dataesr/scientific-paragraphs-categorization huggingface dataset:
0. 1). Novelty of a gene finding was determined by taking all variant predictors of the gene and determining if they were in linkage disequilibrium (LD ≥ 0. 2 in Phase 3 Thousand Genomes Europeans) with a previously reported GWAS index variant. We identified suggestive findings in the discovery stage to be replicated in a second independent dataset. In the discovery stage we employed a false-discovery rate (FDR) threshold of 0. 2 separately for colon transverse and whole blood models. FDR for each gene was calculated using the R statistical package p. adjust, which uses the method of Benjamini and Hochberg to calculate the expected proportion of false discoveries amongst the rejected hypotheses  (Hochberg and Benjamini 1990). Genes meeting this threshold were carried forward for replication.
Replication phase To replicate novel PrediXcan findings (n = 3 genes from colon transverse models) that had a FDR ≤ 0. 2, we used the same GTEx colon transverse, elastic net prediction models (as we had done in the discovery GECCO-CCFR data) to impute genetically regulated gene expression in replication samples from (1) CORECT (pooled across consortium studies), (2) UK Biobank and (3) a pooled dataset of 5 independent GWAS datasets. Multivariate logistic regression was used to test the association of imputed genetically regulated gene expression with colorectal cancer risk in these three datasets and then meta-analyzed effects using inverse variance weighting of Z scores (details provided in Online Resource 1). A two-sided P value less than 0. 05/ (number of genes to be replicated) was considered statistically significant.
Acknowledgements ASTERISK: We are very grateful to  Dr. Bruno Buecher  without whom this project would not have existed. We also thank all those who agreed to participate in this study, including the patients and the healthy control persons, as well as all the physicians, technicians and students. CORECT: The content of this manuscript does not necessarily reflect the views or policies of the  National Cancer Institute  or any of the collaborating centers in the  CORECT  Consortium, nor does mention of trade names, commercial products or organi-staff of the  Department of Pathology and Biobank of CHUS, Instituto de Investigación Sanitaria de Santiago (IDIS) ,  Instituto de Investigación Sanitaria Galicia Sur (IISGS) ,  SERGAS, Vigo, Spain , and  Programa Grupos Emergentes ,  Cancer Genetics Unit, CHUVI Vigo Hospital ,  Instituto de Salud Carlos III, Spain.
DACHS: This work is supported by the  German Research Council [Deutsche Forschungsgemeinschaft ,  BR 1704/6-1 ,  BR 1704/6-3 ,  BR 1704/6-4  and  CH 117/1-1 ); and the  German Federal Ministry of Education and Research  ( 01KH0404  and  01ER0814 ). DALS: This work is supported by the  National Institutes of Health  ( R01 CA48998  to M. L. S. ) EPIC: The coordination of EPIC is financially supported by the  European Commission (DG-SANCO)  and the  International Agency for Research on Cancer. The national cohorts are supported by  Danish Cancer Society (Denmark) ;  Ligue Contre le Cancer ,  Institut Gustave Roussy, Mutuelle Générale de l'Education Nationale ,  Institut National de la Santé et de la Recherche Médicale (INSERM) (France) ;  German Cancer Aid ,  German Cancer Research Center (DKFZ) ,  Federal Vention of diabetes through lifestyle intervention and population studies in Europe and around the World (PREVIEW ) project which received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013 ) under grant no.
Am J Hum Genet. 2009 Nov; 85[5]:679-91 ). The prostate and PanScan study datasets were accessed with appropriate approval through the dbGaP online resource [http://cgems. cancer. gov/data/] accession numbers phs000207. v1. p1 and phs000206. v3. p2, respectively, and the lung datasets were accessed from the dbGaP website (http://www. ncbi. nlm. nih. gov/gap)through accession number phs000093. v2. p2. Funding for the Lung Cancer and Smoking studywas provided by  National Institutes of Health, Genes, Environment and Health Initiative  ( Z01 CP 010200 ,  NIH   U01 HG004446 , and  NIH   GEI U01 HG 004438 ). For the lung study, the  GENEVA Coordinating Center  provided assistance with genotype cleaning and general study coordination, and the  Johns Hopkins University Center  for Inherited Disease Research conducted genotyping.
