dataesr/scientific-paragraphs-categorization huggingface dataset:
These data were thus processed and included in Yersiniomics. One of these studies confirmed for the most part previous results obtained using a similar microarray approach. Other recent RNA-Seq studies did not make their raw sequencing data publicly available and could not be included in our database for now  (63) (64) (65) (66). To expand information on genes of interest, dynamic links to external databases were implemented for several reference strains, allowing the user to access KEGG pathways, UniProt annotation, InterPro domains, IntAct interactors and STRING networks at the gene level. These links also facilitate the access to other data, such as GO terms and Alphafold structure predictions, implemented in UniProt. As new data and data types continue to be published, we intend to update and upgrade the Yersiniomics database with newly published RNA-Seq and LC-MS/MS data, with omics data generated in our laboratory, and with already available data types such as small RNAs (61-68), transcriptional start sites (TSS)  (67) (68) (69) , and riboswitches  (67, 69).
MATERIALS AND METHODS Genomic data collection. Genomic data were browsed on the NCBI database with the keyword "Yersinia" (https://www. ncbi. nlm. nih. gov/data-hub/genome). Assemblies were filtered at the "chromosome" or "complete" level, and "chromosome" assemblies were manually curated to verify data completeness. The most recent RefSeq annotated assemblies were downloaded in December 2022. a WT, wild-type strain; mutant strain (mutated locus). Transcriptomic data collection. Microarray data were collected on Gene Expression Omnibus (https:// www. ncbi. nlm. nih. gov/geo/), and the differential expressions calculated by data depositors were directly used when available. Alternatively, tables and supplemental tables were downloaded from articles and formatted. RNA-Seq data sets were browsed on the European Nucleotide Archive (https://www.
Briefly, either short-read sequencing data or contigs were mapped onto the Y. pestis CO92 reference genome (NC_003143) using the Snippy pipeline with default parameters (https://github. com/tseemann/snippy) to identify variants in the chromosome. Identified variants were inspected, and those falling within repetitive sequences (i. e. , IS) were excluded, while variants associated with putative recombination events were identified using Gubbins v3. 2. 0 (86) and filtered out. The final set of variants (n = 14,273) was then used to reconstruct a maximum-likelihood phylogeny using IQ-TREE 2  (83)  to classify the different Y. pestis genomes present in Yersiniomics into known evolutionary branches (0. PE2, 0. PE4, 0. PE3, 0. ANT, 1. ORI, 1. IN, 1. ANT, 2. ANT, 2. MED, and 4. ANT). For the other species, lineages were assessed from cgMLST.
Homolog database and synteny. A protein BLAST search was automatically performed for each gene of each genome against the other genomes to construct the homolog database, using the BLAST1 command-line tool v2. 13. 0  (87). Synteny was constructed using a best-hit bidirectional BLAST search and implemented in the SynTView framework  (50). The tool was reprogrammed from Flash to JavaScript with the HTML5 2d Canvas JavaScript library Konva (https://konvajs. org/), which enables high-performance animations. Drawing strategies were developed to ensure that graphical outputs remain below the technological limits of drawing more than 10,000 dynamic graphical objects, using a mix of static and dynamic objects. SynTViewJS can be used on a public web application at https://plechat. pages. pasteur. fr/syntviewjs/.
The code repository is at https://gitlab. pasteur. fr/plechat/syntviewjs. Transcriptomic data processing. Downloaded microarray fold change tables were processed through the Bacnet platform. RNA-Seq raw data were processed using Sequana v0. 14. 6, Sequana-rnaseq v0. 17. 0, and Sequana-pipetools v0. 10. 0s (88) , implementing the following software with default parameters: reads were trimmed using fastp v0. 20. 1 (89), processed reads were then aligned on the selected reference genome with Bowtie2 v2. 4. 4  (90) , and strandedness was automatically identified by the Sequana pipeline. Mapped reads were then quantified using featureCounts (package subread v2. 0. 1) (91), and differential analysis was performed on raw read counts using DESeq2 v1. 38. 2 with R v4. 2. 1 with default parameters and Cook's cutoff enabled  (43).
