dataesr/scientific-paragraphs-categorization huggingface dataset:
APOE genotype 5. a cognitive diagnosis (healthy control, MCI, dementia) 6. health information: cardio/cerebrovascular health, diabetes, hypercholesterolemia, smoking, family history of dementia 7. the number of clinical sessions conducted before the selected baseline session (for instance sessions without a matching MRI session) to account for retest effects 2. 2. 2. MRI data MRI data were acquired on Siemens 3T scanners, with the majority coming from a TrioTim model (622 of 662 participants), and the rest from the combined PET/MRI Biograph mMR model. Each participant had between 1 and 4 T1w scans (1. 7 on average). In total, the sample had 1 119 T1w images. The parameter combination most used (in over 1 070 scans) was voxel size = 1 × 1 × 1 Each participant had between 1 and 4 functional resting-state scans (M = 2.
0). In total, the sample had 1 327 functional images. The parameter combination most used (in over 1 300 scans) was voxel size = 4 × 4 × 4 mm 3 , TE = 0. 027 second, TR = 2. 2 seconds, scan duration = 6 minutes. For further information regarding the imaging data see  (LaMontagne et al. , 2019). See Table  S1  for complete information about T1w, T2w and fMRI scans acquisitions.
MRI preprocessing Functional and structural MRI data were preprocessed using the standard processing pipeline of fMRIPrep 1. 4. 1  ( Esteban et al. 2018 ) , which also includes running FreeSurfer 6. 0. 1 on the structural images  ( Fischl, 2012 ). A detailed description of the preprocessing can be found in the supplement ( 6. 1. 1 Details on MRI preprocessing ). Except for basic validity checks in a random subset of participants, data quality of the preprocessed data was not rigorously assessed. Notably fMRIPrep has been shown to robustly work across many datasets  ( Esteban et al. 2018 ).
Input data Input data for the predictive models came from 3 modalities: non-brain , global and subcortical structural ( structGS ), and func-tional connectivity ( func ; Fig. 1 A ). Modalities were entered into the models on their own and in combination. For instance, nonbrain + structGS models received horizontally concatenated input features from the nonbrain and structGS modalities ( Fig. 1  and 2  ). This allowed testing whether combining nonbrain with structural data improved predictive accuracy as compared to nonbrain data alone. The following paragraphs describe the input data modalities and Table  S2  gives an overview of features entered into the models. 2. 4. 1. 1. Nonbrain data. Nonbrain features included demographics, scores of clinical and neuropsychological instruments, APOE genotype, and health information.
Functional MRI (func). Functional connectivity was computed from the fMRIPrep -preprocessed functional scans. Denoising was performed using the 36P model  ( Ciric et al. , 2017 ) , which includes signals from 6 motion parameters, global, white matter, and CSF signals, derivatives, quadratic terms, and squared derivatives. Due to the dimensionality of connectivity matrices, we opted to perform PCA-based dimensionality reduction on the fMRI data. In its raw form, the fMRI connectivity has 44,850 unique features, a prohibitive amount that eclipses the other modalities. Because trees are grown sampling both data points and variables, this number would complicate the training of Random Forests, requiring an increase in the number of trees to reliably expose the different modalities to the model.
4  ), and functional connectivity alone could predict age reasonably well (median R 2 = 0. 33, Figure  S6 ), suggesting that its negligible contribution to decline prediction cannot be attributed to general methodological or data quality issues. In the main analysis, the predictive target of cognitive decline was quantified as a continuous score. To compare our analysis to previous work that predicted classes of cognitive decline, we performed a further analysis that predicted extreme groups of cognitive decline (stable vs decline). Overall, extreme groups could be accurately predicted from the input data (most F1-scores [harmonic mean of the precision and recall] in the range of 0. 8-0. 9; Figure  S7 ).
, 2018 ). In the following, we will sketch possible future developments along 4 themes: implications of and possible improvements to the continuous targets of cognitive decline, multimodal input data, predictive models , and the importance of generalization to new datasets. Quantifying cognitive decline continuously rather than discretely enables a more fine-grained and robust prediction, but also requires methodological choices. By predicting a diagnostic label, previous studies were often restricted to MCI patients and aimed to distinguish stable from converting patients. Considering decline as a continuum better characterizes the underlying change in abilities and allows for capturing changes that occur in healthy aging. Overcoming the scarcity of diagnosed conditions, widens the scope of applications and has methodological advantages: the resulting increased sample size yields more robust models, which is critical to avoiding optimistic bias in estimating prediction accuracy  ( Woo et al.
