dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Acquisition and Pre-processing Data used in this work were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni. loni. usc. edu) and from the Dementia Research Centre, UK. For ADNI, we downloaded all T1 MR images that have undergone gradient warping, intensity correction, and scaling for gradient drift. We included subjects that had at least 3 scans, to ensure we get a robust estimate of the subject specific parameters. This resulted in 138 healthy controls, 235 subjects with mild cognitive impairment (MCI) and 81 subjects with Alzheimer's disease. We also downloaded all AV45 PET images from ADNI that were fully preprocessed, having the tag 'Co-reg, Avg, Std Img and Vox Siz, Uniform Resolution'. This meant that the images were co-registered, averaged across the 6 five-minute frames, standardised with respect to the orientation and voxel size and smoothed to produce a uniform resolution of 8mm full-width/half-max (FWHM).
The DRC dataset consisted of T1 MRI scans from 31 healthy controls, 32 PCA and 23 typical AD subjects with at least 3 scans each and an average of 5. 26 scans per subject. All PCA patients fulfilled the  (Tang-Wai et al. , 2004)  criteria and  (Mendez et al. , 2002)  criteria based on clinical review. The typical AD patients all met the criteria for probable Alzheimer's disease  (Dubois et al. , 2010; Dubois et al. , 2007). Given that the ADNI and DRC datasets contained subjects with different modalities or diseases, we ran DIVE independently on the following four cohorts (see Table  1  for demographics): 1) ADNI MRI: controls, MCI and tAD subjects from ADNI (cortical thickness data) 2) DRC tAD: tAD subjects and controls from the DRC dataset (cortical thickness data) 3) DRC PCA: PCA subjects and controls from the DRC dataset (cortical thickness data) 4) ADNI PET: AV45 scans from ADNI containing subjects with following diagnoses: healthy controls, subjective memory complaints, early MCI, late MCI and Alzheimer's disease.
MR Images On both datasets, in order to extract reliable cortical thickness measures, we ran the Freesurfer longitudinal pipeline  (Reuter et al. , 2012) , which first registers the MR scans to an unbiased within-subject template space using inverse-consistent registration. The longitudinally registered images were then registered to the average Freesurfer template. No further smoothing was performed on these images (FWHM level of zero mm). From these template-registered volumetric images, cortical thickness measurements were computed at each vertex (i. e. point) on an average 2D cortical surface manifold. For each vertex we averaged the thickness levels from both hemispheres. Finally, we standardised the data from each vertex with respect to the values of that vertex in the control population.
Initial Hypotheses Using ADNI and DRC datasets, we aim to recover the spatial distribution of cortical atrophy and amyloid pathology, as well as the rate and timing of these pathological processes. In particular, we hypothesize that these spatial patterns of pathology and their evolution will be: 1. similar on two independent typical AD datasets: ADNI and DRC 2. different on distinct diseases: tAD vs PCA 3. different in distinct modalities: cortical thickness from MRI vs amyloid load from AV45 PET.
Results The optimal number of clusters, as estimated with AIC, was three for the ADNI MRI dataset, three for the DRC tAD dataset, five for the DRC PCA dataset and eighteen for the ADNI PET dataset. Fig. 3A -left shows the results from the ADNI MRI dataset, where in the left image we coloured the vertices on the cortical surface according to the cluster they most likely belong to. We assigned a colour for each cluster (both the brain figures on the left and the trajectory figures on the right) according to the extent of pathology of its corresponding trajectory at a DPS score of 1. The cluster colours range from red (severe pathology) to blue (moderate pathology). In Fig. 3A -right, we show the resulting cluster trajectories with samples from the posterior distribution of each θ k. Similar results are shown for the other three datasets: the DRC tAD dataset (Fig.
3B ), DRC PCA dataset (Fig. 3C ) and the ADNI PET dataset (Fig. 3D ). We notice that in tAD subjects using the ADNI datasets (Fig. 3A ), there is more severe cortical thinning mainly in the inferior temporal lobe (red cluster), with disperse atrophy also in parietal and frontal regions (green cluster), with relative sparing of the inferior frontal and occipital lobes. In tAD subjects from the DRC dataset, we see a relatively similar pattern, however with more pronounced atrophy in the supramarginal cortex (red cluster) compared to ADNI. The spatial distribution of cortical thinning found with DIVE resembles results from previous longitudinal studies such as  (Dickerson et al. , 2008; Singh et al. , 2006). However, in contrast to these approaches, our model gives insight into the timing and rate of atrophy and is also able to stage subjects across the disease time course.
