or both directions at Eurofins Genomics (Germany). The cloned sequences were checked manually then assembled and aligned with their closest matching rbcL sequences retrieved by BLASTn in GenBank database.