dataesr/scientific-paragraphs-categorization huggingface dataset:
SNP Array Genotyping Total DNA was extracted from fin clips and genotyped by Identigen (Dublin, Ireland) by using a 65 K Axiom ® SNP array  (Peñaloza et al. , 2020). The raw genotype data were filtered using the Axiom analysis Suite v4. 0. 3. 3. Samples with a dish quality control <0. 82 and/or genotype calling rate <0. 93 were excluded from further analyses, leaving 801 samples. A total of 53 K SNPs were categorized as PolyHighResolution, and therefore were retained for subsequent analyses. Further quality control using Plink software v1. 09  (Purcell et al. , 2007)  was performed. Specifically, SNPs were excluded for the genomic analyses due to either; 1) call rate <0. 95, 2) minor allele frequency (MAF) < 0. 05 or 3) deviation from Hardy-Weinberg Equilibrium (p < 1 × 10 -6 ). Simultaneously, animals with a call rate <0.
Predictive Ability and Cross Validation The ability to predict the breeding values for the traits using pedigree and genomic approaches was assessed using a fivefold cross-validation approach. To assess the predictive ability among the different models, a total of 100 replicates were used. For each replicate, 80% of fish were used as a training dataset and, for the remaining 20%, the phenotypes were masked and used as the validation dataset. For each model, two cross-validation approaches were applied: 1)Random animal: for each replicate, 80% of all the fish were randomly drawn independently of their family, and used as a training dataset. The remaining 20% of the samples were used as a validation dataset. 2)Random families: for each replicate, 80% of the families were randomly drawn and used as a training set.
Accuracy r y 1 , y 2 h Where y 1 and y 2 represents the predicted EBV and the phenotype, respectively, r is the correlation between these both values, and h is the square root of the estimated heritability. Significant differences in predicted accuracies among models were assessed for each trait using both approaches. A Shapiro-Wilk test was done to evaluate the normality of the distribution of the predicted accuracies. The "rstatix" R package  (Kassambara, 2021) , was used for a pairwise comparison of the marginal means among models. Finally, the obtained p values were adjusted by the number of independent tests.
SNP Densities Since the relatively high cost of high density SNP array genotyping may present a barrier to routine genotyping of large number of animals within a population, the efficacy of reduced density SNP panels for predicting breeding values was assessed. A total of 23 subsets of random SNPs were selected from the high density SNP array. These densities included 0. 1, 0. 3, 0. 5, 0. 7, 0. 9,  1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80  and 90% of the entire SNP dataset. For each SNP density, 100 replicates of a random animal five-fold cross validation were generated. The GBLUP model fitted for each density is the same as that described for the full genotype data set, and the accuracy was predicted as detailed previously.
Genome-wide Association Analyses To identify genomic regions associated with the FE traits, a similar model to that proposed for genomic predictions was applied. However, since random common environmental effect was found to be non-significant, it was not included in the models. The GWAS was performed using the leaving-one-chromosome-out (LOCO) approach from the GCTA software v. 1. 92. 2  (Yang et al. , 2011). Briefly, a suggestive and genome-wide (α 1 and 0. 05, respectively) Bonferroni significance threshold was used to assess the significance of individual SNPs associated with the assessed traits. The former was first proposed by  Lander and Kruglyak (1995) , suggesting that at least one false positive marker is expected under the null hypothesis. Thus, a SNP was considered significant at a suggestive or genomewide level if surpassed the corresponding Bonferroni significance threshold.
The SNPs which were not mapped to a specific chromosome of the Nile tilapia reference genome  (Conte et al. , 2019 ; Genbank accession number GCA_001858045. 3) were assigned to an artificial chromosome "Oni24". For the significant QTLs associated with the traits, candidate genes were identified within a 500 Kb window size flanking the associated markers (250 Kb upstream and downstream, respectively) and used as the basis for a literature search for relevant functions connected to feed intake and efficiency. This size was selected based on  Cádiz et al. , 2020 , which used a similar window size for look candidate genes in three commercial Nile tilapia populations.