SEARCH:  Cancer Research UK  ( C490/A16561 ). The Spanish study was supported by  Instituto de Salud Carlos III , co-funded by  FEDER  funds-a way to build Europe-( PI14-613 ,  PI09-1286 );  Catalan Government DURSI  ( 2014SGR647 ); and  Junta de Castilla y León  ( LE22A10-2 ). Spain: Catalan Government DURSI (2014SGR647); and  Instituto de Salud Carlos III , co-funded by  FEDER  funds-a way to build Europe ( PI14-00613 ). The Swedish Low-risk Colorectal Cancer Study: The study was supported by the  Swedish research council  ( K2015-55X-22674-01-4 ,  K2008-55X-20157-03-3 ,  K2006-72X-20157-01-2 ); and the  Stockholm County Council  ( ALF  project). VITAL: This work is supported by the  National Institutes of Health  ( K05 CA154337 ). WHI: The  WHI program  is supported by the  National Heart, Lung, and Blood Institute ;  National Institutes of Health; United States Department of Health  and  Human Serv  i c e s t h r o u g h c o n t r a c t s (  H H S N 2 6 8 2 0 1 1 0 0 0 4 6 C , H H S N 2 6 8 2 0 1 1 0 0 0 0 1 C , H H S N 2 6 8 2 0 1 1 0 0 0 0 2 C , H H S N 2 6 8 2 0 1 1 0 0 0 0 3 C , H H S N 2 6 8 2 0 1 1 0 0 0 0 4 C , HHSN271201100004C ) The  Genotype-Tissue Expression (GTEx) Project  was supported by the  Common Fund  of the Office of the Director of the National Institutes of Health.
The MCCS was further supported by  Australian NHMRC  grants ( 509348 ,  209057 ,  251553 ,  504711 ), and by infrastructure provided by the  Cancer Council Victoria. Cases and their vital status were ascertained through the  Victorian Cancer Registry  and the  Australian Institute of Health and Welfare , including the  National Death Index  and the Australian Cancer Database. MSKCC: The work at Sloan Kettering in New York was supported by the  Robert and Kate Niehaus Center for Inherited Cancer Genomics  and the  Romeo Milio Foundation. Moffitt: This work was supported by the  National Institutes of Health  ( R01 CA189184 ,  P30 CA076292 );  Florida Department of Health Bankhead-Coley Grant  ( 09BN-13 ); and the  University of South Florida Oehler Foundation. Moffitt contributions were supported in part by the  Total Cancer Care Initiative ; Collaborative Data Services Core; and  Tissue Core at the H.
(b) Data Data on dengue, chikungunya and Zika cases were obtained from SINAN via the Rio de Janeiro Municipal Secretariat of Heath, and are publicly available  [19]. The Municipal Secretariat of Health georeferenced 91% of dengue cases, 95% of chikungunya cases and 92% of Zika cases, using the address of the patient's residence. We analysed notified cases of dengue, Zika and chikungunya (confirmed by laboratory or by clinical-epidemiological criteria) occurring in Rio de Janeiro municipality between 2 August 2015 and 31 December 2016 (epidemiological weeks 31, 2015 and 52, 2016), grouped by epidemiological week and neighbourhood of residence. Case definitions follow Ministry of Health protocols  [13, 20, 21]. Population data by neighbourhood and shapefiles were obtained from the Instituto Pereira Passos  [22].
(c) Space-time analysis To detect spatio-temporal clusters of arboviral diseases in Rio de Janeiro, we used the  Kulldorff  (iii) examines disease dynamics in continuous time; (iv) estimates the relative risk (RR) for each cluster (considering the underlying population); and (v) it can simultaneously evaluate more than one disease  [23]. The scan statistic was applied for each disease individually and all three diseases simultaneously (multivariate scan statistic with multiple datasets). Through moving cylinders across space (i. e. the base of the cylinder) and time (i. e. the height of the cylinder), it identifies high-risk clusters by comparing the observed number of cases to the expected number of cases inside the cylinder  [24]. In our analysis, the neighbourhood was considered as part of the cylinder if its centroid was located within the base of the cylinder.
The RR for each cluster is calculated using the following equation  [23] : RR ¼ c=E½c ðC À cÞ=ðC À E½cÞ : ð2:3Þ The multivariate scan statistic for multiple datasets was applied to simultaneously search for clusters of dengue, Zika and chikungunya that coincided in time and space. This technique calculates for each cluster the LLR for each disease. Then, the LLR for a particular cluster is calculated as the sum of the LLR for the three diseases. As for a single disease, the maximum of the sum of the LLRs constitutes the most likely cluster  [23, 25]. For each model, Monte Carlo simulations (n = 999) were performed to assess statistical significance. We considered statistically significant clusters ( p < 0. 05) with no geographical overlap and that included a maximum of 50% of the city's population (nearly 3.