In this study, we quantified cognitive decline using 2 clinical assessments (CDR and MMSE), which measure a heterogeneous set of cognitive and everyday life functions. While these clinical assessments have the advantage of being used in practice, they lack the specificity to target single cognitive constructs. Measuring cognitive constructs more homogeneously might potentially improve accuracy, especially if those constructs are strongly linked to specific brain regions or networks. This could be achieved by additionally employing neuropsychological assessments. The multitarget approach outlined in this study is well-suited to including these additional targets. Beside additional targets, future studies should also consider additional multimodal input data to characterize the brain in greater detail.
The present study used data derived from structural and functional MRI (T1w and resting-state fMRI). These might be complemented by information from diffusion-weighted imaging, arterial spin labeling, or positron emission tomography  (Rahim et al. , 2016). Additionally, the presently used modalities could also be refined and alternative representations could be considered. For instance, different methods for quantifying brain structure  ( Pipitone et al. , 2014 )  or brain function  ( Rahim, Thirion, and Varoquaux, 2019 ) , and adding data on structural asymmetry  ( Wachinger et al. , 2016 )  or dynamic functional connectivity  ( Filippi et al. , 2019 )  could provide improved predictive performance. Furthermore, the influence of MR data quality on accuracy should be assessed in future studies.
, 2017; Rahim et al. , 2016; Engemann et al. , 2020). While the stacking approach accounts for modality-level interactions it does not consider feature-level interactions across modalities. However, it scales well to high-dimensional data and allows for block-wise missing data, for instance, a missing modality. The present work only included participants if data from all modalities (nonbrain, structural, functional) were available. In clinical practice, this is often not feasible. As we demonstrated previously, stacking can be used to include participants with missing modalities, which increases the sample size and the scope of application  (Engemann et al. , 2020). In practice, the benefit of adding multimodal neuroimaging data to a set of clinical assessments needs to be considered against the additional costs.
(Korolev et al. , 2016). This enables increased accuracy by focusing on high-confidence predictions  ( Tam et al. , 2019 )  and might even suggest a participant-tailored clinical workflow depending on the prediction confidence  (Bhagwat et al. , 2018). While the present study has not yet investigated these effects, it is well set up to determine optimal conditions for model performance. A large number of cross-validation splits yields a distribution of predictive performance, not only a point estimate. This will also allow us to assess whether the predictions across sub-groups are driven by the same features. For a predictive model to be useful in real-world applications, it needs to generalize well to datasets from different sites  ( Scheinost et al. , 2019 ). While characteristics of our study facilitate generalization, a future study is required to empirically establish the generalization of our models to independent datasets.
First, we have aimed to provide full transparency throughout this study to improve reproducibility and generalizability. We used data from a large, publicly available dataset, preprocessed them with well-established open-source tools, and inputted them into wellestablished models. The analysis code is publicly shared and after further developing this approach, trained models will also be shared. Importantly, the analysis was preregistered to avoid overfitting due to analytical flexibility  ( Carp, 2012 ; Hosseini et al. , 2020 ). Second, the OASIS-3 project is set up heterogeneously regarding the number of sessions, the intervals between sessions, and the participants' duration in the study. This heterogeneity is expected to provide less opportunity for an algorithm to overfit to datasetspecific idiosyncrasies, resulting in more generalizable models that also perform well in other settings.
While a heterogeneous dataset and open/reproducible approaches certainly improve generalizability, we trained and tested models using only one dataset. Thus, the cross-validated performance in our study provides a biased estimate of the generalizability to independent datasets. This bias might even be modalityspecific, in that non-brain features might generalize better than brain imaging features  (Bhagwat et al. , 2018). Training predictive models on data from multiple sites has been shown to improve generalization  ( Abraham et al. , 2017 ; Orban et al. , 2018 ; Liem et al. , 2017 ). Hence, future studies should use models trained and tested on data from multiple sites, which requires further suitable longitudinal and publicly available datasets  (Varoquaux, 2018). This also provides an opportunity to take preregistration even further.