DISCUSSION The current study generated genome-wide SNP genotype data from tissue samples collected from a previous study in which genetic parameters for FE traits were estimated in a Nile tilapia breeding population (de  Verdal et al. , 2018b). Through quantitative genetic analyses and using pedigree data with information from up to 15 generations, significant genetic  variation was detected for all the assessed traits, FCR, BWG, RFI and FI. The current study integrated the phenotypic dataset with genome-wide SNP information from recently developed SNP array for Nile Tilapia  (Peñaloza et al. , 2020) , to estimate genetic correlations and genomic heritabilities using almost 50 K SNP markers segregating throughout the Nile tilapia genome. The same data were used to dissect the genetic architecture of feedefficiency traits using a GWAS.
FUNDING This work was undertaken as part of, and funded by, the  CGIAR Research Program on Fish Agri-Food Systems (FISH)  led by  WorldFish. The program is supported by contributors to the  CGIAR Trust Fund. Support was also provided by the  International Fund for Agricultural Development (IFAD)  and the  European Commission-IFAD  Grant Number  2000001539. The authors gratefully acknowledge funding from  Institute Strategic Programme  funding from the  United Kingdom Biotechnology and Biological Sciences Research Council  to The  Roslin Institute  ( BB/P013759/1  and  BB/P013740/1 ).
Polymerase chain reaction (PCR) amplification was carried out with Taq DNA Polymerase (Bioline). All STAT3 (ENST0000264657. 10) exons and exon/intron boundaries, PGM3 (ENST00000513973. 6) exon 4 and exon 8 were amplified by PCR. Amplified products were purified using the EXO-SAP (Thermo Scientific) cleanup procedure and sequenced with the BigDye Terminator kit V3. 1 (Applied Biosystems). The sequencing was performed on an automated sequencer from Applied Biosystems (Applied Biosystems) and the data were analyzed with GENALYS software (CNG, France; http://softw are. cng. fr).
4% vs 2. 3%; adjusted odds ratio (AOR) 0. 53; p < 0. 01) and 30-day mortality (0. 2% vs 0. 1%; AOR 0. 58; p = 0. 07) in comparison with SG. However, there is no large data in the scientific literature comparing 30-day morbidity and mortality of SG and OAGB or RYGB and OAGB. Notwithstanding the lack of such large data, these direct database comparisons are often flawed due to significant differences in the baseline population. Propensity score matching is a valid tool for comparing non-randomised populations by matching them for confounding variables  [6]. To the best of our knowledge, there is only one published study comparing 30-day morbidity and mortality of SG and RYGB  [7] ; and one comparing RYGB and OAGB  [8]  in propensity score-matched populations. Both of these studies emanate from the MBSAQIP database.
In their study, Kapur et al. [7]  found lower adverse events with SG in comparison with RYGB. However, the study by Docimo et al. [8]  comparing the 30-day morbidity of OAGB and RYGB had too few patients to be meaningful. It is probably because the MBSAQIP database is not likely to have large numbers of OAGB, a procedure not endorsed by the American Society for Metabolic and Bariatric Surgery. Global 30-day outcomes after bariatric surgEry duriNg thE COVID-19 pAndemic (GENEVA) study  [9]  is a large, multinational, observational study evaluating 30-day morbidity and mortality of bariatric and metabolic surgery (BMS) during the Coronavirus Disease-2019 (COVID-19) pandemic. The global reach of the study, a large number of patients, and significant numbers of OAGB procedures submitted to this study present a unique opportunity to compare 30-day morbidity and mortality of SG, OAGB, and RYGB in propensity score-matched cohorts.
Study design and population The GENEVA study is an international, multicentre, observational cohort study of BMS performed between 1/05/2020 and 31/10/2020  [9]. The current study included all consecutive patients who underwent a primary SG or RYGB or OAGB during this period. Detailed methods have been published previously  [9] [10] [11]. Data collection included patients' demographics, details of surgery performed, and in-hospital as well as 30-day morbidity and mortality. Complications were categorised using the Clavien-Dindo (CD) Classification system for reporting surgical complications  [12].
Statistical methods Only patients with a complete data entry were included in the analysis. Continuous data were presented as median and interquartile range. Frequencies were used to summarise categorical variables. To examine differences between the three individual procedure types, the Fisher's exact test was used for categorical variables and Kruskal-Wallis analysis of variance testing for continuous variables. Propensity score matching was completed in a step-wise fashion. Pairwise propensity matching was performed to robustly assess the quality of matching. Standardized mean difference (SMD) was used statistic to examine the balance of covariate distribution between treatment groups. Patients were matched using the following features: sex, Type 2 diabetes mellitus (T2DM) status (No diabetes; diet controlled; oral hypoglycaemics; insulin therapy), hypertension, hypercholesterolaemia, obstructive sleep apnoea, smoking status, age, and baseline body mass index (BMI).