P values and adjusted P values were calculated using default DESeq2 parameters consisting, respectively, of the Wald test and the Benjamini-Hochberg correction. Fold change and adjusted P value tables were extracted from DESeq2 results and processed using the Bacnet platform. BAM files generated by the Sequana pipeline were converted to strand-specific. wig files using the strand_cov function from the stranded-coverage package (https://github. com/pmenzel/stranded-coverage. git) and then processed using the Bacnet platform. Raw read counts from featureCounts were also normalized using the TPM method (51) to be displayed in the transcriptome panel of the gene viewer. Proteomic data processing. For the differential analyses of one condition versus another, proteins identified in the reverse and contaminant databases and proteins "only identified by site" were first discarded from the list of identified proteins.
Then, only proteins with at least three quantified intensities in a condition were kept. Differential expression was performed using LFQ when at least 1 unique peptide was detected and at least 2 peptides were quantified in a condition. A normalization was applied within the same condition centered on means of the medians  (92). Remaining proteins without any intensity value in one of two conditions were considered quantitatively present in a condition and absent in another. They were therefore set aside and considered differentially abundant proteins. Missing values were imputed using the SLSA method thanks to the R package imp4p  (93). Differential analysis was performed with a limma t test and an adaptative Benjamini-Hochberg correction to adjust the P values with the cp4P R package  (94).
Proteins with a fold change less than 2. 0 were considered not significantly differentially abundant. Statistical testing of the remaining proteins (having a fold change greater than 2. 0) was conducted using the limma t test  (95). These calculated fold changes were formatted and processed using the Bacnet platform, in parallel to fold change tables downloaded from published articles. Yersiniomics website design. Yersiniomics was constructed with the Bacnet platform  (33) , based on Java and Eclipse e4 RCP/RAP API, and we contributed to its most recent update. Links to external databases. The link to the KEGG database website uses the KEGG species identifier ("ype" for Y. pestis CO92) and the old locus name (for instance, "YPO0001" for the mioC gene of Y. pestis CO92), dynamically queried by URL in the form "https://www.
ACKNOWLEDGMENTS The project received funding from  Institut Pasteur ,  Agence de l'Innovation de Défense (AID-DGA) ,  Université Paris Cité, CNRS ,  LabEX Integrative Biology of Emerging Infectious Diseases  ( ANR-10-LBX-62-IBEID ),  Fondation pour la Recherche Médicale  ( FDT202204015222 ), and the  Inception program  ( Investissement d'Avenir  grant  ANR-16-CONV-0005 ). The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. We thank  Eric D. Merkley  for sharing  Pacific Northwest National Laboratory data sets. We are grateful to all members of the Yersinia research unit and the French national reference center for plague and other yersiniosis for insightful discussions. We declare no conflict of interest.
Additional details on data treatment. The pre-and post-selected states | i � and | ± f � for our experi- ment are given by Eq. (1). With the interaction according to Eq. (  9 ) the expected intensities can be calculated (see 26 ). We get for | + f � and interaction in path I: while the expression for interaction in path II is: For analysis direction | - f � we get expressions that are analogous to the two equations above, but with behavior inverted for the two paths. So the dependency on χ is retrieved for interaction in path II and the χ-independent intensity is retrieved for path I. These expressions can now be normalized with the reference intensities and further simplified by expanding the expressions around α and ignore terms of the order O(α 2 ) or higher (weak regime). The intensities for both analysis directions are: Post-selection | + f � Post-selection | - f � In addition a reference interferograms I ifg ref with the same phase shifter positions have been recorded with the state preparation and the delayed choice coils turned off.
The distance from the first interferometer plate to the ST2 is ∼ 35 cm. The neutrons have a velocity of 2060 ms -1. To guarantee, that the state is completely random and uncorrelated to the time the neutron has entered the interferometer, we have to make sure that a decision or choice to switch the analysis direction is made at least once every 170 µs. Our random signal is generated with the help of a Raspberry Pi, RPi 2 Model B We use the hardware random number generator (HWRNG) functionality of the Pi's system on chip (SoC). With the rng-tools package it is possible to generate true random numbers. We read a random bit of the Raspberry Pi every 50 µs and switch the voltage of a defined output pin (11) between high and low according to the random bit. The software to perform this task on the Raspberry Pi is a script written in the programming language Python 45 , that uses the wiringPi framework to set the the Pi's GPIO-pin 11 to a voltage of 3.