We also find that the cluster trajectories in the DRC tAD dataset have similar dynamics to the ADNI MRI dataset, although they show a clearer separation between each other. In the PCA subjects (Fig. 3C ), we find that atrophy is mainly focused on the posterior part of the brain, mostly the posterior parietal and supramarginal areas, with limited spread in the motor cortex, anterior temporal and frontal areas. There is a clear separation of atrophy extent between the anterior and posterior part of the brain, with the boundary running along the motor cortex. This posterior-focused pattern of atrophy is different from the one found in the tAD datasets, and agrees with previous findings in the literature  (Crutch et al. , 2012; Lehmann et al. , 2011). In ADNI PET (Fig. 3D ) we see that the regions with the highest amyloid uptake are more spatially continuous, comprising the precuneus and anterior frontal areas.
On the other hand, the anterior-superior temporal gyrus shows the least uptake of amyloid. This result closely matches the result by  (Bilgel et al. , 2016) , which used a completely different dataset and modelling technique. The "layers of clusters" starting from the precuneus and frontal lobes, which range from severe to less severe atrophy, suggest a continuum of variation in vertex trajectories in the case of the PET dataset. These trajectories all start with a low amyloid SUVR, between 0 and 0. 25, but in late stages the trajectories for some clusters such as cluster 0 can reach an SUVR of 1. 5. The reason for seeing this continuum might be because the PET images have a much lower resolution than MR images and were smoothed by ADNI during the pre-processing steps.
Summary and Key Findings We presented DIVE, a spatiotemporal model of disease progression that clusters vertex-or voxel-wise measures of pathology in the brain based on similar temporal dynamics. The model highlights, for the first time, groups of cortical vertices that exhibit a similar temporal trajectory over the population. The model also estimates the temporal shift and progression speed for every subject. We applied the model on cortical thickness vertex-wise data from three MRI datasets (ADNI, DRC tAD and DRC PCA), as well as an amyloid PET dataset (ADNI). Our model found qualitatively similar patterns of cortical thinning in tAD subjects using the two independent datasets (ADNI and DRC). Moreover, it also found different patterns of pathology dynamics on two distinct diseases (tAD and PCA) and on different types of data (PET and MRI-derived cortical thickness).
Limitations and future work DIVE has some limitations that can be addressed. First, we assumed that cluster trajectories follow sigmoidal shapes, which is not the case for many types of biomarkers in ADNI which do not plateau in later stages. The assumption of sigmoidal trajectories can be avoided using non-parametric curves such as Gaussian Processes  (Lorenzi et al. , 2017) , which would be straightforward to incorporate into the DIVE framework. Another limitation of the model is that it assumes all subjects follow the same disease progression pattern, which might not be the case in heterogeneous datasets such as ADNI or DRC. This can be a concern, as there might be a pattern of pathology that occurs in a small set of subjects. However, DIVE can be extended to account for heterogeneity in the datasets by modelling different progression dynamics for distinct subgroups, using unsupervised learning methods like the SuStaIn model by  (Young et al.
, 2018). While SuStaIn, just like DIVE, estimates clusters and trajectories within the dataset, the clusters in SuStaIn are made of subjects with similar disease progression, while the clusters in DIVE are made of vertices with similar progression. Future work could combine clustering along both subjects and vertices simultaneously to estimate disease subtypes with distinct spatiotemporal dynamics at the vertexwise level. There are several potential future applications of DIVE. One of the advantages of DIVE is that it can be used to study the link between disconnected patterns of brain pathology and connectomes extracted from diffusion tractography or functional MRI (fMRI). Such an analysis would enable further understanding of the exact underlying mechanisms by which the brain is affected by the disease.