After conducting experiments in an initial dataset, a trained model could be preregistered and applied to an independent dataset that hasn't yet been analyzed. Future research could investigate the impact of different preprocessing strategies on predictive performance. This encompasses everything from individual settings to the software used itself. For example, on the volumetry of subcortical structures, substantial differences are noted between different software  ( Mulder et al. , 2014 ; Bartel et al. , 2017 ).
Conclusions In summary, we have shown that adding structural brain imaging data to nonbrain data (such as memory scores or everyday functioning) improves the prediction of future cognitive decline in healthy and pathological aging. Conversely, adding functional connectivity data, as used in the present approach, did not aid the prediction. Importantly, our work has potential for clinical utility by predicting future cognitive decline, rather than a current diagnosis. Future studies should include additional brain imaging modalities and independent datasets and should determine the potential of functional connectivity using alternative methodological approaches. Quantifying future decline continuously allows for more nuanced predictions on an individual level. In the future, these continuous measures may facilitate dimensional approaches to pathology  ( Cuthbert, 2014 )  Increased personal and societal costs due to healthy and pathological age-related cognitive decline are one of the most pressing challenges in an aging society.
Acknowledgements This work was supported by the  URPP "Dynamics of Healthy Aging " and by the  Swiss National Science Foundation  [  10 0 01C_197480  ] at the  University of Zurich  and partially supported by the grant  ANR-20-IADJ-0 0 02  AI-cog to Alexandre Gramfort. Data were provided by the  OASIS-3 project (Principal Investigators :  T. Benzinger ,  D. Marcus ,  J. Morris ;  NIH P50AG00561 ,  P30NS09857781 ,  P01AG026276 ,  P01AG003991 ,  R01AG043434 ,  UL1TR0 0 0448 ,  R01EB0 09352  ). We thank the participants and organizers of the OASIS-3 project for providing the data.
nobilis resistance to H. pinnae. A BUSCO analysis, with a score of 96%, revealed excellent completeness and integrity of our transcriptome (i. e. , fragmentation of genes) in comparison with molluscan libraries  28. Transcriptomes from different species of mollusks have shown that the expected number of transcripts ranges from approx. 34,794 to 394,251  29. In our study, we found 269,285 transcripts in total, a number within the higher range of what is typically found for mollusks. This demonstrates the high quality of our transcriptome. However, only 46% of transcripts were annotated. This high proportion of non-annotated transcripts is not unusual in transcriptome projects for invertebrate taxa (  30, 31  , including mollusks  29, [32] [33] [34]  ). The lack of detectable sequence orthologues in the public databases may be due to several factors, including taxonomically restricted genes (e.
Data analysis: species confirmation. The species of each sample was verified by comparison of the obtained COI sequences (700 bp) with the reference sequences in the GenBank database using the BLAST-n algorithm (https:// blast. ncbi. nlm. nih. gov/ Blast. cgi). Individuals were assigned to a species level when the identity of the compared sequences was at least 98%. A Principal Coordinate Analysis (PCoA) was performed, using GenAlex  55  , to compare RS samples to individuals from P. nobilis and P. rudis, based on their genotype at the 10 microsatellite loci. Detection of H. pinnae. The presence of H. pinnae was investigated through the amplification of a specific fragment of 600 bp of the small subunit ribosomal DNA (SSU rDNA), using primer pairs: HPN-F3/HPN-R3, developed by  14  ). PCRs were performed using the same protocol as previously described for amplification of the fragment of the COI mtDNA gene and using the following thermal cycling program: an initial denaturing step of 10 min at 94 °C, followed by 40 cycles of amplification (1 min at 94 °C, 1 min at 55 and 1 min at 72 °C) and a final elongation step of 10 min at 72 °C.
Acknowledgements This research was implemented thanks to the financial support of the  Region Occitanie , the  General Council of Pyrénées-Orientales  and the  French Ministry for Ecological Transition. We are grateful for the support of the  DREAL (Direction Régionale de l'Environnement, de l' Aménagement et du Logement) Occitanie  and all of the ports involved in the study: the ports of Sète, Port-la-Nouvelle, Port-Vendres and Frontignan. We also thank the staff from the  Cerbère-Banyuls Natural Marine Reserve  and the  Agathoise Coast Marine Protected Area  for their collaboration and permission to work within their protected areas. We would like to thank  Vincent Laudet ,  Natacha Roux ,  Hanna Abi Akl  and  Jeanine Almany  for constructive remarks on the manuscript and  Peter Esteve  for his help in informatics and data storage.