3 V for random bit being 1 and 0 V for random bit being 0. This random signal is transmitted by wiring the GPIO pin of the Raspberry Pi to the digital input pin of a FPGA board (NI PXI-7833R, National Instruments). On the FPGA card a LabVIEW (National Instruments) program reads the random, logical state of the incoming signal every 100 µs and sends the according +π/2 voltage ( | + f � post- selection) for state being one respectively the -π/2 voltage ( | - f � post-selection) for state being zero to the ST2 coil. The reason for this down-sampling strategy is, that the Raspberry Pi has no real-time functionality, whereas the FPGA board in contrast does. So drifts in the loop time of the random bit signal from the Raspberry Pi are taken care off. The LabVIEW program on the FPGA board handles further the time resolved measurement of the incoming neutrons (single events) and combines the TTL signal of neutron detectors with the random numbers from the Raspberry Pi so that in retrospect the switching state of the ST2 coil can be assigned to the detection times of the neutrons.
In Fig. 6  the situation is schematically depicted. The grey shaded area indicates the time where the neutron can be included to the results. To achieve this classification, the arrival times and the flip state of the ST2 coil had to be recorded and matched. This has been done as a post-processing data analysis step. Figure  6. Timing considerations for the delayed choice switching between |±x� voltages, corresponding to the | ± f � post-selections. The current in the ST2 (see Fig. 2 ) coil has to be constant while the neutrons pass it. The rise time (resp. fall time) from one current level to the other and the neutron travel time through the ST2 coil define periods which cannot be used for the experiment. Only neutrons with specific arrival times in the grey area can be used for the measurement.
Note, however, that there is a residual effect of tides that cannot be corrected. Note also that η is the sea-surface height (SSH) corrected by the geoid. Since the geoid is associated with very large scales, this correction is not relevant for the analysis presented in this study. Therefore, η is also referred to as SSH in the following. GIGATL1 simulation. The GIGATL1 simulation  36  used here has been performed with the Coastal and Regional Ocean COmmunity model (CROCO, 10. 5281/zenodo. 7415055), which is based on the Regional Ocean Modeling System (ROMS  37  ). The integration is performed on an Arakawa C-grid  38  using 100 vertical terrain-following sigma-levels where the bathymetry is taken from the SRTM30plus dataset. Vertical mixing is parameterized with the k-epsilon turbulence closure scheme with applied Canuto A stability function.
The fact that the flux estimated from the one-dimensional SSH data agrees with that from the two-dimensional original shows that the estimation procedure corrects for this issue. We hypothesize that, on average, shorter scale features erroneously imprint at larger scales, and that the decreasing C with increasing scale corrects for this by reducing the pre-estimate (Eq. (1) without C) more at larger scales. Future research needs to address the question, why the estimation works.
Acknowledgements The authors gratefully acknowledge support from the  French National Agency for Research (ANR)  through the project  DEEPER  ( ANR-19-CE01-0002-01 ). J. G. further gratefully acknowledges support from  ISblue "Interdisciplinary graduate school for the blue planet " ( ANR-17-EURE-0015 ) and O. V. from the  French Space Agency (CNES)  via the  French SWOT TOSCA program. This work was performed using HPC/AI resources from  GENCI-TGCC  (Grant  2023-A0090112051 ) and from  DATARMOR at Ifremer, Brest, France.
Participants For both experiments, we recruited 30 participants, the same number as PPZ (though PPZ analysed data from only 23 participants after exclusions). We pre-specified a flexible stopping rule for sample size, requiring a minimum sample size of 30 and Bayes factor of <1/10 or >10 for the critical state by image category interaction in lag2 trials (see Methods, section 2. 6) to stop participant recruitment. Both experiments met the Bayes factor criterion at the first point of inspection, after 30 participants. For E1, we recruited 30 individuals using opportunity sampling, mainly from social media (ages 21-34 years, M = 28. 4, SD = 3. 7; women = 17, men = 13). For E2, we recruited 30 individuals from a research volunteer pool maintained by Newcastle University (ages 20-79 years, M = 42.
Most of these images had been acquired from the International Affective Picture System (IAPS) database  (Lang et al. , 1997) , with additional supplementation from the internet for romantic and food distractors. As there were more images than required in each category, the subset we used may have differed slightly from the subset PPZ used. When selecting food images, we selected an even number of savoury and sweet food images (28 of each). In total, different distractor images were used (56 from each category), alongside 84 landscape images as fillers. An additional 84 landscape images were used as target images; these were duplicated, with one copy rotated 90 • clockwise and one copy rotated 90 • anticlockwise.