Our model, which can estimate fine-grained spatial patterns of pathology, is more suitable than standard ROI-based methods for studying the link between pathology and these structural or functional connectomes, because white matter or functional connections have a finegrained and spatially-varying distribution of endpoints on the cortex. Apart from studying the link with brain connectomes, there are other potential applications for DIVE. The model can be applied to study other types of vertexwise or voxelwise data, such as FDG PET, tau PET, DTI or Jacobian compression maps from MRI. Moreover, the model can also be extended to cluster points on the brain surface according to a more complex disease signature, that can be made two or more biomarkers. For example, using our cortical thickness and amyloid PET datasets from ADNI, we could have clustered points on the brain based on both modalities simultaneously.
Results Table  1  shows the results of the model comparison, on ADNI MRI dataset. Each row represents one model tested, while each column represents a different performance measure: correlations with four different cognitive tests and accuracy in the prediction of future vertexwise measurements. In each entry, we give the mean and standard deviation of the correlation coefficients or RMSE across the 10 cross-validation folds. (u) lk ∝   i,j∈I N (V ij l |f (α i t ij + β i ; θ k ), σ k )     l 2 ∈N l K k 2 =1 z (u-1) l 2 k 2 Ψ(Z l = k, Z l 2 = k 2 )   (8) log z (u) lk ∝   i,j∈I - log (2πσ 2 k ) 2 - 1 2σ 2 k (V ij l -f (α i t ij + β i ; θ k )) 2   + l2∈N l log K k2=1 z (u-1) l2k2 (δ k2k exp(λ) + (1 -δ k2k ) exp(-λ 2 )) (9) We further define the data-fit term D lk as follows: D lk = - log (2πσ 2 k )|I| 2 - i,j∈I 1 2σ 2 k (V ij l -f (α i t ij + β i ; θ k )) 2 (10) This results in: log z (u) lk ∝ D lk + l2∈N l log K k2=1 z (u-1) l2k2 (δ k2k (exp(λ) -exp(-λ 2 )) + exp(-λ 2 )) (11) Finally, we simplify the sum over k 2 to get the update equation for z lk : log z (u) lk ∝ D lk +   l 2 ∈N l log exp(-λ 2 ) + z (u-1) l 2 k (exp(λ) -exp(-λ 2 ))   (12) In practice, we cannot naively compute the exponential term z lk = exp(log(z lk )) due to precision loss.
Fast DIVE Implementation -Proof of Equivalence Fitting DIVE can be computationally prohibitive, especially given that the number of vertices/voxels can be very high, e. g. more than 160,000 in our datasets. We derived a fast implementtion of DIVE, which is based on the idea that for each subject we compute a weighted mean of the vertices within a particular cluster, and then compare that mean with the corresponding trajectory value. This is in contrast with comparing the value at each vertex with the corresponding trajectory of its cluster. In the next few sections, we will present the mathematical formulation of the fast implementation for parameters  [θ, α, β]. Parameter σ already has a closed-form update, while parameter λ has a more complex update procedure for which this fast implementation doesn't work.
Acknowledgements This work was supported by the  EPSRC Centre for Doctoral Training in Medical Imaging  with grant  EP/L016478/1. A. Eshaghi  received a  McDonald Fellowship  from the  Multiple Sclerosis International Federation  (MSIF, www. msif. org),and the  ECTRIMS -MAGNIMS Fellowship. A. L. Young was supported through an  EPSRC doctoral prize fellowship  and  EPSRC  grant  EP/J020990/01. N. P. Oxtoby,  S. Garbarino  and  D. C. Alexander  received funding from the  EU   Horizon 2020 research and innovation programme  under grant agreement No  666992. D. C. Alexander  was also supported by  EPSRC  grants  J020990 ,  M006093  and  M020533. S. J. Crutch  was supported by an  Alzheimer's Research UK   Senior Research Fellowship  and  ESRC/NIHR  ( ES/L001810/1 ) and  EPSRC  ( EP/M006093/1 ) grants.
; https://doi. org/10. 1101/600700 doi: bioRxiv preprint 4 towards antibiotics with different targets. We then used our isolation method to resolve a key outstanding question in the persistence field. Single-cell recovery of persisters after treatment was monitored in a microfluidic 'mother machine' device. These data show that persister awakening occurs at a constant rate, reflecting stochasticity. Our novel approach might prove useful for future single-cell studies of persistence.