Although environmental factors are sufficient to drive population-level differences in strain dominance, multigenerational vertical transmission may also underlie some of these geographic patterns. Here, we assessed codiversification between gut microbial species and their human hosts by comparing their evolutionary histories. Human-and microbial-genomes from the same individuals allowed us to assess codiversification for a suite of gut microbial species. To also gauge short-term vertical transmission, we characterized strain-sharing within mother-child pairs. After enrolling healthy adult women, with an emphasis on mothers with young children, into the study in three countries (Gabon, Vietnam, Germany), we generated chip-based genotype data for the mothers and fecal metagenomes for mothers and children (Adults/Children for Gabon: 171/144; Vietnam: 192/164; Germany: 151/78).
Acknowledgements We thank  Nguyen Thu Ha ,  Anne Pfleiderer ,  Emily Cosgrove ,  Andrew Clark ,  Aleksander Kostic ,  Marla Taylor , Native American tribes ( Mike Bremer ,  Joseph Aguilar ,  Juana Charlie ,  Reylynne Williams ,  Barnaby Lewis ,  Shane Anton ,  Angela Garcia-Lewis , and  Bruce Bernstein ), and members of the  Department of Microbiome Science. Funding: This work was supported by the  Max Planck Society. TwinsUK is funded by the  Wellcome Trust, Medical Research Council ,  European Union ,  Chronic Disease Research Foundation (CDRF) ,  Zoe Global Ltd. , and the  National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust  in partnership with King's  College London.
aeruginosa that are non-pathogenic for humans suggested that this TPS may play a role in the infection of other organisms, prokaryotes or eukaryotes, as it was recently suggested for Pseudomonas protegens  (Vesga et al. , 2020). Here, we examined 191 ExlA sequences retrieved from publicly available databases. The overall amino acid sequence identity of proteins was distributed unevenly throughout the protein sequences. Furthermore, the subclass of ExlA proteins present in the natural fly pathogen P. entomophila (ExlA Pe ) and other soildwelling Pseudomonas exhibited CTDs that were shorter and differed in sequence compared to the ExlA P class. The analyses of the expression and functionality of ExlA homologs in strains of P. chlororaphis (ExlA Pc ), P. putida (ExlA Pp ), and P. entomophila (ExlA Pe ) and their comparison with the well-characterized P.
TPS exolysins display divergent CTDs across Pseudomonas species Our previous study analyzing the promotor sequences and regulation of the exlBA operon identified exlBA sequences in 466 Pseudomonas strains among the over 4,800 Pseudomonas strains present in the Pseudomonas database  (Trouillon et al. , 2020). This intriguing finding led us to investigate here the sequences and the function of the corresponding proteins in selected, commonly used strains of the species P. entomophila, P. putida, and P. chlororaphis that can be genetically manipulated. Interestingly, although the sequence of the overall ExlA protein is conserved in all species, it differs among domains. The TPS domain is 82% conserved, whereas the FHA domains are only 34% conserved and exhibit two different regions in which indels are present.
DISCUSSION In this work, we characterized several ExlB-ExlA TPSs encoded in genomes of environmental Pseudomonas species that are non-pathogenic for humans. Originally annotated in the databases as secreted filamentous hemagglutinins, adhesins or hemolysins, most of the Pseudomonas ExlA proteins share similar size (150-170 kDa), conserved TPS domains and have a central part encoding FHA domains. Based on the prisequences of the TPS domain and their smaller size, they can be phylogenetically separated from the TPS adhesins, proteins with contact-dependent inhibition activity (CDIs) and other TPS proteins with ironacquisition activities  (Guerin et al. , 2017; Mazar and Cotter, 2007). Indeed, the P. aeruginosa ExlA groups with the well-studied hemolysins ShlA from Serratia marcescens and PhlA from Photorhabdus luminescens.