Data analysis Data were analysed and visualised in R  (R Core Development Team, 2020). Our data and code are available at https://osf. io/w5en6/. PPZ excluded participants who reported a lower hunger rating in the hungry condition than in the sated condition. We used the same criteria, leading to no exclusions in E1 and one in E2. PPZ also excluded participants with accuracy more than two standard deviations below the mean for the respective hunger condition. We excluded one participant from E1 and one from E2 based on these criteria. We fitted Bayesian linear mixed models, which followed the structure of the repeated-measures ANOVAs used in PPZ (see Table  S2  for model specifications). Models included a random effect of participant to allow for the repeated measures. We used weakly informative priors of N(1, 10) for all parameters  (McElreath, 2020).
This could account for the lack of state by image category interactions in our replications and those of  Arumäe et al. (2019) , as even subtle differences in image sets may alter whether an EBA occurs  (Santacroce et al. 2023). In these particular experiments, the characteristics of the food images usedsuch as calorie content or whether a food is sweet or savoury could be an important consideration for replication. Calorie data were not available for the images we were provided. We ensured that a balanced sample of sweet and savoury food images were selected for use in food trials. Furthermore, we ran additional analyses on lag2 food trials to assess whether task performance on these trials was affected by whether a food image was sweet or savoury (Table  S10 ). Our results suggest this was unlikely.
Data collection Participants in this study were obtained from multiple sources. Further sample descriptions can be found in Table  1. Argentina. The Argentine samples were recruited in the context of the Alzheimer's participants and/or family members gave their informed consent  [11]. [12] ,  [13]. A total of 1542 peripheral blood or saliva samples were processed to obtain DNA using the QIAmp DNA mini kit (Qiagen); 884 controls, 133 mild cognitive impairment (MCI) and 525 AD cases. Purified DNA samples were genotyped using the Illumina Infinium Global Screening Array (GSA) v. 1. 0 combined to a GSA shared custom content. Chile. The Chilean samples recruited correspond to patients with AD and control subjects, from different studies. Control individuals were recruited from Alexandros longitudinal study  [14] , that belong to 2 cohorts (SABE  [15]  and ALEXANDROS  [16] ) were recruited at Biomedica Research Group, a clinical research center performing industry sponsored international multicenter studies in Santiago.
Global Ancestry Analysis Global ancestry was estimated as described before  [11]. Briefly, 446 ancestry informative markers (AIMs), specifically selected to estimate ancestry in Latin America  [24] , were extracted from the Latin datasets and the reference populations in 1000 Genomes (http://www. internationalgenome. org/): Caucasian (CEU, n=85), Yorubas African (YRI, n=88) and Native American  [25]  (NAM, n=46). From SNPs present in all populations, balanced distributed SNPs among reference populations and chromosomes, were selected to estimate ancestry (n=356). They were all merged in one PLINK v1. 9 file (www. cog-genomics. org/plink/1. 9/), and ancestry was predicted using ADMIXTURE v1. 3. 0  [26]. Plots and analysis were performed with R (www. R-project. org/).
Association Analysis Logistic regression models, adjusted for age, gender and the first six ancestry principal components (PCs) were fitted using PLINK 2. 0  [19]  in both populations. Low imputation quality variants (R 2 < 0. 3) or rare variants (minor allele frequency (MAF) < 1%) were excluded. After study-specific variant filtering and QC procedures, we performed a fixed effects inverse-variance-weighted meta-analysis  [27]  with the Argentine and Chilean summary statistics for AD association. Quantile-quantile plots, Manhattan plots, and the exploration of genomic inflation factors were performed using the R package qqman  [28]. Regional plots were generated with LocusZoom  [29]  and loci were annotated as the closest gene. Genomic regions previously associated with AD  [2]  were also visualized by regional plots.