1 million people). After testing several combinations of temporal and spatial parameters, we chose the combination that resulted in a reasonable number of clusters that could be targeted for local interventions (electronic supplementary material, figure  S2 ). The temporal window was set to be at least one week and a maximum of four weeks. Clusters were restricted to have at least five cases and, in the output parameters, to include a maximum of 5% of the city's population (nearly 315 000 people). SATSCAN™ (v. 9. 5) software was applied within R (v. 3. 4. 4), using the package rsatscan (v. 0. 3. 9200) [26] [27] [28]. The R code is available at https://github. com/laispfreitas/satscan_dzc/blob/ master/script_satscan_dzc_rio  [29]. Maps were produced using QGIS (v. 3. 8. 1) and ggplot2 (v. 3.
(c) Zika cases clusters There were 15 Zika clusters, distributed all over the city, similar to the observed pattern for dengue (figure  5a ). The most likely cluster was located in the West of Rio de Janeiro city, a region where chikungunya clusters were rarely observed. This cluster also had the highest RR, of 13. 57 (electronic supplementary material, table  S3  and figure  S3C ). In contrast with dengue and chikungunya, Zika clusters occurred over a longer period of time, between the end of November 2015 and May 2016 (figure  5b ). The third most likely cluster occurred eight weeks after the first one. The first Zika clusters in time emerged in the North of the city (electronic supplementary material, figure  S4C ). The multivariate scan statistic for multiple datasets detected 16 clusters, of which nine showed dengue, chikungunya and Zika occurring simultaneously; five showed overlapping dengue and Zika outbreaks and two showed only outbreaks of Zika (figure  6 ).
Discussion This is, to our knowledge, the first study exploring space-time clustering of dengue, chikungunya and Zika in an intra-urban region. The data analysed are rare and of great value, as they include triple epidemics with a large number of cases. Also, this study included the first ever epidemics of chikungunya and Zika in Rio de Janeiro city. In brief, detected clusters for each disease presented different dynamics in time and space. Dengue and Zika clusters were found across the city, with Zika clusters persisting over a longer time period. Chikungunya clusters were more concentrated in the North and Downtown regions. Simultaneous clusters of the three diseases were more likely in neighbourhoods with a combination of high population density and low socioeconomic status. Dengue, chikungunya and Zika cases were notified across the whole city.
The similar clinical manifestations of dengue, Zika and chikungunya also represent a limitation. This limitation is inherent of every study using notified cases, as only a small proportion of cases are laboratory confirmed (8. 5%, 30. 4% and 4. 1%, for dengue, chikungunya and Zika, respectively, in our dataset). Also, we did not have information on co-infections within the disease surveillance database. However, as co-infections are rare, this should not have affected our analysis. In a national survey in Colombia, co-infections accounted for 0. 14% of the arboviral diseases cases  [59]. A small percentage of cases (8%) that were not georeferenced (and hence, not included in this study) could potentially result in a selection bias. It is possible that cases occurring in favelas, where addresses are sometimes not standardized, have a higher chance of not being georeferenced.
Data accessibility. The data underlying the results presented in the study are from the Rio de Janeiro Municipal Secretariat of Health. The data can be downloaded at http://www. rio. rj. gov. br/web/sms/exibe-Conteudo?id=6525201. The code was made available at https:// github. com/laispfreitas/satscan_dzc/blob/master/script_satscan_ dzc_rio  [29].
Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES, http://www. capes. gov. br/)-Finance Code 001, to L. P. F. M. S. C. received grants from Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ, http://www. faperj. br/, grant no. E_26/201. 356/2014) and support from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, http://www. cnpq. br/, grant no. 304101/2017-6). R. L. was funded by a Royal Society Dorothy Hodgkin Fellowship (https://royalsociety. org). The funders did not influence the content of this manuscript nor the decision to submit it for publication.
Linear structures were enhanced by computing the smallest eigen values of the hessian tensor thanks to the ImageJ plugin "FeatureJ" (Erik Meijering, Erasmus University Medical Center, Rotterdam, Netherlands. Plugin available from https://imagescience. org/meijering/software/featurej. Images obtained were then binarized and skeletonized. Junction points of the image skeleton, where three or more segments are branched, were then detected by a binary hit or miss operation using the ImageJ plug-in "Morphology" (G. Landini 2008). The junction points were subtracted from the image skeleton and the remaining skeleton branches were counted and measured using the Analyze Particles function of ImageJ. Those branches were considered as representative of the continuous segments of ZO-1 labeling along the cell membranes.