Cytotoxicity assays and microscopy Cells were infected at a multiplicity of infection (MOI) of 10, unless otherwise specified. PI incorporation was followed by fluorescence measuring (excitation 544 nm/emission 590 nm) every 10 min with Fluoroskan Ascent FL2. 5 Microplate Fluorometer (Thermo Corporation), over indicated time post-infection. Data were presented as arbitrary fluorescence units (AU) as a function of time. Cytotoxicity assays of strains carrying chimeric proteins were performed over 10 h at 30 C for P. entomophila in presence of 0. 01% arabinose or 37 C for P. aeruginosa without arabinose. For comparison between strains, the data were presented as area under the curve (AUC) calculated according to the linear trapezoidal rule, expressed in arbitrary units (AU). For microscopy, infections were done as described above and images were captured at the end of infection on a LEICA DMIRE2 microscope with an ORCA-05G camera and with an N PLAN x20 numerical aperture 0.
E-cadherin cleavage A549 epithelial cells were seeded on a 6-well plate in the same conditions as previously described. When about 70% of cells were considered as dead, culture medium was removed, cells were lysed with a lysis buffer (PBS 1x, 1 mM EDTA, 1% Triton X-100, Protease inhibitors (Roche), 1 mM orthovanadate and 50 nM okadaic acid) and scrapped. The suspension was centrifuged at 15,000 g during 15 min at 4 C and the supernatant was recovered. Protein concentrations of the lysates were determined with Micro BCA Protein Assay (Thermo Scientificä) using BSA as standard. Proteins (20 mg sample) were separated by a 10% acrylamide gel, then transferred on to a PVDF membrane and revealed with E-cadherin (BD transduction laboratories) or b-actin (Sigma) antibodies. Quantification of the bands was made with ImageJ software, the amount of E-cadherin full-length was normalized to the amount of b-actin and expressed as percentage referred to non-infected condition, considered as 100%.
Evolutionary analyses The amino acid identity between ExlA/B from different species/strains of Pseudomonas was calculated using both, the SIAS tool with default parameters (http://imed. med. ucm. es/Tools/sias. html) and blastp comparison analyses. The synteny conservation of the genomic regions containing exlA/B was analyzed on selected strains using both the SyntTax webserver  (Oberto, 2013)  and EasyFig 2. 2. 2  (Sullivan et al. , 2011). All completely sequenced Pseudomonas genomes (690) were downloaded from both NCBI (https://www. ncbi. nlm. nih. gov/genome/) and the Pseudomonas genome database (https://www. pseudomonas. com/) (Table  S6 ). We detected both exlA and exlB homologous in 191 genomes that were then selected for further analyses (Sheet 2, Table  S6 ). First, a species tree of Pseudomonas species/strains containing exlAB was constructed using these 191 Pseudomonas genomes and two Burkholderia cepacia genomes that were selected as outgroups for constructing the phylogeny.
For this purpose, the peptides were sampled on a precolumn (300 mm 3 5 mm PepMap C18, Thermo Scientific) and separated in a 75 mm 3 250 mm C18 column (Reprosil-Pur 120C18-AQ, 1. 9 mm, Dr. Maisch). The MS and MS/MS data were acquired by Xcalibur (Thermo Fisher Scientific). Peptides and proteins were identified by Mascot (version 2. 6. 0, Matrix Science) through concomitant searches against the NCBI database (Pseudomonas chlororaphis strain PA23 taxonomy, June 2020 download) or the  Microscope (Medigue et al. , 2019)  database (Pseudomonas entomophila L48 taxonomy, January 2020 download), a homemade database containing the sequences of classical contaminant proteins found in proteomic analyses (human keratins, trypsin, etc. ), and the corresponding reversed databases. Trypsin/P was chosen as the enzyme and two missed cleavages were allowed.
Precursor and fragment mass error tolerances were set respectively at 10 and 20 ppm. Peptide modifications allowed during the search were: Carbamidomethyl (C, fixed), Acetyl (Protein N-term, variable) and Oxidation (M, variable). The Proline software  (Bouyssieet al. , 2020)  was used for the compilation, grouping, and filtering of the results (conservation of rank 1 peptides, peptide length R6 amino acids, peptide score R25, allowing to reach a false discovery rate of peptide-spectrum-match identifications <1% as calculated on peptide-spectrum-match scores by employing the reverse database strategy, and minimum of one specific peptide per identified protein group). Proline was then used to perform a compilation, grouping and MS1 label-free quantification of the identified protein groups based on razor and specific peptides.