The patients were matched against individuals that had other surgeries using the "nearest" method which utilises a greedy search to match each sample with their nearest neighbour. The distance was calculated using the Mahalanobis distance, which estimates the distribution closest for each point  [13]. This procedure was performed in R (R Core Team 2021) using the Matchlt package  [14, 15]. The outcome variable was the presence of a complication at 30-days follow-up. Multivariate analysis was performed to strengthen the resulting statistics from univariate analysis, correcting the influence of each variable on the outcome measured. Multivariate models were created using all the variables used for propensity score matching plus ethnicity (white ethnicity vs other ethnic groups), presence of any co-morbidity, and other unspecified co-morbidity (other than those listed above).
DISCUSSION This study shows that there is no significant difference in 30-day morbidity and mortality of SG, RYGB, and OAGB in propensity score-matched cohorts from a large, global dataset collected during the COVID-19 pandemic. Though RYGB was associated with higher 30-day morbidity in comparison with reference SG (OR 1. 018; 95% CI 1. 005-1. 032) in the unmatched cohort on multivariate analysis, the difference disappeared after propensity score matching (p = 0. 09). In comparison, OAGB was not associated with higher 30-day morbidity in comparison with SG on either multivariate analysis (OR 1. 009; 95% CI 0. 989-1. 030) or propensity score-matched comparison (p = 0. 68). RCTs comparing different bariatric procedures often have weight loss  [2]  or diabetes control  [16]  as their endpoints.
Others  [20]  have also  found dyslipidaemia to be a predictor of complication after bariatric surgery. Standardised mean differences in age, BMI, sex, smoking status, hypertension rates all reduced after matching for both the matched comparisons involving SG in this study. Given that all of these characteristics are known to be associated with increased morbidity after bariatric surgery  [21] [22] [23] [24] [25] [26] [27] [28] [29] , differences in these baseline characteristics may have been in part responsible for why the observed difference in morbidity between SG and RYGB or OAGB disappeared after matching. At the same time, and probably because of the fewer number of bypass procedures in the GENEVA database, matching failed to reduce SMDs for age,   sex, smoking status, and hypertension in the comparison between OAGB and RYGB in this study.
Interestingly that study showed lower complication rates with SG in contrast to our findings. It is however worth noting that these authors do not report standardised mean difference in propensity score-matched populations and given the large numbers matched, it was inevitable that their matching was not perfect with significant difference between the matched populations with regards to important confounding variables such as age, BMI, smoking, insulin-dependent T2DM, etc. This study represents the first large propensity-matched comparison of 30-day morbidity and mortality of SG, RYGB, and OAGB. This data was collected from a large worldwide collaborative study of real-world bariatric surgical practice. Data completion rates were extremely high with 30-day follow data available for 97. 9% of patients across the entire cohort and this represents a significant strength of this study.
| Bioinformatics analysis The data analysis pipeline included the quality trimming of Illumina sequences using Trimmomatic  (Bolger et al. , 2014). Sequences were mapped to an artificial genome reference that contains only the SMN2 coordinates, based on the reference genome (UCSC hg 19 version, build 37. 1). This strategy avoids arbitrarily reporting only one of the possible alignments produced by most mapping algorithms and therefore avoids dispersion of the read-depth signal from all SMN genes. This approach increases the power to detect small changes of coverage and variants with lower AB ratios, which are characteristic of heterozygous variants in multicopy regions. Results show a single location per each read that corresponds to any of the SMN genes. Mapping was performed using burrows-wheeler aligner (BWA)-align and BWA-sample with default parameters.
ACKNOWLEDGMENTS This study was supported by Grants from  Biogen   ESP-SMG-17-11256  and  Galicia AME  (to E. F. T. supporting L. B. -P. ), " Fundación Daniel Bravo Andreu " (to E. F. T. and P. F. -P. ),  SMA Europe  (to E. F. T and P. F. -P. ), and  Spanish Instituto de Salud Carlos III, Fondo de Investigaciones Sanitarias  and cofunded with ERDF funds (Grant No. FIS PI18/000687 ) (to E. F. T. and L. A. ).