Quantitative real-time polymerase chain reaction (RT-qPCR). Cellular material from at least three 6-well plate dishes was pooled for each condition. Total RNA from ARPE-19 was isolated with TRIZOL reagent (Invitrogen, France) according to the manufacturer's instructions, and Superscript II Reverse Transcriptase (Invitrogen, France) was used to reverse transcribe 1 μg of mRNA. Amplification reaction assays contained 1 × SYBR Green PCR Mastermix (Applied Biosystems, France). All real-time PCR oligonucleotide primers were previously experimentally validated by QPCR and BLAST. Primers were designed such that amplicon sizes ranged from 50 to 250 bps (Table  1 ). A hot start at 95 °C for 5 min was followed by 40 cycles at 95 °C for 15 s and 60 °C for 1 min with the 7300 SDS thermal cycler (Applied Biosystems, France).
, 2012 Dawid et al. , , 2017)). This paper is organized as follows. In Section 2, we consider parametric models with tractable likelihoods. We present how the prequential Hyvärinen score can be estimated via SMC samplers, and show that it leads to consistent model selection, under regularity assumptions. In Section 3, we generalize the approach to nonlinear non-Gaussian state-space models, using SMC 2 , and we present a simulation study with Lévy-driven stochastic volatility models. In Section 4, we extend the proposed criterion to discrete observations and compare diffusion models for population dynamics. Possible limitations and directions for future research are outlined in Section 5. Proofs, implementation details, and additional simulations are provided in the supplement. The R code producing the figures is available at github.
A major confounding factor in characterizing the sequence dependence of any protein function, including affinity, is the pervasiveness of epistasis, the phenomenon by which different mutations interact with each other  [5]. Theory  [6] [7] [8]  and genomic data  [9]  suggest that interand intragenic epistasis plays a major role in molecular evolution, by constraining the set of accessible evolutionary trajectories towards adapted phenotypes  [10] [11] [12] [13] [14] , enhancing evolvability through stabilizing mutations  [15, 16] , or slowing down adaptation by the law of diminishing returns  [17, 18]. Evidence for epistasis in antibody affinity include direct observations of cooperativity between mutations  [19, 20] , the dependence of mutational effects on sequence background  [21] , and statistical covariation of residues in large sequence datasets  [22, 23].
Position Weight Matrix model of affinity We analyzed data from  [34]  (https://github. com/ jbkinney/16_titeseq), where Tite-Seq was applied to measure the binding affinities of variants of the 4-4-20 fluorescein-binding scFv antibody, henceforth called 'wildtype'. Libraries were generated by introducing mutations to either the CDR1H or CDR3H domains restricted to 10 amino acid stretches called 1H and 3H (Fig. 1A ). All single amino acid mutants, 1100 random double amino acid mutants, and 150 triple amino acid mutants were generated in multiple synonymous variants and measured, (Fig. 1B ). Using a combination of yeast display and high-throughput sequencing at various antigen concentrations, Tite-Seq yielded the binding dissociation constant K D (in M or mol/L) of each variant with the fluorescein antigen.
The smoothing penalty is approximated by a sum over the squared discretized second derivative: dF |E (F )| 2 ≈ i (E i+1 + E i-1 -2E i ) 2 /δF 3. We minimize O[E] ≈ O[E 1 ,. , E N ] as a quadratic function of its arguments (E i ), while imposing boundary constraints on the PWM prediction and the requirement that E is a increasing function of F (i. e. E i+1 > E i ), using the python package cvxopt  [53]. The hyper-parameter α is evaluated by maximizing the generalized cross-validation of the coefficient of determination R 2 = 1 - s∈S [E \S PWM (s) -E \S (s)] 2 Var s∈S [E \S (s)] S , (6) where E \S and E \S PWM (s) are learned through optimizing Eq. 5, but after removing from the dataset a subset S of the multiple mutants comprising one tenth of the total. The average is over ten non-overlapping subsets S.
