dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene Ontology (GO) For all the proteins identified as variants, a gene ontology (GO) analysis was performed with the ProteINSIDE webservice  (Kaspric et al. , 2015)  to identify the associated biologicals processes. Based on the fact that, in the used database  (Canard_Anas_Caïrina 190524) , the majority of the identified proteins are unreviewed proteins, all variant proteins were evaluated one by one and the ones not related to a function in the liver were deleted before GO analysis. The Human species was used for the GO analysis. For the GO enrichment analyses, only the different biological processes with a p-value < 10 -3 and associated with at least two proteins of the list were considered. As the analysis was performed with a human model, after the GO analysis, all pathways not associated with the liver (viral process, brain development, heart development.
Proteomic Analysis The queries were carried out with the Mascot search engine using MS and MS/MS data from the UniProt sub-bank: Canard_Anas_Caïrina 190524 (32,568 sequences; 14,848,422 residues). As a result, 1444 quantified proteins were identified. Among them, only 235 proteins, exhibiting a p-value < 0. 05 when comparing the two liver weight groups, were kept for the means comparison test. Considering that a significant difference (p-value < 0. 05) in the level of expression of proteins did not necessarily mean a measurable effect at the phenotypic level, only proteins expression with a ratio of HWL/LWL more than 1. 5 or less than 0. 67 were considered because these threshold could possibly have a perceptible effect. Then, by using this significant expression ratio between the 2 groups, only 78 proteins were identified as pure variants.
FUNDING This project was supported in part by the  Région Midi-Pyrénées ,  European funds [Fonds Européens de Développement Régional (FEDER) ],  Toulouse Métropole , and by the  French Ministry of Research  with the  Investissement d'Avenir Infrastructures Nationales en Biologie et Santé program  ( ProFI, Proteomics French Infrastructure  project,  ANR-10-INBS-08 ). BL's Ph. D. grant was supported by  ANR-T 2017/1414.
Data collected and follow-up The screening visit is conducted by the investigating physician. It will take place on the day of the inclusion visit. Before the initiation of any study-related examinations, the investigator will obtain the participant's written, informed consent. To assess generalizability, all patients fulfilling the eligibility criteria in each center will be recorded in a table. The screening visit will include a physical examination, including cardiac blood flow, body temperature, and pain evaluation, and a biological examination, including a blood ion profile, and ASAT, ALAT, total bilirubin, indirect bilirubin, alkaline phosphatase, GGT, amylase, lipase, CRP, hCG (if applicable), procalcitonin levels, and leukocyte count.
End of follow-up visit There will be two post-operative visits, on POD 15 and one month (± 1 week) after the patient's discharge. This visit will be performed by the investigators (and not by the patient's general practitioner) for all included patients. Patients not seen at POD 15 and/or one month will be invited to visit the hospital. The patient's general practitioner (who will have received a letter emphasizing the importance of the primary endpoint) will also be contacted before the patient is considered to have been lost to follow-up. All data will be anonymized.
Randomization An interactive web response system will be used to randomize patients using a randomization list with  a blood ion profile, ASAT, ALAT, total bilirubin, indirect bilirubin, alkaline phosphatase, gamma GT, amylase, lipase, CRP, beta HCG, procalcitonin and leucocyte count. FU follow-up blocks of equal size (with the same of randomized patients in each group, i. e. , antibiotics vs no antibiotics) and stratified by center. At the end of the surgery, investigators will check the inclusion and non-inclusion criteria. If the patient meets all of the inclusion criteria and none of the noninclusion criteria, the investigators will log him/her into the electronic case report form and request that a treatment number be allocated to the patient. The sequence of the study treatments (preoperative and perioperative antibiotic therapy, laparoscopic appendectomy, with or without post-operative antibiotic therapy) will be generated by a data manager using a computer program.