WEB RESOURCES The following web sources have been used to carry out this study: http://www. fda. gov, https://genome-euro. ucsc. edu/, http://www. hgmd. cf. ac. uk/ac/index. php, https://mutalyzer. nl/, https://gnomad. broadinstitute. org/, https://arxiv. org/abs/1303. 3997v2. DATA AVAILABILITY STATEMENT All data and scripts used to generate the analyses of this paper are available upon request unless that the type of request compromises ethical standards or legal requirements. All variants reported in this article are now listed in the ClinVar database (https://www. ncbi. nlm. nih. gov/clinvar/).
In comparison, PS species with one or two di-unsaturated chains seemed less suitable for exchange processes. An equivalent analysis with PI(4)P remains difficult to conduct as much fewer PI(4)P species were assayed. The fact that polyunsaturated PI(4)P, the major brain PI(4)P species, exchanges poorly with 16:0/18:1-PS does not mean that Osh6p is specifically designed for yeast monounsaturated PI(4)P. Indeed, slow transfers were measured when assaying polyunsaturated PI(4)P with ORD8. Collectively our data suggest that Osh6p can efficiently exchange unsaturated PS and PI(4)P species at the ER/PM interface, supporting the notion that Osh6p/7p regulate a pool of unsaturated PS and PI(4)P that together with ergosterol form functional lipid nanodomains  22. However, our investigation suggests that the delivery of PS in the PM, and its exchange for PI(4)P is not specifically promoted by the presence of ergosterol.
5 × F Norm with F Norm = (F-F 0 )/(F Eq -F 0 ). F corresponded to the data point recorded over time, F 0 was the average signal measured before the addition of LTP and F Eq was the average signal measured in the presence of L A-Eq and L B-Eq liposomes that each contained 2. 5% PI(4)P (and additionally 2. 5% PS to normalize data obtained in exchange conditions). At equilibrium, it is considered that one half of accessible PI(4)P molecules, contained in the outer leaflet of L B liposomes, (i. e. , corresponding to 5% of 0. 5 × 200 µM total lipids) have been transferred into L A liposomes. The transfer of PI(4,5)P 2 from L B to L A liposomes was determined using NBD-PH FAPP or NBD-PH PLCδ1 at 250 nM as described for PI(4)P transfer measurements except that F Eq was determined using L A and L B liposomes that each contained 2.
PS transport assay with three liposome populations At time zero, L A liposomes (200 µM total lipid, final concentration) containing 95% DOPC and 5%  × F Norm with F Norm = (F-F 0 )/(F Eq -F 0 ). F corresponded to data points measured over time, F 0 corresponded to the NBD-C2 Lact signal measured when PS was only in L A liposomes and F Eq corresponded to the signal of the probe when PS was fully equilibrated between L A and L B liposomes. F 0 value was obtained by averaging the fluorescence measured after the injection of L C liposomes and before the injection of ORD8. F Eq was the fluorescence of NBD-C2 Lact (250 nM) measured once it was sequentially mixed with L A-Eq liposomes consisting of 97. 5% DOPC and 2. 5% 16:0/18:1-PS and L B-Eq liposomes consisting of 95. 5% DOPC, 2. 5% 16:0/18:1-PS and 2% Rhod-PE, and L C liposomes only made of DOPC (the concentration of each liposome population was 200 µM).
Acknowledgments We wish to thank  Pr. Pietro de Camilli  for providing the plasmid coding for the GST-ORD8 construct, Dr. Enrique Castano  for the plasmid coding for the  GST-PH PLCδ1  and  Dr. Fabien Alpy  for the plasmid coding for the mCherry-D4-His 6. We thank  Dr. Frédéric Brau  for his help in image analysis. We are grateful to Ms. Y. Van Der Does  for her careful corrections and proofreading of the manuscript. This work was supported by the  CNRS  and by a grant from the  Agence Nationale de la Recherche  ( ANR-16-CE13-0006 ). NFL was supported by a fellowship from the  Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation.