Epistatic model The epistatic terms of the pairwise model were made to depend on the biochemical categories of the interacting residues, J ij (s i , s The Lasso penalty λ was learned by 10-fold crossvalidation, and energy terms found in less than 2 sequences were excluded from the fit. Posterior values for J terms were calculated using Bayesian Lasso  [47]. The volume and mutational flux were defined as: V (B) = s Θ(B -K D (s)) (11) A(B) = s Θ(B -K D (s)) 1 19 s |d(s,s )=1 Θ(K D (s ) -B), (12) where  Test of the inference method for learning the nonlinear scale on synthetic data. A PWM was generated randomlly, with drawn from a normal distribution, and applied to the sequences from our dataset to simulate a "true" score (EPWM). Gaussian noise with 50% of the standard deviation of EPWM was added.
Mamiellophyceae (unicellular green algae) are a key phytoplankton group in coastal waters. Although extensively studied over the last 20 years, the overall oceanic distribution of the major species/clades is still poorly known. To address this problem, we analyzed the 2014 Ocean Sampling Day (OSD) metabarcoding dataset providing sequences from the V4 hypervariable region of the 18S rRNA gene for 157 samples collected at 143 mostly coastal stations. Mamiellophyceae were found at nearly all OSD stations and represented 55% of the green microalgae (Chlorophyta) reads. We performed phylogenetic analyses of unique OSD metabarcodes (amplicon single variants, ASVs) and GenBank reference sequences from cultures and from the environment, focusing on the four most represented genera: Ostreococcus (45% of the Mamiellophyceae reads), Micromonas (34%), Bathycoccus (10%) and Mantoniella (8.
7%). These analyses uncovered novel diversity within each genus except Bathycoccus. In Ostreococcus, a new clade (E) was the second most represented clade after Ostreococcus "lucimarinus". Micromonas could be separated into nine clades, exceeding the six species and candidate species already described. Finally, we found two new environmental clades within Mantoniella. each Mamiellophyceae clade had a specific distribution in the OSD dataset suggesting that they are adapted to different ecological niches. In marine waters, the accepted paradigm is that the so-called "red" lineage (mainly diatoms and dinoflagellates) is dominant, while the "green" lineage (land plants) is dominant in terrestrial environments 1. These two lineages are differentiated by the evolutionary origin of their chloroplasts: those of the "green" lineage are surrounded in most cases by two membranes, which is evidence of a single endosymbiotic event, and their major photosynthetic pigments are chlorophyll a and b 1.
light (O. tauri,O. mediterraneus and O. "lucimarinus") and those adapted to low light (Ostreococcus clade B)  19. The second genus within Bathycoccaceae hosts a single species, Bathycoccus prasinos  20. No clades can be delineated within this species based on 18S rRNA gene sequences from cultures and the environment. However, divergence in ITS sequences suggests that B. prasinos probably consists of two different species  21, 22. The European Ocean Sampling Day (OSD) project sampled global coastal waters in 2014 at approximately the summer solstice (21 June) with the aim of analyzing the diversity and distribution of marine microorganisms  23  especially in coastal waters using 18S rRNA metabarcodes (V4 and V9 hypervariable regions)  24. This dataset allowed the distribution of fourteen classes of Chlorophyta to be determined  25.
Mamiellophyceae  7  , the most prevalent class in all coastal environments  26  , did not show any geographic distribution patterns or environmental preference  25. The present paper uses the OSD V4 datasets to analyze the taxonomic diversity and global distribution of four major Mamiellophyceae genera: Ostreococcus, Micromonas, Bathycoccus and the less studied Mantoniella. Our analyses reveal the existence of novel clades within Ostreococcus, Micromonas and Mantoniella and that most species/clades have specific oceanic distributions.
Material and Methods The OSD consortium provided two metabarcoding datasets for 2014 using the V4 region of the 18S rRNA gene: the LGC dataset (sequenced by LGC Ltd. ) consisting of 157 water samples from 143 stations (see http:// oceansamplingday. blogspot. com/2013/04/osd-sites_5. html) filtered through 0. 22 μm pore size Sterivex filters without prefiltration and the Life Watch (LW) dataset consisting of a subset of 29 water samples filtered through 0. 8 μm pore size polycarbonate membranes without prefiltration. The extraction, PCR and sequencing protocols were described previously  24, 25. In brief, the LGC and LW data originated from the same water samples but were processed independently for filtration, DNA extraction, PCR amplification and sequencing (both Illumina 2 × 250 bp). The LGC and LW datasets contained approximatively 5 and 9 million V4 sequences, respectively, resulting in higher coverage for the LW dataset.