Statistical methods Qualitative variables will be presented as numbers (percentages) and compared using a chi-squared test with Yates' correction or (if applicable) Fisher's test. Quantitative variables will be presented as the means ± standard deviation or medians (interquartile range) and compared using Student's t-test or a Wilcoxon signed-rank test, as appropriate. Kaplan-Meier curves for the time to event will be built and compared using a log-rank test. A detailed statistical analysis plan will be defined and validated by the study's scientific advisory board. Any modifications must be made before unblinding of the database and must be validated by the scientific advisory board. Analysis of the primary endpoint Non-inferiority will be established if the upper limit of the two-sided 95% confidence interval of the difference in the proportion of infections between the two groups (placebo armantibiotic arm) is lower than the non-inferiority margin (5%).
This modification was approved by the decision dated January 5, 2006. Amiens University Hospital has signed to comply with this Reference Methodology. In accordance with Article L1121. 15 of the French Public Health Code, the present study is registered in the European EudraCT database under No. 2017-000334-59. This study was also registered on the ClinicalTrials. gov website (http://clinicaltrials. gov/; NCT03688295) on September 28, 2018. The biological sample collection performed in the context of this research was registered with the ANSM at the same time as the application for a clinical trial authorization. After termination of the study, storage of the biological sample collection will be registered with the French Ministry of Research and the Regional Health Agency (Agence Régionale de Santé).
Reverse causality and national culture Reverse causality is a plausible explanation for differences in political beliefs and attitudes between democracies and autocracies. It is possible that it is the values of the educated that cause regime type, and not the other way around, with more conservative societies favoring more autocratic governance systems. The consensus view in the literature however is that many factors unrelated to individual preferences shape regime types, and especially elite bargains and external influences, and that the conditions needed to transition to democracy are numerous and not uniquely related to mass preferences  (North 1  The paper uses a unique longitudinal dataset, collected by the authors, that tracks the first students in colonial schools founded in Benin in the early twentieth century, their direct descendants and extended families, as well as their contemporaries who did not get education.
Data, method, and results We use World Value Surveys' (WVS) 5 waves spanning 1989-2015 (the first wave of 1981 does not include information on education), to maximize the number of countries and also be able to look at changes over time. We also use the European Values Studies data, which largely overlaps with the questionnaires of the WVS, and provides a good coverage of European countries. Together, the data includes 97 countries and about 300,000 individuals, some of which were educated as far back as in the 1920s (with available information on the various variables we use however, the sample size varies for different outcome variables -see the regression tables). The combined data includes 72 country spells of democracy, 22 of anocracy, and 17 of autocracy (see the definition of regime spells below and in the Annex).
17 In associating values with cohorts, we need to be mindful that life-cycle considerations suggest that values change systematically as individuals age, with each stage of life modifying values as a result. Controlling for age allows disentangling cohort effects need from life cycle effects. 18 From the World Bank Indicator database http://data. worldbank. org/ indicator. lowest for those that have studied and live under autocracy; (iv) they are in between for those that have studied in an autocracy and live in a democracy; and (iv) the distance in the effects between the cohorts that live under autocracy vs democracy is smaller among belief values (respect for authority, and strong leader), than for behavioral values (civic engagement, and voting). These results confirm that our hypotheses H1 and H2 are borne out by the data.
e, averaging over our dataset, education always emancipates, and that the political values of the educated (mid, and especially high) are more emancipated in the democratic/democratic case, followed by the autocratic/democratic, and then the autocratic/autocratic case. But the differences among values in different regimes are not as stark as in the case of the effect of education on values, because these effects themselves are not too large (as evident in Table  2 ). The pattern where behavioral values for those living under autocracy (CA and voting) are far below those that live under democracy is evident, especially for higher education.
Robustness checks We conclude by conducting various tests to check if the main results are robust to variations in the way we code our main variables. First, we vary the coding of cohorts. In Table  4 , Panel A, we consider that a regime has to be in power for more than 7 years (instead of 5 years) to be in a position to be able to socialize through education. All the previous results on state socialization remain robust to these changes and are unaffected. In panel B, we tighten the rule used to associate an individual with a regime requiring that all the years between the critical age 18-23 be in that regime (rather than only a majority of those years). The main results are robust to these changes. In Panel C, we vary the definition of mass education, using information from the Barro and Lee dataset (2013), and we include in our restricted sample only the individuals who at age 18 lived in a country/year where at least 50 % of the sample had achieved secondary or higher levels of education (77 countries pass the filter with around 166,000 individuals, with the sample dominated by countries from Eastern Europe).