Fig. 3  shows the districts in the Santiago Metropolitan Region which are considered in the study and Fig. 4  shows the annual budgets for 2016 for the districts under study. For each district, the data needed to calculate the set of indicators to evaluate district smartness (Table  1 ) was gathered for the same year. As an analytical tool, case studies are increasingly common in business research  (Njie & Asimiran, 2014). This method allows us to study a city in relation to its context and enables us to collect data according to our research question. To ensure the quality of the method, we followed the recommendations made by  Yin (2012). External validity is tested in Fig. 3. Districts in the Santiago Metropolitan Region. accordance with the principles of analytic generalisation, replicability and comparison between cases, while reliability is ensured by establishing an initial protocol for procedure and creating a full database.
Clustering analysis The results above were used to perform a cluster analysis through the IBM SPSS Statistics software program, according to the general technical efficiency and the perception of the services delivered by the city. Fig. 5  shows the maximum points highlighted above, as represented by La Cisterna (Maximum in efficiency) and Vitacura (Maximum in perception). Meanwhile, La Pintana and Peñalolén are assigned the minimum of the relation. As for the other groups, the blue stands out, considered as a set of districts with a high perception of the quality of services, but with high inefficiencies in the use of resources. Similarly, the green group stands out for its high efficiency in the use of the municipal budget, but with low expectations in product delivery. Moreover, a global analysis of the performance of the thirty-two districts in the six attributes of a Smart City was made, with the results being presented in Fig.
cDNA was pre-amplified using TaqMan PreAmp Master Mix (Applied Targets of interest covered four putative reference genes (b2 m, ubc, rpl13a, ef1a  [63] ), four regulatory genes (abtb1, ascl1b, kat2a, mapk13) and 24 immune genes involved in innate immunity (marco, mst1ra, mif, il-1β, tnfr1, saal1, tlr2, csf3r, p22 phox , nkefb, sla1, cd97), adaptive immunity (stat4, stat6, igm, cd83, foxp3, tgf-β, il-16, mhcII, tcr-β) and the complement system (cfb, c7, c9)  [32, 33, 46, 64]  (more information in electronic supplementary material, SI. 5). Melting curves were analysed with the Fluidigm Analysis software v. 4. 5. 1. Three targets (il-1β, tgf-β and ascl1b) were excluded from further analyses due to ambiguous melting curves. Raw data were imported into qbase+ 3. 0 (Biogazelle)  [65]  to assess data quality and calculate calibrated normalized relative quantities (CNRQ)  [66].
(f ) Statistical analyses All statistical analyses were performed in R (v. 3. 2. 0; [68] ). Infection rates were analysed with binomial GLMMs using glmer() from lme4  [69]. Response variables were proportional data from infected versus uninfected individuals. Infection rates in copepods were analysed with parasite population and experimental round and their interaction as fixed effects and parasite sibship as random intercept. Infection rates in fish were analysed with host and parasite population and their interaction as fixed effects and experimental round and parasite sibship as crossed random effects. Sympatry was used as predictor for infection rates in Alaskan fish. We accounted for the number of copepods that were not ingested. Significantly different groups and p-values were determined with glht() from multcomp  [70]  with individually defined contrasts or Type III Wald χ 2 -tests using Anova() from car  [71].
Non-parametric permutational multivariate analyses of variance (PERMANOVA  [75] ) were based on Euclidian distances and 10 000 permutations that were constrained within tank. The weight of the fish was included as covariate to account for size related effects. Pairwise PERMANOVAs were used a posteriori to identify significantly different groups  [75]. We tested for differences in baseline gene expression by using data from sham-exposed controls of the three populations. We tested whether host, parasite and/or their interaction affected gene expression of S. solidus exposed stickleback. Since NO parasites did not infect hosts from Wolf, we grouped data from (i) each parasite population and (ii) from each host population (electronic supplementary material, SI. 4. 2). Gene expression profiles of infected, exposed and control fish were compared within each combination of hosts and parasites.
Acknowledgements. Martin Kalbe  was central to development of this project, but sadly he died before submission of this paper. Martin was a dear friend and valued colleague. We mourn his untimely loss and dedicate this paper to his memory. We are grateful to  Withe Derner ,  Daniel Martens ,  Ralf Schmuck ,  Gisela Schmiedeskamp ,  Ines Schulz , Michael royalsocietypublishing. org/journal/rspb Proc. R. Soc. B 288: 20211758.