Data analysis and statistics Images were analysed using NIS Elements D 4. 60. 00 (Nikon Instruments, Japan) and ImageJ (https://imagej. nih. gov/). Flow cytometry data were analysed with FlowJo V10. Statistical analyses were performed in R (https://www. r-project. org/). CC-BY 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under The copyright holder for this preprint (which was not this version posted April 6, 2019. ; https://doi. org/10. 1101/600700 doi: bioRxiv preprint curve was then used to derive the average growth rate of individual cells. After checking normality with a Shapiro-Wilk test, growth rates were compared statistically among different strains using an unpaired, two-sided t-test, with Welch correction in the case of unequal variances (checked with an Ftest).
Acknowledgements We thank  Abram Aertsen  ( KU Leuven ) for sharing the hipA7 strain. E. M. W. and B. V. d. B are Research Foundation Flanders (FWO)-fellows. This research was funded by the  KU Leuven Research Council  ( PF/10/010 ;  C16/17/006 ),  FWO  ( G047112N ;  G0B2515N ;  G055517N ), and the  Flemish Institute for Biotechnology (VIB).
The repertoire of RNA-binding proteins (RBPs) in bacteria play a crucial role in their survival, and interactions with the host machinery, but there is little information, record or characterisation in bacterial genomes. As a first step towards this, we have chosen the bacterial model system Escherichia coli, and organised all RBPs in this organism into a comprehensive database named EcRBPome. It contains RBPs recorded from 614 complete E. coli proteomes available in the RefSeq database (as of October 2018). The database provides various features related to the E. coli RBPs, like their domain architectures, PDB structures, GO and EC annotations etc. It provides the assembly, bioproject and biosample details of each strain, as well as cross-strain comparison of occurrences of various RNAbinding domains (RBDs).
This database houses information from H. sapiens, M. musculus, D. melanogaster and C. elegans, but not from E. coli. The ATtRACT database  [7] , reported in 2016, lists information on 370 RBPs and 1583 consensus RNA-binding motifs, and compiles experimentally validated data from multiple resources, including RBPDB. The latest version (v 3. 0) of the sRNATarBase  [7, 8]  contains more than 750 small RNA (sRNA)target entries collected from literature and other prediction algorithms. Here, we report EcRBPome (http://caps. ncbs. res. in/ ecrbpome), a comprehensive database of E. coli RBPs. The database documents RBPs identified in all complete E. coli proteomes (available in the RefSeq database, as of October 2018) by computational sequence search algorithms and methods as described earlier  [7] [8] [9].
Datasets The overall protocol for data acquisition is described in our previous study  [9] , in which genome-wide survey (GWS) of RBPs was described, but now for 614 complete E. coli proteomes, retrieved from the RefSeq database (October 2018) (please see Additional file 1 for further details on the search method). The start-points for such search methods, were known sequence and structure signatures of RBPs, organised as structurecentric and sequence-centric family Hidden Markov Models (HMMs)  [5]. A total of 11,662 putative RBPs could be identified from 614 E. coli proteomes studied (Table  1 ). The RefSeq accession numbers, FASTA sequences, domain compositions and cross-references to other databases of these RBPs have been made available for the users in EcRBPome ('Browse all RBPs in EcRBPome' under the Browse menu).
Implementation The retrieval of data and manipulation logic at the back-end of EcRBPome has been implemented using CGI-Perl and the interface of the database built on HTML5, CSS, JavaScript, Ajax and JQuery. The basic tables in EcRBPome have been organised as comma-separated text files, and converted to JSon format, for performance improvement through utilities. The display of tables has been implemented using Bootstrap DataTables. The downloadable graphical plots have been generated using R and the interactive bar plots using the CanvasJS library of JavaScript and HTML5.
Browse menu The users can browse through the list of all the E. coli strains present in this database (with links to the assembly, biosample and bioproject details for each strain), all RBPs (with links to the RefSeq page and their downloadable FASTA sequences) and their domain architectures (DAs)  [10]. The pathogenic and the non-pathogenic strains have been represented in red and green fonts, respectively. The pathotype and sequence type (ST) information, wherever available, has been provided for these strains  [11]. The distribution of various RBDs and DAs (domain pairs) in pathogen-specific and nonpathogen-specific proteins have also been represented in various tables (please see Additional file 1 for more details on the identification of pathogen-specific and nonpathogen-specific proteins).