Population admixture in Argentinian and Chilean samples Genome-wide genotyped data was generated in two samples from the southern cone of Latin America (Table  1 ), Argentina (n=1018) and Chile (n=375). We first explored the ancestry admixture of both populations (Figure  1 ). While the admixture of Chilean participants is more homogenous, with 75% of the samples showing 30-50% NAM ancestry, the Argentinian samples showed more diverse admixture along the NAM and EUR axis, with 32% of individuals having >30% NAM ancestry (Figure  1  and Supplementary Fig. 1 ). Beside differences in recruitments between the Chilean (only one city, Santiago) and the Argentinian samples (different cities across the country), dissimilar migratory flows and policies between countries may explain these differences in ancestry proportions.
EADB hits in the Latin population meta-analysis Next, we explore whether the genetic loci previously associated with AD risk  [2]  in European ancestry translate to the Latin genetic admixture included in our study. First, we investigated the 83 sentinel signals reported by EADB in our meta-analysis. Two of them were excluded from the Chilean dataset (PLCG2 p. P522R, rs72824905 and ABI3 p. S209F, rs616338)  [2] ,  [32] , and one from the Argentinian dataset (rs7157106 at IGH gene cluster) due to bad quality; their associations in the remaining population are reported (Figure  2  and Supplementary Table  2 ). Formal replication was observed for nine variants, i. e. , an identical SNP displaying the same effect direction and nominal statistical significance (p<0. 05, Figure  2 ), representing a translation of these signals in the Latin population.
EADB genetic risk score performance in Latin Population Finally, we sought to explore whether the GRS reported by the EADB  [2]  consortium can classify cases and controls accurately in the Latin American population. To compute the GRS in our sample, we included the 80 SNPs that passed quality controls in both, the Argentinian and Chilean datasets, with the effect sizes reported in European ancestry (Supplementary Table  4 ). GRS values were normally distributed and logistic regression analysis revealed an association with AD in both Argentine (GRSmean=50. 4, GRSrange[40. 1-61. 8], OR=1. 06, p=7. 4x10 -4 ) and Chilean (GRSmean=49. 5, GRSrange[39. 3-60. 9], OR=1. 16, p=1. 6x10 -6 ) populations. Since the Latin American population analyzed here are genetic admixtures, we investigated whether the NAM ancestry was affecting the GRS values and/or its association with the disease.
Acknowledgments IdR. is supported by national grant from the  Instituto de Salud Carlos III (ISCIII)   FI20/00215. LC is supported by a doctoral fellowship of  CONICET (Argentina). MCD, PG, EMC, SK and LM are members of the  Research Career of CONICET (Argentina). SK, PS and MCD are supported by the  Agencia Nacional de Promoción Científica y Tecnológica  ( PUE060 ,  PICTO-2021-UCTH00005 ,  PICTO-2021-UCTH00006 ). ARu and MB receive support from the  European Union / EFPIA Innovative Medicines Initiative joint  undertaking  ADAPTED and   MOPEAD  projects (grant numbers  115975  and  115985 , respectively). MB and ARu are also supported by national grants  PI13/02434 ,  PI16/01861 ,  PI17/01474 ,  PI19/01240 ,  PI19/01301  and  PI22/01403. Acción Estratégica en Salud  is integrated into the  Spanish National R+D+I Plan  and funded by  ISCIII -Subdirección General de Evaluación  and the  Fondo Europeo de Desarrollo Regional (FEDER -' Una manera de hacer Europa ').
ARu is also funded by  JPco-fuND-2  " Multinational research projects on Personalized Medicine for Neurodegenerative Diseases, "  PREADAPT project  ( ISCIII  grant:  AC19/00097 ), and  EURONANOMED III Joint Transnational call  for proposals (2017) for  European Innovative Research & Technological Development Projects in Nanomedicine  ( ISCIII  grant:  AC17/00100 ). Ace Alzheimer Center Barcelona  researchers research receives support from  Roche ,  Janssen ,  Life Molecular Imaging, Araclon Biotech ,  Alkahest ,  Laboratorio de Análisis Echevarne , and IrsiCaixa. AS, PO and CG-B are supported by grant  ANID/FONDAP  ( ID15150012 ). JCL is supported by a grant (EADB) from the  EU Joint Programme -Neurodegenerative Disease Research. INSERM UMR1167  is also funded by the  INSERM ,  Institut Pasteur de Lille ,  Lille Métropole Communauté Urbaine and French government 's  LABEX DISTALZ program  ( development of innovative strategies for a transdisciplinary approach to AD ).