All variables are defined in the appendix. The Mass Education sample includes only countries where at least 50 % of the sample has more than secondary education (on average over waves) in the WVS dataset, and in the last panel, in the Barro-Lee dataset. Regime histories with anocracies and transitional regimes are included in estimated models, but omitted from the table. *p < 0. 05; **p < 0. 01; ***p < 0. 001. p-values are adjusted for multiple tests with 5 dependent variables. would suspect that open repression must be particularly prevalent in countries where indoctrination does not work effectively. This speculation offers a rich area for research in the future, especially in country studies, where so far, the emphasis has been placed more on self-selection into the education system than on measuring the impact of selfinterest on political behavior.
The regions of the Black, Caspian, and Azov seas are known for being both (i) the place of extensive crustacean radiation dated to the times of Paratethys and Sarmatian basins, and (ii) present donors of alien and invasive taxa to many areas worldwide. One amphipod morphospecies, Dikerogammarus haemobaphes, is known both as native to rivers draining to the Black and Caspian seas as well as a successful invader (nicknamed demon shrimp) in Central and Western European rivers. Based on mitochondrial (COI and 16S) and nuclear (28S) datasets and 41 sampling sites, representing both the native (19) and the invaded (  22 ) range, we assessed cryptic diversity, phylogeography and population genetics of this taxon. First, we revealed the presence of two divergent lineages supported by all markers and all species delimitation methods.
Sample collection Dikerogammarus haemobaphes was collected at 55 sites in both its native (23 sites) and invaded (32 sites) ranges between 2000 and 2014 (Table  1 ). Some localities were geo-   1 , but one of the two or three indicated with letter A or B), so we decided to combine them with the closest sampling site to simplify further analysis (Table  1 ). As a result, our dataset comprises 41 localities, of which 19 belonged to the native and 22 to the invaded range (Fig. 2 ). All the sampling sites were located in public and non-protected areas. In the native area, sampling was done along the western and northern coast of the Black Sea either in limans of large coastal rivers or lower courses of large rivers (e. g. , Danube, Dniester, Dnieper). The area around the Azov Sea was included in the sampling campaign, but the demon shrimp was not found in any of the 15 sampling sites visited.
The nuclear marker was amplified with 28F and 28R primers and reaction conditions published by  Hou et al. (2007). Relevant voucher information, taxonomic classifications, and the COI barcode sequences are publicly accessible through the public data set "DHAEMOBA" (https://doi. org/10. 5883/DS-DHAEMOBA) on the Barcode of Life Data Systems (BOLD; www. boldsystems. org) (Ratnasingham and Hebert 2007). Newly generated sequences were also deposited in GenBank: COI: MN342874-MN343144, MN343146-MN343307; 16S: MN343308-MN343578, MN343580-MN343741; 28S: MN343743-MN343753.
Runs were examined using Tracer v 1. 6, and all the sampled parameters achieved a sufficient sample size  (ESS > 200). Tree files were combined using Log-Combiner 1. 8. 1  (Drummond et al. 2012) , with the removal of the non-stationary 25% burn-in phase. The maximum clade credibility chronogram was generated using TreeAnnotator 2. 5. 2  (Bouckaert et al. 2014). For additional support of tree topology, the concatenated COI + 16S dataset was analyzed with the Maximum Likelihood (ML) method, using the Tamura 3-parameter model  (Tamura 1992 ) selected through the Bayesian Information Criterion (BIC) with 10000 bootstrap replicates. ML analysis was performed in MEGA X  (Kumar et al. 2018).
Tests for cryptic diversity To visualize molecular divergence of mtDNA haplotypes, a Minimum Spanning Network was generated using Arlequin 3. 5. 1. 2 (Excoffier and Lischer 2010). Pairwise Kimura 2-parameter (K2P) distances were estimated using MEGA X  (Kumar et al. 2018). Four molecular species delimitation methods were applied to reveal the potential Molecular Operational Taxonomic Units (MOTUs) that could represent putative cryptic species within the studied demon shrimp populations. Two methods were distance-based: Barcode Index Number (BIN) System  (Ratnasingham and Hebert 2013) , and the barcode-gap approach using the Automatic Barcode Gap Discovery (ABGD)  (Puillandre et al. 2012). The following two were tree-based, a phylogenetic approach using Generalized Mixed Yule Coalescent (GMYC) model-based method  (Pons et al.