Cross-strain comparisons The various E. coli strains present in this database are compared on the basis of different parameters like, percentage of RBPs in each proteome (downloadable graphical representations, as well as comparative account with the average RBP percentage across all strains) (Fig. 1b ), presence or absence of RBDs in each strain (matrix representation) (Fig. 1c ), as well as percentage of the various RBDs in each strain (graphical representations and downloadable tab separated text files) (Fig. 1d ). A pairwise comparison of two strains based on the presence of RBDs can be carried out. The RBPs obtained from 614 different E. coli strains were compared in terms of sequence, on the basis of single-link clustering method (please see Additional file 1 for a description of the method).
Utility and discussion To the best of our knowledge, EcRBPome is the first database of its kind that organises all RBPs known in a model organism in one platform. EcRBPome records information from all known complete E. coli proteomes (as of October 2018), and also links the data present in this database to other sequence, structure and function annotation resources. Hence, it is a 'one-stop solution' for all researchers who prefer to understand the global landscape of E. coli RBPs, as well as those who are interested in specific strains or proteins. It also predicts the function(s) and cognate RNA partner(s) for each of the RBPs present in this database, through our in-house algorithm, named RStrucFam. A total of 419 gene products, annotated as 'hypothetical protein' could be assigned to one of the RBP families (Additional file 3: Table  S1 ).
On the analysis of the NADP-bound structure of this protein (PDB code: 2O7P), it was seen that RibD uses a different site to bind the oxidised NADP + cofactor, which does not overlap with the RNA-binding site that we have proposed here. Similarly, EcRBPome can be used in conjunction with RStrucFam to understand the RNA-binding properties of many uncharacterised proteins and so-called 'non-RBPs' in E. coli (with moonlighting RNA-binding properties), which might be of special interest to researchers working with the molecular biology of the E. coli model system. These moonlighting RBPs cannot be identified by pure sequence search-based methods, like BLAST, due to the lack of structural restraints in these searches. With the growing advent of next generation sequencing technologies, the gap between protein sequence data and their functional annotation is ever-increasing.
Conclusions RBPs and sRNAs play important roles in bacterial post-transcriptional regulation of gene expression, and have been highly studied over the past decade  [22, 23]. The number of complete genome sequences available has exponentially increased due to the advent of next generation sequencing technologies. Detailed structural and functional characterisation of several RBPs, even within E. coli genome, requires painstaking efforts and huge amounts of time. Computational approaches offer the first glimpse of putative RBPs using mathematical models of known RBPs and searches in whole genomes. EcRBPome is a comprehensive platform for information on all RBPs from a popular model organism, E. coli. Sequences of RBPs reported in this database can also be used to select target gene products for detailed characterisation and to serve as start points for identifying sequence homologues in other microbial proteomes.
Especially, the less studied species, where performing studies using experimental techniques are a challenge. For example, gene products of microorganisms that are highly pathogenic or the ones that are difficult to culture in the laboratory could be studied using this approach. The existing study will be further extended to the ever-growing number of complete E. coli proteomes and the EcRBPome will be updated with cross-references to a greater number of in-house, as well as external databases and softwares, to enrich the existing repository of information. RBPs can then be followed over taxonomic lineages to understand their patterns of conservation.
Acknowledgements We thank the Department of Evolutionary Theory at the Max Planck Institute for Evolutionary Biology in Plön for inviting GWAC, resulting in this collaboration. PC received funding from the Agence Nationale de la Recherche, grant number ANR-14-ACHN-0003-01 provided to Florence Débarre. GWAC thanks the Leverhulme Early Career Fellowship provided by the Leverhulme Trust for funding. We also thank Deborah and Brian Charlesworth for their insightful feedback when presenting this work. We are also grateful to Sylvain Billiard for directing our attention towards diversification rates of mating types and for comments on the manuscript which increased its readability. Lastly, the suggestions of two anonymous reviewers helped to clarify certain aspects of the manuscript. Most notably this improved the discussion around equation (  18 ).