2006) , according to  Monaghan et al. (2009)  and the Bayesian implementation of the Poisson Tree Processes (bPTP)  (Zhang et al. 2013). For both tree-based methods, we have used the already generated Bayesian time-calibrated phylogeny. The BIN method is implemented as part of the Barcode of Life Data system (BOLD;  Ratnasingham and Hebert 2007). Newly submitted sequences are compared together with sequences already available in BOLD. Sequences are clustered according to their molecular divergence using algorithms that aim at finding discontinuities between clusters. Each cluster is ascribed a globally unique and specific identifier (aka Barcode Index Number or BIN), already available or newly created if the submitted sequences do not cluster with previously known BINs. Each BIN is registered in BOLD.
bispinosus  Martynov, 1925, and D. villosus  using the Maximum Likelihood method and the Tamura 3-parameter model selected through BIC as a best fitting model  (Tamura 1992 ). All positions with less than 95% site coverage were eliminated. The bootstrap test was done with 500 replicates  (Saitou and Nei 1987). These analyses were done in MEGA X  (Kumar et al. 2018). Sequences of D. villosus (KF478495, KF478496) and Pontogammarus robustoides  (Sars, 1894 ) (KF478447) used as an outgroup were retrieved from GenBank.
We evaluated the trend in haplotype diversity, nucleotide diversity, allelic richness, and private allelic richness along the Central Corridor of the invasion by calculating Pearson Correlation Coefficient. The distances (Suppl. material 1: Table  S1 ) were estimated using Google Earth v. 7. 1. 2 as a measurement of shortest distance along the waterway (Google Earth, option path, zoomed enough to fit the line in the riverbed) of Central Corridor starting from our sampling locality 11A (Dnieprovski Liman) to locality 32 (Oder). IBD was tested using the Mantel test between F st /(1-F st ) and geographic dis-tance as recommended by  Rousset (1997)  for testing IBD in one-dimensional linear systems, with 100000 permutations, using the ISOLDE software embedded in the GenePop on the Web 4. 2 package  (Raymond and Rousset 1995).
Cryptic diversity within Dikerogammarus haemobaphes In the dataset composed of 433 sequences, we identified 15 haplotypes of 16S (302 bp) and 27 haplotypes of COI (598 bp). The concatenation of both fragments (900 bp) resulted in the recognition of 39 mtDNA haplotypes derived from different combinations of the 16S and COI haplotypes (Suppl. material 2: Table  S2 , Suppl. material 3: Table  S3 ). The pairwise K2P distance between the haplotypes ranged from 0. 002 to 0. 183 for COI and from 0. 003 to 0. 099 for 16S; the highest values suggesting the presence of cryptic diversity within the studied morpho-species. All the COI sequences, represented by 27 haplotypes, were uploaded into the Barcode of Life Data System (BOLD), resulting in two Barcode Index Numbers (BINs) obtained (Fig. 3 ). The BIN AAX9262 was attributed to 406 sequences (23 COI haplotypes) and was already reported in BOLD and GenBank associated with D.
haemobaphes. The mean K2P distance within this BIN was 0. 0023, and the maximal K2P distance was 0. 0263. The BIN ADB9467, associated with 27 sequences (four COI haplotypes), has not been reported before. The mean K2P distance within this BIN is 0. 0013, and the maximal K2P distance is 0. 0051. The closest BIN to ADB9467 is the AAX9262, with an average K2P distance of 0. 176. Automatic Barcode Gap Discovery (ABGD) was performed on two data sets (i) 27 sequences, each representing one of the 27 COI haplotypes and (ii) 433 sequences, representing all individuals sequenced. The first analysis indicated the existence of two MOTUs (lineages A, B), divergent by the 0. 183 maximum K2P distance, that may be interpreted as two potential cryptic species (Fig. 3 ). The individuals belonging to the lineage A included only four haplotypes (27 sequences) and were found in five localities, exclusively in the native range of Dikerogammarus species (two sites in the lower Danube, two in the lower Dniester and one on the Kerch Peninsula).
villosus by  Bącela-Spychalska et al. (2013). High potential for such dispersal was experimentally evidenced also for the demon shrimp (Bącela-Spychalska 2016). The small sample of D. haemobaphes from the Moskva River in Russia, also situated outside the native range of studied species (ca. 3000 km up from the Volga delta), seems to be a sister group to all the other GDUs within lineage B found in the Black Sea as well as in Central and Western Europe (Fig. 3 ). A single available COI sequence mined from GenBank (GenBank accession number: AY529049) and labelled as coming from the Caspian Sea  (Cristescu and Hebert 2005)  represented the same haplotype as eight out of ten sequences from the site in the Moskva River. This suggests that the Caspian Sea served as a donor for this population, which most likely arrived there via the Volga River belonging to the Northern Corridor of invasion.
Acknowledgments The study was partially funded by the  Committee on Scientific Research  ( 3P04F01322 ),  Polish Ministry for Science and Higher Education  grants: sampling ( NN304081535 ,  NN304350139 ,  NN303579439 ), molecular analysis (NN304350139) and partially by the statutory funds of the  Department of Invertebrate Zoology and Hydrobiology of University of Lodz. Tomasz Rewicz  and  Tomasz Mamos  were supported by the Scholarship of the  Polish National Agency for Academic Exchange  ( NAWA) at Bekker Programme  ( TR  project  nb. PPN/BEK/2018/1/00162 ,  TM  project  nb. PPN/ BEK/2018/1/00225 ). The authors thank  Peter Borza ,  Bartosz Janic ,  Ewa Janowska ,  Radomir Jaskuła ,  Krzysztof Jażdżewski ,  Alicja Konopacka ,  Sergiy Kudrenko ,  Marek Michalski ,  Mykola Ovcharenko ,  Maciej Podsiadło ,  Alexander Prokin ,  Agnieszka Rewicz ,  Mikhail Son  and  Oleksandr Usov  for their help in collecting the material for this study.
Follow-up data should be used for both internal quality control (QC) and QA purposes and documented in the ESHRE PGT Consortium online database for international data collection. It is recommended that laboratories follow local regulations or accreditation schemes on storage of clinical samples and patient records. If no local regulations or guidelines exist on storage of clinical samples and patient records, it is recommended as follows: • If embryos have been transferred and/or frozen, all relevant material (e. g. FISH slides, DNA amplification products) from the case should be retained and appropriately stored. Samples should be stored for at least 1 year. Prolonged sample storage could be considered, taking into account the availability of information on delivery and the duration of embryo cryopreservation.
Negative control specimens should be included in preclinical runs to assess contamination. The sensitivity and specificity of genetic testing should be evaluated and compared with in-house samples and/or samples received from other IVF centres. The reporting of the results should be agreed upon. • The IVF/PGT centres should agree on the feasibility, the number and the timing of transport PGT cycles and define a schedule. • It is recommended that all diagnostic results and reports are sent in written form (complying with the GDPR). • The IVF centre and PGT centre should agree on who is responsible for the collection of PGT data and follow-up of PGT children (www. eshre. eu/data collection).
Protein-protein interaction network, EMF proteins and protein groups Predicted human extreme multifunctional (EMF) proteins (238 proteins) and a human PPI network (14046 proteins, 92348 interactions) were downloaded from MoonDB 2. 0 (18) (http://moondb. hb. univ-amu. fr/). The human PPI network was constructed with interactions retrieved from the PSICQUIC web service  (19)  on January 2018, as described in  (14). Network modules were extracted from the PPI network using OCG  (20) , a clustering algorithm that allows proteins to belong to more than one cluster. These network modules are available on MoonDB 2. 0. Briefly, EMF proteins (238 proteins) are proteins that belong to two or more network modules whose Gene Ontology (GO) term annotations ('Biological Process') contain at least two terms that are dissimilar to each other according to PrOnto  (21).